deliverable D3.1

Transcription

deliverable D3.1

FP7 – 609082 – Collaborative Project
Decision support Advisor for innovative business models and useR engagement for smart
Energy Efficient Districts
DAREED
Deliverable 3.1: Development of district energy model
Authors:
CETMA, IAT, UBRUN
Reviewers
(KIT, UNIBO)
Delivery due date:
30.01.2015
Actual submission date
30.01.2015
Status
RE
Deliverable: D 3.1
Organisation: IAT, CETMA
1. Executive Summary
This deliverable entitled ―Development of district energy model‖ reports the results of the work
carried out as part of task T3.1 in Work Package 3 (Modelling and Simulation for ICT platform).
The aim of WP3 is to develop a model based simulation tool for performance analysis of energy
saving procedures at a district level.
According to the Methodology for district modelling (D1.4), using the work done in previous work
packages, in Task 3.1 a district energy system simulation model has been developed to
characterize energy flow (consumption and production) at district level, evaluating the
relationships inside the district and using real data acquisition. The theoretical model considers
the flexibility of energy supply and demand, facing with the availability of renewable energy
sources. Weather conditions are used to predict user’s behaviours and renewable energy
production.
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2. SUMMARY
1.
Executive Summary ........................................................................................................... 2
2.
Introduction ...................................................................................................................... 8
3.
State of the Art Analysis .................................................................................................... 8
3.1
3.1.1
From buildings energy model to district energy model ............................................................................. 8
3.1.2
Decentralized generation technologies .................................................................................................... 13
3.2
4.
5.
State of the art analysis in energy district modelling ............................................................... 8
Overview of existing simulation tools for city district energy modelling ................................ 20
Description of district energy model ................................................................................. 21
4.1
Introduction ........................................................................................................................ 21
4.2
Physical modelling vs. Machine Learning .............................................................................. 23
4.3
Integration of physical models in the DAREED platform ........................................................ 23
4.4
An overall model ................................................................................................................. 24
Components modelling and characterization .................................................................... 25
5.1
5.1.1
5.2
Consumption nodes ............................................................................................................. 27
Buildings ................................................................................................................................................... 27
Production energy units ....................................................................................................... 32
5.2.1
DHW ......................................................................................................................................................... 33
5.2.2
Photovoltaic ............................................................................................................................................. 35
5.2.3
Small wind ................................................................................................................................................ 36
5.2.4
µCHP ......................................................................................................................................................... 38
5.3
Weather data information ................................................................................................... 42
5.4
District energy infrastructures.............................................................................................. 43
5.4.1
Electrical distribution grid ........................................................................................................................ 44
5.4.2
Gas grid ..................................................................................................................................................... 48
5.4.3
District heating and cooling ...................................................................................................................... 48
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6.
Conclusions ...................................................................................................................... 50
7.
References ....................................................................................................................... 52
8.
Annex I. Consuming Black Energy Unit example................................................................ 57
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List of figures
Figure 1: Types and technologies of Distributed generation (adapted from [2]) ........................................................... 14
Figure 2. Process to enrich DAREED platform from physical to model to “Grey-Box Model approaches” ..................... 24
Figure 3. First time data introduction............................................................................................................................. 26
Figure 4. Next time data introduction ............................................................................................................................ 26
Figure 5. Template models categories ........................................................................................................................... 31
Figure 6 Pathways for RE integration to provide energy services, either into energy supply systems or on-site for use
by the end-use sectors .................................................................................................................................................... 43
Figure 7 Efficiency of European Electrical Network compared with rest of the world ................................................... 44
Figure 8 Distribution systems for electrical power ......................................................................................................... 46
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List of tables
Table 1. Features of different building modelling approaches ....................................................................................... 10
nd
Table 2. 2 level classification of building modelling approaches ................................................................................. 10
Table 3: Comparison of application of common energy types (adapted from [2]) ........................................................ 15
Table 4. Overview on existing tools for energy modelling and simulation ..................................................................... 20
Table 5 Black building model inputs and outputs .......................................................................................................... 28
Table 6 Set of parameters required from user for grey building model generation ....................................................... 29
Table 7. DHW model. Inputs ........................................................................................................................................... 34
Table 8. Parameters associated to the selection of a type of collector .......................................................................... 34
Table 9. DHW model. Outputs ........................................................................................................................................ 35
Table 10. PV model. Inputs ............................................................................................................................................. 35
Table 11. Parameters associated to the selection of a type of collector ........................................................................ 36
Table 12. PV model. Outputs .......................................................................................................................................... 36
Table 13. Small Wind inputs ........................................................................................................................................... 37
Table 14. Small Wind parameters .................................................................................................................................. 37
Table 15. Small Wind outputs ........................................................................................................................................ 37
Table 16. µCHP model. Inputs ........................................................................................................................................ 40
Table 17. Parameters µCHP model ................................................................................................................................. 41
Table 18. µCHP model. Outputs ..................................................................................................................................... 42
Table 19. Description of black energy units. ES.ME.SFH.01.Gen .................................................................................... 57
Table 20. Final energy consumption per climate zone. (CBEM) ..................................................................................... 57
Table 21. Constructive elements. U coefficient. (CBEM) ................................................................................................ 57
Table 22. Energy systems description. (CBEM) ............................................................................................................... 58
Table 23. Lighting and equipment consumption per type of building ............................................................................ 58
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Glossary
HVAC – Heating Ventilation Air Conditioning
Energy aspect – final uses of the energy; lighting, air conditioning, equipment, or industrial
processes
DHW – Domestic Hot Water
DEG – Decentralised energy generation
GUI – Graphical User Interface
SW - Software
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2. Introduction
One of the components that will integrate the proposed DAREED’s Platform architecture is the
Energy Performance Simulation tool. The main goal of this document is to provide the
foundations of this architecture component, describing the set of models on which it will be based.
These models will be aligned with the methodology proposed in WP1.
Along this document, a state of the art of district modelling will be reviewed, different modelling
