Delivering the Benefits of Smart Appliances

Delivering the Benefits of
Smart Appliances
EA Technology
A research report completed for the Department for Environment, Food
and Rural Affairs
(September 2011)
Published by the Department for Environment, Food and Rural Affairs
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Delivering the Benefits of Smart Appliances SPMT10_043
Final Report to the Department for Environment, Food and Rural Affairs
September 2011
This research was commissioned and funded by Defra. The views expressed reflect the
research findings and the authors’ interpretation; they do not necessarily reflect Defra policy
or opinions.
EA Technology
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CH1 6ES
TABLE OF CONTENTS
Executive Summary
1
Introduction and Objectives .....................................................................................1
2
Drivers to manage customer demands ...................................................................3
3
Smart Appliances – An Overview...........................................................................10
4
Smart Appliance Experiences ................................................................................13
5
Stakeholder Views and Experiences .....................................................................18
6
Barriers to Deployment and Uptake of Smart Appliances in the UK..................28
7
Impact of Selected Smart Appliances....................................................................35
8
Roadmap for the deployment of Smart Appliances in the UK ............................40
9
Concluding Remarks ...............................................................................................46
Appendix A: End Use Categories .................................................................................A-1
Appendix B: Categorisation of Appliances .................................................................A-4
Appendix C: Smart Appliance Experiences ................................................................A-5
Appendix D: Organisations/ Persons Engaged with for Stakeholder Dialogue....A-26
Appendix E: Case Studies...........................................................................................A-27
Glossary
AC
Alternating Current
DC
Direct Current
DD
Dynamic Demand
DECC
Department of Energy and Climate Change
Defra
Department for Environment, Food and Rural Affairs
DHW
Domestic Hot Water
DNO
Distribution Network Operator
DPCR
Distribution Price Control Review
DSM
Demand Side Management
EV
Electric Vehicle
FIT
Feed in Tariff
GB
Great Britain
GB-TSO
Great Britain Transmission System Operator
HV
High Voltage
L1
Level 1 (as used in Smart Appliance case studies)
L2
Level 2 (as used in Smart Appliance case studies)
LCNF
Low Carbon Network Fund
LV
Low Voltage
MTP
Market Transformation Programme
RHI
Renewable Heat Incentive
SBP
System Buy Price
STOR
Short Term Operating Reserve
SSP
System Sell Price
ToU
Time of Use
UK
United Kingdom
Executive Summary
Smart Appliances are one of the suite of measures that can help the UK meet its
policy objectives in terms of carbon emissions reduction, the facilitation of the uptake
of renewable energy sources and in reducing overall energy consumption.
The development of Smart Appliances is complementary to the ongoing work on
Smart Meters and Smart Grids. In many respects the successful deployment of
Smart Appliances is essential for Smart Grids and Smart Metering to deliver the full
range of anticipated benefits, particularly those in relation to end-use energy
efficiency. Recognising the importance of Smart Appliances in enabling end-users to
adjust their pattern, and to a lesser extent, their total energy consumption, AEA
Technology acting on behalf of Defra commissioned a study to investigate the
potential benefits of Smart Appliances in terms of energy and carbon savings and
enhanced electricity system operation. This report presents the outcome of this
study.
Drivers for Smart Appliances in the UK
Consideration of the main drivers to manage the pattern of demand in the UK shows
that the generic requirements for Smart Appliances fall into two main categories,
namely:
A- Shorter term measures of up to half an hour, which can be used to provide
balancing services (particularly frequency response) to National Grid. This
could also include short duration (i.e. up to half an hour) interruptions to assist
with the avoidance of peak prices.
B- Longer term measures that can be used for a wider range of purposes
including the management of network constraints or the provision of reserve
services to National Grid. This could also include the integration of
renewables, for example during extended periods of low wind.
Smart Appliance Experiences
A review of selected trials and pilots of Smart Appliances highlights that a range of
technologies have been evaluated over a number of years (for example one of the
UK trials dates back to the late 1990s). However, Smart Appliances are still very
much an emerging concept. To date, the majority of trials and pilots have targeted
large single end-uses of energy that contribute towards the peak demands they are
being used to alleviate. The review demonstrates the importance of Standards to
ensure to allow demand response to be implemented using standard operational
instructions and communications. Such a requirement, as yet, does not exist in the
UK.
Another important feature of a number of the trials is the impact of customer
engagement on the project outcome. Some of the smaller trials demonstrate a high
degree of customer involvement, and as such high levels of peak load reduction
were achieved. However, this is not always the case for larger trials. In addition, the
review suggests that not all customers are willing to install control technologies that
i
enable their end use loads to be remotely or automatically controlled. This
demonstrates that customer willingness to participate is an important factor in
evaluating the potential impact of a particular measure.
Stakeholder Dialogue
In addition to consumers, technology developers, appliance manufacturers and
appliance suppliers are key stakeholders in the development and deployment of
Smart Appliances. They can be expected to understand the requirements of
consumers and have direct knowledge of the impact of external factors on their
businesses. Therefore a number of interviews were undertaken with targeted
industry stakeholders to explore their views and opinions of Smart Appliances.
Other industry experts with a specific interest in Smart Appliances and customer
behaviour were also selected.
As might be expected, the stakeholders provided a diverse range of views and
opinions, and in most cases there was little consensus in the responses to the
questions posed. However, key issues raised included:
- The lack of customer awareness of the benefits of Smart Appliances is
regarded as a major obstacle to the deployment of Smart Appliances;
- The difficulty of ensuring benefits can be properly allocated between
consumers, Energy Suppliers and other stakeholders is viewed as another
important factor;
- The need for appliance Standards is considered to be a pre-requisite for the
development of Smart Appliances, particularly in terms of ensuring
interoperability and the provision of ‘plug and play’ solutions;
- The role of Time of Use (ToU) tariffs is generally considered to be an
essential motivator for the development and deployment of Smart Appliances
by those interviewed. Without ToU, many of the stakeholders believe that
there will be little stimulus for the development and deployment of Smart
Appliances.
Smart controls were also recognised in having a role to play in helping to ensure that
heating systems are operating efficiently, i.e. not over or under-heating the occupied
space.
Barriers to Deployment and Uptake in the UK
There are a number of barriers that impact on the potential deployment and uptake
of Smart Appliances in the UK. These include technology related barriers, market
related barriers and customer related barriers. A summary of these is provided
overleaf.
Technology Related Barriers
Not all Smart Appliances respond to inherent changes in electricity network
properties. Those that don’t, rely on a communications system to implement
changes to their pattern of operation. The Smart Meter provides a communications
link directly to the home, thereby providing an opportunity to directly control end-use
loads. Therefore, the readiness of the electricity network to interface with Smart
ii
Appliances is regarded to be an important factor. The wide scale adoption of Smart
Appliances is unlikely to happen until Smart Meters and Smart Grids are in place.
A number of aspects of the rollout of Smart Meters have yet to be finalised. Whilst it
has been decided that data collected from Smart Meters will be channelled through a
central communications hub known as DataCommsCo (DCC), it is not yet clear how
parties such as Energy Suppliers and Network Operators will interact with the DCC
to remotely control end-use loads via the Home Area Network. This is regarded as a
crucial element in the implementation of Smart Appliances, as it enables the existing
two-way communications system for the Smart Meter to be utilised to control the
operation of appliances.
Market Related Barriers
There are a number of market related barriers that impact on the deployment of
Smart Appliances, many of these relate specifically to the structure of the UK
electricity market and the rules governing the way electricity is traded.
ToU tariffs are generally recognised as an important stimulus for the development
and deployment of Smart Appliances. In the UK, the electricity supply market is fully
de-regulated, and it is up to Energy Suppliers themselves to design their tariffs.
Therefore, it is difficult to determine the extent to which innovative ToU tariffs will be
offered by Energy Suppliers in the UK. However, it is important to note that other
incentive mechanisms can be put in place. Alternatives to ToU tariffs might include
one-payments (suitable for all types of DSM measures) or discounts from the Energy
Tariff (applicable only for Supplier led DSM measures).
The UK electricity market is fully unbundled, i.e. the competitive and non-competitive
businesses are fully separated even where they are in common ownership. Thus,
the benefits of Smart Appliances are spread amongst a number of stakeholders,
rather than within a single, vertically integrated utility company. As a result, it is
difficult to ensure that the costs and benefits are shared appropriately between the
relevant stakeholders. This is especially pertinent from the perspective of
Distribution Network Operators, who do not routinely engage directly with end-use
consumers.
The profiling settlement system used to determine the pattern of consumption of
households and other (selected) small users means that it is difficult for Energy
Suppliers to capture the value associated with load shifting. The introduction of
Smart Meters means that it will be possible for all electricity consumption to be
settled according to the actual profile, rather than the ‘deemed’ profile. However,
there is no requirement on Energy Suppliers to move away from the use of the
current profiling system, although they are able to do so if they wish.
Customer Related Barriers
Customer attitudes towards Smart Appliances represents one of the most significant
barriers to the widescale deployment of these devices. In a situation where
‘competitive market forces’ dominate, then the customer is the ultimate decision
maker. If customers do not want the appliances, or if developers do not envisage a
market where customers will want the appliances, then it is evident that the market
for such products will not develop.
iii
Impact of Selected Smart Appliances
A number of case studies have been used to better understand the potential impact
of a selection of Smart Appliances and the specific barriers to their uptake. Five
appliances that contribute most to demand during peak periods (both now and in
future years) and which are suitable for demand response were selected. The
selected appliances are heat pumps, air conditioners, refrigeration, washing
machines and tumble dryers. The control of the charging of Electric Vehicles is
recognised as an important area, however this was outside the scope of the current
study.
The case studies were used to explore the benefits associated with two levels of
Smart Appliances with a ‘Business As Usual’ baseline and to explore the main
barriers to their uptake. Under Level 1 only minimal design changes are required to
existing appliance designs, whilst Level 2 represents more complex options, as
summarised below.
-
-
-
For both heat pumps and air conditioning, Level 1 does not require any
changes to the design of the standard system other than the means of
remotely switching the appliance off for short periods, whilst Level 2 requires
design changes to incorporate thermal storage.
For refrigeration, Level 1 appliances include the capability to provide dynamic
frequency response services to National Grid by automatically adjusting to
changes in system frequency. Level 2 appliances require further
developments to the dynamic demand concept to allow extended interruptions
of, say, half an hour.
For washing machines and tumble dryers, Level 1 does not require any
changes to the design of the existing appliances, but rather relies on changes
in human behaviour to minimise usage during certain periods. Level 2, then
requires design changes to enable these appliances to be automatically
scheduled to operate during prescribed periods. Alternative designs that
allow the operating cycles to be interrupted or to defer the heating load are
also briefly considered.
Delivering the Benefits of Smart Appliances – the Roadmap
The results of the case studies were used to produce a Priority Matrix which
compares the relative impact and ease of implementation of a range of measures.
This approach is useful in identifying the ‘quick wins’, (i.e. measures that have a high
impact and are easy to implement) and those that should be avoided (i.e. those with
a low impact and are difficult to implement).
iv
High
L2A & B
L2A & B
Key:
L1A
L2A
Heat Pumps
L1A
L1A
Washing Machines /
Tumble Dryers
Impact
Refrigeration
Air-conditioners
L1 - Level 1 smart appliance
L1B
L2 - Level 2 smart appliance
L1B
L2B
A - Short term interruptions
L1B
B - Longer term interruptions
L2A
L1A
Low
L2 B
L1B
Low
High
Ease of Implementation
Priority Matrix
In the diagram above, L1A refers to an appliance with a Smartness Level of 1
applied to a short term measure; L2B refers to an appliance with a Smartness Level
of 2 applied to a longer term measure.
The Priority Matrix shows that of the appliances considered, heat pumps and air
conditioners that provide short term interruptions (L1A) represent ‘quick-wins’, i.e.
can be implemented with relatively high ease with reasonable impact.
However, it is important to note that the simple on/off control of heat pumps and air
conditioners are not believed to represent the measures that have the potential to
deliver the highest impact. This is attributed to the provision of longer duration
interruptions by these appliances (L2A heat pumps and air conditioners). However,
it is not known how much more valuable are the benefits arising from these more
complex designs compared to the simpler “quick wins”.
The Priority Matrix was used to establish a prioritised list of actions to enable the
high impact / high ease of implementation measures to be put into action as soon as
possible. These include generic measures that are applicable to all types of Smart
Appliances, and specific measures for heat pumps and air conditioners.
v
Conclusions
The expected increase in the uptake of heat pumps and, perhaps to a lesser extent,
air conditioning is expected to lead to significant increases in electricity consumption
and peak demands. Thus, although these loads do result in increased peak
demands, they could also be potential solutions.
Both air conditioners and heat pumps provide “quick wins” through simple remote
on/off controls. However, it is believed that these may only provide limited benefits
(short term interrupts) whilst it is the application of more complex designs that have
the potential to provide greater benefits (both short term and longer term interrupts).
However, there is a risk that focusing on the ‘quick wins’ in the short term may make
it less likely for the more desirable solutions to be progressed at a later time.
Therefore, further analysis is recommended to quantify the relative benefits to the
UK of different types of DSM measures.
It is also considered that thought should be given to introducing minimum design
Standards for heat pumps in the UK. These Standards could, for example, prohibit
the use of heat pumps incorporating an electric flow boiler, or alternatively ensure
that such heat pumps are required to include the provision for remote or automatic
on/off control of the flow boiler. However, due to the single market rules, such
Standards would need to be delivered at the European level. This would be difficult
unless there is widespread support across Europe for such Standards. Additionally,
consideration needs to be given to the potential role of bivalent heat pumps (i.e
where the peak heating load is met by an auxiliary heating system, usually a gas or
oil boiler) which have the potential to make the implementation of DSM easier to
implement, but could reduce the carbon benefit of heat pumps.
The provision of dynamic frequency response by cold appliances represents the
most developed demonstration of a new1 Smart Appliance in the UK. It is currently
undergoing extensive field trial evaluations in several hundred homes. However, the
major obstacle to the role out of the technology (and thus the reason this is not
considered to be represent a “quick win”) is the lack of any financial mechanism to
encourage the uptake of these appliances by end-users. Therefore, there could be
merit in investigating whether any appropriate financial mechanisms could be put in
place to incentivise customers to purchase appliances with dynamic frequency
response capability. This would also be of relevance to other end use loads suitable
for dynamic frequency response, such as air conditioning and hot water heaters.
Probably one of the most important factors affecting the uptake of Smart Appliances
is customer awareness and acceptance. If customers do not want to allow 3rd
parties to remotely control their appliances or to employ technologies that can
automatically schedule the operation of their appliances, then it is unlikely that Smart
Appliances will be able to deliver any of their potential benefits - even if all the
measures identified here are addressed. Therefore raising awareness of the
importance of the pattern of energy consumption as well as of the overall amount of
energy consumption is considered to be important. Here, it is believed that
behavioural science and behavioural economics can provide useful tools to ensure
such information can be effectively communicated to consumers.
1
The radio teleswitch control of night storage heating is a well established application of a Smart Appliance in the UK used
over a number of years.
vi
1
Introduction and Objectives
1.1
Background
The current pace of change within the electricity industry is unprecedented in recent
times, both within the UK and worldwide. The principal drivers for change relate to
the wide ranging measures being implemented to reduce greenhouse gas
emissions. In particular, the increasing move towards the wide-scale deployment of
time variable renewable generation, particularly wind generation, presents a number
of challenges in relation to the balance of supply and demand. No longer is it
considered viable for electricity to be provided ‘on demand’ in response to the
requirements of end-users. Rather, a co-ordinated approach is required whereby
energy production and demand become integrated to ensure the use of renewables
can be optimised whilst also minimising the use of fossil fired generation.
Smart Appliances are one of a suite of measures that could help the UK meet its
policy objectives in terms of reducing energy consumption, reducing carbon
emissions and the provision of energy from renewable sources. They provide
consumers with the capability to adapt their pattern of energy consumption, for
example, to:
- Optimise the use of electricity from renewable resources
- Reduction of peak demand for electricity, thereby reducing the need for peak
generation plant (usually fossil fired generation), used for only a few hours per
year;
- Assist with the balancing of supply and demand to maximise the use of
existing network assets; and
- Provide support services to the System Operator to maintain the quality and
security of supply.
The development of Smart Appliances is complementary to the ongoing work on
Smart Meters and Smart Grids. HM Government has committed to the roll-out of
Smart Meters to all UK households by 2019, and it is envisaged that technologies to
enable the uptake of Smart Grids could be available by 20152.
This project, therefore, provides an overview of existing experiences of Smart
Appliances, explores the main barriers to their uptake and sets out a roadmap that
addresses these barriers to ensure their successful deployment.
2
Investing in a Low Carbon Britain, Joint Publication from BERR, DECC and DIUS, April 2009
1
The report is structured as follows:







Section 2 provides an overview of the main drivers for the management of
customer demands in the UK, and the generic requirements for a range of
demand management measures that can be met through the deployment of
Smart Appliances;
Section 3 provides an overview of Smart Appliances and describes how they can
help with the management of customer demands;
Section 4 summarises a selection of trials and pilots of Smart Appliances, and
highlights any key learning points relevant to the UK context. Further details of
these trials are found in the Appendices;
The views and experiences of a selection of key stakeholders in relation to the
key benefits and barriers associated with the uptake of Smart Appliances are
summarised in Section 5;
The generic barriers to the uptake of Smart Appliances in the UK are described
in Section 6;
The relative merits of implementing a selection of Smart Appliances are
considered in Section 7 in terms of the relative magnitude of the potential
benefits and the ease of implementation; and
Section 8 sets out a roadmap to facilitate the successful deployment of Smart
Appliances in the UK.
Note – UK and GB
This report focuses on the benefits of Smart Appliances in the UK context. However,
the electricity industry in the UK comprises the Great Britain (GB) wholesale market,
whilst Northern Ireland is part of the Single Wholesale Electricity Market of Ireland
and Northern Ireland. As such, there are a number of differences between the
market arrangements in place in Northern Ireland and in GB. Despite these
differences, the findings of this report are, in general, applicable to the UK as a
whole, and the terms GB and UK are used interchangeably. The Smart Metering
Implementation Programme referred to in this report applies only to GB.
2
2
Drivers to manage customer demands
Interest in the potential for Demand Side Management (DSM) has increased
significantly over recent years, driven by the UK’s commitment to significantly reduce
greenhouse gas emissions over the coming years. DSM is now widely regarded as
one of the suite of measures that will help towards the achievement of these goals.
A more active demand side is an important mechanism for addressing the issues of
improving the overall balance between supply and demand, reducing the reliance on
fossil fuel generation particularly at peak times and increasing the utilisation of
renewable energy sources with variable output.
Households and small commercial buildings account for a significant proportion of
the UK’s electricity consumption, therefore, these sectors have the potential to make
an important contribution towards meeting the UK’s carbon emissions reduction
target. This Section provides an overview of the main drivers for managing customer
demands in the UK.
DSM refers to any action that influences the quantity or pattern of energy consumed
by end-users, as highlighted in the schematic shown below.
Static Load
Management
Off-peak Hot Water
‘Cool’ Storage
Power Factor Correction
Local
Generation &
Fuel Switching
Solar Hot Water
Photovoltaics / Small Wind
Elec to Gas Hot Water/Heat
Industrial Biomass
Wood Stoves
Cogeneration
Energy
Efficiency
Measures
Efficiency Retrofits
Optimised Controls
Efficient Motors & Chillers
Efficient Lighting
Office Equipment
Standby
Generation
Influencing
Customer
Primary Energy
Interruptible Loads
Scheduled Loads
Dynamic Load
Management
Influencing
Customer End
Use Energy
Source: Demand Side Management, Benefits to Industry and the Community, Richard Lee, Energy SA, October 2001
Figure 2.1 Demand Side Management
Thus, DSM encompasses a wide range of measures such as:
- Energy efficiency measures, targeting the reduction of energy consumption in
general or at specific times (such as during network peaks);
- Fuel switching and local generation, that encourage end-users to make use of
alternative primary energy sources, either over the long or short term;
3
-
Load shifting to reduce peak demands, either on a short term flexible basis
(often referred to as demand response) or on a more permanent basis (such
as the move from direct electric heating to night-storage heating).
Smart Appliances have the potential to influence the full suite of DSM measures, as
summarised below.
- Energy Efficiency – through improved awareness of energy consumption and
energy costs, and avoided energy wastage;
- Dynamic load management – via direct load control or automatic response to
Critical Peak Pricing or other dynamic tariffs that deliver short term changes to
the pattern of energy consumption;
- Static load management – automatic response to Time of Use (ToU) tariffs to
encourage users to make a permanent change to their pattern of energy
consumption;
- Fuel switching – by way of the automatic scheduling of on-site generation to
avoid/reduce electricity imports from the network at times of high electricity
prices, to minimise exports onto the network at times of system ‘stress’ or to
maximise the utilisation of on-site generation.
The specific drivers for such changes to the pattern of demand consumption are
highlighted below.
2.1
Management of network constraints
The capacity of electricity networks is determined by the peak demand (MW) for
electricity, and not by the total energy requirement (MWh). Therefore, the larger the
peak demand, the larger the infrastructure needed to meet that demand.
Consequently, the pattern of electricity consumption is as important as the amount of
electricity consumed. Thus measures to reduce peak demand provide network
operators with an alternative to reinforcing the networks.
Figure 2.2 below shows the summer and winter profile for electricity on a range of
example days, including the winter peak day, a typical winter day and a typical
summer day. It shows that peak demand occurs during the winter and extends over
a period of about 3 hours between 4pm and 7pm.
Figure 2.2 GB Summer and Winter Daily Demand Profiles in 2009/10
4
Measures that move demand away from the peak or reduce the peak demand have
the potential to defer or avoid network reinforcement measures. In this example, the
measures would need to be in place over consecutive days over the winter period.
Thus, Smart Appliances that respond automatically to ToU tariffs with high prices
over the winter evening peak, could help reduce network peak demands.
Alternatively, customers could simply avoid using their appliances during this period
in order to reduce their energy costs. Thus appliances that provide customers with
information on the cost associated with their operation at any given time could help
motivate customers to reduce demand at the time of the peak. In the future, as
electric vehicles and heat pumps become more prevalent, such measures could
become particularly important in helping to mitigate against significant increases in
peak demand.
In addition to the winter peak load reduction, demand side management measures
can also help Distribution Network Operators (DNOs) manage their networks in
event of the failure of an asset. Networks are designed such that they have inherent
redundancy, often referred to as the (N-1) criteria, such that the failure of any one
asset does not cause an outage. As a result, assets typically run in pairs such that
they operate at 50% of their rated capacity at peak demand. Thus, assets typically
run at well below their rated capacity, except in a post fault situation. Thus
measures that reduce demand following a network failure, could enable DNOs to
increase the loading on their existing assets above the 50% limit, provided that they
were certain that demand side measures could be implemented when required. In
this case, demand measures would only be required in post fault situations, which
would typically be 1 or 2 times a year, although they may need to be sustained over
a period of 1 to 2 weeks whilst repairs are undertaken.
2.2
Provision of balancing services to National Grid
National Grid, the Great Britain Transmission System Operator (GB-TSO), procures
a range of services to balance demand and supply and to ensure the security and
quality of electricity supply. These services include:
- Frequency response services;
- Reserve services;
- Reactive power;
- System security services;
- Energy related services;
In general terms, these services require flexibility, i.e. they need to be able to deliver
within a tightly defined timeframe. They also need to be dependable, i.e. they need
to be able to deliver whenever the service is required. For this reason, the provision
of these services is largely dominated by fossil fired power stations, either inefficient
peaking plant that can be quickly brought on line or by plant operating inefficiently in
part load conditions. Thus, the use of demand side measures offers the opportunity
to reduce the CO2 emissions associated with the provision of these services. The
services most suited to demand response are frequency response and reserve
services, which are described below.
5
Frequency Response.
Under the terms of its licence, National Grid is required to maintain the system
frequency within prescribed limits. In order to do so, generation and demand
resources are held in automatic readiness, so that they can react to the failure of a
generator or a sudden increase in demand. There are two types of frequency
response: dynamic and non dynamic response. Dynamic frequency response is a
continuously provided service used to manage the normal second by second
changes on the system. Non dynamic frequency response is usually a discrete
service triggered at a defined frequency deviation. The demand side has a wellestablished history of providing non dynamic frequency response, and cement
manufacturers and steel works have provided support to National Grid over a
number of years.
The Table below describes the characteristics required for non dynamic frequency
response services provided by demand customers.
Table 2.1 Frequency Response Characteristics (for demand customers)3
Minimum capacity
Delivery within
Delivery for
Availability
3MW (but can be aggregated over a number of sites)
2 seconds (maximum)
30 minutes (minimum)
24 hours per day
Reserve Services
National Grid requires additional resources in order to deal with unforeseen demand
increases and/or generator unavailability. These resources are provided both by
generators and by the demand side. There are a number of specific services that fall
within this category, but Short Term Operating Reserve (STOR) is the main area
where the Demand Side is regarded as being able to contribute. The table below
describes the characteristics required STOR.
Table 2.2 STOR Characteristics
Minimum capacity
Delivery within
Delivery for
Availability
4
3MW (but can be aggregated over a number of sites)
4 hours (maximum)
2 hours (minimum)
Within prescribed windows
3
Frequency Response by Demand Management, Major Technical Requirements available http://www.nationalgrid.com
accessed 20 May 2011
4
Short Term Operating Reserve, General Description of the Service, 6 May 2011, National Grid Publication available
http://www.nationalgrid.com
th
6
2.3
Energy Peak Price Avoidance
Energy Suppliers purchase energy in half-hourly blocks to cover the needs of their
customers. If the amount purchased does not exactly match the amount consumed,
they face imbalance charges. If a Supplier’s own imbalance position assists with the
overall system balancing, for example if the system is long5 a Supplier that is short6
will receive a payment equivalent to the System Sell Price (SSP). However, if a
Supplier is short when the system is short, the Suppliers much purchase their top-up
electricity at the System Buy Price (SBP).
The imbalance charges are determined by the weighted average value of the bids
and offers into the Balancing Mechanism. The diagram below shows the imbalance
prices faced by Energy suppliers on 7th December 2010, the day with the highest
peak demand over the winter period 1 November 2010 to 28 February 20117.
500
450
400
350
£/MWh
300
SSP (£/MWh)
SBP (£/MWh)
250
200
150
100
50
0
0
6
12
18
24
30
36
42
48
Settlement Period
Figure 2.3 System Buy Prices (SBP) and System Sell Prices (SSP) for 7th December 2010
In this case, the System Buy Price and System Sell prices coincide with the time of
peak demand, and the high prices prevail for a period of around 3 hours. However,
price peaks can occur at any time of the day. Thus demand management provides
Energy Suppliers with the opportunity to minimise their exposure to high imbalance
prices. Energy Suppliers with demand flexibility are also able to bid directly into the
Balancing Mechanism, and thus provide demand reduction services in return for a
direct payment in order to help the System Operator maintain the overall balance
between supply and demand. As is the case in Figure 2.3, the peak prices are likely
to coincide with the time of peak demand. However, as discussed in Section 2.4
below, this may not necessarily be the case in the future as the proportion of
electricity generated using renewable resources increases.
5
The system is ‘long’ when generation exceeds demand overall. A supplier is ‘long’ when the amount of generation
purchased exceeds the amount of energy consumed by their customers, or if consumers’ demand is less than anticipated.
6
i.e. does not have enough contracts in place to cover the energy consumed by its customers
7
Data published by Elexon, and available via http://www.bmreports.com (Accessed 20 May 2011)
7
2.4
Integration of Renewables
UK’s Climate Change Act (2008) established a target of reducing greenhouse gas
emissions by 80% by 2050, with an interim target of a reduction of at least 26% by
20208. In addition, the UK has signed up to the EU Renewable Energy Directive
which includes a UK target of 15% of energy from renewables by 2020. In practice,
this will require that around a third of electricity to be generated from renewable
resources. This decarbonisation of the electricity generation sector combined with
other policy measures is expected to lead to a significant increase in the demand for
electricity, particularly for heating and transport.
The integration of renewable generation resources, particularly wind generation
where the output varies according to the prevailing conditions, poses a number of
challenges in terms of maintaining the balance between supply and demand. As
has been highlighted in Sections 2.1 to 2.3, there are a number of ways that the
demand side can help to assist with the integration of renewable electricity
generation. The following, therefore, discusses how renewables will increase the
need for these services.
Provision of balancing services to National Grid
The provision of support services to National Grid will become increasingly important
as Great Britain moves towards a generation mix dominated by variable output
renewables and inflexible nuclear generation. These generation sources are not well
suited to the provision of balancing services, and therefore the demand side provides
a valuable opportunity to reduce the reliance on fossil fired generation.
Management of network constraints
The move to renewable energy will see a shift towards electrification of certain
energy requirements, particularly heating and transport. Unless the pattern of these
new demands and existing demands are optimised to maximise the use of the
existing assets, the widespread uptake of heat pumps and electric vehicles, could
lead to the requirement for significant investment in infrastructure assets9.
Energy peak price avoidance
In 2009, Pöyry10 conducted a study to investigate the potential impact of renewable
energy on market prices. The study concluded that the dispatch pattern of the
different types of generation plant will be very much different to that in the current
market. This is particularly pertinent for Combined Cycle Gas Turbines (CCGTs)
which are predicted to move from a pattern of demand following (i.e. running during
periods of peak demand) to operating only when there is no wind. As a result, prices
during low wind periods could reach extremely high levels of up to £500/MWh, which
could be sustained over several hours and/or days. More importantly, prices could
8
http://www.legislation.gov.uk/ukpga/2008/27/contents
9
Benefits of Advanced Smart Metering for Demand Response based Control of Distribution Networks, Summary Report
Version 2.0, Joint publication by Centre for Sustainable Electricity and Distributed Generation Imperial College, Centre for
Transport Studies, Imperial College and Energy Networks Association
10
Impact of Intermittency: How Wind Variability Could Change the Shape of the British and Irish Electricity Markets, Summary
Report, Pöyry Energy (Oxford) Ltd, July 2009
8
become increasingly volatile, and in such circumstances the demand side has the
potential to play an increasingly important role in helping Energy Suppliers avoid
peak prices.
The demand side has the potential to play an important role by adjusting its pattern
of consumption in order to maximise the use of renewable resources. For example,
ensuring that any schedulable processes coincide with the availability of wind
generation. The Pöyry study showed that there could be instances where energy
prices become very much lower than today, or even negative, coinciding with high
wind resources. During such periods, it would clearly be advantageous to be able to
bring energy consumption forwards to make use of available generation surplus, and
by doing so reduce energy costs for consumers.
2.5
General requirements for customer demand management
The preceding Sections describe the various drivers for managing customer demand
for electricity in the UK. This information is summarised in the following Table, which
indicates the generic requirements for demand measures meeting the categories
discussed in Sections 2.1 to 2.4.
Table 2.3 Generic requirements for demand management measures
How long?
Management of
network
constraints
3 hours
Balancing
Services
30 mins up to few hours
Energy peak price
avoidance
Renewable
(i)
integration
(i)
In half-hourly blocks, with
some consecutive halfhours
Up to several days (for
example during extended
periods of low wind
output)
How often?
Regularly during the
winter, or occasionally for
a few weeks (following a
network fault)
Continually (for dynamic
frequency response), to
occasionally for other
services
Occasionally, although
more so as wind
generation increases
Occasionally
When required
Early evening peak, during
the winter
Any time of day / year
Any time of day / year
Any time of day / year
In the context of this study, renewables integration specifically relates to matching demand to the wind generation on
a ‘macro’ scale, i.e. to bring demand forward when wind generation levels are high or to delay demand when wind
generation levels are low.
9
3
Smart Appliances – An Overview
There is no industry wide definition of what constitutes a Smart Appliance. However,
in general, the term is used to refer to an “appliance or device that is capable of
modifying its operation in response to signals received from the electricity network or
a communications system.”
In the context of this study, Smart Appliances refer to electrical appliances only and
the study is not concerned with other utilities (gas, water and telecommunications).
The project focuses on Smart Appliances in both domestic and small commercial
locations. Thus, Smart Appliances specifically suitable for industrial and transport
sectors are excluded from the scope of the study.
Electric vehicle (EV) chargers have the potential to represent a large (in the order of
9kWh per vehicle per day11) load in the residential context. The subject of controlling
this charging via the integration of Smart Meters and the Smart Grid is extensive.
There is a large potential for EV chargers to act as Smart Appliances, ensuring
charging takes place either in periods of low demand (overnight) or when surplus
renewable energy is available. The Committee on Climate Change has
recommended that 1.7 million electric vehicles are needed by 2020 to deliver
"dramatic improvements" in the carbon efficiency of transport12. The development of
Smart charging infrastructures for these new electric vehicles is an essential
component for ensuring that these new electric vehicles do not impact on network
peak demands. Whilst this is recognised as an important area, EVs are outside the
scope of the current study.
The commercial sector covers a vast range of building types and functions, from
small corner shops and small offices to vast office buildings and hypermarkets. The
use of sub-metering and building management systems within larger commercial
buildings (i.e. those with maximum demands over 100kW) already provides the
opportunity for these customers to understand and manage their patterns of energy
consumption. Therefore, this project focuses on small commercial properties, i.e.
those with a maximum demand below 100kW and currently falling within the non
half-hourly metering market. The roll-out of advanced meters to this sector offers the
potential to investigate potential synergies for Smart Appliances within the household
sector.
A breakdown of end-use consumption for the domestic and commercial sectors is
found in Appendix A.
11
Based on a vehicle consumption figure of 0.16kWh/km (Cenex, 2008), an average daily mileage of 51km (DfT) and a
charging efficiency of 90%.
12
http://www.theccc.org.uk/sectors/surface-transport/electric-cars
10
3.1
Smart Appliances - functionality
As mentioned previously, a ‘Smart Appliance’ can be defined in general terms as
follows:
“An appliance or device that is capable of modifying its operation in response
to signals received from the electricity network or a communications
system.”13
These appliances can be divided into categories based on the signals to which they
respond, although it should be noted that the two groups are not mutually exclusive:
1. Appliances which respond to inherent changes in electricity network
properties without any consumer intervention. These appliances act
independently of consumers, Smart Meters and Smart Grid devices.
Examples include:
o dynamic demand controllers whereby software within the
appliance’s control unit measures signals from the electricity supply
(e.g. mains frequency) and adjusts power consumption
automatically to compensate14; and
o voltage controllers that respond to network voltage fluctuations and
maintain an optimum voltage level into the household, which can
lead to energy savings in certain appliances15.
2. Appliances which have an integrated communications device and therefore
provide information to consumers, Suppliers and network operators and can
respond to communication signals sent by these parties (i.e. signals distinct
from the properties of the electricity supply). These appliances can utilise
Smart Meter and Smart Grid technology to allow the integration of
communication signals within the network, or otherwise include dedicated
communication and controls.
Appliances can be further categorised according to their technical suitability for
different types of demand management actions, i.e: whether they are suitable for
interruption at little or no notice, whether they are more suited to rescheduling their
operation from one time to another, or a combination of the two, as shown in
Appendix B.
13
SPMT10_043 Delivering the Benefits of Smart Appliances, Issue 0.1 15.10.10
14
See http://www.rltec.com/ for a description of the dynamic demand technology.
15
See http://www.vphase.co.uk/default.aspx for a description of the VPhase device
11
3.2
Smart Appliances – Scope of Current Study
Thus in summary the appliances to be considered within this study are as described
below;
 Electrical appliances in the residential and small commercial (<100kW)
sector;
 The primary focus will be on individual appliances which are either
interruptible, schedulable or hybrid (see Appendix B), or ‘add-on’ devices
that enable the pattern of consumption of individual appliances to be
optimised in response to certain external stimuli. As such, devices such
as voltage controllers that optimise the overall supply into a property,
rather than optimise the usage pattern of individual devises, are not
considered here;
 Smart Appliances are those that fall into the categories described below;
o Those that respond automatically to inherent changes in electricity
network properties without any consumer intervention. These
appliances act independently of consumers, Smart Meters and
Smart Grid devices; and/or
o Appliances which have an integrated communications device and
therefore provide information to consumers, suppliers and network
operators and can respond to communication signals sent by these
parties (i.e. signals distinct from the properties of the electricity
supply).
 Air conditioning, electric space and water heating will be included within
the scope of the project; and
 The charging of electric vehicles is excluded.
12
4
Smart Appliance Experiences
This Section of the Report provides an overview of a selection of trials and pilots of
Smart Appliances to explore their impact on energy consumption and to highlight
issues affecting the deployment of Smart Appliances in the UK. This Section does
not attempt to capture all trials and pilots that have been undertaken, rather a
relatively small number of examples have been selected to highlight key learning
points relevant to the UK context. Full information on the pilots and trials reviews is
found in Appendix C.
Table 4.1 focuses on UK experiences whereas Table 4.2 highlights selected
overseas examples of Smart Appliance trials. The information summarised in these
Tables highlights that, in the vast majority of cases, the pilots and trials that have
been conducted to date have focused on large single end-uses of energy that
contribute significantly to the peak loads that they are helping to alleviate, i.e. air
conditioning and electric heating loads. The main exception to this is the trial of cold
appliances with dynamic frequency response in the UK. One of the important
learning points from this trial is that demonstrating the technical viability of a concept
and ensuring that the benefits outweigh the costs are not sufficient for the successful
deployment of a technology. In this particular example, the key barrier is the inability
to establish the route to market. There is no financial mechanism to appropriately
allocate the costs and benefits between stakeholders and the consumers
themselves, particularly within the UK context.
The direct load control programme implemented by ETSA utilities, Australia,
demonstrates the importance of product Standards. The programme discovered that
not all air conditioners could be controlled using the communication device being
trialled, and as such the scale of the trial had to be reduced from 2,400 sites to 750
sites. This has led to a requirement for all new air conditioners sold in Australia to
incorporate a standard interface to allow demand response to be implemented using
standard operational instructions and communications. Although a specification16
has been developed at a European level that sets out the requirements that devices,
objects and systems must comply with in order for them to be capable of
interoperability, it is not an official Standard. Thus, there is no Standard in place in
the UK to ensure that demand response can be implemented using standard
operational instructions and communications. Any Standard would need to be
developed at the European level due to single market rules, and would require
widespread support across Europe.
16
An ‘Interoperability framework requirements specification for service to the home (IFRS)’, A CENELEC Workshop
Agreement, published June 2010.
13
Another important feature of a number of the trials includes the impact of customer
engagement. Some of the smaller trials demonstrate a high degree of customer
involvement, where-as this is not always the case for larger trials. For example, in
the California Statewide Pricing Programme, around one-third of the customers in
the larger demand group (> 20 kW) declined the offer of a free Smart thermostat to
enable their air conditioning load to be automatically controlled during high price
periods. As a result, the demand reduction of these customers was very much less
(around half) than that for customers who did take up the offer. Therefore, the
resulting impact of Smart Appliances is highly dependent upon customer attitudes
and behaviours.
14
Table 4.1 Summary of selected UK Experiences of Smart Appliances
Trial
npower and RLTec
Dynamic Demand Trial
(domestic cold
appliances)
(up to 3000 households)
Sustainable Blacon
(50 households)
Energy Wastage Study
ETHOS Multimedia
Energy Management
System
(100 households)
Wattbox Heating Control
(control of gas and
electric space heating)
Early Adopters Research
Outcomes
Key learning points
Trial still underway however analysis suggests
that:
 Each fridge will save between £0.70 and
£5.60 per year
 Each device costs £4 per appliance
 A potential carbon saving of between 17 and
44kg of CO2 per fridge per annum is
estimated
Trial is still underway however:
 A large scale adoption of the technology would be necessary to access
system balancing value, however the technology could be deployed in a
greater range of appliances.
 No financial mechanism to encourage consumers to purchase appliances
that provide dynamic frequency response technology.
 Legislating for the inclusion of this technology in all new products not
practicable because appliance Standards set at EU level. Frequency control
does not pose the same challenges across the mainland Europe that it does
within the UK and Ireland.
 Further investigation required on the effect on appliance efficiency and
general wear and tear.
Trial sill underway – no results yet available
Trial still underway
 Survey of 2,000 households shows extent to
which customers leave unattended appliances
switched on, either deliberately or
accidentally.
 25% reduction in peak demand on the
relevant section of the distribution network.
 8% reduction in average electricity
consumption in participating households.
 Increased customer comfort.
 Potential opportunity for Smart Appliances to avoid energy wastage, but
there is a lack of reliable and cost effective occupancy detection sensors.
 Focus on electric space and water heating to reduce localised network peak
demands.
 Technology readiness at the time of the trial (1996-1998) was an issue.
 Introduction of profile settlement system considered to be a barrier to roll-out
at the time of the trial.
 Energy consumption decreased by 14% in
trials
 Many households do not cope well with conventional heating controls.
 There could be an opportunity to link this sort of device to Smart Meters to
further optimise heating controls based on price signals.
 Results of focus groups undertaken for three
water authorities gauged customer receptivity
to Smart Home concept.
 Results indicate an interest in simple, optional controls to allow appliances to
be operated when electricity is cheaper.
15
Table 4.2 Summary of selected Overseas Experiences of Smart Appliances
USA
Country
Trial
California State Wide
Pricing Program
(Commercial and
Industrial)
California State Wide
Pricing Program
(Residential)
LIPAedge
Australia
(direct load control of
23,400 air conditioners)
17
ETSA Utilities Air
Conditioning Direct
Load Control Program
(15 households
increasing to 750)
Outcomes
Customers with Smart thermostats
reduced peak period energy use by 13%
more than those without
Customers with enabling technologies
saved 18% more than those without, and
on Super Peak days this increased to
26%.
 16,067MW average load reduction,
(680kW/air conditioning unit)
 Cost of US$515 per customer
(approximately £320 per customer)
yielding a combined average cost of
US$487/kW (approximately £300/kW) of
demand reduction17.
 Reduction in peak load
 Change in appliance Standards so that
enabling technologies can be easily
fitted to applicable devices
Based on an exchange rate of US$1 = £0.62
16
Key learning points
 Air conditioning loads targeted via range of ToU tariffs.
 Enabling technologies helped customers to be more price responsive.
 Not all customers took up offer of a free Smart thermostat to enable
air conditioners to respond automatically.
 Air conditioning loads, electric water heating and pool pumps targeted
via range of ToU tariffs.
 Recent blackout in California had made householders aware of
system constraints and potential supply shortages
 Time of Use pricing is already used in many parts of the USA and is
therefore a familiar concept
 Householders were able to automate their own electricity loads after
being given future price tariff information
 Householders have ability to override technology under some
circumstances
 Number of interruptions was limited
 Long Island Power Authority (LIPA) is a non-profit making municipal
company with different drivers to UK companies
 LIPA run the supply, transmission and distribution company on Long
Island, amalgamating benefits in one company and providing a more
extensive relationship with the customer
 Much of peak load growth attributable to growth in air conditioning.
 Careful initial research undertaken to monitor customer comfort
levels.
 Small initial trial to gain experience with technology.
 For some householders air conditioning was still a novelty.
 A marketing trial was undertaken to explain the trial. It successfully
helped to changed public perceptions of electricity.
 A strong regulator encouraged DNOs to undertake such trials.
Australia
(continued)
Country
Trial
Western Sydney
Interruptible Air
Conditioning Rebate
Trial
(90 households)
Denmark
Elkraft
(control of electric
heating load)
(25 households)
Outcomes
 Technology worked correctly
 Customers preferred shorter more
frequent off cycles rather than
prolonged interruptions
 High administration cost
 High cost of metering
 Billing was problematic
 Customer set own preferences for
interruptions.
 40% used the facility to stop an
interruption.
 41% of interrupted consumption was
used afterwards to bring temperature up
to required level.
 24% of householders set a maximum
interruption length.
 Installation costs were about 800€ per
house.
Key learning points
 Long history of direct load control (for DHW), including strong
customer support framework for DHW scheme, and high price returns
for customers on DHW scheme
 DNO has direct relationship with customers - also their energy
supplier.
 Billing of a similar scheme in the UK would be difficult until Smart
Meters are rolled out.
 The unbundling of the UK energy market has resulted in financial
incentives for such a scheme being thinly spread across multiple
stakeholders.
 Participants’ electric heating loads were greater than
16,000kWh/year.
 High degree of customer satisfaction (participants would recommend
the system to friends).
 Very small scale trial.
 Customers happy to suggest appliances that could also be included
in the scheme
 Most were “satisfied” or “nearly satisfied” with the financial incentive
paid.
 Most customers were happy with three hour interruptions to their
heating.
 Need for interruptions to be synchronised with meter settlement
periods identified.
Norway
Oslo ‘Ebox’
(control of domestic hot
water loads)
 Peak load reduction of up to 15% (of
maximum metered peak load in
participating households)
 Very small trial.
 Significant potential to manage hot water loads.
 Inconvenience to residents kept to a minimum.
 High prevalence of electric water
heating resulted in a large peak
reduction, resulting in a financial benefit
of NZ$12m/yr year (approx £6m/yr) for
DNO and NZ$6m/yr (approx £3m/yr)for
18
transmission .





New
Zealand
(20 households)
18
Orion Network DSM
Project
(control of domestic hot
water loads)
Based on an exchange rate of NZ$1 = £0.50
17
High consumer price benefits.
Strong consumer support network.
Long standing program.
High prevalence of electric water heating in New Zealand.
Strong financial incentive on DNO to reduce peak demand.
5
Stakeholder Views and Experiences
Technology developers, appliance manufacturers and appliance suppliers are key
stakeholders in the development and deployment of Smart Appliances. They can be
expected to understand the requirements of consumers and have direct knowledge
of the impact of external factors on their businesses. Therefore, a number of
interviews were undertaken with targeted industry stakeholders to explore their views
and opinions of Smart Appliances. The stakeholders were selected from the
following two groups:
-
Equipment and device developers, manufacturers and suppliers (from here-on
referred to as ‘manufacturers’); and
Other industry experts, with a specific interest in Smart Appliances particularly
from the consumer perspective (from here-on referred to as ‘industry
experts’).
Pro-formas were used as the basis for telephone interviews with these two
stakeholder groups. The findings of the interviews were captured in the pro-forma,
and participants were invited to review the findings. None of the responses are
attributed to individual participants. However, a list of those interviewed is found in
Appendix D.
The following sections provide an overview of the comments provided.
5.1
Definition of a Smart Appliance
All respondents were asked at the outset to clarify their understanding of the
definition of a Smart Appliance. As would be expected from a group of individuals
targeted for their knowledge and experience of Smart Appliances, there was a high
level of consistency amongst the respondents from both stakeholder groups.
All respondents agreed that Smart Appliances provide a higher level of connectivity
and communication. One of the respondents specifically referred to the use of bidirectional communication. Some, but not all, identified that the additional
connectivity would allow increased energy efficiency through measures such as
helping users to learn how to operate their appliances more energy efficiently,
allowing appliances to respond automatically to external signals, or help users to
make more informed decisions. The importance of the additional connectivity in
being able to offer additional services such as remote control, diagnostic tools and
extra programmable functionality was highlighted as an important factor.
In addition to exploring their understanding of Smart Appliances, respondents were
asked to comment on which appliances were thought to offer the greatest potential
to customers in terms of the anticipated benefits that could be delivered. Responses
were sought for domestic customers and commercial customers, as highlighted
below.
18
Domestic Customers
There was no clear consensus from the respondents on which appliances could
provide the greatest potential benefits to customers. Responses focussed very
much on the types of loads, rather than on specific end-uses. Thus, it was thought
that appliances that provide flexibility of use, the ability to reduce energy
consumption and include the capability to be remotely controlled were seen as
having the greatest potential for domestic customers. Some respondents cited
specific end-uses of energy, i.e. those with the highest energy demand or those most
suited to adjusting their pattern of consumption, as likely to have the greatest
potential (both for energy efficiency and load shifting). Heating, ventilation, air
conditioning systems and tumble dryers were identified.
Energy consumption was identified as being a longer term goal for Smart
Appliances, dependent on market conditions such as ToU tariffs, while in the shorter
term customer care solutions that provide flexibility of use may be more
predominant. It was also suggested that appliances which could be remotely
controlled have the greatest potential.
The respondents were split in their opinions on the type of appliances that
consumers are most likely to favour. Some thought that it would be appliances that
assisted their lifestyle either though time efficiencies or making a task easier, while
others suggested appliances that assisted the consumer in energy efficiency or load
shifting may be attractive. However, it is interesting to note that some of the industry
experts questioned whether consumers were interested in reducing or moving their
energy loads or indeed if there were any domestic loads worth shifting.
The respondents were also split on the benefits of Smart Appliances for load shifting
and energy reduction. One suggested that the benefits would be in appliances with
the largest energy consumption such as electric vehicles, freezers and fridges.
Another highlighted that presently, consumers have no understanding of why they
should participate in load shifting so education is necessary, combined with some
form of (probably financial) incentive. Another proposed that experience suggests
that consumers will not participate in load shifting because Energy Companies will
not share any benefits with them. It was also suggested that the benefits of Smart
Appliances would depend on the context of the customer. For example, people
living in flats may be limited by the extent to which they are able to shift loads due to
the impact on their neighbours, e.g. the noise from washing machines operated
overnight could cause represent a nuisance for neighbours. However, this could be
equally true for all households regardless of the type of property, i.e. householders
may not wish to operate their appliances at night because they themselves are
affected by the noise.
Commercial Customers
In terms of commercial customers, the greater energy savings (in absolute terms)
that could be made by individual users was cited as an important factor, potentially
making this market more important than the domestic market. The fact that
appliances sold to this sector were subject to fewer regulations and Standards, i.e.
the energy labelling of appliances, was also viewed as being a significant factor in
making this a potentially attractive market for Smart Appliances.
19
The ability for customers to be able to remotely monitor the performance of individual
appliances or processes was seen as a key advantage for this customer segment.
For example, the ability to check that overnight processes are occurring according to
plan could highlight inappropriate control implementation. Air conditioning units were
identified as being the most attractive appliance for load shifting amongst
commercial customers. Additionally, commercial lighting and air conditioning units
were thought to represent key applications for ‘Smart Goods’ because of their high
power use, especially when facilities are unoccupied during the evening peak. Thus,
in this context, the ‘Smart’ features would focus on avoiding energy wastage in
unoccupied premises. Smart controls were also recognised in having a role to play
in helping to ensure that heating systems are operating efficiently, i.e. not over or
under-heating the occupied space.
Respondents drew attention to devices that could increase productivity, move loads
or increase convenience. It was also suggested that the commercial sector may be
a good place to start introducing Smart Appliances because of the large energy and
financial savings available. In addition, the roll-out of Smart Appliances to this sector
could help to reassure the domestic sector about the additional benefits of Smart
Appliances.
5.2
Development of Smart Appliances
Respondents were asked to comment on the main drivers for the development of
Smart Appliances. In addition, insights into the current developments by the
respondent’s own organisations were sought.
Not all the manufacturers interviewed were developing products in the Smart
Appliance arena. Of those that were developing new products, the main focus of
their activities was on the development of communications platforms, Smart water
and storage heaters and household appliances. Factors driving these developments
included UK energy policy goals, international (particularly North American)
regulations, the price of energy, maintenance savings and customer demand for
increasing functionality in their new appliances.
The timing of the emergence of these new appliances ranged considerably. Some
manufacturers had prototypes that would be piloted later this year, while others
spoke about achieving full market readiness in up to ten years.
Important factors for encouraging the development for new products included:
- Ensuring that customers are aware of the potential benefits to them from
Smart Appliance ownership;
- Ensuring customers are in a position to take advantage of these benefits; and
- Preventing a consumer backlash.
In general, it was noted that manufacturers develop products for a global market
although some develop products specifically for individual geographical regions. It
was suggested that global Standards, rather than for example just EU Standards,
were required to create the volumes and prices required to drive prices for Smart
Appliances to an effective level. Other respondents focused more on the need for
20
EU wide Standards, particularly on the need for Standards applicable across a wide
range of appliances, for example for both white and brown goods.
The industry experts had a divided opinion about who would be driving the push
towards Smart Appliances. Some thought that manufacturers were seen as having
the greatest motive, although they would be more interested in developing remote
diagnostic functions, learning more about how products are used and integrating
appliances into communication networks for the consumers’ convenience. Being
able to integrate appliances with renewable generation is also highlighted as a
potential driver. It was proposed that the organisations with most interest in Smart
Appliances would be Network Operators, as they provide them an opportunity to
control loads to improve energy security and reduce CO2 emissions. This has driven
device development in North America and Australia and was thought likely to
become more important here as the installed capacity of wind increases. However,
in the UK, it is the Energy Suppliers, and not Network Operators, that lead the rollout of Smart Meters to consumers.
5.3
Barriers to the development of Smart Appliances
The experts provided a wide range of views on the barriers to the development of
Smart Appliances. Issues raised focused on the following aspects:
 The high capital cost associated with product development;
 Energy companies not yet agreeing on how they want appliances integrating, or
not yet being in a position to utilise them;
 A lack of government policy and regulation for Smart Appliances, including the
impact of building regulations on the promotion of electric heating;
 Customer attitudes to Smart Appliances.
 Lack of consistent appliance Standards;
 The costs of developing appliances are not necessarily borne by those who
benefit from their use;
 The long timescale associated with the roll-out of Smart Appliances due to the
infrequency of appliance replacement;
 Different manufacturers having different communication platforms;
 The requirement for an override function to make Smart Appliances acceptable
to consumers; and
 A lack of consumer confidence that they will be able to use and understand the
technology.
One interviewee identified the inefficiencies converting between AC (alternating
current) and DC (direct current) power supply through the energy supply chain,
especially within houses with microgeneration as a barrier, although this is not
specifically an issue relating to Smart Appliances.
Measures suggested for overcoming these potential barriers included:
 The need for consistent international and national appliance Standards to be
implemented;
 Trials to prove the business case for Smart Appliances;
 Making appliances useful to householders;
 A greater awareness of the consequences of personal energy use,
21


