Supply and Demand Dynamics in Energy Systems Modeling

Transcription

UNIVERSIDADE TÉCNICA DE LISBOA
INSTITUTO SUPERIOR TÉCNICO
Supply and Demand Dynamics in
Energy Systems Modeling
André Alves Pina
Supervisor:
Doctor Paulo Manuel Cadete Ferrão
Co-supervisor: Doctor Carlos Augusto Santos Silva
Thesis approved in public session to obtain the PhD Degree in
Sustainable Energy Systems
Jury final classification: Pass with Distinction
Chairperson: Chairman of the IST Scientific Board
Members of the Committee:
Doctor António Manuel Barros Gomes de Vallêra
Doctor António Manuel de Oliveira Gomes Martins
Doctor Luı́s Manuel de Carvalho Gato
Doctor Carlos Augusto Santos Silva
Doctor Stephen Robert Connors
2012
UNIVERSIDADE TÉCNICA DE LISBOA
INSTITUTO SUPERIOR TÉCNICO
Supply and Demand Dynamics in
Energy Systems Modeling
André Alves Pina
Supervisor:
Co-supervisor: Doctor Carlos Augusto Santos Silva
Thesis approved in public session to obtain the PhD Degree in
Sustainable Energy Systems
Jury final classification: Pass with Distinction
Chairperson: Chairman of the IST Scientific Board
Members of the Committee:
Doctor António Manuel Barros Gomes de Vallêra, Full
Professor (Retired) of Faculdade de Ciências,
Universidade de Lisboa
Doctor António Manuel de Oliveira Gomes Martins, Full
Professor of Faculdade de Ciências e Tecnologia,
Universidade de Coimbra
Doctor Paulo Manuel Cadete Ferrão, Full Professor of
Instituto Superior Técnico, Universidade Técnica de Lisboa
Doctor Luı́s Manuel de Carvalho Gato, Associate Professor of
Instituto Superior Técnico, Universidade Técnica de Lisboa
Doctor Carlos Augusto Santos Silva, Invited Assistant Professor
do Instituto Superior Técnico, Universidade Técnica de Lisboa
Doctor Stephen Robert Connors, Researcher, Massachusetts
Institute of Technology
2012
Resumo
Actualmente, os maiores desafios dos sistemas de energia são a mudança
para sistemas com elevadas penetrações de energias renováveis e o aumento
da eficiência energética, tanto na oferta como na procura. O desenvolvimento de planos de transição para sistemas sustentáveis de energia que sejam
económica e tecnicamente possı́veis requer o uso de modelos energéticos detalhados. No entanto, as metodologias mais utilizadas não estão preparadas
para realizar análises a longo-prazo e simultâneamente levar em conta as
dinâmicas de oferta e procura.
O trabalho apresentado nesta tese analisa a importância de considerar
as dinâmicas das fontes de energia renováveis e da introdução de mudanças
nos padrões de consumo de electricidade, através da implementação de medidas de redução de consumo ou da introdução de veı́culos eléctricos, na
optimização de investmentos em nova capacidade geradora.
Uma nova metodologia com resolução temporal elevada que permite
modelar a evolução no longo-prazo de sistemas de energia é proposta e
testada para desenvolver planos de investimentos para sistemas com altas
penetrações de fontes de energias renováveis. Os resultados mostram que
considerar as dinâmicas é crucial para evitar o sobreinvestimento em nova
capacidade geradora e aumentar a eficiência do sistema.
Palavras-chave: modelação de sistemas energéticos; fontes de energia
renová-veis; TIMES; sistemas de armazenamento de energia; veı́culos eléctricos;
eficiência energética.
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Abstract
The shift towards high penetrations of renewable energy sources and the
increase in energy efficiency, on both the supply and demand sides, are
currently the main challenges that energy systems are facing. To design
economically and technically feasible transition pathways to more sustainable energy systems, detailed energy models are required. However, the
most commonly used modeling methodologies are not prepared to perform
long-term analysis of energy systems while taking into account the inherent
hourly dynamics of supply and demand.
The work presented in this thesis analyzes the importance of accounting for the dynamics of renewable energy sources availability and of the
introduction of changes in the patterns of electricity consumption, through
the implementation of demand side management policies or the deployment
of electric vehicles, in the optimization of investments in new generation
capacity.
A new framework that allows modeling the long-term evolution of energy
systems while having a high temporal resolution is proposed and tested to
develop investment plans for systems with high penetrations of renewable
energy sources. The results show that accounting for the dynamics is crucial
for avoiding the overinvestment in new generation capacity and increase the
cost-effectiveness of the systems.
Keywords: energy systems modeling; renewable energy resources; TIMES;
energy storage systems; electric vehicles; energy efficiency.
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Acknowledgments
I am grateful to many people, more than I can enumerate by name, for the
guidance, help, encouragement and friendship during the development of
this work.
To my Supervisor Professor Paulo Ferrão, for all the opportunities he
provided and the challenges he put me through which allowed me to learn
more than I could ever expect from this whole experience.