approaches will be compared selecting the most suitable one, and the proposed models will be
described.
3. State of the Art Analysis
3.1 State of the art analysis in energy district modelling
3.1.1 From buildings energy model to district energy model
Nowadays building energy consumption and CO2 emission in Europe are measured over 40%
and 36% of total, respectively [55]. The reduction of these numbers is one of the main objectives
to fight against the global warming and preserve the environment, as it has been proclaimed by
the European Union through several actions such as the EPBD (Energy Performance of Building
Directive), for instance.
In order to achieve such targets, building energy forecasting models are of critical
importance. Therefore, the energy performance of a building or the integration of initiatives
concerning energy efficiency are firstly tested via software thanks these energy building models.
However, there are several typologies of buildings with their own characteristics that complicate
the modelling: residential, offices, schools and hospitals are the most important among the
commonly considered. In addition, there are a wide variety of devices in the market which are part
of the system being modelled: HVAC systems, boilers, solar collectors, lighting, photovoltaic
facilities and equipment, concept that includes a high variety of devices such as computers,
printers, etc.
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As a result, accordingly with the literature, there are different modelling approaches that will be
described in following sections according to [52], [54], [53], [55].
The higher level for the classification of building modelling approaches is based mainly in the data
origin. Thus three different levels are identified [54]
White-box modelling approach
White-box modelling is physics-based and beforehand knowledge about the system is required.
This approach is based on solving the equations that rule the physical behaviour of the heat
transfer (thermal model equations). Models of space heating, natural ventilation, air
conditioning system, passive solar, photovoltaic panel, occupant behaviour and others are
included. These models are mostly generated based on software such as EnergyPlus or
TRNSYS.
Black-box modelling approach
Black-box modelling includes data-driven methods. Unlike physical methods, black-box modelling
does not require any physical information about parameters or equations since these statistical
methods use machine learning. These models are based on the implementation of a function
deduced from samples of training data which describe the behaviour of the specific system.
Black-box modelling is the most suitable when building parameters are not known. On several
occasions, the physical meaning of the problem is lost and result interpretation is not obvious.
The most used statistical techniques are: the linear multiple regression, the genetic algorithm, the
artificial neural network and the support vector machine.
Grey-box modelling approach
This approach is a combination of the two above, as it combines input/output data together with
physical models. The combination of both white-box and black-box approaches allows to
overcome the disadvantages of each one. Several strategies are contemplated in this approach.
The first one consists in using machine learning to estimate the physical parameters. Other
strategy is to use black-box models to implement a learning model based on a physical approach
to describe the building behaviour. Another strategy is to use statistical method in fields where
physical models are not effective or accurate such as end-uses disaggregation.
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The allowance to consider only a certain number of data is one of the main strengths. In addition,
the input parameters regarding building geometry and thermal behaviour do not need to be too
accurately neither fixed at the beginning of the simulation. Furthermore, a physical interpretation
is maintained with this method.
For instance, [52] exposed a method where a resistance and capacitance (RC) network was use
to model and predict building cooling load. The parameter values were determined by non-linear
regression method of on-site measured operation data. Numerous different grey-box models can
be found in literature [53].
To sum up, the main features of each model is perfectly presented in the table below, extracted
from [53].
Table 1. Features of different building modelling approaches
Method
Building geometry
description
Training data
requirements
Physical
interpretation
White-box
High
Low
High
Black-box
Low
High
Low
Grey-box
Medium
Medium
Medium
Additionally to this first level classification pending from White and Black approaches it could be
possible to identify following modelling strategies.
Table 2. 2nd level classification of building modelling approaches
1st level classification
2nd level classification
The CFD approach
White-box or physical model
The zonal approach
The multi-zone approach
Grey-box
Black-box or predictive models
Multiple linear regression or conditional
demand analysis(CDA)
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1st level classification
2nd level classification
Genetic algorithm (GA)
Artificial neural network (ANN)
Support vector machine (SVM)
Under physical modelling three categories could be proposed
The CFD approach
The CFD method is a 3D approach able to model in detail even the fluid flow field. On the other
hand, a huge computation time is required and the implementation of the models is complex.
Since its application fields are very large, CFD models are used by several software such as
FLUENT, COMSOL Multiphysics, MIT-CFD or PHOENICS-CFD.
The zonal approach
The zonal approach is a simplification of the CFD technique. It consists in dividing each zone into
several cells. Therefore it is considered a 2D approach where local state variables such as
temperature, concentration, pressure and airflow could be measured in a large volume.
Computational time, though less than in the CFD method, still are very large. Another
disadvantage is the requirement of a detailed description of the flow field and flow profiles. Some
software tools that use this method are: SimSPARK and POMA.
The multi-zone approach
The multi-zone or nodal method considers that each building zone is a homogenous volume
characterised by uniform state variables. Thus, a zone is approximated to a node described by a
single value of the variables (temperature, pressure, concentration, etc.). Therefore, the nodal
method is considered as a one-dimensional approach. This enormous simplification turns into a
huge reduction of computation time. In addition, the implementation is much easier. Nevertheless,
it is unable to study local effects as heat or pollutant source and the study of large volume
systems becomes more difficult. The most popular software using this procedure are: TRNSYS,
EnergyPlus, IDA-ICE, ESP-r, Clim2000, BSim and BUILDOPT-VIE.
In the case of predictive or black-box models the possibilities are the following;
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Multiple linear regression or conditional demand analysis (CDA)
Multiple linear regression or conditional demand analysis (CDA) is a linear multivariate regression
technique consisting on predicting an output as a linear combination of the input variables and an
error term. This method is valid to forecast the energy consumption as well as the evolution of the
energy demand. As inconveniences stand the large amount of training data and non-collinearity
between variables.
Genetic algorithm (GA)
Genetic algorithm (GA) is a stochastic optimization technique deduced from an analogy with the
evolution theory of Darwin. This method starts from an equation form imposed by the user and
the main advantage is the powerful optimization algorithm it consists on. Still, a large amount of
training data is required as well as a considerable computation time to adjust the algorithm
parameters.
Artificial neural network (ANN)
Artificial neural network (ANN) is a nonlinear statistical technique mainly used for the prediction.
The algorithm is multilayer composed where the outputs consist on the sum of the weighted input
variables. No starting hypothesis is needed but the physical interpretations are not easy to
stablish. They have a huge training faculty, though an exhaustive and representative data is
required.
Support vector machine (SVM)
Support vector machine (SVM) is an artificial intelligence technique to solve classification and
regression problems. The kernel function should be imposed by the user. This model is able to
forecast the energy consumption or temperature requiring a reasonable amount of training data.
The difficulty of this algorithm resides on the determination of the kernel function and the
adjustment of certain parameters.
Hitherto, single building energy models have been presented. Another step on is to move up to
district modelling which includes several of the single building models. Therefore, some points
should be kept in mind in order to effectively do so.
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Firstly, the computation time required for black-box model becomes unacceptable given the great
amount of buildings to model from a district overview. Consequently, it is hard to work with a large
amount of training data, besides the difficulty of compiling and acquire all this information.
On the other hand, white-box models require much less computational time considering the nodal
method instead of zonal or CFD methods. Still, with these approaches, many parameters are
needed, focusing the problem on this point.
A grey-box approach look to be the most suitable option to district modelling as it stands at an
intermediate point between the two previous techniques. Thus, the number of known geometrical
parameters needed could be adjusted to achieve a trade-off where computational time is not
excessive and the physical interpretation does not get lost along the way at the same time that
the strengths of statistical methods could support the integrated model.
Furthermore, a district wide model should as well integrate the behaviour of other energy
consumer, producers and distribution systems in them such as public lighting, decentralized
generation technologies or infrastructures auxiliary equipment such as pumping stations for DHC
networks.
Thus, generating a district wide model could be tackled by coupling and aggregating the
aforementioned energy unit models. In this interconnected modelling approach, a statistical
analysis should be performed in order to assess how the uncertainty associated to different
energy unit models is propagated into the district wide model, and therefore how the latter’s
accuracy is affected.
3.1.2 Decentralized generation technologies
The concept of Distributed Generation (DG in advance) is not at all new but it is an emerging
trend in the electricity industry, market, and deregulated systems [11]. In the existing literature DG
is loosely defined as small-scale electricity generation [1], [7] and there are several terms used to
refer to distributed generation [2], [3] for example:

In Europe and some Asian countries it is referred to as ―Decentralized generation‖

In North America it is referred to as ―Dispersed generation‖

In South American countries it is referred to as ―Embedded generation‖
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According to Little [10], although DG has been defined in various ways, a general definition for
distributed generation is ―the use of stand-alone or grid-connected small, modular electric
generation devices which are located close to the point of consumption‖ [10]. The key defining
characteristics of DG technologies encompassed the size of the power production of the
technology and the location and application of the device [11]. From a practical point of view, DG
is a facility for the generation of electricity that may be located at or near the end users within an
industrial area, a commercial building, or a community (district).
Types of Distributed Generation
DG compromises a wide range of technologies for specific applications. Figure 1 adapted from ElKhattam and Salama [2] graphically depicts some of the different types of DG from the
constructional and technological points of view. These applications and technologies vary
according to the load requirements (thermal needs, stand-alone or grid-connected electrical
power, size, and requirements of power quality, environmental issues in the site, etc.).
Figure 1: Types and technologies of Distributed generation (adapted from [2])
A comparison of the application of some of the common DG energy types has been presented in
Table 1. These types of DGs could be compared to each other to support decision making with
regards to which kind is more suitable to be chosen in different situations.
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Application of Common Energy Types
MicroTurbines
Fuel Cells
Wind
Turbines
Photovoltaic
Support for
peak load
shaving, cogeneration,
and as a base
load.
Fit to provide
CHP for airconditioning,
cooling, and
heating
purposes.
Stand alone and
base load in some
rural applications if
combined with
batteries.
Commercially
available in
small units
with sizes 30–
75kW [8].
Commercially
available in
small units
with sizes 3–
250kW and
connected as
modular to
serve large
loads [8].
It can be
considered as a
maintenance free
supply for
telecommunication
and road lighting
and advertising.
Remote
homes and
farms and
process
industry
applications.
Traditional
internal
combustion
engines
(diesel
engines)
Central
Power
generation
In use for
several years,
but generate
high
emissions.
Operation and
maintenance
costs are also
high in
addition to
diesels
hazardous
during
transportation
to remote
consumers [8].
Main
electricity
generation
as the main
base load.
Mostly used
for peak load
shaving and
backup
operation (for
reliability
purposes) not
for continuous
operation.
Mostly used
for peak
load shaving
and backup
operation.
Large stations
are suitable
for base load
applications.
Table 3: Comparison of application of common energy types (adapted from [2])
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In DAREED the main focus is concerned with DG technologies and types of the renewable
energy sources such as PhotoVoltaic (PV) and WindTurbine (WT) based on the most common
technologies applied nowadays in the European market.
Key Drivers of Distributed Generation Technology
In the last decade, there has been a renewed interest in DG due to technological innovations and
a changing economic and regulatory environment. IEA (International Energy Agency) have
confirmed this and have listed five major factors that has resulted in driving the renewed interest
in DG [4]. As per Driesen and Belans [1] these factors could be further reduced to two major
driving forces, i.e. electricity market liberalization and environmental concerns. Liberalization of
electricity market encompasses four major factors, namely; (a) Standby capacity or Peak Use
capacity, (b) Reliability and Power quality, (c) Alternative to Expansion or Use of the Local
Network, (d) Grid support and the fifth major factor is the (e) environment concerns.
Liberalization of Electricity Markets:
There has been an increased interest by electricity suppliers in DG as they see it as a tool that
can support them address niches in the market, in which customers look for the best suited
electricity service. In the electricity sector DG allows players to respond in a flexible manner to
changing market conditions. In liberalized markets, it is significant to adapt to the changing
economic environment in a flexible manner. DG technologies provide flexibility because of their
small sizes and assumed short construction lead times compared to most types of larger central
power plants. However, the lead time reduction is not always that evident. For example,
resistance to wind energy and use of landfill gasses may be very high by the public.
Environmental Concerns:
In Europe, environmental policies are probably the major driving force for the demand for DG.
Environmental legislations and regulations force players in the electricity market to look for
cleaner energy solutions. In this context, DG can play a key role as it allows optimizing energy
consumption of firms that have a large and constant demand for heat. In addition, most
government policies aiming to promote the use of renewables also results in an increased impact
of DG technologies, as renewables, except for large hydro and wind parks (i.e. off-shore), have a
decentralized nature.
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Particularly on sites where there is a constant demand for heat, it is sensible to consider the use
of combined generation of heat and electricity instead of generating the heat in a separate boiler
and buying electricity from the grid. In this context, compared to separate fossil-fired generation of
heat and electricity, CHP (Combined Heat and Power) generation may result in a primary energy
conservation, varying from 10% to 30%, depending on the size (and efficiency) of the
cogeneration units [5], [6].
Benefits and Challenges associated with Distributed Generation Technology
DG provides benefits for the consumers as well as for the energy utilities, especially in sites
where the central generation is unfeasible or where there are deficiencies in the transmission
system [13]. Some of the benefits of distributed energy sources are as follows:

Highly efficient CHP plants, and backup and peal-load systems could provide increased
capacity. In addition, it enables the use of waste hear and improves overall system
efficiency [14].

Increased use of DG resources such as renewable energy sources will help reduce fossil
fuel consumption and greenhouse gas emissions, as a result benefitting the environment
[13].

On-site production can help reduce the amount of power that needs to be transmitted from
a centralised plant, and avoids resulting in loss of transmission and distribution as well as
cost reduction due to the fact that generation business and consumption are closer [11].

DG may provide ancillary services or network support [13]. The connection of distributed
generators to networks generally leads to a rise in voltage in the network. Therefore, in
areas where voltage support is difficult, installation of a distributed generator may improve
quality of supply.
Apart from the abovementioned benefits, some of the key challenges associated with increased
penetration of DG can be classified into three main categories, namely technical, commercial and
regulatory [9]. These are now discussed below.
Technical:
There are many factors that add to the technical challenges of DG which are as follows [9]:
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
Voltage rise effect: this is a key factor that limits the amount of additional DG capacity that
can be connected to rural distribution networks.

DG Protection: A number of different aspects of DG protection can be identified:
Protection of the generation equipment from internal faults; protection of the faulted
distribution network from fault currents supplied by the DG; anti-islanding or loss-of-mains
protection (islanded operation of DG will be possible in future as penetration of DG
increases) and impact of DG on existing distribution system protection.

Quality of power: Two aspects of power quality are usually considered to be important: (1)
transient voltage variations and (2) harmonic distortion of the network voltage. Depending
on the particular circumstance, DG plant can either decrease or increase the quality of the
voltage received by other users of the distribution network. Power quality is an
increasingly important issue and generation is generally subject to the same regulations
as loads.

Stability: Traditionally, distribution network design did not need to consider issues of
stability as the network was passive and remained stable under most circumstances
provided the transmission network was itself stable. However, this is likely to change as
the penetration of these schemes increases and their contribution to network security
becomes greater.
Commercial:
Existing case studies have indicated that active management of distribution networks can enable
significant increases in the amount of DG that can be connected to the existing networks [9].
Although the cost associated with the operation of active distribution networks is still to be
identified, it is expected that the benefits are likely to considerably outweigh the cost of its
implementation. However, distribution companies that operate wires businesses have no
incentives to connect DG and offer active management services
Regulatory:
As there is a lack of clear policy and associated regulatory instruments on the treatment of DG, it
is highly questionable that this type of generation is going to thrive. In order to nurture the
required changes, there is a clear need to develop and articulate appropriate policies that support
the integration of DG into distribution networks [9].
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Development Trends in Distributed Generation Technology
New energy and renewable energy sources includes hydropower, wind energy, solar energy,
biological energy, geothermal energy and ocean energy. In the field of electrical engineering, the
use and development of new energy, wind power generation, solar photovoltaic generation and
fuel cell technology is a major research area and some of the development trends in DG are as
follows [12].

Wind power technology is emerging as one of the most important renewable technologies.
The wind power generation technology is used to convert wind energy into electrical
energy power generation. It can be classified into two broad categories: constant speed
constant frequency (CSCF) and variable speed constant frequency (VSCF). As VSCF
power generation technology has merits of capturing the maximum limit wind power, the
wide rotational speed movement scope, flexible adjustment of the system active power
and reactive power, as well as the advanced PWM control, it has gradually became the
mainstream technology of the current wind power generation. Reviewing the fast
development route of global wind power generation in recent years, the latest
development trend and research progress are [2]: larger rated power, variable blade pitch,
variable speed constant frequency (VSCF), no gearbox driven (direct driven), gridconnected full power converter, low voltage ride through (LVRT), intelligent control for
wind power generation, remote wireless network wind farm monitoring system, and so on.