Consumer awareness of override functions; and
Ensuring appliances are as easy to use as ‘non smart’ appliances, and are able
to communicate with other appliances.
A coordinated approach, addressing all issues and stakeholders was seen to be
important. The importance of Energy Companies considering the needs of
consumers was also highlighted. The need for appliances to be developed for
consumers with microgeneration, to enable them to maximise the use of their on-site
generation, was identified.
One of the industry experts also pointed out that the development of appliances that
did what the consumers themselves wanted would overcome many of the barriers to
the uptake of Smart Appliances.
5.4
Key factors influencing uptake of Smart Appliances
The stakeholders were asked to comment on the key factors affecting the decision to
purchase Smart Appliances. The manufacturers were split about the importance of
cost and economic returns to consumers buying products. Some saw this as being a
big barrier while others thought that other benefits of Smart Appliances would make
cost less of a barrier. Interestingly, the green credentials of appliances were seen as
being less important by most of the interviewees. Manufacturers also suggested that
it was important to appropriately pitch the appliance cost, i.e. so that the technology
is taken seriously but the appliance is not too expensive.
A number of other key factors that may influence the uptake of Smart Appliances
were raised. One of the most important was the need for appliances to be ‘plug and
play’, so that they could be easily connected and operated by users.
It was generally regarded by the majority of respondents that Smart Appliances
should provide additional benefits to consumers over and above ‘non smart’
products. Appliances should be equal in all other respects compared to ‘non smart’
version, i.e. appliances should provide the same service to customers, but at a lower
energy cost. Alternatively, products should provide additional features that are
valued by the end-users themselves, e.g. they provide time saving features, make
things easier for consumers or improve quality of life. A system that allowed users to
link all their household appliances to a single control system may be seen as an
advantage by certain users.
Effective product labelling, so that consumers can readily understand the benefits of
Smart Appliances, was highlighted as a key factor in encouraging the uptake of
these appliances. This should include both information on the kWh/year savings or
the annual energy cost savings (compared to their overall energy costs).
Consumers need to have the confidence that they will be able to operate the
technology effectively, before they will make the decision to purchase. Thus
education is regarded to be an important factor in helping consumers understand the
benefits of Smart Appliances. However, it was recognised that the focus here should
be on explaining the benefits of Smart Appliances to the consumers themselves,
22
rather than in terms of societal benefits. In particular the benefits to the individual
need to outweigh any extra costs associated with the purchase of Smart Appliances.
Whilst a desire to have the ‘latest’ technology could stimulate certain users to
purchase Smart Appliances, it was suggested that Smart Appliances should be
aimed at the mass market as opposed to being seen as ‘geeky’.
For commercial customers, manufacturers and industry experts agree that quality,
reliability, return on their investment and the ability to reduce costs are important.
They have a greater potential for energy and financial savings and potentially
rewards from load shifting. Environmental benefits are a low priority according to a
number of the respondents, however others disagreed with this.
Running costs and capital costs
While initial costs are an important part of the decision to purchase an appliance,
running costs were seen as being of increasing, if not equal importance. This could
be particularly relevant as energy prices rise. While one appliance manufacturer
pointed out that their green appliances had sold well, most of those interviewed
suggested that environmental concerns were important only in so far as reducing
energy consumption resulted in lower bills. One respondent pointed out that running
costs are of less importance to the buyers of high end products, which is the part of
the market that Smart Appliances are being targeted at currently. These consumers
want an improved lifestyle.
The difficulty in estimating the running costs of an appliance for an individual user
due to the diversity of energy tariffs available was highlighted.
It was suggested that running costs may be more of an issue for items such as
electric forms of heating and electric vehicles. Running costs of smaller items were
not considered to be important at the current time. However, as users become more
aware of the cost of running individual appliances, this may also be a consideration
for these appliances in the coming years.
A number of respondents also commented that better labelling of an appliance’s
energy rating and running cost would help consumers understand the reduced cost
of running Smart Appliances.
Environmental Impact
Manufacturers generally thought that environmental concerns were less important to
consumers than saving money. However, it was acknowledged that existing,
environmental products do sell well. Local issues may also make environmental
concerns more important. The industry experts agreed that while consumers liked
the feeling that they were helping the environment, this is not regarded to be a main
driver for the majority of householders. Environmental concerns may influence
commercial decisions more because of Government policies and customers
examining companies’ ‘green’ credentials.
23
Awareness
Respondents were in agreement that customers, both domestic and commercial,
had little awareness of Smart Appliances and Smart Grids. However, it was thought
that consumers on Economy 7 tariffs are better placed to understand the concept.
Some industry experts suggested that little work had been done to investigate the
extent to which consumers were aware of Smart Appliances. Greater dialogue may
also help manufacturers to understand applications that customers may find useful.
It was thought that the introduction of Feed in Tariffs (FITs) had the potential to help
users with microgeneration understand the benefits of using electricity at particular
times, i.e. to minimise exports, and thus users understand the benefits of Smart
Appliances. It was suggested that a change in the relationship between consumers
and their energy company would be required before consumers could fully
understand the benefits (expressed in simple terms) of Smart Appliances. There
was also the view that consumers would need to change their attitudes towards
energy consumption in general, before the impact of Smart Appliances (i.e. the
impact of the pattern of consumption) could be fully appreciated.
Manufacturers suggested a number of reasons why purchasers may choose a Smart
Appliance ahead of a non smart one. These included saving money, extra
functionality, desire to keep up with the latest features because appliances last so
long (i.e. future proofing is important), lifestyle improvements and rebates for
participating in schemes.
More basic but appealing models may initially be useful while consumers get used to
the concept. The electricity network must also be ready to utilise the flexibility of
appliances, and the savings proven using real data. Local arguments, for example,
not having to build a new power station may help to influence consumer decisions.
Prices of the appliances must also be realistic in order to encourage consumers to
purchase them in preference to other ‘non smart’ products. Several of the industry
experts suggested that rather than letting householders have a choice between
Smart and conventional appliances, all appliances should be sold ‘Smart ready’, in
much the same way that TVs are currently sold HD ready.
It was suggested that Smart features may only be appeal to a minority of the market.
The importance of an override function, allowing users to have ultimate control over
how their appliances operate, was seen as a pre-requisite. It was also suggested
that consumers’ relationship with their Energy Supplier and attitude towards energy
needs to change drastically before they would actively choose Smart Appliances.
Financial savings may also be imperative. Others said that a ‘social revolution’ in
householders’ attitude towards energy was necessary.
Manufacturers suggested that a holistic education programme is required to promote
the benefits of Smart Appliances and associated technologies to the consumer.
Government, energy companies and manufacturers were all identified as having a
role in improving awareness. According to industry experts, Smart Appliances could
be better appreciated by consumers through improved product labelling and better
communication of the benefits. It was recognised that certain appliances appeal to a
certain segments of the population. One suggestion was also made for making
24
Smart Appliances the ‘default’, but provide consumer with the facility to ‘opt out’, i.e.
with an override function.
The importance of trust (i.e. that consumers trust that Smart Appliances are
providing benefits) was highlighted. The role of organisations such as Chambers of
Commerce, magazines (such as Which?, Radio Times, Women’s and DIY
magazines) were recognised has having the potential to play an important role with
regards to promoting trust.
The need for change to the current settlement system (the mechanism by which
energy trades are settled for each half-hourly trading period) was identified as a key
factor to unlocking the benefits of Smart Appliances, particularly for matching supply
to demand. Similarly, time-of-use tariffs were viewed as an important mechanism to
encourage changes to the pattern of consumption of electricity, and thus stimulate
the uptake of Smart Appliances.
Other benefits
Respondents were asked to comment on additional benefits associated with Smart
Appliances that could be an important factor in promoting their uptake. Suggestions
included:
- communication and connectivity features that can help improve lifestyle as
well as providing energy saving features. They may also be valued for
helping customers save money.
- a remote control function (that allows the users to remotely control their own
appliances);
- appliances that integrate into a domestic renewable system to optimise a
households’ power usage and thus optimise income from FITs.
However, it was also emphasised that Smart Appliances must offer the same or
better functions than normal or ‘non smart’ appliances. They also need to provide
demonstrable energy savings.
Role of Smart Meters
Some of the manufacturers’ representatives identified the rollout of Smart Meters as
being essential for the later adoption of Smart Appliances. This is because they
provide both energy consumption and cost information to households or because of
the reliance on the Home Area Network for the successful operation of Smart
Appliances. However, others suggested Smart Meters were not essential, with
Smart Appliances able to operate independently of Smart Meters.
However, it was generally agreed that Smart Meters that provide real time energy
information were important as an educational tool to allow householders to
understand the impact of individual appliances on their electricity load.
Smart Appliances as premium products
The manufacturers’ representatives nearly all agreed that Smart Appliances will
become ‘the norm’. Some will have additional functionality and be classed as
premium within the classification. Some suggested that support may be required
initially to help this happen however others suggested that widespread adoption
could happen without incentives. One suggestion was that Smart Plugs could be
25
provided by Energy Suppliers at a low cost to familiarise consumers with the concept
of Smart Appliances. Some industry experts suggested that Smart Appliances
should have enough value without being incentivised. Another suggested that all
appliances should be Smart and ToU tariffs used to incentivise off peak electricity
usage. It was also pointed out that those people who need to save money the most
may not be able to afford Smart Appliances and that schemes may be required to
help them.
Time of Use (ToU) Tariffs
Some manufacturers thought that ToU tariffs would be beneficial for helping to
promote more widespread adoption of Smart Appliances. All respondents suggested
that the products they would develop would have the functionality to be able to
decide when to run if ToU tariffs were introduced, although the importance of an over
ride function was stressed by one representative. One representative suggested
that ToU tariffs would be more acceptable to consumers if they were predictable
although this would be less cost effective to the utilities. Industry experts point out
that the public is familiar with Economy 7 so understand that electricity can be more
expensive at certain times, however some thought that it would be quite a jump from
this to understanding ToU pricing. Most suggested that for ToU tariffs to work a
substantial incentive may be required to reward consumers. One respondent
suggested that customers would need evidence that ToU tariffs were worth the risk
and inconvenience. Another suggested that they may assist with improving the
understanding of how much energy consumers are using, especially if they receive
financial benefits. It was generally thought that a far better understanding of
household’s energy use and attitudes towards energy is required. Another
suggested that attention should not be drawn away from reducing demand rather
than just moving it.
5.5
Barriers to the uptake of Smart Appliances
A range of issues were identified as representing potential barriers to the uptake of
Smart Appliances by users. Although none of the users were asked to comment on
how the costs of Smart Appliances would compare to ‘non smart’ products, there
was a general consensus that Smart Appliances would be more expensive and that
the additional costs could prove a barrier to uptake. The ease with which Smart
Appliances can be purchased was also cited as another factor that clearly influences
uptake, e.g. are they readily available from a range of outlets or only from specialist
retailers. The time pressures on commercial customers require that products must
be easy to use. Users must also be willing to change their energy use patterns.
Not all Smart Appliances respond to inherent changes in electricity network
properties. For those that don’t, a communications system is required to implement
changes to their pattern of operation. The Smart Meter provides such a
communications link directly to the home, thereby providing the opportunity to
directly control end-use loads. Other options include the installation of a dedicated
communications system or use of internet and WiFi enabled devices. However, the
readiness of the electricity network to interface with Smart Appliances is regarded to
be an important factor. The wide scale adoption of Smart Appliances is unlikely to
happen until Smart Meters and Smart Grids are in place. The need for an operating
26
platform into which appliances can be readily connected was highlighted, together
with interoperable appliances.
Concerns over data privacy, security and access to data are seen as a major barrier
to the implementation of Smart Appliances, particularly in terms of control by 3rd
parties. One respondent thought that trials and reports highlighting the advantages
to consumers may overcome some of these attitudes. Monetary incentives could be
used to stimulate the market through a transition period, although it was recognised
that users would require time to get used to the idea of control by 3rd parties. One
respondent suggested that a surcharge could be imposed for opting out of 3rd party
appliance control. The term ‘control’ was regarded as unhelpful and that the
decision must always remain with the consumer as to whether to respond – if this
option is removed then consumers will not like the concept.
It was generally agreed that consumers may not be happy to cede control of their
appliances to 3rd parties and safeguards would be required, for example, to limit the
maximum temperature in remotely controlled refrigerators, or to ensure that electric
cars were fully charged by a certain time. The intrusion may also be unwelcome.
Clear agreements about data use and data privacy would be required and customers
would have to be involved in any agreement between Energy Companies and 3rd
Parties. Some form of personalised feed-back may also make it more acceptable. It
was highlighted that in the UK, domestic electricity loads are not dominated by single
large loads as much as in, for example, regions of the USA where air conditioning
units cause a problem and can be readily targeted for load control. Simple
understandable systems may also be advantageous. It was also suggested that the
concept of load control by 3rd parties may be problematical because any advantage
is to the Energy Suppliers and Network Operators and not the consumer.
27
6
Barriers to Deployment and Uptake of Smart
Appliances in the UK
There are a number of barriers that impact on the potential deployment and uptake
of Smart Appliances in the UK. Some are generic barriers that apply to all types of
Smart Appliances whilst others are specific to particular appliance groups. This
Section describes the generic barriers, whilst those specific to particular appliance
groups are considered in Section 7.
The barriers to the successful deployment of Smart Appliances can, generally, be
classified into the following three categories:



Technology Related Barriers, i.e. those relating to the Smart Appliance itself
and any related infrastructure required to deliver the associated ‘Smart
functionality’;
Market Related Barriers, i.e. those relating to the structure of the electricity
market, the rules governing the way electricity is traded and the way that market
stakeholders interact with one another; and
Customer Related Barriers, i.e. those relating to customer attitudes towards
Smart Appliances and the way that customers interact and use their Smart
Appliances.
These are considered in further detail in the following Sections.
6.1
Technology Related Barriers
With the notable exception of night storage heaters remotely controlled using
teleswitching signals sent via the national radio broadcasting infrastructure, Smart
Appliances are not, as yet, mainstream products. However, devices are now
beginning to emerge that enable customers to remotely control individual appliances
and end use loads. A number of manufacturers have announced their intentions to
launch Smart products in the UK in the near future, but these are not yet readily
available.
Importantly, no clear consensus has yet emerged on the level of functionality that will
be offered by Smart Appliances. For example, Smart Appliances could comprise
any or all of the following features:
-
-
An appliance that can respond automatically to tariff signals sent by a Smart
Meter, such that the usage of these appliances is prevented or reduced during
high price periods, or rescheduled to coincide with low price periods;
An appliance that can be remotely disconnected by a third party for a limited
period of time;
An appliance that can schedule its operation to optimise the use of on-site
generation;
28
-
An appliance that can automatically detect the changes in the network, for
example frequency or voltage changes and respond automatically;
The term could equally describe appliances that provide non energy related benefits
to consumers. For example, appliances that can be remotely controlled by users for
added convenience.
Without an industry wide consensus on what constitutes a Smart Appliance, it is not
possible for customers to compare the functionality of one Smart Appliance with
another. For example, the term ‘Smart’ could be used to describe an appliance that
provides additional benefits over an above ‘standard’ models, i.e. a ‘Smart’ washing
machine may be one that provides customers with the ability to remotely update
washing cycle programs over the internet, rather than specifically relating to ‘Energy
Smart’ features.
Similarly, there are currently no criteria that define the minimum functionality that
must be achieved in order for an appliance to be classified as ‘Smart’. For example,
the Energy Saving Trust Recommended energy labelling scheme19 permits products
that meet strict criteria on energy efficiency to carry the ‘Energy Efficiency
Recommended’ logo, and a similar scheme could be applied to Smart Appliances.
The dialogue with key industry stakeholders highlighted that the readiness of the
electricity network to interact to Smart Appliances was seen as an important factor;
until Smart Meters and Smart Grids are in place it is difficult to foresee the wide
scale adoption of Smart Appliances that can be fully utilised. There is a need for a
standard operating platform into which appliances can be easily connected, rather
like the ‘plug and play’ platform adopted by Microsoft and the ‘apps’ used on Smart
Phones.
The most recent proposals for the rollout of Smart Meters in the UK are contained in
a suite of documents published by DECC on 18 August 201120. These include a
consultation on the obligation of Suppliers to rollout Smart Meters and on the
specification of metering equipment in the home.
It has been decided that the data collected from domestic Smart Meters will be
channelled through a central communications hub known as DataCommsCo (DCC).
This hub will provide two way communications with the Smart Meter. Parties such
as Energy Suppliers and Network Operators will be able to access data from Smart
Meters, as required to meet their licence obligations, or as authorized by individual
customers. The data flow is represented in Figure 6.1.
19
http://www.energysavingtrust.org.uk
20
Accessed via http://www.decc.gov.uk/en/content/cms/consultations/cons_smip/cons_smip.aspx
29
Figure 6.1 Data flow in proposed UK Smart Metering model
In addition to accessing data, the Smart Meters include the functionality to send
pricing or other messages to the customer premises. This will be facilitated through
a Home Area Network, as indicated in Figure 6.2 below. However, the mechanism
by which network operators and other third parties implement the control of end-use
loads such as Smart Appliances has yet to be defined.
Source: Smart Metering Implementation Programme: Statement of Design Requirements, 27 July 2010
Figure 6.2 Home Area Network
Thus, the Home Area Network is regarded as a crucial element in the
implementation of Smart Appliances, as it enables the existing two-way
communications system for the Smart Meter to be utilised to control the operation of
appliances. In this way, the appliances themselves may not therefore need to be
‘Smart’, but rather the decision making is undertaken within the Home Area Network.
However, the details of the Home Area Network have yet to be defined. Thus, the
specification of the Home Area Network is seen as a priority in terms of stimulating
the development and deployment of Smart Appliances.
6.2
Market Related Barriers
There are already a number of mechanisms in place that incentivise customers to
provide demand side services, for example frequency response and reserve services
to National Grid. Whilst these could include demand side services implemented
using Smart Appliances, there are a number of market related barriers that impact on
30
their deployment. Many of these relate specifically to the structure of the electricity
market and the rules governing the way electricity is traded.
Current thinking about the Smart Meter rollout strategy is contained in ‘Smart
Metering Implementation Programme: Rollout Strategy’21. It has been decided that
the rollout of Smart Meters will be led by Energy Suppliers.
Once Smart Meters are in place, the potential exists for Suppliers to offer a much
wider range of electricity tariffs than is currently available. This could include:
- Static ToU tariffs: to reflect the varying costs of providing electricity at different
times of the day (evening, peak, night etc);
- Real time (or day ahead tariffs), where prices vary half-hourly in line with
market price fluctuations; and
- Critical peak pricing, whereby the high prices are applied during specified
hours on a certain number of days, although the timing of these ‘critical days’
are not fixed in advance.
Energy retailing in the UK is a fully competitive market, with Energy Suppliers free to
decide the level and the structure of the energy tariffs they offer. Therefore, it is
difficult to predict how such tariffs will evolve as Smart Meters are rolled out.
However, dynamic tariffs (either as real time day ahead prices or critical peak
pricing) are regarded as a major stimulus for the deployment of Smart Appliances:
without some form of financial incentive it is unlikely that there will a significant
demand for Smart Appliances. It is, however, important to note that static ToU, i.e.
tariffs that do not vary on a day by day basis (other than perhaps seasonally) do not
require ‘Smart’ features, and instead require more simple behavioural changes or
simple timer functions.
It is important to note that ToU tariffs are not a prerequisite for the implementation of
Smart Appliances. Other incentive mechanisms can be put in place. Alternatives
might include one-off payments (suitable for all types of DSM measures) or
discounts from the Energy Tariff (applicable only for Supplier led DSM measures).
As discussed previously, as the proportion of wind generation increases over the
medium to longer term, it is likely that Suppliers could see wholesale electricity
prices fluctuate considerably, reflecting the availability of wind. Under such
conditions, Suppliers could face huge price risks and therefore, may be more likely to
introduce dynamic tariffs. However, in the short term, it is difficult to determine the
extent to which these tariffs will be offered by Energy Suppliers.
It is worth noting here that HM Government has recently published a White Paper
focused on reforms to the electricity market22. The Paper identifies a need for the
introduction of capacity payments to encourage investment in flexible reserve plant.
The introduction of such payments could reduce the ‘peakiness’ of electricity prices,
and hence on one of the drivers for Smart Appliances. However, the payments
would be equally available to demand side participants, thus providing an incentive
to implement demand side programmes.
21
22
Smart Metering Implementation Programme, A consultation on draft licence conditions and technical specifications for the
roll-out of gas and electricity smart metering equipment, August 2011
Planning our electric future: a White Paper for secure, affordable and low carbon electricity, July 2011
31
As discussed in Section 2, Smart Appliances have the potential to assist Network
Operators manage network constraints, and thus defer or avoid network
reinforcement. However, the unbundling of the electricity industry means that DNOs
no longer have any direct engagement with end use customers. The charges levied
by DNOs to recover the costs associated with distributing electricity to end-users
(known as Distribution Use of System (DUoS) charges) are collected by Energy
Suppliers rather than directly via the DNOs. Therefore, it is it is up to the Energy
Suppliers how these charges are bundled together with the energy tariff for HV and
LV customers. Whereas DNOs have introduced ToU distribution charging for HV
and LV customers with half-hourly metering (i.e. with maximum demands above
100kW), such charging is not in place for domestic and small commercial users.
These customers are currently charged on a flat unit rate, or a two rate tariff for
those on an off peak tariff. Therefore, DNOs are faced with making direct
contractual arrangements with end-users, or third party aggregators. The details of
these arrangements are yet to be fully established, and are currently being
investigated as part of a number of the Low Carbon Network Fund trials that are
currently underway.
In an unbundled electricity system, the benefits of Smart Appliances are spread
amongst a number of stakeholders, rather than within a single, vertically integrated
utility company. Thus it is difficult to ensure that the costs and benefits are shared
appropriately between the relevant stakeholders. For example, the benefits of peak
load reduction could be shared amongst:
- the Energy Supplier (via reduced purchase costs of electricity at the peak);
and
- the DNO (via deferred or avoided network investment)
The profiling settlement system23 that is currently used to allocate the total annual
consumption of households and other small users24 into half-hourly blocks means
that it is difficult for energy suppliers to capture the value associated with load
shifting. Under the current arrangements, the pattern of consumption of customers
is determined according to a profile, and total consumption is allocated to specific
half-hours according to these profiles. Customers are classified into one of eight
profile classes. However, within these classes, customers with demand
management are treated in the same way as customers without demand
management. This is regarded to have been a major barrier to the introduction of
innovative tariffs or direct load control schemes since the system was introduced in
1998.
The introduction of Smart Meters means that it will be possible for all electricity
consumption to be settled according to their actual profile, rather than a ‘deemed’
profile. However, there is no requirement on Energy Suppliers to move away from
the use of the current profiling system for domestic customers and some small nondomestic customers25, although they are able to do so if they wish.
23
For a description of the load profiles and their use in electricity settlement see
http://data.ukedc.rl.ac.uk/browse/edc/Electricity/LoadProfile/doc/Load_Profiles.pdf
24
With a maximum demand less than 100kW
25
A proposal to introduce half-hourly settlement for all customers currently in profile classes 5 to 8 is pending, but no such
requirements are yet proposed for customers in profile classes 1 to 4 which includes domestic customers.
32
6.3
Customer Related Barriers
Customer attitudes towards Smart Appliances represents one of the most significant
barriers to the widescale deployment of these devices. In a situation where
‘competitive market forces’ dominate, then the customer is the ultimate decision
maker. If customers do not want the appliances, or if developers do not envisage a
market where customers will want the appliances, then it is evident that the market
for such products will not develop.
As outlined in Section 2, there are a number of clear drivers for Smart Appliances.
However, the ‘business case’ for their deployment in the UK is far from clear. In
particular, it is difficult to quantify the impact (i.e. MWh of electricity saved, MW of
peak demand reduction and tonnes of CO2 saved) or to quantify the implementation
costs (i.e. development costs, capital costs and on-going operational costs). The
case studies summarised in Section 7 go some way toward exploring some of the
issues and demonstrate the difficulty of quantifying the benefits.
Whilst a number of pilots and trials of Smart Appliances have been undertaken,
many of these have taken place overseas and have focussed on large single enduse applications such as heating or air conditioning. No detailed studies have been
undertaken to quantify the potential magnitude of savings in the UK and whether the
costs of achieving these savings are economic.
The dialogue with key industry stakeholders highlights the importance of increasing
customer awareness of Smart Appliances. However, the issue of motivating
customers to become more energy aware must not be underestimated. The use of
behavioural economics is now becoming seen as increasingly important in the role of
encouraging energy efficiency. For example, it is becoming increasingly evident that
customers do not make decisions on a purely rational economic basis26, and
therefore, a purely economical argument that customers will save money if they
adopt Smart Appliances is unlikely to be sufficient to promote their adoption.
Similarly, whilst it is widely recognised that customers need to be made aware of the
benefits of Smart Appliances, there is growing evidence to show that the way
information is presented to customers is an important factor. A good example of this
is provided in Nudge27, which suggests that presenting energy information in terms
of how much money customers have just lost by not pursuing a certain action may
be far more effective that presenting information on how much could be saved.
Similarly, a recent report from Consumer Focus28 recognises the importance of the
role of information in changing energy related behaviours. It seems reasonable to
suggest that such arguments will apply equally to Smart Appliances as they do to
energy efficiency.
Consumers are likely to need reassurances before they will permit third parties to
remotely control their appliances. This will require understanding of customers own
views and concerns regarding Smart Appliances. Also, as highlighted in Section 5,
26
Predictably Irrational, Dan Ariely
27
Nudge – improving decisions about health, wealth and happiness, Thaler and Sunstein
28
Missing the Mark, Consumers, Energy Bills, Annual Statements and Behaviour Change, Hannah Mummery and Gillian
Cooper, Consumer Focus, June 2011
33
close liaison with customer representatives who are well placed to solicit the view of
customers, as well as being seen as trusted sources of information, will be useful in
terms of allaying customer concerns. This could be particularly relevant where there
is conflicting advice from different organisations. For example, a campaign by
Tayside Fire Department recommended that customers do not operate appliances at
night or when their properties are unoccupied because “more house fires are caused
by electrical appliances than anything else”29.
29
http://www.taysidefire.gov.uk/page/Switch_Off_173.html accessed 1 June 2011
34
7
Impact of Selected Smart Appliances
Smart Appliances enable consumers to modify the amount of energy they consume
and/or the pattern of their consumption in response to certain external stimuli.
Although a project is currently underway to monitor the end-use consumption
patterns in a group of UK households, no data is currently available from this trial30.
However, a number of illustrative load profiles were produced for a range of
European countries as part of a research project to investigate the potential of Smart
Appliances. The data for an average UK household is shown in Figure 7.1, and
shows a break-down of the pattern of consumption by different electricity end uses
for domestic customers, both now and for 2025. These profiles do not include
lighting, consumer electronics and entertainment loads. Although electric heating
loads are included, the profiles are believed to represent the average heating load
profile across all households (i.e. those with and without electric heating). Thus, the
profiles below do not provide a good representation of the load profile of homes with
electric heating only.
2010
2025
WH = Water heating
CP = Circulating Pumps
A/C = Air conditioning
DW = Dishwasher
OS = Oven / stove
RF = Refrigeration
TD = Tumble dryer
FR = Freezers
WM = Washing Machine
EH = Electric heating
Source: Synergy Potential of Smart Appliances, D2.3 of WP 2 of from the Smart-A Project
Figure 7.1 Estimated Daily Load Curve of an Average UK Household
30
A Study of the Energy Consumption of Domestic Products in UK Households on Behalf of the Department of the
Environment, Food and Rural Affairs, the Department for Energy and Climate Change and the Energy Saving Trust,
AEA/TEN/244
35
As discussed in Section 3, many end-uses are not well suited to demand
management, namely lighting and consumer electronics. Thus, of those loads
shown in Figure 7.1 that can be interrupted or rescheduled (see Appendix B for
details of the classification of loads for DSM), those that contribute most to system
peak demands, both now and in the future are:




Refrigeration
Central heating circulating pumps
Washing machines
Tumble dryers
It is interesting to note that central heating circulating pumps (circulators) represent a
significant end-use of electricity in domestic properties. EcoDesign31 requirements,
introduced by the EU Commission, impose energy efficiency Standards on a range
of products to reduce their environmental impact. The Ecodesign requirements for
circulators are set out in the relevant legislation32: The efficiency requirements will
become mandatory in January 2013, and once in force it would be expected that the
energy efficiency of circulating pumps will increase significantly. Therefore, the role
of Smart circulators is not considered within the scope of this study.
The decarbonisation of the electricity generation sector will see significant growth in
new electric loads, particularly heating and transport. As mentioned previously,
electric vehicles are not within the scope of this current study. In terms of the growth
in electric heating, this is likely to be dominated by heat pumps rather than direct
electric heating or electric storage heating. For example, a number of potential
pathways that lead to an 80% reduction in carbon emissions by 2050 have been
considered by the Department of Energy and Climate Change (DECC) in its ‘2050
Pathways Analysis’33. Although the pathways do not attempt to forecast particular
outcomes, the six illustrative pathways that were developed demonstrate an
expectation that heat pumps will be the main form of heating for both the domestic
and commercial sectors in future years. Consequently heat pumps are likely to
contribute significantly to increases in both overall electricity consumption and peak
demand within both domestic and commercial buildings in the future. However, they
are also well suited to demand management and are therefore a potentially valuable
resource to help mitigate against increases in peak demand attributed to their
uptake.
It is important to note that, although not specifically considered here, direct electric
heating and storage heating are also well suited to demand management. For
example, electric storage heating is a well established DSM measure in the UK,
developed specifically to optimise the use of low cost electricity from nuclear
generation during the off-peak period. However, as it is generally anticipated that
growth in electric heating will be attributable to heat pumps, only this form of electric
heating is considered in the case studies.
31
http://www.eup-ecodesign.com/
32
http://ec.europa.eu/enterprise/policies/sustainable-business/documents/eco-design/legislation/implementingmeasures/index_en.htm
33
2050 Pathways Analysis, available from http://www.decc.gov.uk/
36
Very little is known on the breakdown of energy consumption patterns within the
small commercial sector, with even less known on the pattern of electricity
consumption within this sector. Therefore, it is difficult to identify particular end uses
that are well suited to demand management within small commercial buildings.
However, as highlighted in Appendix B, air conditioning currently represents around
10% of electricity end-use consumption in all commercial buildings, and there is an
expectation that air conditioning loads will increase in future years. In addition, air
conditioning was highlighted as a key area for Smart Appliance development in the
commercial sector during the dialogue with the key Stakeholders. Therefore, air
conditioning is also considered here.
7.1
Case Studies
A number of case studies have been used to better understand the potential impact
of a selection of Smart Appliances and the associated barriers to their uptake. The
five appliance types considered are as follows:
 Heat pumps;
 Air conditioners.
 Refrigerators (cold appliances);
 Washing machines; and
 Tumble dryers;
Each case study considers the potential impact of each of the selected appliances
under three scenarios as described below;



Business As Usual. This is the baseline against which the Smart Appliance
options are compared. It represents what is likely to happen in the future if no
specific measures are put in place to encourage the implementation of Smart
Appliances over and above the measures that are currently in place or planned.
Thus, the Business As Usual incorporates the role of Smart Meters to all
consumers by 2019 and assumes that customer attitudes to energy consumption
do not change significantly over the period. This scenario does, however,
incorporate improvements in appliance efficiencies arising through an ambitious
and feasible programme of policy measures34.
Smart Appliances – Level 1: This scenario explores the potential impact of
Smart Appliances that incorporate a ‘minimal’ degree of smartness, and requires
relatively modest developments to existing appliance designs.
Smart Appliances – Level 2: This scenario explores what could be achieved
through a greater degree of smartness, and considers the extent to which Smart
Appliances could enable consumers to modify their energy consumption.
The following provides a summary description of the Level 1 and Level 2 for each of
the Smart Appliances under consideration. Detailed descriptions of each of the case
studies can be found in Appendix E.
-
34
For both heat pumps and air conditioning, Level 1 does not require any
changes to the design of the standard system other than the means of
The Market Transformation Programme’s Policy Scenario
37
remotely switching the appliance off for short periods. Level 2 requires design
changes to incorporate thermal storage.
7.2
-
For refrigeration, Level 1 appliances include the capability to provide dynamic
frequency response services to National Grid by automatically adjusting to
changes in system frequency. Level 2 appliances require further
developments to the dynamic demand concept to allow extended interruptions
of, say, half an hour.
-
For washing machines and tumble dryers, Level 1 does not require any
changes to the design of the existing appliances, but rather relies on changes
in human behaviour to minimise usage during certain periods. Level 2, then
requires design changes to enable these appliances to be automatically
scheduled to operate during prescribed periods. Alternative designs that
allow the operating cycles to be interrupted or to defer the heating load are
also briefly considered.
Case Study Findings
As described in Section 2, there are a number of drivers to manage the pattern of
demand in the UK, including:
- The provision of balancing services to National Grid;
- The management of network constraints;
- The avoidance of peak energy prices; and
- The integration of renewable generation resources.
Smart Appliances enable customers to adjust their pattern of electricity consumption
in a coordinated and controlled manner so that the modified pattern of demand can
be relied upon for the purposes described above. As such, Smart Appliances are
one of the suite of measures that could help the UK meets its policy objectives in
terms of carbon emissions reduction, the facilitation of the uptake of renewable
energy sources and in reducing overall energy consumption.
The generic requirements for demand management are described in Section 3,
which highlights how requirements vary depending upon the purpose for which the
demand response is to be used. Thus, not all end-use loads are suitable for all
purposes.
The case studies outlined in Appendix E describe the potential impacts associated
with the deployment of the selected Smart Appliances, and the specific barriers to
the uptake of each technology. This information has been used to compare the
benefits and barriers associated with the selected Smart Appliances. The
comparison is presented in Table 7.1, which provides a qualitative assessment of the
Level 1 and Level 2 scenarios compared to the Business As Usual baseline.
38
Table 7.1 Overview of Case Study Findings
Appliance
Heat
Pumps*
Benefits
(Relative Impact compared to
Busines As Usual)
Network
constraints
management
Barriers
(Ease of
Implementation)
Smartness
Level
Technology
*
1
Air
Conditioners
2
1
2
Cold
Appliances
1
2
Washing
Machines
1
2
Tumble
Dryers
1
Ancillary
Services
Energy peak
price
avoidance
Renewables
integration
Market
Customer
Bi-valent and fuel substitution options are not included under heat pumps (see Appendix E for
discussion) but would, in principle, provide significant benefits and relative ease of implementation
Key:
Benefits
Benefits compared to the ‘Business As Usual’ baseline are potentially significant
Potential benefits compared to the ‘Business As Usual’ baseline, but these are not
regarded to be significant
Minimal or no benefits compared to the ‘Business As Usual’ baseline
Barriers
Minimal barriers to deployment
Some barriers to deployment
Significant barriers to deployment
39
2
8
Roadmap for the deployment of Smart
Appliances in the UK
As described in the preceding Sections, there are a wide range of barriers to the
uptake of Smart Appliances. In order to identify specific actions to overcome these
barriers, and the timescales over which they these should be undertaken, a number
of case studies were developed (see Section 7). These provide an overview of the
potential impact of Smart Appliances compared to a ‘Business As Usual’ scenario
and consider the ease with which the various measures can be implemented in a UK
context. This information has been used to produce a Priority Matrix, as illustrated
Figure 8.1. This provides a useful framework for prioritising activity by comparing the
relative impact and ease of implementation of a range of measures. It is particularly
useful in identifying the ‘quick wins’, (i.e. measures that have a high impact and are
easy to implement) and those that should be avoided (i.e. those with a low impact
and are difficult to implement).
High
High Impact
High Ease of Implementation
“quick wins”
Impact
High Impact
Low Ease of Implementation
“review”
Low Impact
Low Impact
Low Ease of Implementation High Ease of Implementation
“avoid”
“possible”
Low
Low
Ease of Implementation
High
Figure 8.1 Illustrative Priority Matrix
Therefore Figure 8.2 illustrates the relative merits (i.e. the impact) and the ease of
implementation of two levels of Smart Appliances (Level 1 and Level 2 as described
in Section 7) compared to a non smart version (i.e. the baseline or business as
usual). In this context, easy to implement includes measures that require little to no
changes to existing appliance design and/or measures that have no significant
barriers to entry.
It is important to note, however, that the case studies presented in Section 7 are not
based on extensive modeling to quantify the relative impacts of different approaches
and the ease of implementation. Rather, they represent a largely qualitative
approach that considers the situation for each of the appliances considered, e.g.
does a Smart washing machine offer significantly improved benefits compared to a
40
non-Smart appliance. In addition, the case studies do not explore the potential
impact of the level of penetration of Smart Appliances. Therefore, further work may
be required to establish a detailed comparison between different appliances for
varying penetration levels. Thus, the comparison shown in Figure 8.2 provides a
qualitative assessment of the level of demand response and the associated ease of
implementation that could be reasonably predicted to be achievable for the case
studies under consideration.
In each case, the impact is considered in terms of specific applications for the
resulting demand response. The impact for each appliance (and for each Level of
Smartness) is considered for the following two options:
A. For shorter term measures, i.e. balancing services (primarily frequency
response services lasting typically up to half and hour). This could also
include short duration (i.e. up to half an hour) interruptions to assist with the
avoidance of peak prices.
B. For longer term measures, i.e. managing network constraints (up to three
hours), and renewables integration (action over several days). Also included
within this category is the provision of reserve services, typically required for a
period of 2 hours.
High
Thus, in Figure 8.2, L1A refers to an appliance with a Smartness Level of 1 applied
to a short term measure; L2B refers to an appliance with a Smartness Level of 2
applied to a longer term measure.
L2A & B
L2A & B
Key:
L1A
L2A
Heat Pumps
L1A
L1A
Washing Machines /
Tumble Dryers
Impact
Refrigeration
Air-conditioners
L1 - Level 1 smart appliance
L1B
L2 - Level 2 smart appliance
L1B
L2B
A - Short term interruptions
L1B
B - Longer term interruptions
L2A
L1A
Low
L2 B
L1B
Low
High
Ease of Implementation
Figure 8.2 Impact / Ease of Implementation Matrix for Selected Smart Appliances
41
8.1
Actions Required to Deliver the Benefits of Smart Appliances
High Impact / High Ease of Implementation (“quick wins”)
Those appliance groups falling within the ‘high impact, high ease of implementation’
area are:
- Heat pumps, L1A: simple on/off control
- Air conditioners, L1A: simple on/off control
Thus, actions to be considered over the short term include that relate specifically to
the uptake of heat pumps and air conditioners are:
-
A better understanding of the level of interrupt that is acceptable to
householders and commercial customers is needed, i.e. for how long can
these appliances be switched off without comfort levels being significantly
affected, and what the system impacts might be of the post-interrupt restart.
-
An investigation into the relative value (to the UK) of the capability of heat
pumps and air conditioners to deliver short term interrupts compared to the
value of delivering longer term interrupts is required. As seen in Figure 8.2,
none of the appliance measures considered in Section 7 are positioned in the
far top-right of the ‘quick wins’ quadrant. The measures with the highest
impact sit in the top-left quadrant, i.e. high impact / low ease of
implementation. Therefore, it is considered essential to obtain a more
detailed understanding of the relative benefits of different DSM measures to
the UK, i.e. how much more valuable are Level 2 heat pumps and air
conditioners compared to Level 1 versions. If the benefits are significantly
greater, then there may be merit in focusing on the higher impact solutions
rather than just on the quick wins. In particular, there is the possibility that
focusing on the ‘quick wins’ in the short term may make it less likely for the
more desirable solutions to be progressed at a later time.
-
Even though the measures here involve the simple on/off control of heat
pumps and air conditioners, experience from international field trials show that
minimum appliance Standards will be required particularly in terms of the
interface between the appliance and (in the UK context) the Home Area
Network.
In addition to the appliance specific measures listed above, there are a number of
more generic actions required to facilitate their uptake in the UK. Thus actions
applicable to all Smart Appliances are:
-
The role of the Smart Meter is a crucial element in providing a cost effective
communications interface with the end-user. Therefore, the mechanism by
which Energy Suppliers and other 3rd parties issue instructions to these
appliances via the Smart Meter and the Home Area Network needs to be
established. In particular, the role of the Data Communications Company
(DCC) in handling these communications needs to be defined, including the
requirements for the speed of communications (i.e. how quickly can
messages be sent), the prioritisation of messages and the frequency of
communications (i.e. how often can messages be sent).
42
-
The protocols for communication with Smart Appliances need to be agreed, in
the form of either voluntary or mandated Standards. These Standards will
need to be developed within an EU context as appliances are not generally
produced specifically for a UK market. However, it will be essential to ensure
that the specific requirements of the UK market structure and the Smart Meter
specification currently being finalised for the UK roll-out are addressed.
-
A better understanding is required of the financial mechanisms that need to
be put in place to reward customers for allowing their appliances to be
remotely controlled by 3rd parties. This should include a review of the role of
ToU tariffs and other financial rewards.
-
Whilst ToU tariffs can be implemented within the current settlement
arrangements, the use of standard profiles for households and selected
smaller non-domestic customers35 could limit the ability to capture the benefits
of modifying the load profile for these customers. Therefore, consideration
needs to be given to the potential implementation of half-hourly settlements
for all customers once Smart Meters are rolled out.
-
Customer engagement and awareness is a key factor in ensuring the
successful uptake of Smart Appliances. Therefore, a better understanding of
customer acceptance of 3rd party control of appliances is regarded to be an
essential prerequisite. Otherwise, there is a risk that products and services
are developed without due regard for the concerns of the customers
themselves.
High Impact, Low Ease of Implementation (“review”)
Those appliances with the potential to provide a high impact, but with a lower ease of
implementation compared to those listed above include:
- Heat Pumps, L2A & L2B: fleet operation or incorporation of storage to allow
maximum flexibility for the full range of DSM measures
- Air conditioners, L2A & L2B: incorporation of storage
- And Refrigeration (cold appliances) L1A and L2A: short-term interrupts using
varying levels of appliance control complexity.
Many of the actions highlighted above (under “quick wins”) apply equally to the
appliance groups falling in the “review” quadrant. Therefore, additional actions to be
considered over a longer time horizon to facilitate the uptake these potentially
desirable measures are:
-
35
Further investigation and research into the role of thermal storage in providing
heat pumps with more flexibility for DSM measures. This includes obtaining a
better understanding of the advantages of thermal storage versus the
increase capital costs and the impact on space requirements. This also
applies to air conditioning, although arguably to a lesser extent that for heat
pumps as space limitations maybe less onerous in a commercial building
context.
Non maximum demand customers (<100kW) settled according to profile classes 3 and 4
43
-
The benefits associated with the provision of dynamic frequency response
services to National Grid by cold appliances is well understood, and
appliances incorporating this technology are currently undergoing extensive
field trial evaluations. However, the major obstacle to the role out of the
technology (and thus the reason this has been placed in the “review” quadrant
rather than the “quick wins” quadrant) is the lack of any financial mechanism
to encourage the uptake of these appliances by end-users. Therefore, there
could be merit in investigating whether any appropriate financial mechanisms
could be put in place to incentivise customers to purchase appliances with
dynamic frequency response capability. This is especially relevant given that
the annual benefits amount to a few pounds per customer per year.
Low Impact, High Ease of Implementation (“possible”)
Those appliances falling within the ‘low impact, high ease of implementation’ area
include:
- Heat Pumps, L1B: simple on/off control;
- Air conditioners, L1B: simple on/off control;
As the impact associated with these measures is considered to be low, no specific
actions are considered appropriate at this time. However, there may be merit in
revisiting this area once all other actions have been implemented and the impacts
measured.
Low Impact, Low Ease of Implementation (“avoid”)
Those appliances falling within the ‘low impact, low ease of implementation’ area
include:
- Washing Machines & Tumble Dryers, L1A and L1B: user education
- Washing Machines & Tumble Dryers, L2A and L2B: enhanced operational
capability to allow washing machine and tumble dryer loads to be
automatically scheduled (B) and/or interrupted mid cycle (A);
- Cold Appliances, L1B and L2B
In all of these cases, the low impact reflects the relatively small average energy use
of the appliances concerned.
As both the impact and the ease of implementation associated with these measures
are considered to be low, no specific actions are considered appropriate at this time.
However, raising consumer awareness of the importance of the pattern of energy
consumption as well as of the overall amount of energy consumed is nevertheless
considered to be important. Here, behavioural science and behavioural economics
may provide useful tools for how this information can be best communicated to
consumers.
8.2
Timelines for Implementation
Figure 8.3 provides a suggested timeline for the implementation of the actions
described in Section 8.1, which also includes key dates in the Smart Meter
Implementation Programme36.
36
Smart Metering Implementation Programme, Response to Prospectus Consultation, Overview Document, March 2011
44
2011
2012
2013
2014
2015
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 …..
Smart Metering Implemention Programme
Draft technical specification
Draft technical specification complete
EU Notification period for technical specification

(*)

Regulatory Obligations on Suppliers come into force - Tranche 1
(*) Mandated roll-out completion date, Installation code of practice
DCC licence application process
DCC licence awarded
DCC service providers appointed


Smart' change of supplier arrangements become standard

Mass roll-out
Delivering the Benefits of Smart Appliances
Generic actions
Procedures for 3rd party communication with Smart Appliances via DCC and HAN
Standard protocols for communicating with Smart Appliances
Review of profile settlement arrangements
Customer engagement
Appliance specific actions
Relative value of short vs long interuptions for heat pumps, air conditioning loads to UK.
Level of interupts acceptable to customers (heat pumps, air-conditioning loads)
Define programme of work
Undertake analysis/trials (two heating/cooling seasons)
Interim findings
Delivery of findings