To my Co-Supervisor Carlos Silva, for his continuous teaching, support
and, even more important, his friendship. His optimism and enthusiasm are
definitely contagious.
To Stephen Connors, from MIT, who provided valuable feedback throughout my work and enabled me to work and contact with incredible people
from MIT.
To Miguel Carvalho and Edward Spang, whom I had the pleasure to work
closely with for the MIT Portugal Program and the Green Islands Project,
as well as Ana Quaresma, João Fumega and everyone at the MIT Portugal
Program I contacted with. All of them provided an incredibly friendly and
supportive working atmosphere.
To Gustavo Souza, Vı́tor Leal, Gonçalo Pereira and Alexandra Moutinho
and all others who I was able to collaborate with for all their comments,
suggestions and discussions.
To my parents, my brother and my family for always encouraging me to
keep improving and for providing a loving environment for me to do it.
To all my friends, for the many funny and crazy times we had together.
To Patrı́cia, whom I admire deeply for being an example of excellence,
courage, dedication and determination. Thank you for all the patience and
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support.
Finally, this work would not have been possible without the financial
support of the MIT Portugal Program and FCT Portuguese National Science Foundation though scholarship funding SFRH/BD/35334/2007.
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Acronyms
CO2 Carbon dioxide
EU27 European Union 27 countries
EV Electric Vehicle
DER-CAM Distributed Energy Resources Customer Adoption Model
DSM Demand Side Management
GHG Greenhouse Gases
IPAC Integrated Policy Assessment model for China
MARKAL MARket ALlocation
NEMS National Energy Modeling System
LEAP Long-range Energy Alternatives Planning System
OECD Organisation for Economic Co-operation and Development
RES Renewable Energy Sources
TIMES The Integrated Markal-Efom System
V2G Vehicle to Grid
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Contents
1 Introduction
1.1
1
Problem statement . . . . . . . . . . . . . . . . . . . . . . . .
1
1.1.1
High penetration of renewable energy sources . . . . .
3
1.1.2
Energy storage systems . . . . . . . . . . . . . . . . .
5
1.1.3
Demand side management . . . . . . . . . . . . . . . .
6
1.1.4
Electric vehicles
. . . . . . . . . . . . . . . . . . . . .
7
1.2
Research questions . . . . . . . . . . . . . . . . . . . . . . . .
8
1.3
Research strategy . . . . . . . . . . . . . . . . . . . . . . . . .
8
1.3.1
Extended TIMES modeling methodology . . . . . . .
9
1.3.2
Hybrid modeling framework . . . . . . . . . . . . . . .
11
1.4
Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
1.5
Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
1.6
Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . .
13
2 State of the art in energy systems modeling
2.1
15
Existing modeling methodologies . . . . . . . . . . . . . . . .
17
2.1.1
LEAP . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
2.1.2
NEMS . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
2.1.3
MARKAL . . . . . . . . . . . . . . . . . . . . . . . . .
23
2.1.4
TIMES - The Integrated Markal-Efom System . . . .
25
2.1.5
HOMER . . . . . . . . . . . . . . . . . . . . . . . . . .
27
ix
2.2
2.1.6
DER-CAM . . . . . . . . . . . . . . . . . . . . . . . .
28
2.1.7
EnergyPLAN . . . . . . . . . . . . . . . . . . . . . . .
29
Evolution of energy models . . . . . . . . . . . . . . . . . . .
29
2.2.1
Before 1980 - The energy crisis and the push towards
nuclear . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2
1981-1990 - The diversification of alternative energy
sources
2.2.3
. . . . . . . . . . . . . . . . . . . . . . . . . .
2.3
2.4
3.2
34
2001-2010 - The emergence of sustainable energy systems . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
Hybrid methodologies . . . . . . . . . . . . . . . . . . . . . .
36
2.3.1
PERSEUS and AEOLIUS . . . . . . . . . . . . . . . .
37
2.3.2
IPAC . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
2.3.3
TIMES and EMPS . . . . . . . . . . . . . . . . . . . .
41
2.3.4
GEMINI-E3 and TIAM . . . . . . . . . . . . . . . . .
42
2.3.5
The Altos Integrated Market Model Suite . . . . . . .
43
Key findings . . . . . . . . . . . . . . . . . . . . . . . . . . . .
45
3 Case studies
3.1
33
1991-2000 - Renewable energy sources as a response
to environmental concerns . . . . . . . . . . . . . . . .
2.2.4
32
47
The Azores islands . . . . . . . . . . . . . . . . . . . . . . . .
47
3.1.1
São Miguel . . . . . . . . . . . . . . . . . . . . . . . .
48
3.1.2
Flores . . . . . . . . . . . . . . . . . . . . . . . . . . .
50
Mainland Portugal . . . . . . . . . . . . . . . . . . . . . . . .
51
4 High-temporal resolution modeling of the dynamics of energy systems
4.1
55
Modeling hourly electricity dynamics for po-licy making in
long-term scenarios . . . . . . . . . . . . . . . . . . . . . . . .