Solar photovoltaic technology directly converts solar energy into electrical energy by
photovoltaic effect of semiconductor material. Photovoltaic generation system is divided
into separate photovoltaic systems and grid-connected photovoltaic system. Photovoltaic
generation system typically uses two power converters. The first one is the Direct Current
(DC) DC / DC converter, using Boost step-up circuit to achieve the transformation of solar
output voltage and photovoltaic arrays maximum power point tracking (MPPT) control. The
second one is used to convert the direct current into alternating current by voltage source
inverter to the utility grid, and the inverter controls the DC constant voltage and inputs
reactive power of the utility grid. At present the biggest hurdle of photovoltaic generation is
the high price of solar cells, which accounts for over 60% the price of the whole solar
photovoltaic (PV) generation system, so the solar cells research such features as cheap
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Deliverable: D 3.1
price, high efficiency, high reliability, high stability, long lifetime has become the world's
focus [12].

Fuel cell technology is considered as one of the power generations with high efficiency,
energy saving, environmental protection in the 21st century [12].Fuel cell is a generation
facility which can directly convert the chemical energy stored in the fuel and oxidizer into
electricity power efficiently. The FC converts fuel and air directly to electricity, heat, and
water in an electrochemical process. It also has some merits in the fuel diversification,
clean exhaust, low noise, low pollution, high reliability and good maintainability.
3.2 Overview of existing simulation tools for city district energy
modelling
As assessed in D2.5, there are evidences of tools to simulate aspects of a city or urban areas
separately. In the following table extracted from D2.5 provides a general overview.
Table 4. Overview on existing tools for energy modelling and simulation
DAREED Components
Existing Tool Name
EnergyPlus –
Modelling and
Simulation
Consumption
monitoring,
analysis and
control
Energy
management
X
simulation engine
DOE-2 – simulation
X
engine
Lucid’s BuildingOS and
X
Dashboard
Dexma’s DexCell
X
Energy Manager
C3 Energy
X
eSightenergy
X
EnergyCAP
X
US Department of
X
X
X
20
Decision
support and
energy
awareness
Deliverable: D 3.1
DAREED Components
Existing Tool Name
Modelling and
Simulation
Consumption
monitoring,
analysis and
control
Energy
management
Decision
support and
energy
awareness
Energy’s BEopt
Toshiba’s CEMS
X
RETScreen (only for
X
models)
BeAware Project
X
Efergy Engage
X
Platform
BuildVisTool (issued
X
by an FP7 project,
X
X
called KnoholEM)
However, there are not exists or at least high deploy an integrated software tool able to offer
different services from a district perspective, which guarantee the innovation of DAREED
platform.
4. Description of district energy model
4.1 Introduction
According to the approaches set in D1.4 Definition of a methodology for district modelling,
the concept of Energy District was defined as a combination of elements called Energy Units
responsible for energy production and/or consumption.
In this sense a district energy model could be understood as a linear sum of the mentioned
Energy Units as follows;
⌋
∑
∑
⌋
In addition to this, and foreseeing the lack of real data information as well as the interest of
different stakeholders, associated with the concept of Energy Units, three different levels of
accuracy were defined;
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


Black level
Grey level
White level
and their associated



Black Energy Unit
Grey Energy Unit
White Energy Unit
At the black level, black energy units are characterized by data from bibliography. Therefore
Black Energy Units are static providing aggregated energy consumption per year including in the
case of buildings or industries different energy aspects such as; lighting, equipment and HVAC
systems. Regarding other infrastructures i.e. public lighting the information provided under this
approach is just the total annual amount of electric consumption depending on the technology
installed. The time-scale for this black approach could be annual or monthly.
In the case of the grey level, energy units are created based on dynamic physical models which
are composed by a set of inputs parameters and a set of outputs in an hourly basis. Considering
the case of buildings, the number of inputs in buildings models is tremendous so it is necessary to
review the aim of this deliverable, the district modelling
Regarding district modelling, users may not be interested in a deep district element analysis
especially if real data are not available mainly because the huge amount of elements that could
be found in district makes an accuracy effort useless.
In other words the process of district simulation could be composed by following step depending
on the information available (Figure 2).
1) Developmet of the district energy models based on black energy units
2) Progressive Replacement of black energy units per grey units once district manager or
responsable gets information about inputs selected per element.
3) Replacemente of certain grey energy unit as long as this specific unit is monitor by DAREED
platforma and white model could be develop by the platform itself.
The lack of real data information has been one of the major concerns taking into consideration the
amount of information required from a district perspective. So the methodology already introduced
in D1.4 tries to overcome this barrier by offering above-mentioned alternatives.
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Deliverable: D 3.1
It is important to note that the terminology used by DAREED project does not refer to the
modelling approaches presented in Section 3. The discussion regarding types of energy
modelling chosen to develop DAREED platform is introduced in the next section.
4.2 Physical modelling vs. Machine Learning
The selection of certain modelling provides advantages as well as disadvantages in comparison
with other methods. The key point is the identification of the most suitable modelling according to
DAREED platform needs.
Generally speaking and as it is stated in [53] physical methods (PM) are suitable in situation in
which building design data are available and specially for new building when real data do not
exist. On the negative side, physical models require a big amount of input data including
geometry, envelopes, energy systems, building use patters, occupancy, etc.
On the contrary, Machine learning modelling (MLM) is suitable in the opposite situation when
design data are not available but operational data are, including energy and comfort information.
One of the weaknesses of this last approach is that MLM requires high quality and amount of
available data to train models.
All in all, MLM requires less information and seems easier to be developed. But, if a physical
interpretation is required PM is the most suitable solution.
In the DAREED framework, taking into consideration the major concern regarding real time data
acquisition, physical models is needed as starting point.
4.3 Integration of physical models in the DAREED platform
The selection of physical models is mainly based on the fact that from a district perspective the
collection of real data from a high amount of building is really time consuming specially for
gathering a great number of final users.
As mentioned before, physical modelling guarantees a first starting point for a district simulation,
to increase lately the accuracy of the different models in case real data are available.
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Deliverable: D 3.1
Figure 2. Process to enrich DAREED platform from physical to model to ―Grey-Box Model
approaches‖
At the beginning of the platform operation, a set of generic building will be created and store in
the data layer of DAREED platform. These generic building models are created based on
standard of uses and building national codes as explained in next sections
Once, pilot building are including in the tool implying the acquisition of building design information,
pattern of use and energy invoiced, associated physical model will be tuned creating a new
element in the DAREED database
4.4 An overall model
As mentioned in section 4.1, the overall district model comprises the sum of elements with certain
energy behaviour in the sense of producing or consuming energy.
Thus, to know and assess the energy demand and consumption in a certain area, DAREED
platform users will aggregate ―Energy Units‖ responsible for energy consumption, such as
24
Deliverable: D 3.1
buildings, public lightings, etc. as well as those responsible for energy production, including;
photovoltaic installations, domestic hot water systems (DHW), small wind turbines, etc.
5. Components modelling and characterization
This section represents the main output of this deliverable. In following paragraphs different level
approaches are explained in detail and will be implemented and located in the DAREED
database.
In
general
the
models
will
be
composed by following elements;




Parameters
Inputs
Model
Outputs
Parameters are those variables fixed or linked to a certain selection done by DAREED users. This
information will be stored in the Data layer.
The input box includes all the information DAREED platform requires from user to run physical
models.
Relation between parameters and inputs
It is important to note that it is possible some inputs become parameters based on the knowledge
manager.
An example of that is the modelling of a building. The first time associated building manager
introduces the description of the building most of the information will be asked as Inputs such as
building total area, height, uses, etc. but after that if user wants to assess the effect of improving
building glazing just variables related to windows will be asked as windows, uploading the rest of
variables as Parameters from the first time user introduced this information.
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Deliverable: D 3.1
Figure 3. First time data introduction.
Figure 4. Next time data introduction