Design specification for heat pumps
Figure 8.3 Timeline for Implementation
45
2019
2020
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
9
Concluding Remarks
This report highlights that Smart Appliances represent one of a suite of measures
that could help the UK meet its goals for reducing energy consumption, reducing
carbon emissions and the provision of energy from renewable sources. However, as
have been cited throughout this document, there are wide ranging barriers to their
uptake in the UK. Many of these barriers are generic to all types of Smart
Appliances, whilst others relate to issues particular to specific Appliances. All are
equally important – unless they are addressed it will be unlikely that the use of Smart
Appliances will become widespread.
The roadmap presented in Section 8 demonstrates the relative merits of a range of
Smart Appliances in terms of their impact (in providing certain DSM measures) and
the ease with which they can be implemented. This shows that of the appliances
considered, heat pumps and air conditioners that provide short term interruptions
represent ‘quick-wins’, i.e. can be implemented with relatively high ease with
reasonable impact. In order for these to be implemented, therefore, the priority
actions to be completed are as summarised below:
-
A better understanding of the level of interrupt that is acceptable to
householders and commercial customers is needed, i.e. for how long can
these appliances be switched off without comfort levels being significantly
affected, and what the system impacts might be of the post-interrupt restart.
-
Even though the measures here involve the simple on/off control of heat
pumps and air conditioners, experience from international field trials show that
minimum appliance Standards will be required particularly in terms of the
interface between the appliance and (in the UK context) the Home Area
Network.
-
The role of the Smart Meter is a crucial element in providing a cost effective
communications interface with the end-user. Therefore, the mechanism by
which Energy Suppliers and other 3rd parties issue instructions to these
appliances via the Smart Meter and the Home Area Network needs to be
established. In particular, the role of the Data Communications Company
(DCC) in handling these communications needs to be defined, including the
requirements for the speed of communications (i.e. how quickly can
messages be sent) and the frequency of communications (i.e. how often can
messages be sent).
-
The protocols for communication with Smart Appliances need to be agreed, in
the form of either voluntary or mandated Standards. These Standards will
need to be developed within an EU context as appliances are not generally
produced specifically for a UK market. However, it will be essential to ensure
that the specific requirements of the UK market structure and the Smart Meter
specification currently being finalised for the UK roll-out are addressed.
46
-
A better understanding is required of the financial mechanisms that need to
be put in place to reward customers for allowing their appliances to be
remotely controlled by 3rd parties. This should include a review of the role of
ToU tariffs and other financial rewards.
-
Whilst ToU tariffs can be implemented within the current arrangements, the
current use of standard profiles for households and selected smaller nondomestic customers37 could limit the ability to capture the benefits of
modifying the load profile for these customers. Therefore, potential
implementation of half-hourly settlements for all customers once Smart Meters
are rolled out needs to be given consideration.
-
Customer engagement and awareness is a key factor in ensuring the
successful uptake of Smart Appliances. Therefore, a better understanding of
customer acceptance of 3rd party control of appliances is regarded to be an
essential prerequisite. Otherwise, there is a risk that products and services
are developed without due regard for the concerns of the customers
themselves.
It is important to note that the simple on/off control of heat pumps and air
conditioners are not believed to represent the measures that have the potential to
deliver the highest impact. This is attributed to the provision of longer duration
interruptions by these appliances.
The expected increase in the uptake of heat pumps and, perhaps to a lesser extent,
air conditioning is anticipated to lead to significant increases in electricity
consumption and peak demands. Thus, although these loads do result in increased
peak demands, they could also provide part of the solution. Both air conditioners
and heat pumps provide “quick wins” through simple remote on/off controls.
However, it is believed that these may only provide limited benefits (short term
interrupts) whilst it is the application of more complex designs that have the potential
to provide greater benefits (both short term and longer term interrupts). A crucial
question is thus: how “valuable” are the benefits arising from these more complex
designs compared to the simpler “quick wins”.
It is therefore considered essential to obtain a more detailed understanding of the
relative benefits of different DSM measures to the UK, i.e. how much more valuable
are short term interruptions than longer term interruptions. If the benefits are
significantly greater, then there may be merit in focusing on the higher impact
solutions rather than just on the “quick wins”. There is a risk that focusing on the
‘quick wins’ in the short term may make it less likely for the more desirable solutions
to be progressed at a later time. Therefore, further investigation and research into
the role of thermal storage in providing heat pumps and air conditioning more
flexibility for DSM measures could be considered as a priority activity over the short
term. This includes obtaining a better understanding of the advantages of thermal
storage versus the increase capital costs and the impact on space requirements.
37
Non maximum demand customers (<100kW) settled according to profile classes 3 and 4
47
Consideration should be given to introducing minimum design Standards for heat
pumps in the UK. These Standards could, for example, prohibit the use of heat
pumps incorporating an electric flow boiler, or alternatively ensure that such heat
pumps are required to include the provision for remote or automatic on/off control of
the flow boiler. Due to the single market rules, such Standards would need to be
delivered at the European level. This would be difficult unless there is widespread
support across Europe for such Standards. Additionally, consideration needs to be
given to the potential role of bivalent heat pumps (i.e where the peak heating load is
met by an auxiliary heating system, usually a gas or oil boiler38), which have the
potential to make the implementation of DSM easier to implement, but could reduce
the carbon benefit of heat pumps.
The provision of dynamic frequency response by cold appliances represents the
most developed demonstration of a new39 Smart Appliance in the UK, and is
currently undergoing extensive field trial evaluations. However, the major obstacle to
the role out of the technology (and thus the reason this is not considered to be
represent a “quick win”) is the lack of any financial mechanism to encourage the
uptake of these appliances by end-users. Therefore, there could be merit in
investigating whether any appropriate financial mechanisms could be put in place to
incentivise customers to purchase appliances with dynamic frequency response
capability. This would also be of relevance to other end use loads suitable for
dynamic frequency response, such as air conditioning and hot water heaters.
Probably one of the most important factors affecting the uptake of Smart Appliances
is customer awareness and acceptance. If customers do not want to allow 3rd
parties to remotely control their appliances or to employ technologies that can
automatically schedule the operation of their appliances, then it is unlikely that Smart
Appliances will be able to deliver any of their potential benefits - even if all the
measures identified here are addressed. Therefore raising awareness of the
importance of the pattern of energy consumption as well as of the overall amount of
energy consumption is considered to be important. Here, it is believed that
behavioural science and behavioural economics can provide useful tools to ensure
such information can be effectively communicated to consumers.
38
39
- see Appendix E – Heat Pump Case Study for a description of bivalent heat pumps
The radio teleswitch control of night storage heating is a well established application of a Smart Appliance in the UK used
over a number of years.
48
Appendix A: End Use Categories
An appliance can be very loosely defined as:
“A device or piece of equipment designed to perform a specific task”40
In the context of energy consumption, the term ‘appliance’ generally refers to the
following specific domestic energy end-uses:
 Cold appliances, such as fridges and freezers;
 Wet appliances, such as dishwashers, washing machines and tumble
driers;
 ICT; such as home computing, mobile phones and home printing;
 Entertainment, such as TVs, digital receivers, music systems; and
 Cooking; including microwaves, toasters and kettles.
As indicated in Figure A.1 below, electric space and hot water heating account for
around a quarter of the electricity used by all UK households. These end-uses can
respond to Time of Use (ToU) price signals and/or be controlled remotely by third
parties and therefore represent potentially valuable demand resources.
Space heating
13%
Hot Water
14%
Cooking
6%
Lighting & Appliances
67%
Figure A.1 Domestic Electricity Consumption, 200841
Electric heating is ideally suited to demand management, as it can be interrupted
with little or no notice and often with minimal short term impact on end-users.
40
Oxford Dictionairies Online. Accessed 25/01/11 from:
http://oxforddictionaries.com/view/entry/m_en_gb0035470#m_en_gb0035470
41
Energy consumption in the United Kingdom, Domestic Data Tables, available at
http://www.decc.gov.uk/en/content/cms/statistics/publications/ecuk/ecuk.aspx
A-1
However, a significant proportion of this load currently occurs during the off-peak
period and thus its management would have little impact on peak demand for
electricity. However, as the amount of electricity generated from wind increases over
the coming years, electric heating provides a valuable resource to help optimise the
use of this variable generation resource during the off-peak period. Additionally, the
de-carbonisation of the electricity generation sector over the coming years, and the
move towards zero carbon homes, will see an increase in the number of homes
heated with electricity, with heat pumps seen as a key technology. As such, electric
heating is considered to be a valuable resource for helping to manage the demand
pattern of households. Similarly, it is suggested that hot water heating also be
included within the scope of the project.
Whilst air conditioning does not represent a significant demand for electricity within
the domestic sector, it does account for a sizeable proportion of electricity consumed
in the commercial sector, as indicated in Figure A.2 below. Therefore, it is
suggested that the control of air conditioning loads, for example via the
implementation of ‘Smart thermostats’ be included within the scope of this study.
Warehouses
Sport and Leisure
Retail
Other
Catering
Computing
Hotel and Catering
Cooling and Ventilation
Hot Water
Heating
Health
Lighting
Other
Government
Education
Communication and
Transport
Commercial Offices
0%
25%
50%
75%
100%
Figure A.2 Electricity Consumption by End-Use in the Commercial Sector, 200842
It is worth noting here that there is a general lack of data on the energy consumption
categorised by different size of end-consumer (defined in terms of annual energy
consumption). This will inevitably impact on the results of this project. The data in
Figure A.2 encompasses the whole of the commercial sector, and therefore includes
a wide range of premises from small shops and offices up to large offices and
hypermarkets. Therefore the cooling loads shown above will include large central air
42
Energy Consumption in the UK Service sector data tables 2010 update, Department of Energy and Climate Change, July
2010, URN 10D/799
A-2
conditioning and refrigeration systems, as well as the split type air conditioning units
more suited to smaller properties and which therefore fall within the scope of this
project.
Therefore, within the context of this study, the term ‘Smart Appliances’ has been
expanded to also include heating, hot water and air conditioning.
A-3
Appendix B: Categorisation of Appliances
Interruptible
Definition
Typical end-uses
Schedulable
Definition
Convenience
Hybrid
Typical end-uses
Definition
Typical end-uses
Definition
Typical end-uses
Loads can be interrupted with minimal or no notice.
Consumers are more interested in a level of service and are
less time sensitive as to when this service or process occurs.
For example, for electrical space heating, providing storage
heaters provide sufficient heat at the correct time, the
consumer is not concerned when the heaters are ‘charged’,
or if this load is interrupted.
Typical end uses are electric space and water heating, and
air conditioning.
The inclusion of some form of thermal storage within the
system may be required to facilitate this.
These loads are more problematical to interrupt.
For example, for washing machines, it may be possible to
advance or delay the start of a cycle, but it not advised to halt
a cycle during the washing process due to potential damage
to clothes.
A more significant period of notice is required (anticipated to
be in the region of 4 to 6 hours).
They are usually relatively significant loads, especially within
small consumers’ sites.
Types of end uses include wet appliances.
These are loads that can be interrupted, but there is a limit to
how often or for long they can be interrupted. Scheduling
interruptions may increase the time that the loads can be
interrupted for.
Types of end uses are cold appliances. These can be
interrupted but, presently, only for a limited period without
impacting on food quality.
Although outside the scope of this present study, electric
vehicle (EV) charging would also be included within this
category. EVs must have sufficient charge available at the
start of each journey. Within this requirement charging can
either be interrupted or re-scheduled.
These are loads that are associated with either the comfort or
convenience of the consumer. Interruptions are unwelcome,
unless chosen by the consumer. Consumers may change
their behaviour in response to a price signal but are unlikely
to be willing to accept direct interventions by other parties.
Types of end uses are lighting, cooking, computing and
consumer appliances.
The primary focus of this study is on the first three of these categories, namely,
interruptible, reschedulable and hybrid loads.
A-4
Appendix C: Smart Appliance Experiences
C.1 UK Experiences
Following the establishment of the Low Carbon Network Fund (LCNF)43 during the
latest Distribution Price Control Review (DPCR5) for the Distribution Network
companies, there has been a significant increase in interest in the control of
customer loads, particularly those of domestic customers. The Fund allows up to
£500m support to projects sponsored by the DNOs to try out new technology,
operating and commercial arrangements to help DNOs meet the challenges faced in
a low carbon future. There are two tiers of funding available; Tier 1 focuses on small
scale projects, while larger projects fall within Tier 2 and are funded through an
annual competition. In the first year (2010/11), four projects were awarded Second
Tier funding and eleven projects were registered under the First Tier. A number of
these projects are looking specifically at the role of the customer in helping the
DNOs to operate and manage their assets more effectively, including demand side
response and Smart Appliances. No results are yet available from these projects.
Other examples of trials and applications of Smart Appliances within the UK are
highlighted below.
C1.1 The Potential for Dynamic Demand. Prepared for The Department of
Energy and Climate Change (DECC). November 2008.44
This report provides an overview of the potential for Dynamic Demand in the UK. It
defines Dynamic Demand technology as that which would allow non-time critical
electronic appliances such as domestic refrigerators to play a role in system
balancing (i.e. by automatically switching off when frequency is low, and on when
frequency is high). An additional report was prepared by the UK Centre for
Sustainable Energy and Distributed Generation which considered the economic and
environmental impact of dynamic demand, relating specifically to domestic
refrigerators, under a number of alternative future generation development
scenarios.
The conclusions of the report were as follows:
 Annual energy cost savings between £0.70 and £5.60 per fridge per
annum. This reflects cost savings in power stations resulting from a
reduction in the number of “part loaded” generation units currently
providing Frequency Response Services. The savings from the
consumer’s point of view are less clear.
 Outside of the development costs, Dynamic Demand control increases
component costs by around £4 per appliance45.
43
http://www.ofgem.gov.uk/NETWORKS/ELECDIST/LCNF/Pages/lcnf.aspx
44
Available from: http://www.supergen-networks.org.uk/filebyid/50/file.pdf (Accessed 14/02/11)
45
BNXS41: Dynamic Demand Control of Domestic Appliances.
http://efficient-products.defra.gov.uk/spm/download/document/id/658 (Accessed 17/02/11)
A-5


The use of Dynamic Demand refrigerators has a potential annual carbon
saving of between 17 and 44kg of CO2 per fridge per annum, dependent
on the generation mix.
A number of issues were raised as worthy of further investigation, as
follows:
o The effect on the overall efficiency of appliances operation including
wear and tear and associated costs as a result of providing
Dynamic Demand services; and
o The impact of the pattern of use of fridges (i.e. door opening) on the
value of Dynamic Demand which could be provided.
In November 2008 Ofgem approved an application under the Carbon Emissions
Reduction Target (CERT) Scheme by npower to carry out a ‘demonstration action’
using refrigerators fitted with Dynamic Demand. Indesit provided and distributed the
fridge-freezers with RLTec Dynamic Demand technology installed for the trial46. The
first tranche of fridge-freezers were delivered in May 2010 to residents in the West
Midlands. A further three hundred Smart fridges were scheduled to be part of the
initial stage of the trial, with up to three thousand to be included in the later stages.47
The benefits associated with the Dynamic Demand concept applied to domestic
refrigeration were demonstrated to be significant, as evidenced by the theoretical
study and the field trials, which are believed to support the theoretical analysis. In
addition, the benefits have been shown to outweigh the additional costs. However,
the benefits accrue to the Transmission System Operator (National Grid), whilst the
costs accrue to customers. Therefore, there is no incentive for customers to
purchase a ‘Smart’ product rather than a conventional ‘non smart’ device, as the
benefits are effectively shared amongst all network users through reduced energy
balancing costs. Although it would be possible for National Grid to provide a
financial incentive to customers to reward them for purchasing a Smart Appliance, a
payment of up to £5.60 per household per annum is unlikely to prove attractive. In
addition, it would seem likely that the costs of administering such a reward could
outweigh the incentive itself. Thus despite a strong economic argument in support of
the concept (i.e. benefits greater than costs), there are significant barriers to the
implementation of Dynamic Demand refrigeration equipment. One solution that has
been suggested is the mandating of the Dynamic Demand technology in all cold
appliances sold in the UK. However, as is highlighted in Section 1, manufacturers
do not generally develop products specifically for the UK market, but rather for the
wider European market. Consequently, the mandate would need to apply to all
products sold within the EU, rather than products sold within the UK. Frequency
control does not pose the same challenges across the mainland Europe that it does
within the UK, which as an island experiences a wider range in frequency.. As such,
frequency response refrigeration products are specific to the needs of the UK. Thus,
here it is essentially the ‘energy market barriers’, pertaining to the UK energy market
structure that represent the main barrier to the implementation of this concept.
46
http://www.renewableenergyfocus.com/view/5041/indesit-rltec-and-npower-dynamic-demand-smart-grid-technology/
(Accessed 16/02/11)
47
http://www.npowermediacentre.com/Press-Releases/The-cool-way-to-cut-CO2-Europe-s-first-smart-fridges-trial-starts-inUK-dcd.aspx (Accessed 16/02/11)
A-6
Summary of Dynamic Demand Trial
 Scale of planned trial: 300 hundred refrigerators in the first stage, 3,000 in the
later stages.
 Drivers for the device development: ability to play a role in system balancing
 Solutions facilitating the deployment of Smart Appliances: this technology does
not require additional communication technology and operates based on
frequency of supply only
 Outcomes: Estimated financial benefit of between £0.70 and £5.60 per annum.
Carbon dioxide savings of between 17 and 44kg of CO2 per fridge per annum
 Key Learning Points:
o Further investigations required in relation to the overall effect on
operational efficiency and wear and tear on appliances.
o In order for this type of appliance to be adopted by a large number of
consumers they would need to be able to access the value from system
balancing, potentially through an aggregating company
o This type of technology could potentially be expanded to a greater range
of appliances (see RLtec Smart Grid Solutions, below)
C1.2 RLtec Smart Grid Solutions
The DECC report outlined above has highlighted a requirement for increased
Dynamic Demand services. In addition to the trial of Smart fridge-freezers with
npower and Indesit, RLtec have also received research and development funding
from the Carbon Trust48 for other appliances. This grant funding has been used to
enable RLtec to test Dynamic Demand technology for data centre air conditioning
units, in a trial hosted by Department for Environment, Food and Rural Affairs
(Defra). The company also received a £260,000 grant from DECC’s Smart Grid
Demonstration Fund. The Carbon Trust funded project seeks to transfer the
Dynamic Demand technology outlined for fridge-freezers above to the cooling,
humidification and de-humidification needs of a data centre. This trial illustrates how
Dynamic Demand has the potential to be expanded to other technology areas,
although this specific application is likely to be outside the scope of this study.
Results from the trial are not publically available at the time of writing.
48
http://www.rltec.com/sites/default/files/Carbon%20Trust%20Case%20Study%20-%20CTS248.pdf (Accessed 16/02/11)
A-7
C1.3 Sustainable Blacon49
Blacon, a suburb near Chester in Cheshire, is one of the many communities
participating in DECC’s Low Carbon Communities Challenge. As part of its activities
to reduce carbon emissions, a trial of 150 households is currently underway to
actively manage household electricity consumption. The trial does not involve Smart
Appliances; rather it is evaluating the impact of an energy management system
which controls individual appliances via the use of ‘Smart plugs’. The system being
trialled is the AlertMe50 system, although a number of other similar systems are
available. The trial involves: 50 ‘active' homes equipped with the AlertMe system,
50 ‘passive' homes using systems which will allow passive monitoring of energy
usage, and 50 ‘control' homes. In addition, all three groups will take part in a
community based education programme. No results are yet available from this trial.
C1.4 ETHOS Project Trial of Multimedia Energy Management Systems51
This trial was designed to test whether multimedia energy management systems
could be used to achieve demand management outcomes. The trial involved 100
residential customers of the (former) South Wales Electricity Company (SWALEC),
between 1996 and 1998. The aim was to reduce the peak load on the distribution
network.
Two systems were trialled - Low Cost CELECT (LC-CELECT) and Credanet. The
LC-CELECT system controlled space heating, utilising customer settings, room
temperature and electricity cost information to meet the required temperature at
minimum cost. A single zone with one temperature setting could be controlled by the
LC-CELECT system. The Credanet system used dedicated heaters with integral
transceivers but did not include data logging equipment, therefore individual
temperature recorders were used to collect room temperature information. However,
the Credanet system had three zones and allowed different temperatures to be set
for each zone (e.g. one temperature for occupied rooms, and a lower temperature
elsewhere). All houses in the trial were also provided with prototype DICE water
heater controllers in order to meet the customer’s requirements for hot water at
minimum cost. It was not possible to proceed with a planned trial of direct load
control dishwashers and clothes washer/dryers due to the unavailability of
controllable models.
Overall, the trial achieved a 25% reduction in peak demand on the relevant section
of the SWALEC distribution network. LC-CELECT improved comfort for customers
and achieved an average reduction in electricity consumption of 8%. The Credanet
heating system and DICE water heater controllers were also successful in reducing
electricity consumption.
49
http://www.sustainableblacon.org.uk/
50
http://www.alertme.com/
51
Worldwide Survey of Network-driven Demand-Side Management Projects. Task XV of the IEA DSM Programme. Oct 2008.
Available from: http://www.ieadsm.org/ (Accessed 17/02/11)
A-8
The introduction of electricity supply competition for domestic customers in 1999 is
believed to have contributed to the decision to not pursue the concept beyond the
trials noted here. The unbundling of supply and distribution companies meant that
the benefits of load shifting were split between two companies, rather than to a
single vertically integrated utility. In addition, and possibly more importantly, the
introduction of the profiling settlement system meant that it was not possible for
energy suppliers to capture the value associated with load shifting. Under the load
profiling arrangement, the energy profile of customers is determined according to a
profile, and total consumption is allocated to specific half-hours according to these
profiles. Thus, customers with demand management are treated in the same way as
customers without demand management.
Summary of ETHOS Trial
 Scale of scheme: 100 residential customers in South Wales
 Drivers for the study: to determine if multimedia energy management systems
could be used to achieve demand management outcomes
 Outcomes: 25% reduction in peak demand
 Key Learning Points:
o Controlling electric space and water heating in the UK can be used to
achieve a reduction in peak demand
o In this study, the unavailability of appliances was a barrier
C1.5 Energy Waste Due to Leaving Electrical Appliances Switched On When
Not in Use. Prepared for Logicor. 2010.52
This survey was carried out to determine which appliances are routinely left switched
on and unattended, both deliberately and accidentally. Research was also
conducted into the average power consumption of these appliances. Although this
survey does not relate to, or consider Smart appliances specifically, this ‘energy
wastage’ is something which could be minimised through Smart appliance design,
i.e. appliances that could turn themselves off when they are no longer required. A
recent study53 has, however, shown that there is a lack of reliable and cost effective
occupancy detection sensors to enable appliances to be turned off.
The Table below shows the results from the survey of 2,051 respondents.
52
Available from: http://www.logicor.co.uk/wasted.pdf Accessed 14/02/11
53
Smart Energy Management Solutions: Smart Metering, Smart Appliances and Interoperability, Presentation by Professor
th
Philip Moor, Director of Research De Montfort University and TAHI Board Member, AMDEA workshop, London, 11 May 2011
A-9
Table C.1 Appliance Usage Patterns
Appliance
Electric Kettle
Electric Blanket
Lamp
Tumble Dryer
Microwave
Immersion/ Water Heater
Modem
Computer
Computer Screen
CD Player/ Hi-Fi
Set-Top Box
TV
Iron
Electric Portable Heater
Hair Care Appliance
Electric Hob
Mobile Phone Charger
Printer
Power (W)
2100
150
240
3750
1050
5000
10
100
150
30
22
110
1400
1125
1440
4600
4
35
Average Time (minutes) left on
9.0
3.6
99.3
16.4
5.6
73.0
289.5
172.6
122.7
33.0
189.1
92.3
0.6
5.3
1.8
1.5
97.8
83.4
Summary of Energy Wastage Survey
 Scale of scheme: survey of 2,051 respondents
 Drivers for the study: estimating the potential benefits for a device which could
reduce energy consumption from leaving appliances switched on when not in use
 Outcomes: The survey calculated that households could save around 1,300 kWh/
year, equivalent to approximately £200
 Key Learning Points:
o It may be possible for ‘Smart Appliances’ to include some elements which
eliminate this wastage of energy, thereby providing a financial benefit to
the consumer
C1.6 Wattbox Heating Controller
The Wattbox is an alternative design of heating controller for both residential and
small commercial premises. The system controls space and water heating (both gas
and electric) and reduces energy consumption by monitoring and learning occupant
behavior patterns and temperature preferences to program the controller
accordingly, whilst minimising energy consumption. Adjustments can be made by
the user using simple commands- “more heat”, “less heat” and ”more hot water”54.
This monitoring and learning process can replace manual programming of the
controller which the manufacturer suggests 30% of householders “do not cope well
with”55. The controller can operate with a range of heating appliances, including new
technologies such as micro combined heat and power (micro-CHP) units. The
energy savings as a result of using a Wattbox controller are variable, depending on
54
Wattbox Technical Description. Available from:
http://www.wattbox.com/Wattbox/Products_and_Services_files/Wattbox%20Technical%20Description.pdf (Accessed 17/02/11)
55
http://www.wattbox.com/Wattbox/Home.html Accessed 17/02/11.
A-10
the effectiveness of the original controller in avoiding over or under heating of the
occupied space. Savings of between 9 and 22% were observed from computer
modelling, whereas the trial results showed a decrease in energy consumption of
14%56. When the product is launched commercially, Wattbox are aiming for a retail
cost of around £200 (in-line with existing time-clock controllers in the UK). The
Wattbox is currently undergoing trials as part of the Technology Strategy Board
‘Retrofit for the Future’ project, supplying 28 controllers.57
Wattbox has the functionality to work alongside Smart Meters and other home
automation products56.
Summary of Wattbox Controller
 Drivers for the device development: reducing energy demand through optimised
automatic programming of space and water heating
 Outcomes: Decreased energy consumption of 14% shown in trials
 Key Learning Points:
o There could be an opportunity to link such a heating controller with
information provided by a Smart Meter to further optimise heating controls
based on price signals
C1.7 Early Adopters Research- Summary Report. Prepared for Three Water
Authorities. November 2001
This report58 summarised the results of a number of focus groups undertaken for
three water authorities (East of Scotland Water Authority, North of Scotland Water
Authority and the West of Scotland Water Authority). The aims of the focus groups
were to understand customer demand and expectations for the future use of
technology in the home, gauge customer reactions to the “Smart home” concept and
test the appeal, benefits and drawbacks of various services. It should be noted that
this work was carried out in 2001 and therefore some of the services offered have
now become commonplace or have been superseded. The key findings of
relevance to Smart Appliances were as follows:

In terms of White Goods Monitoring (i.e. the potential for an email to be
generated when the system detects that white goods have developed a fault),
members of the focus groups welcomed “ideas which are likely to lead to
savings both in terms of personal finance and the environment and the idea of
a ‘green button’ on appliances, which switches them on when electricity is
cheapest was well received”.
 Focus groups also indicated that “the possibility of saving money via more
accurate heating and best use of cheaper electricity tariffs is seen as a
tangible benefit”.
Summary of Early Adopters Research
56
Wattbox Technical Description. Available from:
http://www.wattbox.com/Wattbox/Products_and_Services_files/Wattbox%20Technical%20Description.pdf (Accessed 17/02/11)
57
http://www.wattbox.com/Wattbox/RfF_Results.html (Accessed 17/02/11.)
58
Early Adopters Research – Summary Report, Prepared for The Three Water Authorities by George Street Research
Limited, November 2001, Ref: 2963
A-11