55
4.1.1
Aim of the study . . . . . . . . . . . . . . . . . . . . .
56
4.1.2
Main results . . . . . . . . . . . . . . . . . . . . . . . .
57
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4.2
4.3
The relevance of the energy resource dynamics in the mid/longterm energy planning models . . . . . . . . . . . . . . . . . .
60
4.2.1
Aim of the study . . . . . . . . . . . . . . . . . . . . .
60
4.2.2
Main results . . . . . . . . . . . . . . . . . . . . . . . .
62
Key findings . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
5 Modeling the dynamics of electricity demand
5.1
5.2
5.3
65
The impact of demand side management
strategies in the penetration of renewable electricity . . . . .
65
5.1.1
Aim of the study . . . . . . . . . . . . . . . . . . . . .
66
5.1.2
Main results . . . . . . . . . . . . . . . . . . . . . . . .
67
Modeling the introduction of electric vehicles in an island . .
69
5.2.1
Modeling methodology . . . . . . . . . . . . . . . . . .
69
5.2.2
Scenarios . . . . . . . . . . . . . . . . . . . . . . . . .
71
5.2.3
Main results . . . . . . . . . . . . . . . . . . . . . . . .
72
Key findings . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75
6 Hybrid modeling framework for high penetrations of renewable energy sources
6.1
6.2
77
Integrated Modeling Framework for Energy Systems Planning 77
6.1.1
Aim of the study . . . . . . . . . . . . . . . . . . . . .
78
6.1.2
Main results . . . . . . . . . . . . . . . . . . . . . . . .
79
High-resolution modeling framework for the planning of electricity systems with high
6.3
penetration of renewables . . . . . . . . . . . . . . . . . . . .
81
6.2.1
Aim of the study . . . . . . . . . . . . . . . . . . . . .
81
6.2.2
Main results . . . . . . . . . . . . . . . . . . . . . . . .
82
Key findings . . . . . . . . . . . . . . . . . . . . . . . . . . . .
86
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7 Conclusions and future work
7.1
State of the art in energy systems modeling . . . . . . . . . .
7.2
High-temporal resolution modeling of the dynamics of energy
87
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systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88
7.3
Modeling the dynamics of electricity demand . . . . . . . . .
90
7.4
Hybrid modeling framework for high penetrations of renew-
7.5
able energy sources . . . . . . . . . . . . . . . . . . . . . . . .
91
Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
Bibliography
93
Papers
111
Contributions in each paper . . . . . . . . . . . . . . . . . . . . . . 111
Paper I - Modeling hourly electricity dynamics for policy making
in long-term scenarios . . . . . . . . . . . . . . . . . . . . . . 113
Paper II - The relevance of the energy resource dynamics in the
mid/long-term energy planning models . . . . . . . . . . . . . 127
Paper III - The impact of demand side management strategies in
the penetration of renewable electricity . . . . . . . . . . . . . 137
Paper IV - Integrated Modeling Framework for Energy Systems
Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Paper V - High-resolution modeling framework for the planning of
electricity systems with high penetration of renewables . . . . 159
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List of Figures
1.1
State of development of technologies for Sustainable Energy
Systems in terms of development stages and challenge for implementation . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2
2
Expected costs of different electricity generation technologies
using renewable and non-renewable energy sources for the
years 2007, 2020 and 2030 . . . . . . . . . . . . . . . . . . . .
1.3
4
Characterization of different energy storage systems in terms
of rated power (MW) and discharge time (hours) . . . . . . .
5
1.4
Types of Demand Side Management strategies
. . . . . . . .
7
2.1
Types of models typically used for energy analysis . . . . . .
16
2.2
Development of new modeling tools since 1970 . . . . . . . .
29
2.3
Overview of existing modeling tools based on their time horizon and the number of time periods per year they consider .
2.4
Evolution of the number of energy models, temporal resolution and time horizon from before 1980 until 2010 . . . . . .
2.5
31
Evolution of the electricity generation mix from 1970 until
2007 for a combination of countries . . . . . . . . . . . . . . .
2.6
30
32
Development of hybrid modeling frameworks and their general purpose depending on the types of energy modeling approaches that are combined . . . . . . . . . . . . . . . . . . .
2.7
Framework developed to combine the models PERSEUS and
AEOLIUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8
37
39
Framework of the Integrated Policy Assessment model for
China (IPAC) . . . . . . . . . . . . . . . . . . . . . . . . . . .
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40
2.9
Modeling framework using EMPS and TIMES . . . . . . . . .
42
2.10 Coupling framework using GEMINI-E3 and TIAM . . . . . .
43
2.11 Application of the Altos Integrated Market Model Suite . . .
45
3.1
Electricity production in São Miguel, by generation type,
from 1994 until 2009 . . . . . . . . . . . . . . . . . . . . . . .
3.2
Electricity consumption in São Miguel, by sector, from 1994
until 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3
51
Main inputs and outputs of a typical TIMES model for electricity systems . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2
50
Electricity consumption in Flores, by sector, from 1994 until
2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1
49
Electricity production in Flores, by generation type, from
1994 until 2009 . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4
48
56
Comparison of electricity production from wind energy from
models with different time resolution, for the time periods of
2010-2015, 2010-2020 and 2010-2025 . . . . . . . . . . . . . .