Green arrows represent interaction with GUI
Yellow arrows represent interaction with Component layer. Uploaded information from DAREED
platform
Blue arrows represent interaction with Data layer. Information to be stored in DAREED platform
Physical Models are the set of equations that solve specific energy problem such as photovoltaic
production, building energy demand or consumption, etc. In the next section these physical
models are explained in depth for both consumption and production nodes.
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Deliverable: D 3.1
For example in the case of a DHW the physical model is governed by the following mathematical
equation;
where
η is the performance of the collectors
A collector surface
I solar radiation
Finally, outputs are the result of solving the above-mentioned equations. In the example of the
DHW model, Q values are the output of the model.
5.1 Consumption nodes
5.1.1 Buildings
Probably buildings are the most complex elements to be modelled in a district as there exist a
great number of parameters that can affect their energy behaviour including; user patterns,
weather conditions, building envelopes or equipment. To overcome this complexity different levels
of modelling have been foreseen.
Black approach
At an early stage in which DAREED Platform’s adoption in a district might be low, lacking the
minimal required data in order to simulate building energy behaviour. Thus, an alternative to such
cases might be provided.
The use of this modelling approach will be limited to district wide simulations in order to provide a
consumption estimation for those buildings not enrolled in DAREED Platform.
Nevertheless, users will be required to provide some basic information based on which the
estimations will be performed using knowledge extracted from bibliography and previous works.
For instance, the approach followed in TABULA Project, where annual consumption for different
building typologies is provided for regions all over EU.
Under black approach, buildings will be modelled with the following inputs and outputs:
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Deliverable: D 3.1
Table 5 Black building model inputs and outputs
Inputs
Model
Type of buildings
Output
Output
=
Building
area
∙
Energy ratios ∙ Energy Aspect
Total surface (sq-m)
Breakdown
Annual energy consumption per
source (kWh)
Annual
energy
demand
per
energy aspect (kWh)
Grey
The black modelling approached detailed along the previous paragraphs will provide a last resort
solution in the event of very limited building characterization data and/or consumption data
availability. Due to this lack of input data, the accuracy and data resolution of output variables
from simulation and forecast tasks will be presumable low.
In order to fulfil DAREED Platform objectives and to be able to provide some of the services
described in Deliverable 2.4, a higher accuracy and output data resolution is required.
To achieve this accuracy requirement, a physical modelling approach is proposed. The
advantages of such approach are:

The installation of devices is not required and, therefore, no budgetary constraints affect
this approach

The amount of data in which physical models are based is small compared to a machine
learning model, and could be easily provided by the end-user.

This modelling approach, unlike machine learning models, is suitable for the assessment
of EEM in a particular building.
Although machine learning models can achieve a higher accuracy for forecasting purposes,
physical models present more flexibility, being suitable for both forecasting and EEM assessment
tasks.
The creation of an accurate physical model is a challenging task, which requires a comprehensive
definition of a vast amount of parameters, which will characterize thermal behaviour of building,
energy systems behaviour as well as usage patterns. Together with the amount of parameters,
the mayor barrier to overcome is the fact that most of this input data would not be easy to identify
and provide for a user with no technical background.
To surmount this issue, the end-user will be asked to provide a limited set of parameters, easily
identifiable for a non-expert person, based on which the DAREED platform will generate the
28
Deliverable: D 3.1
physical model taking into consideration some hypothesis integrated in the model generation
logic.
Table 6 details the preliminary set of parameters to be provided by the end-user in order to allow
DAREED Platform to generate a physical building model1, some of which might be optional in the
final version.
Table 6 Set of parameters required from user for grey building model generation
Parameter
Input method
Building location
Selectable on map view
Selectable from a predefined
Building orientation
list
Building typology
list
Building use
list
Construction period
list
Number of floors
Façade
surfaces
Numeric input
and
orientation
Façade constructive solution
Approximate glazed surface
Window technology
Useful or conditioned surface
Approximate use schedule
1
Numeric input [sq-m]
list
list
list
list
Number of building users
HVAC System technology
Some services might require additional parameters to the proposed set.
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Deliverable: D 3.1
Parameter
Input method
list
Existing
on-site
technologies
generation Selectable from a predefined
list2
To ease the physical model generating task, the platform will be provided with a set of template
parametric models which will be adjusted based on characterization information entered by the
platform’s user.
These parametric models will be categorized in a tree-like structure consisting on the following
preliminary set of levels:
2

Geographical location

Building typology

Construction period

Building use typology
Generation technologies will required additional parameters to construct their models.
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Deliverable: D 3.1
Figure 5. Template models categories
The simulation tool selected for building simulation and forecast will be an integration of
EnergyPlus, whose characteristics were detailed in D2.5, and JEnergyPlus, which has been
described in D3.2. Therefore, the models will be generated in an appropriate format, compatible
with the selected simulation tool. EnergyPlus model specifications have been extracted from the
software documentation, EnergyPlus Input/Output Reference.
White approach
As it has been stated, physical modelling approach is the most suitable one to fulfil DAREED
Project objectives, taking into consideration its constraints.
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Deliverable: D 3.1
Nevertheless, consumption monitoring data availability could be exploited to achieve a higher
level of accuracy through a tuning process of the physical model already existing for a building.
This fine tuning process will consist on the identification of the optimal parameters for the physical
model in order to minimize the deviations between the forecasts performed by the DAREED
Platform and the real consumption data gathered from buildings.
The procedure would be executed as follows:

A physical model would be generated based on user inputs (grey model)

Consumption data will be uploaded to the DAREED Platform through the monitoring
devices installed on site.

The discrepancies identified between the real consumption data and the predictions
generated by DAREED Platform will be assess attending to several indicators, i.e.
baseline and peak loads deviations, total energy consumption, etc.

Depending on the aforementioned indicators, a set of model’s parameters will be selected
to be adjusted.

The identified sub-optimal parameters in the model will be corrected using a data fitting
technique to match as accurately as possible the real energy consumption.
In the event of considerably large deviations from physical model results and monitoring data, the
user will be asked to reassess building characterization data in order to avoid errors derived from
an incorrect characteristics identification or transcription to the platform.
The tuning procedure described could be executed recursively, triggered whenever certain
discrepancy indicators are higher than a predefined threshold.
5.2 Production energy units
The case of production nodes is much simpler than consumption ones. As it has explained in
previous sections, buildings in general include different energy uses as well as energy fuels.
For that reason, production nodes are modelled from a grey approach. In the case platform user
requires a black district simulation; aggregation of results will be carried out from the grey
approach to the black as follow,
If monthly basis information is required for the black approach modelling
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Deliverable: D 3.1
⌋
∑∑
⌋
where
j is the calculated month
N is the number of days of the month
k is the hour of the day
If annual basis information is required for the black approach modelling
⌋
∑∑
⌋
where
k is the hour of the day
Taking into consideration the most representative solution that can be found in a district, following
technologies has been selected to be implemented;