Drivers for the focus group: to understand customers demands and expectations
for the future use of technology in the home
Key Learning Points:
o In 2001, consumers were interested in some form of optional control
(‘green button’) to switch appliances on when electricity was cheaper.
This feature could be provided through Smart Appliances
C.2
Overseas Experiences
C2.1 USA
The electricity market in the United States of America is split into distinct regions,
with each having regulation and balancing responsibility. Each region has particular
issues. The Section below captures some of the experiences from a small sample of
the pilot trials and schemes involving Smart appliances in the USA.
California State-Wide Pricing Program (Commercial and Industrial)59.
Following the “energy crisis” in California in 2000 and 2001 that resulted in short
term energy shortages, combined with fears about long-term generation and
transmission capacity, California Public Utilities Commission sanctioned several
trials of automated demand response systems.
Two elements of this program have been reported in relation to Demand Response.
The first, which is described here, was a trial undertaken in the summer of 2004 and
2005 and related to the introduction of ToU and Critical Peak Period (CPP) pricing
schemes. Smart thermostats for adjusting air conditioning units were used by some
participants in this trial.
During this pilot ToU and CPP pricing was introduced for small commercial and
industrial customers in the Southern California Edison service territory. The critical
peak price was around $1.00/kWh compared to a normal peak price of $0.17/kWh
for customers with demand less than 20kW. Customers were divided into the
following two groups:
- LT20, customers with demands less than 20kW; and
- GT20, customers with demands greater than 20kW
All customers were offered free Smart thermostats as part of the trial to automatically
adjust their air conditioning settings during critical peak pricing periods. Around one
third of the LT20 group accepted the free Smart thermostat, compared to around
60% of the GT20 customers. The key results in terms of the impact of enabling
technology (Smart thermostats) in conjunction with Critical Peak Pricing were as
follows:
 LT20 customers were not price responsive on normal weekdays, regardless
of whether they had enabling technology installed or not.
59
California’s Statewide Pricing Pilot: Commercial & Industrial Analysis Update, June 2006
A-12




LT20 customers without enabling technology were also not price responsive
on Critical Peak Pricing days.
LT20 customers with enabling technology reduced peak-period energy use on
critical days by more than 13% across two summers.
There was no statistically significant difference in price response on normal
weekdays and critical days for GT20 customers without enabling technology
On critical days, GT20 customers with enabling technology were roughly twice
as price responsive as customers without technology.
California State-Wide Pricing Program (Residential)
The California Public Utilities State-Wide Pricing Program was a pilot scheme started
in 2003 to assess different time-based tariff schemes. Residential customers were
provided with a free advanced digital electricity meter to provide energy information
and facilitate energy management. They also had access to an online tool to check
their energy usage. A free Honeywell programmable thermostat was also provided.
It is not clear if any direct link was provided between the operation of the thermostat
and pricing signals, or whether consumers were required to interpret the ToU tariffs
when programming the thermostat in order to make cost savings. Participants were
randomly enrolled into one of the three tariff groups listed below:
 ToU Pricing (peak and off-peak price)
 Critical Peak Pricing (peak, off-peak and super-peak price)
 Information Only (no price change but received all the programme
information)
A small number of enrolled customers were also provided with additional equipment
including communication links with electric water heating, the pool pump and
thermostat. These customers could view real time interval demand and trends in
historical consumption via a web portal and could then alter climate control and pool
runtime preferences. These customers saved up to 18% more energy than other
pilot customers who had not been issued with this equipment. On super peak days
they used 26% less peak energy than other pilot customers60. This demonstrates
the importance of customers having access to information (and education with
regard to how to use this information) from their Smart Meters, and appliances which
allow them to alter their behavior.
Summary of Californian Trials
 Drivers for the study: to assess different time-based tariff schemes
 Solutions facilitating the deployment of Smart Appliances: Smart Appliances were
used in conjunction with ToU pricing and/ or additional information
 Outcomes: 18% additional energy saving for customers with additional Smart
Appliances compared to those without. On super peak days this group used 26%
less energy than other pilot customers.
 Key Learning Points:
60
State-Wide Pricing Results, Overview and Results,
http://www.nwcouncil.org/energy/dr/library/drrc_presentation.pdf
th
accessed 16 March 2011 and A Scoping Study: Demand Side Measures for Small Business and Residential Customers
on Ireland’s Electrical System, Kema Ltd and UK Department of Trade and Industry, November 2005, p25-27
A-13
o Householders were given information about future price tariff periods and
were allowed with this information to automate their household electricity
loads
o Time of use pricing already exists in parts of the United States so the concept
is already understood,
o Recent blackouts in California had made householders aware of the system
constraints and potential supply shortages
The use of ToU and critical peak tariffs could be a potential barrier to the roll-out of
this model in the UK as these do not exist in the UK and are thought be to unpopular
with consumers. Such tariffs are therefore unlikely to be introduced by customer led
electricity supply companies
Long Island Power Authority LIPAedge- Sponsoring Organisation: Long Island
Power Authority
Long Island Power Authority is a non-profit making municipal electricity provider who
own and operate the distribution and transmission networks on Long Island, New
York State. They also act as an electricity supplier. They do not operate any
generation plants.
LIPAedge was initially introduced in 2001 to reduce peak demand where generation
was insufficient or where the network was constrained. The scheme was the largest
residential direct load control program in the USA using two way communications. In
July 2003 the scheme was closed to new participants because it had sufficient air
conditioning units under direct control.
Scheme participants agree to have their central air conditioning system adjusted for
a maximum of seven days over a period of four summer months, between the hours
of 2pm and 6pm. Participant households were given US$25 and a Comfort Choice
programmable remotely accessible internet thermostat. Participants can override
events, although the system operator can block overrides if necessary. The system
communicates with the air conditioning unit using the SkyTel pager network. The
network operator has flexibility to address all, one of a group of 15 or just a single air
conditioning unit, requesting it to change its set point between particular hours, by
cycling air conditioning compressors for a portion of each hour or to curtail
immediately.
The scheme operators expected that a 24.9MW peak reduction was possible from
the full 23,400 controlled air conditioners. Response was found to be highly
dependent on temperature, day of the week and time of the day. Over three
curtailment events during the summer of 2002, the average load reduced was
16,067MW (equivalent to around 680kW per air conditioning unit), and the average
energy saving during each curtailment event was 66,613MWh.
The program cost US$515 per residential customer. It yielded a combined average
cost of US$487/kW of demand reduction.
A-14
Summary of LIPAedge Scheme
 Scale of scheme: 23,400 controllable air conditioners
 Drivers for the study: reduction in peak demand
 Solutions facilitating the deployment of Smart Appliances: two-way
communications with programmable thermostat, override option and financial
incentive
 Outcomes: Average load reduction of 16,067MW. Cost of US$515 per customer.
 Key Learning Points:
o Householders were given remote control thermostats
o The number of annual interruptions that each household would be subject
to was limited, potentially increasing the willingness of consumers to enter
the scheme
o Households could override events under some circumstances
o The relatively low incidence of households with air conditioning in the UK
could be a barrier to the success of similar scheme in the UK– this model
may be more applicable to commercial premises
o Long Island Power Authority is run as a non-profit making municipal
company and therefore has different drivers to companies in the UK which
may limit the possibilities to conduct such a scheme in the UK
o Long Island Power Authority runs a supply company and transmission and
distribution on Long Island, this may allow them to conduct such a
scheme, which may be more difficult in the UK
C2.2 Australia
The Australian electricity system consists of two markets, one covering the Western
and Northern States, and the other covering Queensland, New South Wales,
Victoria, Tasmania and Australian Capital Territory. The electricity market is not unbundled, and some network companies are state owned. Load increase, especially
peak load, has become a growing problem because of urban growth and the
increased ownership of air conditioning units.
ETSA Utilities Air Conditioner Direct Load Control Programme, Adelaide
The electricity load profile in Southern Australia is especially ‘peaky’. This is
attributed to a high incidence of domestic air conditioning. The peak network
demand in this region was 2,563MW on 16 January 2007. The local distribution
operator, ETSA Utilities, estimate that the peak load on a very hot day over the
summer of 2006/07 was approximately 1,000MW greater than the average daily
peak demand. In 2003 Essential Services Commission of South Australia, the
industry regulator, introduced a regime to encourage ETSA to investigate and
implement DSM options61.
An initial trial of the remote management of domestic air conditioners of 20
customers was carried out over the summer of 2005/06. They were paid AU$100 to
61
Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the
International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr.
David Crossley, pp 71-72
A-15
participate. ETSA’s radio network was used as the chosen communication medium.
The trials aims were to:
 Determine customer perception of changes in comfort
 Determine the impact on aggregated demand
 Gain installation and operational experience of the control technology
 Test the performance of the control technology
 Gain experience in quantifying change
The initial trial customers were given a high degree of support, for example by being
given a named ETSA staff contact to report any adverse impacts or problems to, and
their experiences and perceptions were thoroughly monitored. A selection of cycling
strategies was also tested62.
The following summer a much larger trial was implemented. Following a large
marketing campaign 1,700 domestic and 700 commercial air conditioning systems
were identified to participate in the scheme. However it transpired that the
communication device was unsuitable for use on some newer generation air
conditioners. This reduced involvement to 750 sites although a subsequent trial was
conducted with the most suitable of the new generation air conditioners. As part of
the larger trial, ninety randomly selected sites were monitored at the site level, while
the remaining sites were monitored at either street or transformer level. A variety of
switching periods were employed62.
An important lesson drawn from the pilot program was that a random overlapping of
switching programs is required, otherwise, if the overlap is none random, a “saw
tooth” demand profile is resultant. The figure below shows the aggregated demand
from the 68 properties involved in the initial trial on 11 March 2006. The area
highlighted in red shows the period when the air conditioning units were under direct
load control for 15 minutes of every 30 minute period. A clear reduction in load can
be observed.
62
Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the
International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr.
David Crossley
A-16
Figure C.1 Aggregated Demand on 11 March 2006 for the Houses Involved
in the ETSA Utilities Initial Trial63
A further trial was initiated in three different suburbs of Adelaide to gain more cost
benefit data on the direct load control of larger air conditioners, investigate effects on
the distribution network, gain more customer participation data and to compare the
impact of direct load control relative to other geographical trial areas. The data from
these trials was used to create estimates of the load reduction that could be
expected from an event under similar circumstances to the trial conditions64.
Cost benefit analysis of these schemes revealed that the benefits accrued primarily
to customers, energy retailers, and the transmission company whilst the schemes
costs fell on the distributor and energy generator65.
As a result of the problems installing the DRED Peakbreaker technology on some
models of air conditioners a voluntary compliance Standard was adopted in
December 2009, namely “AS4755 Demand response capabilities and supporting
technologies for electrical products Part 3.1 Interaction of demand response enabling
devices and electrical products – Operational instructions and connections for air
conditioners”. This Standard requires all air conditioning units sold in Australia to
have a standard interface. The Australian Standards committee is investigating
making similar requirements for pool pumps and hot water controllers and
investigating Smart Meter interoperability.65
63
Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the
International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr.
David Crossley,p78
64
Demand Management Program Interim Report No. 3, June 2010, ETSA Utilities, p27-28, accessed from
http://www.etsautilities.com.au/centric/our_network/demand_management.jsp, June 2011
65
Demand Management Program Interim Report No. 3, June 2010, ETSA Utilities, p30
A-17
Summary of ETSA Direct Load Control Programme
 Scale of scheme: initial trial of 15 customers followed by larger trial of 750 sites.
 Drivers for the study: evaluating the technology, its impact on the customer and
potential impact on aggregated demand
 How trial participants were recruited: Participants in the larger trial were recruited
via a marketing campaign. Residents demonstrated a new attitude towards
electricity and were happy to “do their bit” to help their community.
 Outcomes: Reduction in peak load
 Key Learning Points:
o Careful initial research was carried out to determine customer perception
of comfort changes which may have contributed to the success of the
model;
o Carrying out a small initial trial to test the system and gain operational
experience of the communications system allowed installers to familiarise
themselves with the technology;
o As air conditioning was generally only recently installed and therefore not
an established essential, customers may be willing to accept the change in
its operation;
o A large marketing trial was undertaken to explain the reasons for the trial
and to recruit volunteers;
o A strong regulator encouraging a change of behaviour within the
distribution network operator may have contributed to the success of the
trial;
o The relatively low incidence of households with air conditioning in the UK
could be a barrier to the success of similar scheme in the UK– this model
may be more applicable to commercial premises;
o Load reduction was dependent on location because of different build types
and ages and climatic conditions; and
o Much of the load peak can be attributed to a single appliance group (air
conditioning) however this is not true in the UK which may limit the impact
of such as scheme.
Western Sydney Interruptible Air Conditioning Rebate Trial
Integral Energy distributes electricity to 2.1million households and businesses across
Western Sydney, Illawarra and the Southern Highlands. They also hold licences to
retail electricity across all the regions of Australia covered by the National Electricity
Market. Integral is motivated by a number of factors to engage in demand
management:
 Combined customer maximum demand, usually driven by weather patterns
 A high growth in demand due to urban consolidation
 Demand growth, mainly driven by air conditioning
Integral has been involved in two forms of residential load management.
 Domestic hot water
 Air conditioning66
66
Submission to the Legislative Standing Committee On Public Works – Inquiry into Energy Consumption in Residential
Buildings, Integral Energy, August 2003, p3
A-18
Domestic Hot Water
Prior to the formation of Integral in 1995, its predecessor organisations had
experience of direct load control of hot water. The control of this load was useful to
network operation because historically it coincided with the winter evening peak.
Ripple signals carried on the 11 kV or lower voltage systems are used at a variety of
different frequencies to stagger channels of hot water systems to avoid the daily
peak. The hot water is on a separate circuit and meter. A large price incentive is
offered to customers in return for surrendering control of their hot water system. If a
customer runs out of hot water, a twenty four hour helpline and emergency help crew
can be called upon to rectify the problem. In 2003, Integral had an estimated
1,556MW of domestic hot water load under this form of control, reducing peak
demand by 389MW 67.
Air Conditioning
In summer 2001 Integral sponsored a trial to investigate the technical and
commercial feasibility of directly controlling residential air conditioning. The motives
for this trial were:
 To resolve network issues and defer expenditure
 To resolve retail issues caused by exposure to high pool prices
 To test the reliability and response time of the equipment
 To investigate the impact of cycling on load profile
 To gauge customer experience and acceptance
The trial involved 90 participants. A proportion of the participants were used as a
control sample and experienced no load cycling of their air conditioning. Two control
methods were trailled, namely pager and ripple technology. Customers received a
$150 incentive payment upon completion of a final survey at the end of the trial. The
following conclusions were drawn from the trial:
 The technology worked correctly apart from on two occasions when the pager
did not re-activate the air conditioning systems automatically
 Customers preferred shorter, more frequent, off cycles rather than prolonged
interruptions
 The program had high administrative costs due to customer inquiries and
information gathering
 Electronic metering costs were higher than expected, and
 Billing was problematic because of complications due to the rebate as well as
the need to estimate bills during an unusually hot summer67.
67
DM Programs for Integral Energy, Charles River Associates (Asia Pacific)Pty Ltd, August 2003
A-19
Summary of Western Sydney Interruptible Air Conditioning Trial
 Scale of scheme: 90 participants in the air conditioning trial
 Drivers for the study:
o Domestic Hot Water: winter peak load reduction
o Air Conditioning: Resolving network issues and deferring expenditure,
reduce exposure to peak pool prices, evaluating the performance of the
technology, assessing impact on consumers
 Outcomes:
o Domestic Hot Water: 389MW reduction in peak demand
o Air Conditioning: technology operated correctly in the majority of cases,
customers preferred shorter, more frequent interruptions than longer ones,
and high administrative costs were experienced
 Key Learning Points:
o The scheme may have been successful due the long history of direct load hot
water control
o A strong support framework for the hot water scheme may have increased its
success as consumers were confident that support would be provided if
necessary
o The high level of price return for households on the hot water scheme may
have increased its level of success
o Integral Energy has relationships with customers as both their distribution
operator and supplier. Market barriers in the UK prevent this occurring
o The unbundling of the UK electricity sector means that the costs of high
electricity pool prices and the high cost of network reinforcement are split
across different industry participants. Financial incentives are therefore
spread over multiple players and less acute limiting the replicability of such a
scheme in the UK.
o Billing for such a scheme in the UK may be difficult until the roll out of Smart
Meters is complete
C2.3 Scandinavia
The electricity markets in the three Scandinavian countries of Denmark, Norway and
Sweden are highly interconnected. There is a high potential for demand
management because of the prevalence of electric heating in the region. A number
of pilot trials that included a Smart Appliance element have been undertaken in the
Scandinavian countries. A selection of these are summarised in the Table below.
Table C.2 Scandinavian trials of Smart Appliances
Country
Size of trial
Denmark
25
Norway (Oslo)
20
Control
technology
Internet and
mobile phone
technology
Internet,
wireless radio
and an “Ebox”
A-20
Appliance
controlled
Electric heating
Electric heating
Denmark
Liberalisation of the electricity market in Denmark began in the 1990s. Supply and
distribution companies have been separated and supply competition now exists,
however switching electricity supplier is rare amongst Danish households. The
Transmission System Operator (Energinet.dk) is state owned. Most generation is
owned by one of two companies. The wholesale electricity market is integrated into
the Nordic power market
The Danish pilot trial in 2003/04 was sponsored by Elkraft Sytem, Eltra, Seas and
Energy Piano. It was undertaken as part of the EFFLOCOM (Energy Efficient and
Load Curve Impact of Commercial Development in Competitive Markets) project.
The houses included in the trial each had electric heating, with an annual
consumption greater than 16,000kWh. Trial participants were able, via the internet,
to set limits for the duration of any interruption for up to five zones in their home for
different periods of the day. They could also end any interruption if required. They
received a report on their electricity consumption and the bonus they had saved.
The savings as a result of demand response were reported separately, increasing
the visibility of the benefits to consumers68. One hundred hours of high prices was
simulated over the winter with bonuses varying between 0.13€ and 0.40€69 per kWh.
Demand response prices were applied during two time periods- 06:00-11:00 and
16:00-19:00
The communication configuration used can be seen in the Figure below.
Figure C.2 Overview of Danish Pilot
69
Electricity consumption for heating was recorded and stored every quarter hour.
This quarter hour consumption was collected via daily remote meter reading using
GPRS. Customers were able to set preferences for the duration of interruptions
68
Energy efficiency and load curve impacts of commercial development in competitive markets. Results from the
EFFLOCOM Pilots. Available via
http://www.efflocom.com/pdf/EFFLOCOM%20report%20no.%207%20Pilot%20Results.pdf (Accessed 24/03/11)
69
IEA DRR Task XIII Subtask 5 – DK technology database, DK Tech Case #1: EFFLOCOM pilot: DR offered by households
with direct electric heating, Casper Kofod
A-21
based on the location (five zones), time of interruption (06:00-11:00 and 16:0019:00) and level of bonus68.
The following lessons were drawn from the trial:
 The installation costs were about 800€ per home (for 1,000 houses)
 Only 40% of customers used the facility to stop an interruption
 Customers didn’t change the level of interruption that was acceptable based
on the level of bonus
 24% of houses used the facility to set the maximum duration of interruption
 Total customer savings/benefits were about 120€ per year, 80€ bonus from
demand response, and 40€ from reduced consumption during interruptions
 Only 41% of interrupted consumption was used afterwards to bring the
temperature back to the required level
 Interruptions and meter times need to be synchronised
 Failsafe messages were introduced
A customer survey was carried out following the trial. The main findings of this
were68:
 Customers were interested in both the graphs and figures of consumption
over time and the reports of bonuses received from demand response
 Most customers were either “satisfied” or “nearly satisfied” with the level of
bonus received from participating in the project
 Some customer suggested that dishwashers, washing machines and tumble
driers could also be included in the management
 Nearly all customers stated that a three hour interruption was acceptable
 No customers in the trial used additional portable electric heating during
interruptions
 All customers would recommend the system to other users and would like to
continue participating in the demand response scheme
Oslo
The electricity sector in Norway is de-regulated, however many of the participating
companies are state-owned. A single state owned company, Stattnet, operates the
Transmission Network, while small local companies operate the local distribution
networks. Retail is competitive. Norway is a member of the Nordic wholesale power
market
The objective of this trial was to avoid or defer reinforcement of the electricity
network and to learn more about consumer behaviour. It consisted of two parts, the
first of which was for the electricity network company to control hot water production
for an apartment block. This was achieved with no impact on hot water consumption
for the residents. The second part of the trial involved residents using an “Ebox” to
programme the heating schedule for their property. The network company was also
able to control the “Ebox” and override the householders programmed schedule
according to the load at a local overloaded transformer. The reaction of the
participants to the technology was monitored. The combined result of the two trials
A-22
was a reduction in peak usage by up to 15% (of the metered maximum demand of
the households).70
Summary of Scandinavian Trials
 Scale of scheme: a single apartment block
 Drivers for the study: to avoid/ defer network investment and to learn more about
consumer behavior
 Outcomes: Reduction in peak load by 15%
 Key Learning Points:
o The initial trial may have been particularly successful as there was a large
potential to manage hot water systems in blocks of flats while limiting
inconvenience to residents to a minimum
C2.4 New Zealand
New Zealand has a competitive electricity supply and generation market, with
regulation imposed upon the natural monopoly transmission and distribution
elements. Some of the companies operating in the market are state or trust owned
while the rest are privately owned. The electricity retail market uses a locational
pricing auction, calculating prices at each transmission node. Some retailers and
industrial users bid to reduce their capacity. All the major generating companies
have energy retail businesses. Full retail competition exists allowing consumers the
freedom to choose their electricity supplier.
Orion Network DSM Project
Orion New Zealand Ltd. is the state owned distribution operator for the Christchurch
region of New Zealand. The New Zealand transmission operator, Transpower,
charges distribution operators based on an average of the twelve highest peaks, by
half hour, at the transmission grid exit points. This creates an incentive to manage
peak demand, which Orion has been doing since 1990. This is being done with
direct load control of hot water using ripple control. Two control regimes are
employed71:
 Peak Control Water Heating
 Night only Water Heating
In 1996, 88% of hot water systems in New Zealand were electric. A study published
in 2006 suggests that a large proportion of residential electricity (31%) is used to
provide hot water – this represents 8% of all electricity use.72 Thus, the focus is on
the control of a large, single load group that can be readily interrupted with minimal
or no impact on the end-users, and which contributes significantly towards system
peak demands.
70
Scoping Study: Demand Side Measures for Small Business and Residential Customers on Ireland’s Electrical System,
Sustainable Energy Ireland, November 2005, p32-33
71
Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the
International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr.
David Crossley, pp 91-93
72
Hot Water Over Time – The New Zealand Experience, Conference Paper No 132 (2007), Nigel Isaacs, Dr Michael
Camilleri, Lisa French, Branz 2008, ISSN: 0111-7505, p7
A-23
Peak Control Water Heating
This system is generally employed by households with smaller water tanks or
consumers who require larger quantities of hot water. The ripple system is used to
switch off water heating elements during peak periods and to rotate through sixteen
groupings of water heating load. Orion has set service levels so that it aims to have
individual systems turned off for no more than seven hours in any day, and no more
than four hours in a seven hour period. Between 2005 and 2007 this system was
used to switch water heating for an average of 54 hours a year, usually over the
winter. When demand approached a threshold (570MW in 2007), water heaters
were switched off in groups. These groups are cycled to limit customer impact.
Night Only Water Heating
This system is generally used by customers with bigger water tanks. The ripple
system is used to turn on their system overnight. The system is so successful that
the tank deployment has to be staggered to avoid creating night-time peaks. An
afternoon boost period is also an available option to customers on this regime73.
These regimes are made attractive using pricing regimes – night only hot water
customers pay only about 50% of the Anytime Use tariff for night time hot water
heating and Controlled Load tariff save about 10% on the Anytime Use tariff.
Table C.3 Tariff schedule for Electricity Retail Contract (May 2008)
Tariff type
Variable price*
Anytime use tariff
19.267 cents/kWh
Controlled load tariff
17.129 cents/kWh
Day/night – day tariff
21.765 cents/kWh
Day/night – night tariff
9.256 cents/kWh
* All tariffs pay a fixed tariff of 60.53 cents per day in addition
About 90% of water heating is controlled using these regimes in Orion’s’ distribution
area. The result of these schemes is demonstrated in the Figure below. The yellow
area shows the percentage of load shed during the reduction period. The red line
shows the expected load had load reduction not been employed, and the blue line
shows the actual load.
73
Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the
International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr.
David Crossley, pp 93-94
A-24
Figure C.3 Orion Daily Load Management Summary, Thursday 28 June 2007
74
Based on estimations, Orion suggest that without this scheme, delivering peak load
would cost an additional NZ$12 million per year for distribution and NZ$6 million for
transmission.
Summary of Orion DSM Project
 Drivers for the study: reducing peak demand due to charging of DNOs based on
an average of the twelve highest peaks by half hour at the transmission grid exit
points
 Outcomes: Reduction in peak load with a total estimated financial benefit of
NZ$12 million per year for distribution and NZ$6 million for transmission
 Key Learning Points:
 High consumer price benefits, with limited inconvenience to the customer and
a strong support network are likely to have contributed to the success of the
scheme
 The program has been in operation since 1990 and is therefore well known to
consumers
 The benefits experienced in New Zealand are relatively high due to the high
prevalence of electric heated water storage systems. This may not be
applicable in the UK where there is currently a relatively low incidence of
households with electric heated water storage systems
 There is a strong financial incentive on DNO to limit peak demand in New
Zealand, which may have contributed to a successful peak load limiting
system being developed. This is not the case in the UK so it may be more
difficult to replicate such a system.
74
Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the
International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr.
David Crossley, pp 93-98
A-25
Other Industry Experts
- Referred to as “Others”
Technology Developers/
Manufacturers / Suppliers
- Referred to as “Manufacturers “
Appendix D: Organisations/ Persons Engaged
with for Stakeholder Dialogue
Person
Organisation
Conor Mullaney
Glen Dimplex
Elisabetta Bari &
Vanessa Butani
Electrolux
Manufacturer of white appliances.
Marc Stanton
Cool Power
Products
Victor Mackaij
Echelon
Developers of a device to match demand to local
generation
Networking and Smart Grid software and
hardware solutions.
Gale Horst
Electric Power
Research
Institute
Hamish Neale
Control 4
Dan Taylor
Centrica
John Counsell
University of
Strathclyde
Sarah Darby
Environmental
Change
Institute;
University of
Oxford;
Ritsuko Ozaki
Imperial
College London
A-26
Information
Manufacturer of electrical heating (storage
heaters and heat pumps)
Project manager of Smart Grid demonstration
project (and formerly from Whirlpool)
Supplier and developer of home automation
products
Energy supplier, active in Smart Metering and
Smart Home technologies
Professor, Chair of BRE Centre at Strathclyde
University, with interest in electric heating,
advanced controls and energy efficiency.
Research Fellow focusing on what householders
are able to learn from developments in energy
metering, billing and displays and how effective
these developments may be in reducing and
managing demand.
Senior Research Fellow, with a focus on energy
consumption by households. Currently working
with UK Power Networks on the Low Carbon
Network Fund looking how householders'
understanding of their own energy use patterns
(time of day) and levels (actual usage) can help
change their behaviours.
Appendix E: Case Studies
E.1 Heat Pumps
The following is written primarily from the perspective of space heating for domestic
properties, although it is also applicable to heating other buildings, and producing
domestic hot water.
There are a wide variety of heat pump systems, some of which are more suited to
DSM than others. Common system types are:
 Air to air
 Air to water
 Ground to water
 Water to water
Where, in each case, the first term refers to the source of heat and the second to the
type of heat sink (or heat distribution system). In addition to these basic types, the
presence of any supplementary heating is also important, both to the need for DSM
(as will be explained below) and to the ease with which DSM can be implemented.
The following terms are often used to describe the type of supplementary heating.
 Mono-valent – heat pump with no integrated supplementary heating
 Bi-valent – two fuel (i.e. electricity to drive the heat pump and a fossil fuel fired
boiler as the back-up system)
 Mono-energetic – a single form of energy supply driving the heat pump and
providing any supplementary heat (i.e. an electrically driven heat pump with
an integral electric flow boiler)
Most UK designs of heat pump are mono-valent (e.g. Kensa75, Calorex76) and many
overseas designs are also supplied in this mode. Mono-valent heat pumps can be
utilised as a part of a bivalent system – typically by running the heat pump parallel
with a pre-existing fossil fuel fired boiler.
Many European designs are mono-energetic (both Scandinavian and German
makes are very common in the UK). Here the design philosophy is to size the heat
pump to cope with 80 or 90% of the design day heat load of the property and bring
on the electric flow boiler to increase the system output on the few days of the year
when more heat is required than the heat pump alone can deliver. This approach
clearly adds to the maximum demand on the electricity system (both in terms of
network capacity and generation capacity) – hence increasing the need for DSM.
However, switching the flow boiler off in times of network or supply constraints is a
simple DSM measure.
75
Kensa Heat Pumps, http://www.kensaengineering.com/
76
Calorex Heat Pumps, http://www.calorex.com/
A-27
The heat source has little bearing on the DSM capability. However, air source
systems are more likely to require back-up or supplementary heating as their heating
capacity falls more rapidly with falling ambient temperature than do either water or
ground source heat pumps, and thus may have a ready means for achieving some
DSM capability.
Water distributed heating systems (radiators, under-floor heating, fanned radiators)
offer a much wider range of DSM capabilities than do air distributed systems, simply
because the water provides a natural buffering or thermal storage capability.
Thermal storage can take the form of:
 The building fabric
 The heating system (e.g. water content of radiators)
 Separate storage vessels or buffer tanks.
It is currently common practice to install buffer tanks with water distributed systems
to reduce the prevalence of on/off cycling of the heat pump (particularly for the fixed
speed, UK and European, designs).
The following explores a number of DSM options and matches them to the types of
heat pump, first exploring simple options requiring little or no changes to current
standard design practices and then exploring more complex options or the use of
alternative fuel sources.
Table E.1summarises a number of options for implementing DSM that do not require
any changes to standard heat pump design practices. Rather, they require changes
to the way the systems are controlled and operated. However, there may well be a
need to change current industry advice (and Renewable Heat Incentive (RHI)
provisions) to encourage their adoption. Table E.2 summarises a number of more
complex options for implementing DSM that require changes to standard heat pump
design practices.
In many ways, the bi-valent heat pump with secondary gas boiler operation provides
the simplest and most effective DSM technique (indefinite time interruptions).
However, there are good arguments against the use of these systems:
 The aim in using heat pumps is to move away from fossil fuels;
 Maintaining two systems adds to the running costs (one of the often cited
advantages of heat pumps is their low maintenance requirement compared to,
say, gas or oil boilers).
On the other hand, liquid / gaseous fuels have very high energy storage densities. A
long term, non-fossil fuel option is hydrogen (or derivates thereof), which can be
produced by electrolysis from zero / low carbon sources of electricity. An integrated
heat pump with, for example, bottled gas (c.f. Calor) to produce a single package
that would only require one maintenance visit, is conceivably a viable product and
one that would give very good DSM capabilities. In a similar vein, a Canadian
design of air-source heat pump with an integral mains gas burner was on the market
in North America – allowing the use of air-source heat pumps in very cold climates.
A-28
Table E.1 Simple DSM Options
Option
Heat distribution
system type
Air and water
DSM implementation
approach
Raise / lower set point, via
Smart thermostat
Mono-valent heat
pump with
underfloor heating
with screed
Mono-valent heat
pump with buffer
tanks
Mono-energetic
heat pump with
electric flow boiler
Bi-valent heat pump
with secondary gas
boiler
Water
Raise / lower set point, via
Smart thermostat
Water
Switch on/off heat pump,
whilst allowing circulating
pump to continue operating
Switch on/off of electric flow
boiler whilst allowing heat
pump to continue operating
Fuel substitution – i.e. only
operate boiler during ‘DSM
events’
Bi-valent heat
pumps with
secondary heating
system
Air and water
Mono-valent heat
pump with fabric
thermal storage
Water
Water
Use of secondary heating –
i.e. use of wood burning
stoves during ‘DSM events’.
Limitations / Issues
Interruption duration
Only realistic option for air-distributed
systems.
Longer interrupts are possible with buildings
designed to give high fabric thermal storage
(for example the Termodeck77 system)
Only limited use of underfloor heating systems
in UK homes.
Short (possibly up to 1 hour)
Generally only provides short terms storage,
as existing buffer tank designed to reduce
on/off heat pump cycling
Whilst this does give impression of DSM
capability, it is essentially just providing a
solution to a self-inflicted problem.
Requires the controls of the boiler and the
heat pump to be integrated.
System must be carefully designed to
maintain low flow temperatures for heat pump
use.
Advanced warning of high prices could enable
householders to use the secondary form of
heating on days with high electricity costs.
UK households do not currently require the
installation of a secondary heating system.78
Medium (potentially several
hours)
Short (up to 1 hour)
Short (up to 1 hour)
Long (several hours, maybe
even days provided boiler is
sized appropriately)
Long (several hours, maybe
even days provided boiler is
sized appropriately)
77
Termodeck has been used in a limited number of large non-domestic building in the UK. It uses hollow concrete slabs to deliver heated fresh air to the building
http://www.tarmacbuildingproducts.co.uk/products_and_services/termodeck.aspx
78
It is compulsory for electrically heated households in Finland to have some form of reserve heating, such as a heat storing fireplace (typically a wood-burning stove surrounded by stone that
stores the heat and releases it slowly over time).
A-29
Table E.2 Complex DSM Options
Option
Mono-valent heat
pump with large
buffer tanks
Mono-valent heat
pump with large
buffer tanks using
phase change
materials (PCMs)
Mono-energetic
heat pump with
electric storage
heating
Mono-valent heat
pump with local
electricity storage
Mono-valent heat
pump with longer
term thermal
storage
79
DSM implementation approach
Limitations / Issues
Water
Heat pump can then be switched
off during ‘DSM events’ and heating
requirements met by the thermal
storage capacity within the buffer
tank.
If heat pump used to charge up the thermal store, the
storage temperature will be limited, thus requiring
potentially large storage volumes. If off-peak direct
electricity used to store energy in the buffer tank, higher
storage temperatures can be utilised, thus increasing
storage capacity for a given volume, but could impact on
heat losses.
Several hours
(depending upon
capacity of buffer
tank).
Water
As above, but use of PCMs allows
for increase energy storage density
within buffer tank
Energy storage using PCMs for this application is largely
unproven79.
Several hours
(depending upon
capacity of buffer
tank).
Water
Utilise storage heater technology
(thermal bricks) to provide heat
directly to a water distributed
system, thus allowing heat pump to
be switched off during ‘DSM
events’
Unproven technology. A similar system was developed in
the 1970s / 1980s but never reached commercialisation.
Potentially greater energy storage density than water or
PCM systems.
Several hours
(depending upon
capacity of thermal
storage bricks).
Water and Air
During ‘DSM events’, the local
electricity store is used to operate
the heat pump.
Not a Smart appliance solution, but included for
completeness.
Several hours
(depending upon
capacity of buffer
tank).
Use of seasonal energy storage to
allow heat pump to be turned off
during ‘DSM events’.
Range of options currently being researched, although
none are considered to be proven technologies. Options
include:
Adsorption systems (e.g. zeolites)
Gravel beds
Large (buried) PCM stores
Several days
(depending upon
capacity of buffer
tank).
Heat
distribution
system
Water
Interruption
durations
Ice storage has been widely used for shifting the electrical load of air conditioning systems, other PCMs are available with a range of phase change temperatures suitable for heating applications.
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Heat Pump DSM Scenarios
The following discussion is based on the 3 scenarios introduced in Section 7, i.e.
Business as Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2.
Business as Usual
If current predictions for the uptake of heat pumps, as part of an overall strategy to
decarbonise the supply of space heating, are correct, then “Business as Usual” will
lead to large increases in demand for electricity in domestic properties. In particular
winter demands, both through the day and peak demands, will increase
substantially. The degree of increase will depend on:
 Heat load of properties
 Performance of the heat pump (winter coefficient of performance)
 Operating pattern (i.e. 24 / 7 operation or intermittent heating)
 Penetration level (the numbers of houses with and without heat pumps in a
given geographic area)
The following Figure provides an illustrative example of the possible effect – this for
24 / 7 operation of the heat pump. It shows the additional demand of the heat pump
imposed on the standard domestic electricity profile. This relatively small increase
(~33% in maximum demand) can be regarded as the increase for 100% penetration
of heat pump use in well insulated properties (or lower penetration levels in less well
insulated properties).
Half Hourly Power Demand With and Without Heat Pump (January Weekday)
Without HP Elec Demand (kW)
1.4
With Heat Pump Power Demand (kW)
1.2
Power Demand (kW)
1.0
0.8
0.6
0.4
0.2
0.0
00:00
03:00
06:00
09:00
12:00
15:00
18:00
Time
Figure E.1 Daily Demand Profile for a Household with a Heat Pump
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21:00
Smart Appliances – Level 1
Here we assume minor changes to control or system design, so that simple DSM
measures can take place. Typically these will take the form of responses to signals
to turn the heat pump off for short periods. Alternatively, with a slight increase in
sophistication, a Smart thermostat could be used to raise and lower the set points
either side of a call to interrupt supply.
How useful this will be depends on the particular DSM driver. If we consider
reducing the maximum demand as the driver and calling for a short interrupt of all
heat pumps during the period of maximum demand, then, modifying Figure E.1
produces the profile shown in Figure E.2.
Electricity Demand for a January Day
1.40
1.20
Electricity Demand (kW)
1.00
0.80
0.60
0.40
Simple Measure with HP Electricity demand
(kW)
BaU With HP Electricity Demand (kW)
0.20
Base Electricity Demand (kW)
0.00
00:00
03:00
06:00
09:00
12:00
15:00
18:00
21:00
00:00
Time
Figure E.2 Illustrative Demand Profile - Short Interruption to Heat Pump
Here we see a large reduction in demand for a short period, but, perhaps counter
intuitively, very little change in overall maximum demand.
Thus simple interrupts, such as shown here, will be of little benefit to some types of
DSM driver (maximum demand). However, they may well still be useful to some of
the other DSM drivers (such as the Frequency Response service required by
National Grid).
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Smart Appliances – Level 2
Significant improvements can be made to the situation shown in Figure E.2 by
redesigning the system to include significant levels of thermal storage. Such a
scenario can be illustrated by Figure E.3.
Half Hourly Power Demand With and Without Heat Pump (January Weekday)
Without HP Elec Demand (kW)
1.400
With Heat Pump Power Demand (kW)
1.200
Power Demand (kW)
1.000
0.800
0.600
0.400
0.200
0.000
00:00
03:00
06:00
09:00
12:00
15:00
18:00
21:00
Time
Figure E.3 Illustrative Demand Profile - Peak Lopping to Heat Pump
Here we see the heat pump continuing to operate during the peak hours, but load
sharing with output from a thermal storage that has been charged in an earlier period
of the day. This form of operation can result in load being capped and held at the
maximum allowable level for several hours, provided that storage levels are
sufficient.
An alternative that would achieve a similar profile to that shown here, would be to
operate many heat pumps as a fleet – staggering their off periods. Thus each
individual heat pump would have a profile akin to that shown in. Figure E.2 but each
with a different interrupt time, with the resulting overall network profile as shown in
Figure E.3
Thus we see that the Smart Appliance Scenario Level 2 can be achieved with either:
 Complex heat pump designs (Table E.2), or
 Simple (mono-valent and mono-energetic) heat pumps (Table E.1) with
complex, fleet-operated, network controls.
Key to the choice of approach is the level of simple interrupt that is acceptable to the
householder (i.e. how long can a heat pump, with little or no in-built storage, be
switched off for without comfort levels being significantly affected. This will depend
on householder preferences, type of property (thermal mass) and, probably,
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operating regime (interrupts may be more acceptable if heat is supplied continuously
throughout the day, and hence the building has achieved more of a steady state
condition, than the case where heat is supplied intermittently and the central heating
may only have been on for a few hours). This is an area where more information is
required and one that deserves further research.
The choice will also depend on the time periods for the DSM operation and the level
of increase in the “Business as Usual” profile over the current profile. The greater
this increase, the more storage will become the preferred DSM approach.
Whilst thermal storage would appear to give more flexibility for DSM measures, it
does have significant disadvantages in terms of additional capital cost and physical
space requirements. Further information and research is required in this area.
Smart Appliances – the bi-valent option
Deserving of separate mention is the question of fuel substitution and bi-valent
operation. Unlike all other appliances, heating is the one area where the appliance
(the heat pump) can be enhanced or substituted with another fuel source.
Although this is a relatively simple measure, and hence included as simple DSM
options in Table E1, it can meet the full range of DSM drivers, including the extreme
case of interruptions lasting several days (e.g. matching demand to periods of low
wind generation). Of course how acceptable this is depends on how the energy
supply market develops, and on Government policy and incentives.
Clearly the concern with bi-valent and other fuel substitution approaches is that the
secondary fuel gets used more than the heat pump, hence defeating the reasons for
installing the heat pump in the first place. Good price signals (or carbon based
signals) feeding into automated control systems should avoid this possibility.
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E.2 Air conditioning
The following will focus on air conditioning for relatively small offices. At present the
uptake of air conditioning in domestic properties is very low. Whether this will
change is difficult to assess - there being conflicting opinions arising from the need to
minimise energy use on the one hand, and the expectation of rising summer
temperatures on the other. For now (at least in the domestic sphere) cooling is still
widely seen as something that can be lived without, rather than being regarded as an
essential in the way that heating is.
There are many similarities between the opportunities for using DSM with heat
pumps, discussed above in Appendix E.1, and those for air conditioning. Short term
interrupts may be feasible with little or no changes to the design and operation of the
air conditioning equipment, whilst longer term interrupts are possible through the use
of (negative) energy storage, or “coolth” storage as it is sometimes known. The main
difference is that the range of possibilities is more limited with air conditioning than is
the case with heat pump as will be discussed below.
Air conditioning utilises a similar range of technologies to heat pumps (apart from
there being no equivalent to the bi-valent and mono-energetic designs of heat
pump). Gas fired / heat driven absorption air conditioning is possible and does find
uses (e.g. heat driven air conditioning as part of CHP schemes) but they are not
used as a back-up / bi-valent alternatives to electric driven air conditioning.
The most common designs of electrically driven air conditioning are air-to-air
systems. These are often reversible, providing cooling in summer and heating in
winter (i.e. they act as heat pumps during winter). Such systems usually comprise
an indoor unit and an outdoor unit, connected together by pipe-work through which
refrigerant is circulated (so called “split” and “multi-split” system – the latter having
several indoor units fed by a single outdoor unit).
Similar in effect are small local units (through-the-wall, window units, temporary / adhoc units), which also rely on air-distribution of the cooling.
Water distributed systems are also widespread – particularly in larger sizes where
they are often referred to as chillers. In very large sizes, hybrid water / air systems
are common, with a single, central, chiller feeding several air-handling units.
Designing out the need for cooling is a particularly important option (the energy
efficiency option for DSM – see Figure 2.1) – a case of smart designs rather than
smart appliances:
 low energy appliances (e.g. laptop performance rather than desk-pc
performance);
 local task lighting, low energy lighting;
 window shading;
 local air movement (comfort cooling for people);
 dehumidification (comfort cooling for people);
 heat tolerant machines (e.g. separate server / data base rooms);
A - 35
There are also a number of low energy cooling strategies such as night cooling
options (e.g. Cool-phase from Monodraught80) and “free” cooling by ground source
heat pumps (i.e. using the ground loop water directly with, for example, chilled
beams - the only energy use then being the relatively small amount required for the
water circulation pump).
Accepting that air conditioning cannot be designed out / avoided in all cases and that
there will inevitably be some electrically driven air conditioning, then there are a
number of DSM options.
Table E3 summarises a number of the simpler options for implementing DSM that do
not require any changes to standard air conditioning designs and are, perhaps,
particularly appropriate for air to air systems.
Table E4 summarises a number of more complex options for implementing DSM that
require changes to standard air conditioning designs. These are generally more
suited to water distributed systems, with the exception of enhanced fabric-storage
designs, such as the Termodeck81, which often use air distribution as an integral part
of the design.
The more complex DSM options will generally result in longer interrupt periods than
are possible with the simpler systems.
80
See www.cool-phase.com
81
Termodeck has been used in a limited number of large non-domestic building in the UK. It uses hollow concrete slabs to
deliver heated fresh air to the building http://www.tarmacbuildingproducts.co.uk/products_and_services/termodeck.aspx
A - 36
Table E.3 Simple DSM Options
Option
Air to air
Splits & multi-splits
Cooling
distribution
system type
Refrigerant / air
DSM implementation
approach
Limitations / Issues
Interuption durations
Simple interrupt
e.g. off for 15min in each 30
min DSM period
Impact on comfort – unknown – some
studies in e.g. Australia (see Appendix C)
Circa 30 min
or
chillers
Staggering of off periods required to smooth
demand
water
Prioritise cooling zones
A sort of cooling rationing – only allowing it
in critical areas of the building at certain
periods of the day.
Possibly > 30 min (of partial
load reduction)
Raise / lower set point, via
smart thermostat
Length of interrupts achievable are
unknown, but, as for heating-only heat
pumps, will probably be quite limited
Circa 30 min
A - 37
Table E.4 Complex DSM Options
Option
Chillers with
water buffer
tanks
Cooling
distribution
system
Water
Chillers with
PCM stores
(including ice)
Water
Enhanced
fabric storage
Air
DSM implementation approach
Limitations / Issues
Interuption
durations
Air conditioning equipment can be
switched off during ‘DSM events’ and the
cooling requirements met by the thermal
storage capacity within the buffer tank.
Similar to water tanks, but large capacity
per unit volume (kWh / m3) due to latent
heat capacity of PCM
Limited capacity – low DT within store –
hence large volumes per kWh of “coolth”
Limited
Energy efficiency will be related to the
temperature of phase change. Ice is a low
temperature (and hence low energy
efficiency) storage means but will have the
highest energy storage density.
Several
hours
Longer interrupts are possible with
buildings designed to give high fabric
thermal storage (e.g. Termodeck)
A - 38
Several
hours
Air conditioning DSM Scenarios
The following discussion is based on the 3 scenarios introduced in Section 7 - (Business as
Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2).
Business as Usual
Under “Business as Usual” there is expected to be a growth in the demand for air
conditioning and hence an increase in use of electricity, particularly in the summer months.
It is likely that some parts of the electricity network will become summer peaking – indeed
this is already happening in some major cities.
One potential mitigating factor is the obvious synergy between air conditioning and building
mounted PhotoVoltaics (PV). However, demand on the network may well still increase
leading to potential supply and network constraints.
Smart Level 1
As discussed earlier in Appendix C2.2 (Overseas Experiences, Australia), there is evidence
from overseas that very simple DSM measures can be used to provide significant benefits.
The simplest is simply a remote call for air conditioning to be interrupted at certain times of
the day, although, as noted in the Australian example, staggering of the switch-off and
switch-back-on times is desirable.
As with the Level 1 heat pump case study, there is the likelihood that simple interrupts will
not have a large impact on system maximum demand, simply altering the time at which this
occurs.
Smart Level 2
As with the heat pump Level 2 example, storage greatly increases the ability for the Demand
Side to deliver system benefits for all DSM Drivers. This includes benefits requiring both
short-term interrupts (network / ancillary services) and longer term interrupts (network
constraints, peak demand reduction and renewables integration).
A - 39
E.3 Refrigeration
UK households typically have a combination of either a separate fridge
(undercounter or tall designs) and freezer (undercounter, tall or chest designs), or a
combined fridge-freezer. The small commercial premises under consideration in this
study may have similar sizes of fridges for the use of employees. Small shops with
demand less than 100kW (i.e. within the scope of this study) may also have
refrigeration units for products, for example small convenience stores.
Data from the Market Transformation Programme82 estimates both ownership rate
(% of households owning a given appliance) and lifespan (years). The data for 2010
is shown in the Table below.
Table E.5 Ownership and Lifespan of Cold Appliances
Appliance
Ownership Rate (% of households) Lifespan (years)
Fridge Freezer
65%
17.5
Chest Freezer
16%
16.7
Upright Freezer
30%
15.5
Fridge (only)
43%
12.8
Refrigerators and freezers operate cyclically- i.e. they operate a cooling cycle and
maintain a low temperature between cycles through the use of good insulation and
the thermal mass of the contents of the unit. It is estimated that an appliance is ‘on’
for 33% of the time. The length of time the unit is cycling ‘on’ is increased by
consumer activity such as opening the door and adding warm food to the
refrigerator. Analysis as part of the European Smart-A project has suggested that
this activity accounts for 25% of the total energy consumption of the appliance. It
could therefore be suggested that there are more cycles in the evening, when these
activities are more likely to take place.
82
BNC08: Assumptions underlying the energy projections for domestic cold appliances. Version 3.1. 15/01/2008. Available
from: http://www.greenandeasy.co.uk/upload/file/Fridges_Freezers_2008January15.pdf Accessed 10/06/11.
A - 40
DSM Options for Refrigeration
There a number of potential ‘Smart’ solutions which could be developed for cold
appliances, set out as follows:



Business-as-Usual: consumers are encouraged to purchase energy efficient
appliances through energy labelling. It may also be possible to encourage
consumers to buy a suitable size of appliances with a minimal number of
features to decrease energy consumption, although this may be at odds with
a tendency for manufacturers/ retailers to ‘up-sell’ (i.e. persuade the
consumer to choose a more expensive model with additional features).
Smart Level 1: A dynamic demand technology is used to provide frequency
response services to the System Operator (National Grid).
Smart Level 2: The cycling of appliances could be interrupted for extended
periods with more complex monitoring of the temperature set point (e.g.
through a Smart thermostat). The use of Smart thermostat could reduce the
temperature of the unit prior to an event (e.g. the half hour of highest demand)
and then stop cycling during the event. A potential alternative, or
complementary, solution could make use of phase change materials as inbuilt storage.
The following Sections will provide further details of each of these scenarios and the
benefits of each.
Business As Usual
Energy efficiency labelling for appliances was introduced in 1992, using classes A-G.
Additional classes were introduced in 2004 (A+ and A++)83. The Market
Transformation Programme (MTP) have estimated the stock (i.e. number of
appliances within UK homes) of different groups of appliances for each year from
2004 to 2030 for chest freezers, refrigerators, fridge freezers, and upright freezers.
The total energy consumed per year (GWh/ year) of each class of appliances is also
estimated. From this consumption and total stock data it is possible to estimate the
energy consumption for each type of appliance by energy labelling band for a
number of reference years. The case for 2010 and 2020 is illustrated in the Table
below.
Table E.6 Energy Consumption (kWh/year) in 2010 and 2020 for
cold appliances by energy label category
Energy
Label
A+++
A++
A+
A
B
C
D
Fleet
Average
83
Chest Freezer
2010
N/A
123
170
203
254
309
539
323
2020
83
104
146
195
258
309
546
183
Fridge Freezer
Refrigerator
2010
N/A
195
268
372
431
529
541
446
2010
72
91
128
170
211
244
257
200
http://www.energylabels.org.uk/eulabel.html (Accessed 13/06/11)
A - 41
2020
156
195
266
363
422
531
523
286
2020
72
90
123
165
211
243
259
124
Upright
Freezer
2010 2020
N/A
100
140
125
196
172
226
225
298
304
380
374
387
385
316
189
This illustrates the decreasing total energy consumption under each class of
appliance, and the introduction of A+++ Standard.
The power demand from any single appliance over a 24 hour period is a function of
the cycling of the appliance. This is related to the ‘normal’ operation of the appliance
and additional ‘events’ such as the door opening and warmer food being placed in
the appliance.
The Smart-A project has estimated total energy consumption of 404kWh per year
(considerably less efficient than the figures for the MTP above), with an average
power demand of 138W, assuming the appliance is ‘on’ (cycling) for a third of the
time84. For the energy efficiency standards above, a 2010 A+ rating is equivalent to
a power demand of 45W, and a D rating 87W when cycling. The timing of the load
(cycling) is dependant on the thermal properties of the cold appliance, its
surroundings, and the consumer behaviour. A University of Bonn study, cited within
the Smart-A project has correlated energy demand to the frequency of door opening
events. It is assumed that 25% of the total energy consumption of the fridge is due
to consumer behaviour. The resulting curve is shown below for the Smart-A
appliance with a total annual energy consumption of 404kWh per year. This takes
into account the increased energy consumption during periods when the door is
most likely to be opened. It should be noted that this results in a peak in the early
evening, coincident with the existing peak in overall network demand.
Figure E.4 General Pattern of a daily load curve of a refrigerator in an average European
household84
If it is assumed that all types of cold appliance follow the same profile of door
opening, but the their total energy consumption differs, then a series of curves could
be plotted for A+++ to D rated chest freezers, refrigerators, fridge freezers and
84
Synergy Potential of Smart Appliances. D2.3 of WP2 from the Smart-A project. November 2008. Prof. Dr. Rainer
Stamminger, Rheinische Friedrich-Wilhelms-Unversität Bonn.
Available from: http://www.smart-a.org/WP2_D_2_3_Synergy_Potential_of_Smart_Appliances.pdf (Accessed 13/06/11)
A - 42
upright freezers. An example pair of curves is shown below for the fleet average
fridge freezers in 2010 and 2020 on the following Figure.
Figure E.5 Business as Usual Fridge Freezer Load 2010 and 2020
In the Business as Usual scenario, consumers would be encouraged to purchase the
most energy efficient appliances through labelling. This could increase the size of
the energy reduction between 2010 and 2020 but would not alter the shape of the
power demand curve.
Smart Level 1
A technology has been developed by RLtec85 which can be integrated into
refrigerators. The control unit senses the frequency of electricity supplied to the
appliance and adjusts the power demand accordingly- i.e. using power when
demand is low and frequency is high, and vice-versa when frequency is low. It
applies this method within a widened ‘band’ of temperature set point, such that the
maximum temperature is increased when frequency is low, and decreased when
frequency is high. This ensures that although the fridge/ freezer load is increased or
reduced, the appliance remains at a safe temperature to maintain food safety. By
aggregating the action of a number of these devices (“several thousands or millions
of appliances” to emulate the current Frequency Response Services procured by
National Grid86) it would be possible to provide balancing services to the National
Grid. Initial field trials of the technology have focused on cold appliances, but Rltec
85
http://www.rltec.com/ Accessed 13/06/11
86
The Potential for Dynamic Demand. Department of Energy and Climate Change. URN 08/1453. November 2008.
Available from: http://www.supergen-networks.org.uk/filebyid/50/file.pdf (Accessed 13/06/11)
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suggest the technology could be extended to air conditioning and hot water
heaters87.
DECC commissioned a study to assess the potential for Dynamic Demand (DD).
The study was completed by the Centre for Sustainable Energy and Distributed
Generation86. The objectives for the study were to:
 To assess the potential for DD to reduce the requirement for the System
Operator to commission Balancing Services;
 To estimate DD carbon saving potential, both in current electricity generation
scenario and in scenarios featuring greater integration of renewable energy
generation; and
 To estimate the potential carbon savings on a per-appliance basis.
The study is suitable to estimate the benefits of using such a technology as ‘Smart
Level 1’ within this work.
The following diagram illustrates the ‘normal’ and ‘Dynamic Demand’ operation of the
refrigerator.
State representation
Refrigerator temperature and compressor state
Figure E.6 ‘Normal’ operation of a refrigerator
88
87
Background Information. RLtec. Available from: http://www.rltec.com/sites/default/files/090427_product_background_0.pdf
(Accessed 13/06/11)
88
Economic and Environmental Impact of Dynamic Demand. Centre for Sustainable Electricity and Distributed Generation.
November 2008.
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Figure E.7 DD Refrigerator Behaviour with a Step Change in System Frequency88
In the ‘Dynamic Demand’ scenario, the duty cycle length is changed in proportion to
the change in system frequency. This is illustrated in the figure above, where the
grid frequency changes from 50Hz to 49.5Hz at the 2 hour point. After this point the
shortened duty cycle and higher minimum temperature of the refrigerator can be
seen. This shortened duty cycle leads to a lower average power demand.
The power relieved for response per appliance under various scenarios is
summarised in the following table:
Table E.7 Power potentially relieved by refrigerators while delivering frequency response
DD Penetration (%)
In fed Loss (MW)(*)
System Demand
(GW)
Power relieved for response (W per appliance)
1320
1800
25
40
60
25
60
100
19
21
18
29
24
75
19
25
20
29
27
50
19
28
23
28
27
25
20
29
26
28
28
(*) In fed loss – sudden loss of a single generator
This illustrates that in the most extreme scenario- i.e. the loss of the largest possible
generation unit (1800MW), at the period of highest demand, when levels of DD
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penetration are low, each appliance would provide 28W of relieved power, relative to
a rating of 150W. The analysis assumed that the appliances would be able to
provide this level of response for a sufficient period of time (30 minutes) and that all
refrigerators would instantly recover their temperature. The Figures in the table
above are therefore at the upper limit of what could be provided. The other main
conclusions of the study are summarised below:



The simplified modelling approach showed that Dynamic Demand
refrigerators could contribute between 728 and 1174MW of Frequency
Response Services, based on 40 million appliances under different generation
scenarios.
The potential value of annual fuel savings per appliance were estimated to be
between £0.70 and £5.60, dependent upon the precise mix of coal, gas,
nuclear and wind generation and demand levels. These savings are as a
result of reduced fuel costs by reducing the number of ‘part-loaded’ and
therefore less efficient generators running. It is not clear what proportion of
this cost saving could be passed onto consumers and through what
mechanism. Assuming a ten year life and a discount rate of 10% the
capitalised present value of the technology is between £4.40 and £34.10.
By reducing the use of part loaded and therefore less efficient generation, the
DD technology could save between 17 and 44kg of CO2 per refrigerator per
year, depending upon the generation mix.
The following issues were identified for further study:


The results are sensitive to modelling technique used and further insight is
required into:
o Whether the resource could provide sufficient balancing services over
the time period required (presently 30 minutes);
o Whether increased reserve requirements would be required once the
refrigeration load is reconnected, as their power demands may be
greater as the temperature would rise during the ‘off’ cycle. The SEDG
report highlights the importance of this research as follows: “This is
clearly of fundamental importance for the value of DD and further
research in this area is essential”88; and
o The impact of alternative control algorithms for DD on providing
frequency response services.
A demonstration project was identified as necessary, to understand practical
issues such as:
o The impact of DD on the efficiency of appliances and ‘wear and tear’
costs;
o How much affect the pattern of use of the appliance (e.g. door
openings) has on the potential value of DD; and
o The effect of future changes in appliance design on DD potential.
A trial of this technology was approved by Ofgem under CERT legislation as a
‘demonstration action’ in November 2008. The trial was originally scheduled to begin
in 200986. This was delayed, with the first tranche of fridge-freezers being delivered
in May 2010 to residents in the West Midlands. A further three hundred Smart
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fridges were delivered in the initial stage of the trial, with up to three thousand to be
included in the later stages of the trial over the next two years89.
However, the major obstacle to the role out of the technology is the lack of any
financial mechanism to encourage the uptake of these appliances by end-users.
Although demonstrated to be cost effective (i.e. benefits outweigh the costs),
mandating the inclusion of dynamic frequency response within these appliances is
not practicable. This is due to the fact appliance Standards need to be implemented
on a EU-wide basis, rather than for the UK region, and the benefits of the technology
relate specifically to the UK region.
Smart Level 2
Smart Level 2 would represent an extension of Level 1, such that an extended
interruption (time of reducing the proportion of time which the appliance is cycling
for) could be acceptable. This would extend the dynamic demand concept to enable
the appliance to avoid peak pricing periods, or provide services to the System
Operator for a longer period time. This could potentially extract a higher value than
Smart Level 1.
One potential method of extending this interruption would be to provide a
communications link to the Smart cold appliance to provide a warning of a future
interruption. This would enable the unit to cool to a lower temperature before an
interruption, extending the period of time for which the cycle could be interrupted
before a maximum temperature is reached. The graph in Figure E.7 illustrated the
potential for a shortened duty cycle without pre-cooling. Figure E.8 below shows
show refrigerator duty cycles- the first for Smart Level 1 (a reproduction of Figure
E.7) and the second for Smart Level 2.
Figure E.8 Refrigerator Duty Cycles for Smart Level 1 and 2
89
http://www.npowermediacentre.com/Press-Releases/The-cool-way-to-cut-CO2-Europe-s-first-smart-fridges-trial-starts-inUK-dcd.aspx (Accessed 14/06/11)
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This shows the lengthening of the duty cycle prior to the interruption at hour 2, and
shorter duty cycles. The effect on the temperature of the cold appliance would
require further consideration. For refrigerators there is a minimum suitable
temperature, below which food can be damaged. The temperature should not be
less than this value during the pre-cooling. During the interruption, the maximum
temperature must still stay below the maximum acceptable value. Further
investigations would be required to determine if pre-cooling would meet these
criteria.
Pre-cooling clearly relies on prior warning of an event. This event could be a
requirement to provide services to the System Operator such as frequency
response, or a peak price. Under some conditions, such as the unforeseen failure of
a generator requiring frequency response services, it would not to be possible to precool the appliances. This could limit the potential benefit from the pre-cooling
functionality.
The size of the benefit is likely to be of a similar magnitude to Smart Level 1 (i.e. tens
of Watts per appliance). The potential benefits could be slightly higher as a longer
interrupt could be provided. The length of this interruption which is achievable and
the likelihood of the necessary warning for pre-cooling would require greater
investigation. An assessment could then be made of the additional benefits and
costs involved in Level 2 relative to Level 1.
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E.4 Washing Machines
Washing machines are owned by around 80% of UK households90 and consume
approximately 4.4 TWh of UK electricity per year, as indicated in Table E.8 below.
Table E.8 Ownership and Energy Consumption of washing machines in the UK
2010
2020
2030
Number of UK
Households ('000s) (i)
26,795
29,458
32,021
Households with a
washing machine ('000s)
21,082
23,566
25,617
79%
80%
80%
4,369
4,564
4,692
207.3
193.7
183.2
(ii)
% ownership
Energy Consumption
(GWh) of washing
machines (iii)
Average household
consumption (kWh/year)
(i)
(ii)
BNXS25: UK Household and Population Figures 1970 – 2030, Version 3.0, Market Transformation Programme
(iii)
Market Transformation Programme
BNW01: Combined Laundry: Government Standards Evidence Base 2009: Key Inputs Version 1.1, Market
Transformation Programme
It is not generally regarded to be practicable to interrupt washing machines mid-cycle
due to the potential impact on clothing. Therefore, DSM options are predominately
limited to scheduling the use of the appliances to avoid consumption during certain
periods of the day. Examples include:
Lifestyle changes by householders to limit washing machine usage during
certain times of the day.
Utilisation of ‘in-built’ timer functions to delay the start of washing machine
cycles, thus avoiding peak-time consumption.
These are relatively simple options that could be implemented with existing washing
machine designs.
Alternative options, that require changes to washing machine designs could include:
Interrupting the hot water heating cycle for a short time (a few minutes)
Integration of the washing machine with a Home Area Network to enable
washing machine usage to be adapted to dynamic tariffs
In-built hot water storage91 to enable hot water heating load to be deferred,
without impacting on the customer.
Table E.9 summarises a number of options for implementing DSM for washing
machines.
90
Excludes households with washer driers, which account for a further 16% of households
91
For example, use of Phase Change Materials which can be heated using low cost electricity, and then used to pre-heat the
incoming cold water supply.
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Simple Options
Table E.9 DSM Options for Washing Machines
DSM implementation approach
Change of user behaviour, to avoid usage
during peak periods.
Limitations / Issues
No technology requirements – can be implemented with all existing
appliances.
Range of approaches possible, including
feedback from In-Home Displays or
awareness campaigns.
Change of user behaviour, whereby users
delay the start of their wash cycle using
the ‘in-built’ timer function.
Suitable for reducing use during a pre-defined period. Therefore only
suitable to avoid network peaks demands.
Raise awareness of potential role of ‘inbuilt’ timers
Complex Options
Interruption to hot water heating cycle.
Would require one-way communication to
the washing machine, possibly via the
Smart Meter and Home Area Network
Optimisation of washing machine usage
with remote signals (e.g. dynamic tariffs)
via the Smart Meter / Home Area
Network.
In-built hot water storage facility to allow
electrical load for hot water heating
element to be deferred, with no impact on
customer.
Not all washing machines have ‘in-built’ timer function.
Interuption durations
Up to a few hours
Up to a few hours
Customer perception of risks of fire or flood damage if appliances
turned on during night or when house in unoccupied
Suitable for reducing use during a pre-defined period. Therefore only
suitable to avoid network peaks.
Suitable for reducing use during very short periods only (a few
minutes), therefore limited only to balancing services applications.
Short intervals (few
minutes)
No suitable technology currently available.
Suitable for reducing use during more flexible range of timings.
Up to a few hours
No Standard protocols exist for way that remote signals are issued
via the Home Area Network.
Suitable for reducing use during more flexible range of timings.
No suitable technology currently available.
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Up to a few hours
Washing Machine DSM Scenarios
The following discussion is based on the three scenarios introduced in Section 7, i.e.
Business as Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2.
It was not possible within the scope of this study to undertake detailed studies to
evaluate the potential impact of washing machines on managing network constraints,
providing balancing services to National Grid or integration of renewables.
Therefore, the following Sections explore the potential magnitude of savings that
might be achievable with Smart Appliances under two potential scenarios (Level 1
and Level 2) compared to the ‘Business as Usual’ baseline.
Business as Usual
As indicated in Table E.8, “Business as Usual” will lead to a modest growth in energy
consumption by washing machines from 4.4TWh to around 4.7 TWh over the period
2010 to 2030. This growth is driven by increased appliance ownership, which is
offset to a large extent by improvements in energy efficiency.
Analysis of around 2,500 households across 10 European countries conducted for
the Smart-A project determined the general pattern of energy consumption of
washing machines, as indicated in Figure E.9 below. Whilst the pattern will
undoubtedly differ from household to household, it is nevertheless a useful measure
across a group of households.
Source: Synergy Potential of Smart Appliances, D2.3 of WP2 from the Smart-A project,
Figure E.9 Daily Pattern of Consumption of Washing Machines
The data presented in Figure E.9 is based on an average annual consumption
across Europe of 150kWh per year per household, and around 170 wash cycles per
year (i.e. around 0.89kWh/cycle). The equivalent data for UK households is shown
in Table E.10 below.
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Table E.10 Average Energy Consumption by Washing Cycle for the UK, 2010 to 2030
2010
2020
2030
207.3
193.7
183.2
Average number of cycles
260
260
260
Average consumption per cycle (kWh/cycle)
0.80
0.74
0.70
Average household consumption (kWh/year)
Source: Data provided by Market Transformation Programme
Therefore, adjusting the data shown in Figure E.9 to reflect UK average usage
information provides the following estimate of the pattern of consumption for washing
machines. This is taken to be the ‘Business As Usual Scenario’ against which the
Smart options will be later considered.
60
50
Load (W)
40
30
20
2010
2020
2030
10
0
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
00:00
Time of Day
Figure E.10 Business As Usual Average Energy Consumption Pattern of
Washing Machines in UK Households
As shown in Figure E.10, the average washing machine demand at the time of the
system peak (i.e. during the early evening) is currently around 43W per household,
falling to around 38W per household by 2030.
Thus, based on the pattern of consumption determined in the Smart-A project,
washing machines account for around 0.9GW of UK demand at the time of the
system peak, i.e. around 1.5% of current total demand. If it is assumed that
households with washer driers have a similar pattern of consumption, then total
demand due to clothes washing in households would represent around 2% of total
demand.
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Smart Level 1
As discussed previously, simple options for washing machines involve changes to
customer behaviour that lead to reduced washing machine usage during peak
periods, and thus in themselves do not rely upon ‘Smart Appliances’.
The willingness of customers to be more flexible in their usage of certain appliances
was explored in a recent survey commission by Ofgem92. The survey of 2,000
households gauged the level of interest in moving specified end-uses of electricity,
and the results are summarised below.
Table E.11 Customer receptivity to moving end-uses of electricity
Measure
Heat water at different times of day
Use certain appliances after midnight
Install technology to turn off appliances
when prices are high
Carry out chores or cook meals in
cheaper periods (e.g. after 7pm)
Use electric storage heaters
% of respondents very/fairly likely
to adopt measure
56%
51%
47%
41%
35%
It would seem unlikely that 50% of electrical load for washing machines could be
avoided through behavioural changes alone. Therefore, if is assumed that 10% of
households would be willing and able to significantly reduce their washing machine
usage between the hours of 17:00 and 20:00, network peak demand could be
reduced by up to 0.1GW (i.e. 10% of 0.9 GW peak demand attributable to washing
machines).
Smart Level 2
The results of a study on the impact of tariff structure and technology on the level of
demand response achieved in almost 70 pilots and trials showed that93, in general,
dynamic pricing combined with automatic control technologies lead to greater levels
of demand response. Therefore, it would be expected that the Level 2 scenario
would contribute more peak load reduction compared to Level 1 scenario. It is not
possible to confirm whether this would be the case for this particular example, nor is
it possible to quantify the additional benefits compared to the Level 1 scenario.
However, the benefits, in absolute terms are likely to be modest. For example, even
if the impact on peak load could be doubled compared to the Level 1 scenario, the
impact would represent a reduction of around 1.8 GW.
It is however important to note that peak demand is expected to grow significantly in
future years as the electricity supply sector is decarbonised. Therefore, as a
percentage of total growth, it is unlikely that washing machines would be able to
have a significant impact on overall demand.
92
Demand Side Response, a discussion paper, 15 July 2010, Ref82/10 Ofgem
93
Rethinking Prices, The changing architecture of demand response in America, Ahmad Faruqui, Ryan Hledik and Sanem
Sergici, Public Utilities Fortnightly, January 2010
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E.5 Tumble Dryers
Tumble driers are owned by just under half of UK households94 and consume
approximately 4.4 TWh of UK electricity per year, as indicated in Table E.12 below.
Table E.12 Ownership and energy consumption of tumble dryers in the UK
2010
2020
2030
Number of UK
Households ('000s) (i)
26,795
29,458
32,021
Households with a
tumble dryer ('000s) (ii)
11,860
13,348
14,509
44%
45%
45%
4,405
4,470
4,125
371.5
334.9
284.3
% ownership
Energy Consumption
(GWh) of tumble dryers
(iii)
Average household
consumption
(kWh/year)
(iv)
(v)
BNXS25: UK Household and Population Figures 1970 – 2030, Version 3.0, Market Transformation Programme
(vi)
Data provided by Market Transformation Programme
BNW01: Combined Laundry: Government Standards Evidence Base 2009: Key Inputs Version 1.1, Market
Transformation Programme
Whilst it is not generally regarded to be feasible to turn off tumble dryers mid-cycle, it
would be possible to either interrupt the heating cycle (whilst continuing to tumble the
clothes) or reschedule the cycle, thereby providing a range of DSM options similar to
those discussed for washing machines in the previous Section. Thus, rescheduling
of tumble dryer loads could be achieved with existing appliances in the following
ways:
- Lifestyle changes by householders to limit washing machine usage during
certain times of the day; and
- Utilisation of ‘in-built’ timer functions to delay the start of washing machine
cycles, thus avoiding peak-time consumption.
Alternative options, that would require development to existing appliance designs
include:
- Interrupting the heating element for a short time (a few minutes);
- Integration of the tumble dryer with a Home Area Network to enable its usage
to be adapted to dynamic tariffs;
- In-built heat storage95 to enable heating load to be deferred, without impacting
on customer.
Table E.13 summarises these options and the issues associated with their
deployment.
94
Excludes households with washer driers, which account for a further 16% of households
95
For example, use of Phase Change Materials which can be heated using low cost electricity, and then used to pre-heat the
incoming cold water supply.
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Simple Options
Table E.13 DSM Options for Tumble Dryers
DSM implementation approach
Change of user behaviour, to avoid usage
during peak periods.
Limitations / Issues
No technology requirements – can be implemented with all existing
appliances.
Range of approaches possible, including
feedback from In-Home Displays or
awareness campaigns.
Change of user behaviour, whereby users
delay the start of their wash cycle using
the ‘in-built’ timer function.
Suitable for reducing use during a pre-defined period. Therefore only
suitable to avoid network peaks demands.
Raise awareness of potential role of ‘inbuilt’ timers
Complex Options
Interruption to heating element.
Would require one-way communication to
the washing machine, possibly via the
Smart Meter and Home Area Network
Optimisation of washing machine usage
with remote signals (e.g. dynamic tariffs)
via the Smart Meter / Home Area
Network.
In-built heat storage facility to allow
electrical load for heating element to be
deferred, with no impact on customer.
Not all tumble dryers have ‘in-built’ timer function as standard.
Interuption durations
Up to a few hours
Up to a few hours
Customer perception of risks of fire or flood damage if appliances
turned on during night or when house in unoccupied
Suitable for reducing use during a pre-defined period. Therefore only
suitable to avoid network peaks.
Suitable for reducing use during very short periods only (a few
minutes), therefore limited only to balancing services applications.
Short intervals (few
minutes)
No suitable technology currently available.
Suitable for reducing use during more flexible range of timings.
Up to a few hours
No standard protocols exist for way that remote signals are issued via
the Home Area Network.
Suitable for reducing use during more flexible range of timings.
No suitable technology currently available.
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Up to a few hours
Tumble Dryer DSM Scenarios
The following discussion is based on the three scenarios introduced in Section 7, i.e.
Business as Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2.
It was not possible within the scope of this study to undertake detailed studies to
evaluate the potential impact of tumble dryers on managing network constraints,
providing balancing services to National Grid or integration of renewables.
Therefore, the following Sections explore the potential magnitude of savings that
might be achievable with Smart Appliances under two potential scenarios (Level 1
and Level 2) compared to the Business as Usual baseline.
Business as Usual
As indicated in Table E.12, “Business as Usual” will lead to a modest decrease in
energy consumption by tumble dryers from 4.4TWh to around 4.1 TWh over the
period 2010 to 2030. Although the number of appliances increases over this period,
advances in energy efficiency lead to an overall reduction in consumption.
Analysis of around 2,500 households across 10 European countries conducted for
the Smart-A project determined the general pattern of energy consumption of tumble
dryers, as indicated in Figure E.11 below. The extent to which the pattern of
consumption varies between countries is not known, however, in the absence of UK
specific data it does provide a useful measure of the average pattern across a group
of households.
Source: Synergy Potential of Smart Appliances, D2.3 of WP2 from the Smart-A project,
Figure E.11 Daily Pattern of Consumption of Tumble Dryers
The data presented in Figure E.11 is based on an average annual consumption
across Europe of 251kWh per household, and around 102 wash cycles per year (i.e.
around 2.46 kWh/cycle). The equivalent data for UK households is shown in Table
E.14 below.
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Table E.14 Average Energy Consumption by Tumble Dryer Cycle for the UK, 2010 to 2030
2010
2020
2030
371.5
334.9
284.3
Average number of cycles
140
140
140
Average consumption per cycle (kWh/cycle)
2.65
2.39
2.03
Average household consumption (kWh/year)
Source: Data provided by Market Transformation Programme
Therefore, adjusting the data shown in Figure E.11 to reflect UK average usage
information provides the following estimate of the pattern of consumption for washing
machines. This is taken to be the ‘Business As Usual Scenario’ against which the
Smart options will be later considered.
70
60
Load (W)
50
40
30
20
2010
2020
2030
10
0
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
00:00
Time of Day
Figure E.12 Business As Usual Average Energy Consumption Pattern of
Tumble Dryer in UK Households
As shown in Figure E.12, the average tumble dryer demand at the time of the system
peak (i.e. during the early evening) is currently around 50W per household, falling to
around 35W per household by 2030.
Thus, based on the pattern of consumption determined in the Smart-A project,
washing machines account for around 0.6GW of UK demand at the time of the
system peak, i.e. around 1% of current total demand. If it is assumed that
households with washer driers have a similar pattern of consumption for clothes
drying, then total demand due to clothes drying in households would represent
around 1.5% of total demand. However, the assumptions here assume that tumble
dryer usage is approximately constant throughout the year. In practice, demand will
be higher during the winter.
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Smart Level 1
As discussed previously, simple options for tumble dryers involve changes to
customer behaviour that lead to reduced washing machine usage during peak
periods, and thus in themselves do not rely upon ‘Smart Appliances’.
Using a similar approach to that used for washing machines, if it is assumed that
10% of households would be willing and able to significantly reduce their tumble
dryer usage between the hours of 17:00 and 20:00, network peak demand could be
reduced by less than 0.1GW (i.e. 10% of 0.6 GW peak demand attributable to tumble
dryers).
Smart Level 2
Here it is assumed that the Level 2 scenario could contribute more peak load
reduction compared to Level 1 scenario. However, it is important to stress that no
evidence does exist to substantiate this assertion. Nevertheless, assuming a 20%
impact at the time of system peak still represents a modest reduction in peak
demand of around 0.1 GW.
It is however important to note that peak demand is expected to grow significantly in
future years as the electricity supply sector is decarbonised. Therefore, as a
percentage of total growth, it is unlikely that tumble dryers would be able to have a
significant impact on the overall pattern of demand.
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