58
4.3
Wind energy generation capacity for the different scenarios .
59
4.4
Hourly load curve for 13 days for January 2020 for Flores,
supply scenario D, dynamic balance method in EnergyPLAN
63
5.1
Scenarios description according to the options for DSM . . .
67
5.2
Utilization rates of renewable electricity generators for each
hour of the average day in 2020 for scenarios a) without
standby power elimination b) with standby power elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3
67
Fraction of possible shiftable loads that is shifted in each year
for the different scenarios . . . . . . . . . . . . . . . . . . . .
68
5.4
Framework considered for modeling the introduction of EVs .
70
5.5
Electricity consumption from EVs in the scenarios considered
71
5.6
Share of the daily demand of electricity by the EVs assumed
for each hour in the fixed charging strategy scenario . . . . .
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72
5.7
Share of RES on the electricity that was produced for EVs
5.8
Share of RES on the monthly electricity produced for EVs in
5.9
.
73
the year 2025 . . . . . . . . . . . . . . . . . . . . . . . . . . .
73
Optimal average share of the daily demand of electricity by
the EVs in each hour for the scenarios with flexible charging
in the year 2025 . . . . . . . . . . . . . . . . . . . . . . . . . .
74
6.1
Proposed integrated modeling framework . . . . . . . . . . .
79
6.2
Electricity production by source from TIMES initialization .
80
6.3
Electricity production by source after the last iteration . . . .
80
6.4
Modeling framework proposed . . . . . . . . . . . . . . . . . .
81
6.5
Installed capacity from renewable energy sources a) before
the first iteration b) after the last iteration . . . . . . . . . .
6.6
83
Electricity mix in the different scenarios a) in the 1036 MW
scenario, iteration 1 b) in the 1036 MW scenario, last iteration 84
6.7
Percentage of excess electricity from renewable energy sources
in the different scenarios a) for all renewable energy sources
and b) considering only solar, wind offshore and wave energies 85
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List of Tables
1.1
Studies performed and corresponding section of thesis, paper
where it is presented, case study used and summary of the work 14
2.1
Modeling tools analyzed . . . . . . . . . . . . . . . . . . . . .
18
2.2
Overview of the MARKAL family of models . . . . . . . . . .
24
3.1
Installed generation capacity in São Miguel . . . . . . . . . .
49
3.2
Installed generation capacity in Flores . . . . . . . . . . . . .
51
3.3
Generation capacity (MW) in the Portuguese electricity system
3.4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Total installed capacity (MW) of renewable energy sources
proposed by the Portuguese Government . . . . . . . . . . . .
4.1
53
Range of capacity factors in each temporal level, for each
resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2
52
57
Qualitative characterization of renewable resource dynamics:
+ (important), O (mild), - (not important) . . . . . . . . . .
60
4.3
Generation capacity (MW) considered in each scenario . . . .
61
4.4
RES results using the integral, semi-dynamic and dynamic
balance methods for all scenarios . . . . . . . . . . . . . . . .
5.1
62
Scenarios for the penetration of EVs and time of charging
strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xvii
71
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Chapter 1
Introduction
Worldwide, energy systems are characterized by an intensive use of fossil
fuels to respond to an ever increasing demand. In 2007, coal, peat, oil and
gas accounted for around 81.4% of the primary energy supplied[1]. For most
countries, this implies depending heavily on imports to meet their domestic
needs, which has significant economic and security of supply risks. In 2008,
the EU27 had an estimated average dependence of 53.8%, with Denmark
being the only net exporter of energy [2]. Furthermore, the combustion of
fossil fuels results in the emission of greenhouse gases (GHG), which have
been considered as one of the most important factor influencing climate
change.
One of the most important energy vectors is electricity. In 2007, around
31% of the primary energy consumption worldwide was used for electricity
production, with an average efficiency of 39%, and electricity accounted for
around 17% of total final energy consumption (21% for OECD countries)
[1]. To address this, policy makers have been developing policies for reducing
final energy demand, increase energy efficiency and use endogenous resources
with the goal of promoting more sustainable energy systems.
1.1
Problem statement
The main policies that are being discussed by policy makers include the investment in renewable energy sources (RES) for electricity and heat supply,
installation of energy storage systems to improve the efficiency of electricity
1
systems, promotion of demand side management (DSM) policies to increase
energy efficiency and support of technological changes, such as the introduction of electric vehicles (EVs), to shift the consumption of traditional fossil
fuels to more efficient and sustainable alternatives.
The wide range of options that can be adopted results from the fact
that no silver bullet exists to address the sustainability issues of an energy
system (national, regional or local), with different technologies having different degrees of development. While some are already mature technologies
or ready to be deployed, others are still in demonstration, development or
even research stage. The multitude of technologies to develop cleaner energy
systems that exist or are currently being developed is shown in Figure 1.1,
based on [3, 4]. This large number of options requires policy makers to assess and analyze what different solutions exist for their system in order to
identify what are the benefits that can be achieved and develop transition
pathways for the implementation of the most suitable set of options.