DHW – Domestic Hot Water Systems
PV – Photovoltaic installations
µCHP – micro-Combined Heat & Power
Small Wind
Public lighting
5.2.1 DHW
Domestic hot water systems are probable the most extended DEG technology, supported by
national regulations that require the integration of this type of systems in new building
constructions. Typically the size of these installations in terms of power rate is not large unlike the
number of them.
In addition DHW represents an important energy use in dwellings, representing for example in the
case of Spanish dwellings up to 8% of total energy consumption [58].
33
Deliverable: D 3.1
The model to implement this grey producing energy unit is based on the following equation;
where
η is the performance of the collectors
A collector surface
I solar radiation
To implement the model based on the previous equation a set of input and parameters are
required to produce outputs
Table 7. DHW model. Inputs
Inputs
Unit
Type of collector
-
Description
Type of collector available in
DAREED platform database
2
Surface
m
Aperture area
Location
-
Urban area under study
Orientation
-
N/NE/E/SE/S/SW/W/NW
The selection of a certain type of collector implies the selection of following parameters
Table 8. Parameters associated to the selection of a type of collector
Inputs
ID
Unit
Description
a0
-
Intercept of the collector efficiency
a1
kJ/h/m2/k
Efficiency slope
a2
kJ/h/m2/k2
Efficiency curvature
Cp
kJ/kg/K
Specific heat collector fluid
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Deliverable: D 3.1
Table 9. DHW model. Outputs
Outputs
ID
Unit
Description
Tout
°C
Temperature of the outgoing flow from the collector
Q
kWh
Transmitted energy to the flow
η
-
Instantaneous efficiency
5.2.2 Photovoltaic
Photovoltaic installations are also well-deployed technologies in urban areas. The combined
effects of energy price increase rate and the fast decrease of the technology price [62] have
increased the investments in these technologies. National Policies have also contributed to the
implementation of this technology. As a result there exist an important amount of installations
mainly roof-installations and still a high potential to install new ones. These are the reasons why
Photovoltaic has been considered as a production energy unit.
The model to implement this grey producing energy unit is based on the following equation;
PVpower,act = Iglob * Apv * effpv
where
PVpower,act is the active power output of the panels
Iglob is the global solar radiation normal to the panel
Apv is the area of panel
effpv the constant efficiency of the panel
To implement this model based on the previous equation a set of input and parameters are
required to produce outputs.
Table 10. PV model. Inputs
Inputs
Unit
Type of collector
-
Surface
m2
Description
Type of collector available in
DAREED platform database
Aperture area
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Deliverable: D 3.1
Inputs
Unit
Description
Location
-
Urban area under study
Orientation
-
N/NE/E/SE/S/SW/W/NW
Table 11. Parameters associated to the selection of a type of collector
Inputs
ID
Unit
effpv
Description
-
Constant efficiency of the panel
Table 12. PV model. Outputs
Outputs
Model
ID
Output
Unit
Description
Electricity
PVpower,act
kWh
produced
by
the
installation
5.2.3 Small wind
Small wind is probable the less common technology applied in urban areas. This technology is
mostly applied for higher installation, called wind farm, far from consumption areas depending on
the wind resource availability.
On one hand the availability of wind source in cities is lower than in open areas where there not
exist physical barriers that could stop wind flows. But on the other hand the impact on the
environment is much lower in the case of urban areas, especially concerning wildlife.
Regarding cities two main installations are foreseen; horizontal and vertical axis. The equations
are the following;
(kinetic energy equation)
And including losses;
where
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Deliverable: D 3.1
PW is the wind turbine power produced
ρLocal is the air density at a certain height
AR is the swept area
VLocal is the wind velocity
Cp is the power generation coefficient
The calculation of intermediate parameters will be done by EnergyPlus SW, from the user
perspective inputs and outputs required are:
Table 13. Small Wind inputs
Inputs
Unit
Description
AR
m2
Swept area
Location
-
Height
m
Place where the wind turbines
are installed
Height where the turbines are
placed
Table 14. Small Wind parameters
Parameters
Unit
Description
Type of turbine
-
Horizontal or vertical axis
Number of blades
Table 15. Small Wind outputs
Outputs
Model
ID
Output
Unit
Description
Electricity
PVpower,act
kWh
installation
37
produced
by
the
Deliverable: D 3.1
5.2.4 µCHP
Micro Combined Heat and power systems are systems able to provide both thermal and electrical
energy. Unlike elder systems where heat was dissipated, new systems take advantage from the
heat produced in a combustion process, providing it as an output as well as the electricity.
This means the efficiency of these systems could reach up to 90% much higher than the only
production of electricity (around 40%) [59].
For that reason these technology has become popular in last years. Its scaling has allowed not
only implementing at a high scale but also at lower scale, being 5kW-CHP installations suitable
application for dwelling and block of buildings [60].
It is also common, especially in Northern, Central and Eastern Europe, the existence of District
Heating Networks some of the feed by CHP technologies. But limitation in terms of level of
investments and regarding urban barriers limit the deployment of this solution in consolidated
urban areas.
In any case the model presented is applicable to both large and small scale.
To implement this model based on the previous equation a set of input and parameters are
required to produce outputs. Mathematical equations are the followings;
1. Case of Internal Combustion Engine (ICE):
This model represents a 6 valves engine. The model is based on the "Baud Rochas" Cycle (four
strokes engine). The four strokes are described below:
1.
2.
3.
4.
5.
6.
Intake of air at atmosphere pressure
Isentropic compression (All valves closed)
Combustion, (constant pressure heat input)
Expansion stroke (all valves closed)
Heat rejection (constant volume, exhaust valve open and intake valve closed)
Exhaust stroke at constant pressure (exhaust valve open and intake valve closed)
First, the volume of each combustion chamber has to be defined:
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Deliverable: D 3.1
Power from thermodynamic transformation at each stroke is calculated for each time step of the
simulation time:
1. Intake of air at atmosphere pressure
First, the amount of substance (number of moles) has to be defined:
The temperature out of this isentropic compression could be defined with the Laplace equation:
The power needed for this compression could be calculated with this expression:
(
)
2. Isentropic compression (All valves closed)
Combustion, (constant pressure heat input)
(
)
3. Expansion stroke (all valves closed)
The exhaust temperature is depending on the volume ratio, γ and the temperature out of the
combustion:
(
)
The effective mechanical work could be calculated with this expression:
(
)
4. Heat rejection (constant volume, exhaust valve open and intake valve closed)
Exhaust stroke at constant pressure (exhaust valve open and intake valve closed)
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Deliverable: D 3.1
(
)
The cycle work is calculated from this expression:
Cycle work leads to obtain the power calculation engine for a number of cycle per minute:
|
|
The electrical power is obtained from the mechanical power:
The power produced by the combustion transformation is:
|
|
The thermal power from exhaust gas is
|
|
Table 16. µCHP model. Inputs
Intputs
ID
Unit
Description
T1
K
Inlet ambient air temperature (For ICE and micro turbine)
P1
kPa
m
kg/s
Inlet air flow rate (For microtubule)
T3
K
Combustion temperature
Inlet
air
(For ICE and micro turbine)
40
pressure
Deliverable: D 3.1
Intputs
ID
Unit
Description
pr
-
ICE: Volume ratio Micro turbine: Compression ratio
nc
-
Compressor efficiency
nt
-
Turbine efficiency
Vbdc
m3
Volume bottom dead center
rpm
Tr/min
Revolution per minute
nelec
-
Alternator efficiency
K
-
Gamma
R
J/mol/K
Ideal gas constant
Table 17. Parameters µCHP model
Parameters
ID
Unit
Description
T3
K
Combustion temperature
pr
dimensionless
nc
dimensionless
Compressor efficiency
nt
dimensionless
Turbine efficiency
3
Default value
ICE : 2100
Micro turbine : 1240
ICE volume ratio
ICE : 8
Micro turbine : Compression ratio
Micro turbine : 10
Volume bottom dead centre
ICE : 0
Micro turbine : 0.83
ICE : 0
Micro turbine : 0.87
ICE : 10-3
Vbdc
m
rpm
Tr/min
Revolution per minute
η elec
dimensionless
Alternator efficiency
0.95
η therm
dimensionless
Combustion efficiency
0.99
Micro turbine : 0
ICE : 2500
Micro turbine : 0
Constant parameters
K
dimensionless
γ : ratio Cp/Cv
1.4
R
J.mol-1.K-1
R : Ideal gas constant
8.314
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Deliverable: D 3.1
Table 18. µCHP model. Outputs
Outputs
ID
Unit
Description
T1_mean
K
Mean ambient temperature
T4
K
Exhaust temperature
Pmec_mean
W
Mechanical Power
Pin_mean
W
Mean combustion power
Ptherm_mean
W
Mean heat Power
Pelec_mean
W
Mean electrical power
Pactive_mean
W
Mean active power
Preactive_mean
W
Mean reactive power
5.3 Weather data information
For the above models, weather data information is required as in all of them weather information
is required as input.
Data information will be collected from open-source weather information database compiled by
U.S. Energy Department and linked to Energy Plus.
This relation between weather information and Energy Plus SW tool will facilitate the processes of
integration in further project steps.
In a nutshell the parameters gathered in these files are;