Figure 1.1: State of development of technologies for Sustainable Energy
Systems in terms of development stages and challenge for implementation
To support the necessary studies of how technological development can
2
help increase the sustainability of a region, a large of number of modeling
tools have been developed with different objectives and scope. Generally,
the scope of energy system modeling tools can be categorized as at least one
of the following:
• to understand the relation between changes in the energy system and
economy;
• to analyze possible evolutions of energy systems;
• to analyze the balance between energy sources and demand;
• to study how different components of the electricity supply sector, such
as individual plants or the electricity grid, work on a specific energy
system.
While all these issues are important for assessing how well a policy will
work, most studies performed focus only on either the multi-year analysis
that allows assessing the long-term implications of a certain policy or the
high temporal resolution modeling that allows to accurately calculate the
true impacts of a policy. The hypothesis of this thesis it that the combination
of different approaches would allow policy makers to design more efficient
policies and understand the impacts each policy can have on others.
The work presented in this thesis focuses on combining the multi-year
modeling approach with the balance modeling approach for the analysis of
electricity systems by using TIMES [5], an existing energy modeling tool,
with a high temporal resolution. This allows accounting for supply and
demand dynamics to study the topics of high penetrations of RES with and
without energy storage and the impact of DSM strategies and EVs, which
are technologies on the demonstration or deployment phase, as shown in
Figure 1.1.
1.1.1
High penetration of renewable energy sources
The incorporation of RES (such as wind, hydro and solar) in the electricity
mix of a system allows reducing the use of fossil fuels, which can increase the
security of supply and have significant environmental benefits. However, the
introduction of RES must be analyzed in detail due to their higher capital
3
investment costs, higher levelized cost and variability. The higher costs when
compared to fossil fuels result, generally, from three main factors: early stage
development of technologies, lack of economies of scale and low capacity
factors. The increasing investment in electricity generation from RES is
expected to enable a lowering of the costs of these technologies, as shown
in Figure 1.2 based on [6], due to reductions in capital and maintenance
costs and increases in efficiency and lifetime. With the costs of technologies
using fossil fuels expected to increase until 2030, some of the technologies
using RES can achieve grid parity during this period. The problem of RES
variability is particularly important for the introduction of non-dispatchable
sources such as small hydro systems, wind and solar.
To assess the large scale investment in RES in energy systems, two approaches are typically used: long-term energy models with low temporal
resolution or short-term energy models with high temporal resolution. The
first approach is able to consider long-term changes in costs such as technology development and variability in fuel prices, while the second approach
is capable of accounting for the hourly fluctuations of electricity production
from RES.
Figure 1.2: Expected costs of different electricity generation technologies
using renewable and non-renewable energy sources for the years 2007, 2020
and 2030
For an efficient energy planning, however, all these factors need to be
considered as the costs determine how much new generation capacity can
be built, while the amount of electricity that is effectively introduced in the
grid will ultimately influence the cost-effectiveness of the investment.
4
1.1.2
Energy storage systems
Energy storage systems can be used to address different issues in electricity
systems such as guaranteeing power quality, providing bridging power and
allowing a better energy management. Power quality applications is when
energy storage systems are charged and discharged in a time scale of seconds or less, only to help guarantee the quality of power in every instance.
Bridging power is when energy storage systems are used to help make the
transition while a new power plant comes online, which can take from seconds to minutes. Energy management is considered to be when a storage
unit is used to store energy for large time periods, hours or higher, in order
to utilize electricity that is generated when it is not needed in another period
of time where it is needed but would be not be readily available. Examples
of energy storage systems and their characteristics are shown in Figure 1.3
[7].
Figure 1.3: Characterization of different energy storage systems in terms of
rated power (MW) and discharge time (hours)
The use of energy storage systems is particularly important when plan-
5
ning systems with high penetrations of RES, since their variability and intermittency can lead to mismatches between electricity supply and demand
as well as grid operation issues. The planning of systems with high penetrations of RES must therefore consider the option of using energy storage
systems.
1.1.3
Demand side management
Demand side management strategies have been used for many decades in
order to promote a more efficient energy consumption. Generally, DSM
strategies allow a better management of the available energy resources and
can help reduce the cost of a system, as well as avoid interruption of service. There are six different objectives for DSM strategies [8], as shown in
Figure 1.4:
• Peak clipping consists on the reduction of electricity demand during
high consumption periods, thus avoiding high electricity costs;
• Valley filling consists on the increase in demand during low consumption hours, which may enable the production of electricity at a low
cost;
• Load shifting consists on changing the consumption of electricity from
hours with high consumption to hours with low consumption, which
generally results in no savings in consumption but can help decrease
the overall costs;
• Strategic conservation is the overall reduction of demand through technological advances such as appliances with higher efficiencies;
• Strategic growth is the overall increase of electricity demand, which
can result from the introduction of new applications;
• Flexible load shape consists on controlling appliances from the endusers so that they can respond to signals from the supply side and
change their consumption according to the suppliers needs (demand
response).