Site location including latitude, longitude, time zone and elevation
Design weather days
Monthly Average values; dry temperatures, dew points, relative humidity, wind speed and
velocity, solar radiation, ground temperatures, etc.
Hourly values of temperatures, humidity and radiation.
Reader can find more details regarding weather data information in the following link;
http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data2.cfm/region=6_europe_wm
o_region_6
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Deliverable: D 3.1
5.4 District energy infrastructures
District energy strategies have to start from the analysis of distribution technologies to supply
electricity, gas and cooling/heating services in a city. In fact, alongside the traditional energy
infrastructure supplies that comprise both primary energy (gas, fossil fuel) and electrical energy,
in the last years new types of energy supply networks are being studied and developed. These
concerns with new heat and cool distribution network, district heating and cooling (DHC) enable
the carrying of energy from one or several production units, using multiple energy sources, to
many energy users.
Figure 6 Pathways for RE integration to provide energy services, either into energy supply systems or on-site
for use by the end-use sectors
Now, for evaluating the district energy needs, the most widely used drivers that supply energy to
a city or a neighborhood for buildings energy demands are:

Gas network

Electric distribution grid

DHC
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Deliverable: D 3.1
The 68% of the total residential buildings primary energy consumption is used for space heating
by heat pumps (HP), electric heater (EH), gas boiler (GB) and combined heat and power (CHP).
The demand of gas or fuel fossil is mostly due to space or water heating (GB, CHP), so it’s
closely related to the energy features of each buildings. As consequence an improvement in
buildings thermal performance leads to a reduction of gas or fuel fossil.
The true potential of the city district energy system, able to support the flexibility of energy supply
and demand and to support the integration of RES, involves the electrical energy supply
infrastructure. This allows taking in consideration in the district energy model all the aspects that
could not be investigated through the sum of single building analysis.
Therefore, this approach in district infrastructure energy modelling allows taking in consideration
local energy generation, storage technologies, the increasing of electric vehicles (EV), electrical
appliances in transportation, public lighting and facilities.
5.4.1 Electrical distribution grid
In the European Union region, the efficiency of Electric networks is highly efficient, with losses
around 6% and values at Country level spanning from 2% (Slovak Republic) to 21% (Lithuania).
Without considering losses due to failures, typical of countries with high losses, losses are due to
unbalancing of reactive power consumption, to ohmic losses of the lines, to losses in
transformation stations.
Figure 7 Efficiency of European Electrical Network compared with rest of the world
In an efficient network, failures are quickly repaired. There is an accurate active management of
reactive power by means of compensation systems, the best compromise is found between
length of lines and their capacity, and between lines voltage and voltage elevation/reduction
44
Deliverable: D 3.1
passages (if the voltage is high then the losses diminish, although this implies normally a larger
number of transformation passages)
Narrowing the distance between electricity production and consumption is an effective strategy to
reduce losses, since ohmic and transformation losses are both reduced.
Considering this, a way to increase grid efficiency could be distributed power generation with
small plants connected to the distribution grid at low voltage and electricity used by the prosumers
(producers/consumers) themselves or by their closer neighbours.
New solutions and approaches of management will be required in the next future to convert the
traditional grids into smart grids, since the rapidly growing electric production from distributed,
unpredictable power sources (such as renewable energy sources) makes that the production
follow the weather condition instead of the power demand stressing the traditional grid
management policies.
New features of grids require electricity buffers, production forecast systems linked to weather
forecasts and dynamic pricing, tools to favouring energy use in moments of high availability and
low demand and discouraging consumption in situations of low production and high demand.
Integrating buildings and the electricity grid is important to guarantee a reliable grid operation if
the fraction of renewable energy increases. Intermittent and variable generation sources and
loads, such as those of electric vehicles and renewable on-site sources (e.g. PV panels or wind
turbines), are being installed on the grid in increasing numbers and at more distributed locations.
Examples are buildings that typically produce energy on-site from renewable sources in order to
compensate their electric energy consumption. When many renewable energy sources are
located in the same district, the fluctuations of the electric power generated may be high and
usually not aligned with the demand of electric power. In order to avoid problems, efficient
transactions between buildings and the grid need to become a reality.
The district model platform could incorporate the electrical energy supply infrastructure in order to
provide information about the state of the network, at the same time it has to allow the
characterization of energy generation, storage, loads types (residential, commercial, tertiary,
public facilities).
The versatility of energy in electrical form, the ability to transport it across large distances (nearly)
instantaneously, and its necessity for the deployment of modern technology and the advancement
of economic and social development has resulted in a dramatic increase in the demand for
electricity. This growth of electricity demand coupled with the geographically dispersed nature of
45
Deliverable: D 3.1
many renewable sources makes electricity an attractive energy vector to harness RE where
adequate network infrastructure is available.
The fundamental purpose of electrical distribution systems is to move power from a few sources
of supply to a very large number of points of consumption. The major building blocks (or
elements) of distribution systems are the line segments and the transformers. Distribution lines
serve as channels. Numerous line segments are connected in order to deliver power to
consumers. Transformers are inserted into the power flow path to change the voltage levels
because such changes are needed in order to increase the efficiency of power transportation.
Figure 8 Distribution systems for electrical power
The object of our model will be essentially related to the distribution and sub-distribution network
related to district level, specifically medium voltage (MV) and low voltage grid.
These are the distinguishing features:

The object of the MV grid is to carry electricity from the transmission network to points of
medium consumption. These consumer points are either in the public sector, with access
to MV/LV public distribution substations, or in the private sector, with access to delivery
46
Deliverable: D 3.1
substations for medium consumption users. The number of these customers is only a
small proportion of the total number of customers supplied directly with LV. They are
essentially from the tertiary sector, such as hospitals, administrative buildings, small
industries.

The object of LV grid is to carry electricity from the MV network to points of low
consumption with access to LV customers. It represents the final level in an electrical
structure.
The distribution system typically starts at the distribution substation, and is fed by one or more
sub-transmission lines. Each substation is designed to serve one or more primary feeders. Most
of the utility distribution feeders are radial, i.e. power flows from the substation to the metered
user. An important characteristic of radial distribution feeders is having only one path for power to
flow from the source to each customer. A typical distribution system is composed of distribution
substations having one or more feeders. The distribution lines may be overhead or underground
depending on the feasibility and requirement, with distinct electrical characteristics. Voltage
regulators adjust the voltage settings, to keep the voltage at all nodes within IEC limits. Some of
the primary main feeders have in-line transformers to serve large industrial consumers. To
provide reactive power support to the feeder at critical nodes, single phase or three phase
capacitor banks are used. Smaller distribution transformers, also known as service transformers
supply customers at 380/230 V level. The distribution feeder supplies single phase, two phase
and three phase loads categorized as smaller residential consumer as well as large industrial
consumers. Each device in a distribution feeder has unique electrical characteristics that must be
determined before the power flow analysis of the feeder. Distribution systems for electrical power
possess a hierarchical structure. Each level in this hierarchy corresponds to a specified voltage.
The structure at each level consists of nodes and links between nodes. An assembly of links
between nodes links the structure of one level to the upper structure. An assembly of
transformers that are commonly referred to as sources links the structure of one level to the upper
structure.
The generation part consists of the power plants that are located in certain locations (at discrete
points) on the territory. Seen from the point of view of a single consumer, the power stream that
the consumer receives flows through the sequence of the upper subsystems.
47
Deliverable: D 3.1
5.4.2 Gas grid
The largest gas suppliers for Europe are the Northern Sea, former Soviet countries and North
Africa.
In gas networks large diameter, high pressure and gas speed pipelines connect the production
areas to the use areas covering thousands of miles. They are sided by the naval transport.
The natural gas is liquefied into Liquid Natural Gas at cryogenic temperatures at shipping
harbours, transported by gas carrier ships and brought to gas phase again into receiving Liquid
Natural Gas terminals at arrival harbours, where it enters the transmission network again.
Transport of liquid gas is more expensive, in terms of energy and of cost, than pipelines, but it
allows the access to diversify the suppliers thus reducing risks related to price changes and gas
availability.
From the transmission network, gas passes at local level in distribution networks, where pressure
is reduced to low values.
In general, gas networks have low losses due to the leaks, thanks to the very high care in
avoiding and repairing the leaks for safety reasons.
Energy losses in transmission and distribution losses are due to gas compression at the
production sites, gas-pumping stations along the lines, gas free decompression without energy
recovery at pressure reduction station.
For Liquid Natural Gas transportation, large energy inputs are needed for the liquefaction process
at shipping harbours to bring gas at cryogenic temperatures and further losses occur from
refrigeration systems on Liquid Natural Gas carrier ships. Part of the liquefaction energy is
recovered at regasification terminals at arrival harbours.
At the user side, pressure reduction normally occurs through simple lamination valves with no
energy recovery. An emerging technology is the mechanical energy recovery from the pressure
drop through turbo expander systems generating electricity, which is the only possible energy
efficiency improvement at district level in gas distribution networks.
5.4.3 District heating and cooling
District Heating and Cooling systems increase the overall efficiency of the energy system by
recycling heat losses from a variety of energy conversion processes. Heat which otherwise would
be lost is recovered and placed on the market to meet thermal demands in buildings and
industries.
48
Deliverable: D 3.1
Also renewable sources which otherwise would be difficult to use, such as many forms of
biomass and geothermal energy, can be exploited. By aggregating a large number of small,
variable heating and cooling demands, District Heating and District Cooling provide the key to
wide scale primary energy and carbon emission reductions in whole communities.
District heating networks bring heat from large production systems, where heat often comes as a
by-product of industrial processes or of power generation, to the end users in districts. Normally
the heat is carried by hot water, superheated water or steam.
Losses occur by heat transmission through pipes walls, by water or steam leaks due to holes on
pipes, heat exchangers and fittings and, in case of steam systems, by condensate return lines
and by damaged steam traps.
In an efficient DH network, losses due to thermal transmission are minimized thanks to high
insulation levels of piping and losses due to damages are limited by careful maintenance.
Normally, in a District Heating Network where the maintenance is performed well, the losses are
kept below 10% of thermal energy entered into the grid.
Aside improving maintenance, efficiency of district heating increases by maximising the ratio
between the number of users and the length of the network.
Despite good maintenance, losses due to leaks in District Heating networks keep being a main
problem also in the best cases.
One strategy to detect leaks, put in place since long times, are related to colouring water flowing
in District Heating pipes that permit to detect immediately failures on heat exchangers and leaks
along lines. Other strategy is to measure delivery and return flow rates.
Efficiency reduction due to wearing and aging of components in District Heating networks is one
of the fields where ICT technologies and the availability of more precise sensors play a major role
in improving energy efficiency.
The installation of meters along networks to detect anomalies in flow rate, pressure and
temperature is today much cheaper than in the past, with more precise measurements and more
powerful automatic monitoring systems able to detect anomalous behaviours and report them to
the District Heating managers.
A large field of energy efficiency improvement along District Heating systems is related to shaving
demand peaks. In the majority of District Heating systems, part of the heat comes from cheap
waste heat sources (industries or power plants), that produce it as a by-product at a certain rate
regardless of the heat need of the city.
49
Deliverable: D 3.1
Very often, the waste heat sources are sufficient to cover a part of demand, but often the peak
load has to be covered in a more expensive way through dedicated boilers.
Reducing demand peaks and shifting demand on different times can be therefore a way to
increase the waste heat use and to reduce primary energy use. Normally, fluctuations in heat
demand decrease with the size of the District Heating system.
At network level, strategies that are effective in shaving peaks are related to mixing users with
different heat demand profiles and different peak demand hours and to storing heat.
District Cooling is an environmentally optimized cooling solution, using local, natural resources or
absorption chillers using heat to produce cooling. As with District Heating, the customer is
connected to the cooling production via a pipe network. Chilled water is distributed to the
buildings where it loses its cold content, thus cooling down the building temperature.
6. Conclusions
Through this document, a set of models have been proposed and described considering:

State of the art of district modelling

Modelling approach proposed in WP1

DAREED Platform objectives and functionality

DAREED Platform constrains
The suitability of two modelling strategies has been analysed: physical modelling and machine
learning based models. It has been concluded that physical modelling approach is more
convenient to fulfil the objectives established and to overcome the identified barriers.
As it was proposed in WP1, the district will be modelled as a set of energy units, which will be
modelled, extracting district-wide information by results aggregation. These energy units have
been categorized as buildings, energy generating units, energy distribution units and
infrastructures.
In the case of buildings, different models have been proposed with increasing levels of precision
in order to overcome lack of building characterization information. The simulation tool chosen for
building energy behaviour simulation will be an integration of EnergyPlus and JEnergyPlus.
A set of models have been proposed for energy generating units in the district, namely, DHW,
photovoltaic, small wind and µCHP. The accuracy achieved by the simplified physical models
proposed for energy generating units will be suitable for DAREED Platforms objectives.
50
Deliverable: D 3.1
Regarding energy distribution infrastructures, the integration of existing simulation tools will be
described in D3.4.
51
Deliverable: D 3.1
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Deliverable: D 3.1
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Deliverable: D 3.1
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Department of Energy.
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8. Annex I. Consuming Black Energy Unit example
In this section, an example of black energy unit is presented based on the work developed in the
framework of the TABULA project.
Table 19. Description of black energy units. ES.ME.SFH.01.Gen
ES.ME.SFH.01.Gen
Code
Climate Zone
Mediterranean Climate
Period of construction
Before 1900
Type of construction
Single Family House
Habitable Area (sq-m)
50
Volume (m3)
124
Compacity V/S
1,38
Number of floors
2
Number of dwelling
1
According to the climate conditions defined in Spain, final energy consumption is the following;
Table 20. Final energy consumption per climate zone. (CBEM)
Climate Zone Final Energy (kWh/m2 yr)
B3
107,20
B4
94,60
C1
138,60
C2
128,80
C3
144,50
D1
188,70
E1
211,40
And the following constructive elements;
Table 21. Constructive elements. U coefficient. (CBEM)
Element
U (W/m2K)
Pitched roof
5,56
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Deliverable: D 3.1
Façade
0,24
Floor
2,38
Ground floor
0,66
Windows
4,96
And energy systems
Table 22. Energy systems description. (CBEM)
System
Description
Performance
Heating
Electric
1
system
DHW
Gas Heater
0,8
Additionally to this information, it is required the total amount of energy linked to lighting and
equipment covering the three main aspects found in residential and tertiary sector; lighting,
equipment and HVAC.
According to information provided in [58], depending on the type of dwelling lighting and
consumption is as follows;
Table 23. Lighting and equipment consumption per type of building
Type
dwelling
of Lighting
consumption
2
SFH
Prevalence
Equipment
Energy
of
consumption
Efficiency
2
(kWh/m yr)
technology
(kWh/m yr)
Level
3,17
Incandescent
23,77
A Class
58

deliverable D3.1

Transcription

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