6
Figure 1.4: Types of Demand Side Management strategies
While on the one hand, the planning of electricity systems must take
into consideration the DSM strategies that are being undertaken in order
to avoid over or under investment in new generation capacity, on the other
hand, the design of new DSM policies must be analyzed in detail as they
can have an impact on the cost-benefit of the existing generation capacity.
Furthermore, the development of demand response programs can benefit the
investment in new generation capacity from RES by enabling a better match
between electricity supply and demand.
1.1.4
Electric vehicles
Electric vehicles are currently considered as one of the best alternatives for
the use of conventional fossil fuels in the transportation sector, which has
led major vehicle manufacturers to develop their own vehicles. The switch
from conventional internal combustion engines to EVs enables the reduction
of local GHG and particule emissions, as these are made during electricity
production.
The full benefits of the introduction of EVs are determined by the energy
source that is used to charge the vehicles, which depends on two crucial
factors: the electricity supply system and the charging hours of the vehicles.
In a system with a low penetration of RES or nuclear, the environmental
benefits of EVs are less significant since the traditional transportation fuels
are being substituted by other fossil fuels such as natural gas, coal or fuel
oil. However, in systems with a high penetration of RES, the amount of
GHG emissions relative to the transportation sector can reduce significantly.
7
In what concerns the time of charging, peak consumption hours have, in
general, higher marginal GHG emission factors due to the use of fossil fuel
plants whereas low consumption periods can have excess of electricity from
RES that can be used to charge EVs.
Furthermore, the large scale introduction of EVs can pose a real problem
for electricity systems if the additional electricity demand occurs during peak
hours and more investment is necessary in peak power plants. However, if
an intelligent charging strategy is adopted, EVs can be an important asset
in the management of electricity systems as the charging of the batteries
can be done in periods with low consumption or in periods in which there
is excess of electricity production from RES. This would allow running the
necessary power plants more efficiently as well as increasing the use of RES.
1.2
Research questions
The research questions addressed in this thesis are:
• Are the current modeling methodologies able to consider the necessary
dynamics of electricity supply and demand and what is the impact of
using different temporal resolutions?
• What impact different DSM strategies have in the planning of future
electricity systems?
• How can the shift to electric mobility help reduce CO2 emissions and
what is the impact on the electricity system?
• How can we account for the seasonal and daily variability of supply
and demand in order to develop expansion plans of electricity systems
with high penetration of RES, and taking into account the existence
of energy storage systems?
1.3
Research strategy
The research questions expressed in the previous section were supported by a
comprehensive literature review on the existing modeling methodologies. It
8
was mainly intended to identify the shortcomings in the modeling strategies
of systems with high penetrations of RES. The challenges for energy modeling tools were analyzed and the main gap identified was associated with the
lack of optimization approaches for the investment in additional renewable
energy generation capacity that may take into account the seasonal, daily
and hourly dynamics of energy supply and demand.
This gap was addressed with the development of two new modeling
methodologies:
• an ”Extended TIMES modeling methodology” that is able to analyze
a period of 40 years with 288 periods per year;
• a ”Hybrid modeling framework” that couples the extended TIMES
model with a short-term model to enable an hourly temporal resolution.
Both methodologies minimize the total costs of energy systems from a
holistic system approach, while assuming that all agents (consumers, policy
makers, utilities and all other relevant companies) work together to enable
the best possible outcome for each scenario.
1.3.1
Extended TIMES modeling methodology
The extended TIMES modeling methodology consists on an innovative approach designed in TIMES, a bottom-up optimization model for multi-year
analysis of energy systems. This new modeling methodology diverged from
the previous applications of TIMES as it provides a higher temporal resolution by dividing each year into 4 seasons, 3 typical days per season (Saturday,
Sunday and weekday) and 24 hours per day. This increase in temporal resolution allows for the consideration of seasonal, daily and hourly dynamics
of energy demand and supply.
The modeling of electricity supply and demand dynamics with the extended TIMES model is explained and validated in terms of its accuracy in
Paper I - ”Modeling hourly electricity dynamics for policy making in longterm scenarios”. The results showed that the increase in temporal resolution
is crucial to achieve better estimates for the potential of RES and allow the
design of more robust investment plans for electricity systems.
9
The extended TIMES modeling methodology was compared with two
other methods with different temporal resolutions concerning the estimation
of electricity production from RES in Paper II - ”The relevance of the energy
resource dynamics in the mid/long-term energy planning models”. This
comparison showed that while the extended TIMES modeling methodology
can help improve the analysis of the investment in RES, the modeling of
very high penetrations of RES requires modeling frameworks with higher
temporal resolutions.
This new methodology was used to perform two innovative analysis on
the use of DSM strategies and different time of charging strategies for EVs
as means of increasing the penetration of RES.
The impact of promoting different DSM strategies on the planning of
electricity systems was tested in the extended TIMES modeling methodology in Paper III - ”The impact of demand side management strategies in the
penetration of renewable electricity”. In particular, this methodology was
used to study the introduction of demand response in the residential sector
in Flores island, which has hourly dynamics and impacts on the investment
decisions in new generation capacity. The introduction of dynamic demand
technologies was shown to help delay the investment in new generation capacity by reducing peak demand and enabling a better management of the
already existing capacity.
The use of different time of charging strategies for EVs to increase the
share of RES in the electricity consumed by the vehicles was studied by
using the extended TIMES modeling methodology and a short-term model
with hourly resolution. The extended TIMES modeling methodology was
used to optimize the investment in new generation capacity over a large
time horizon and the short-term model to calculate the effective electricity
production from RES for each year, while considering the installed capacities
as decided by TIMES. The results showed that the use of flexible time of
charging strategies can help increase the share of RES in the electricity
produced for the charging of the EVs by providing a better match between
supply and demand.
10
1.3.2
Hybrid modeling framework
To improve the accuracy of the modeling of systems with very high penetrations of RES, a new hybrid modeling framework to perform multi-year
optimization of energy systems was developed. This framework consists of
using an iterative algorithm in which the results obtained from the shortterm model are used to introduce constraints in the TIMES model whenever
an operation condition is not met for one year.
Two applications of this framework were performed in order to develop
transition pathways to systems with high penetrations of RES. One application concerned the development of an investment plan for the island of São
Miguel that took into account the dynamics of Geothermal and Wind energy as well as the variability of demand. This work, presented in Paper IV
- ”Integrated Modeling Framework for Energy Systems Planning”, showed
that the increase in temporal resolution allows avoiding the overinvestment
in new generation capacity by delaying the investments which would lead
to excesses in electricity production from RES that cannot be used by the
system.
The second application of the hybrid modeling framework was developed
for mainland Portugal to study the importance of considering RES variability and complementarity for different cases of energy storage systems
capacities. The results, presented in Paper V - ”High-resolution modeling
framework for the planning of electricity systems with high penetration of
renewables”, showed that for systems with lower energy storage capacities a
detailed model of energy systems is crucial to develop investment plans that
consider accurate estimations of the electricity production from each RES.
1.4
Case studies
The case studies used in these works were the islands of São Miguel and
Flores in the Azores archipelago and mainland Portugal. The main purpose
of working with the islands of the Azores is that they represent excellent
case studies due to having well defined boundaries, facing severe security of
supply issues and having a large potential for the use of RES.
Furthermore, the research developed using the islands of the Azores was
11
a part of the Green Islands Project, a research project on sustainable energy systems planning that is being developed as a collaboration between
the MIT Portugal Program and the Government of Azores. This project
has the purpose of developing sustainable pathways for the energy systems
of the Azores islands, which requires the use of comprehensive modeling
methodologies to understand which challenges and opportunities can arise
in each island.
As such, São Miguel was used as a case study for the development of the
proposed modeling methodologies due to its particular constraints regarding
the use of geothermal energy, which is considered as a non-dispatchable
energy source by the local electricity company. The island of Flores was used
to test the impact of the introduction of DSM strategies and of EVs since
it presents already a considerably large penetration of RES, with several
hours during the year having 100% renewable electricity. This creates an
opportunity to use dynamic demand technologies as a way of increasing the
penetration of RES.
The mainland Portugal case study was used to test the proposed modeling framework in a system with a diversified electricity mix and that can
invest in several different energy sources. This case study also allowed understanding the need for hybrid modeling methodologies with high temporal
resolution depending on the storage capacities that are considered to exist.
1.5
Publications
The work developed in this thesis resulted in several conference and journal
papers, some of which included in this thesis:
• Paper I - ”Modeling hourly electricity dynamics for policy making in
long-term scenarios” by André Pina, Carlos Silva and Paulo Ferrão
published in the journal Energy Policy [9], 2011.
• Paper II - ”The relevance of the energy resource dynamics in the
mid/long-term energy planning models” by Gustavo Haydt, Vı́tor Leal,
André Pina and Carlos A. Silva published in the journal Renewable
Energy [10], 2011.
12
• Paper III - ”The impact of demand side management strategies in the
pe-netration of renewable electricity” by André Pina, Carlos Silva and
Paulo Ferrão published in the journal Energy [11], 2011.
• Paper IV - ”Integrated Modeling Framework for Energy Systems Planning” by Carlos Silva, André Pina, Gonçalo Pereira and Alexandra
Moutinho published in Volume 3 of the Proceedings of the 23rd International Conference on Efficiency, Cost, Optimization, Simulation
and Environmental Impact of Energy Systems [12], 2011.
• Paper V - ”High-resolution modeling framework for the planning of
electricity systems with high penetration of renewables” by André
Pina, Carlos Silva and Paulo Ferrão to be submitted to the journal
Applied Energy.
1.6
Outline of thesis
The thesis is organized in seven chapters. In Chapter 2, the existing energy
systems modeling tools are compared in order to identify the gaps for effective policy making regarding electricity systems and different tools and
hybrid methodologies are described. The energy systems used as case studies are presented in Chapter 3. Chapter 4 analyzes how different modeling
methodologies are capable of accounting for the dynamics of electricity systems and the impact of considering the seasonal and daily variability of
electricity supply and demand for designing optimal expansion plans in systems with high penetrations of RES. In Chapter 5, the impact of changes
in electricity demand patterns through the promotion of DSM strategies or
the introduction of EVs is analyzed. The development of a hybrid modeling
framework for analyzing systems with high penetrations of RES that combines, iteratively, two types of models is presented in Chapter 6. Finally,
Chapter 7 presents some conclusions of the work that was performed and
presents the basis for future work.
The studies included in this thesis are identified in Table 1.1 in terms
of the section of the thesis and paper in which they are presented, the case
study to which they were applied to and their main purpose.
13
Table 1.1: Studies performed and corresponding section of thesis, paper
where it is presented, case study used and summary of the work
Section
4.1
Paper
I
Case study
São Miguel
4.2
II
Flores
5.1
III
Flores
5.2
-
Flores
6.1
IV
São Miguel
6.2
V
Mainland Portugal
14
Main purpose
Development of the extended
TIMES modeling methodology
and analysis of using different
number of time steps within a
day
Comparison of the accuracy of
using different temporal resolutions for modeling energy systems using the extended TIMES
methodology, EnergyPlan and
LEAP
Use of the extended TIMES
methodology to assess the impact of energy efficiency and demand response on the planning
of electricity systems
Use of the extended TIMES
methodology to estimate the use
of renewable electricity for electric vehicles under different time
of charging strategies
Development of the hybrid modeling framework and analysis
of investment in new renewable
generation capacity for electricity production
Use of the hybrid modeling
framework to analyse the investment in new generation capacity
for electricity production under a
CO2 restriction and considering
different sizes of energy storage
systems
Chapter 2
State of the art in energy
systems modeling
The development of tools for the modeling of energy systems has been seen
as crucial for a few decades now [13, 14]. With these tools, governments,
energy agencies and private investors can analyze different scenarios and
make better and more informed decisions regarding investments on renewable energies, energy efficiency plans, subsidies, etc [15, 16, 17].
There are four major types of energy analysis models, as shown in Figure 2.1, with very different focus and temporal and spatial resolutions:
• Macro-economic models, such as GEMINI-E3 [18], are models used
to analyze the relations between energy systems and the rest of the
economy;
• Energy systems planning models, such as LEAP [19] and TIMES [5],
are models used to analyze the possible evolution of energy systems;
• Energy systems balancing models, such as HOMER [20] and EnergyPLAN [21], are models used to analyze the balance between energy
sources and demand and calculate how different energy sources can
contribute in the supply side of an energy system;
• Grid operation and dispatch models, such as DYMONDS [22], are
models used to study how the electricity grid is able to cope with
15
different energy mixes and how specific power plants can be used in
an energy system;
In this work, the focus will be on energy systems planning and balancing models. Generally, energy modeling tools of these types are prepared to be applied to either the micro- or the macro- scales [23]. The
micro-scale models are usually detailed technology models for short-term
simulation/optimization of components, technologies, buildings or facilities,
with high temporal resolution [24, 25]. The macro-scale models are aggregated models for long-term planning of large energy systems or making
economic/policy recommendations for countries or groups of countries [26].
Due to the size of the regions being studied in the macro models, they usually have yearly time steps, which remove many of the dynamics that enable
the matching of supply and demand [27]. This development of energy modeling tools has been pushed by the continuous development of new energy
technologies, which needed to be studied before being implemented.
Figure 2.1: Types of models typically used for energy analysis
However, the technologies that are currently being developed have created the need to incorporate high detail into long-term modeling, which has
pushed for the development of new modeling methodologies to narrow the
16
gap between the micro- and macro- scales [28, 29]. The convergence to this
meso-scale has been done, mainly, by stretching the boundaries of existing tools from the two types of models to include more dynamics and new
disaggregation levels or by combining different methodologies and tools.
In this Chapter, a total of 90 modeling methodologies are compared
based on different characteristics and their development is analyzed based
on the evolution of energy systems through the years. Furthermore, a description is provided for the most used energy models, as well as some of the
hybrid methodologies that have been developed in the last years.
2.1
Existing modeling methodologies
The number of modeling tools for electricity systems has increased significantly in the last few decades, with each model being developed for a specific
focus. While several studies have been made regarding the classification of
energy models [30, 31, 32, 33], a different analysis is made here focusing on
the time horizon, temporal resolution and the mode of operation. A total
of 90 modeling tools, as shown in Table 2.1, were analyzed and categorized
based on different characteristics:
• Year of development;
• Time horizon, which is the number of years considered by the tool: 2
years or less, between 2 and 30 years, more than 30 years;
• Temporal resolution, which is the number of time periods per year:
8760 or more (hourly or higher), between 8760 and 1 (between hourly
and yearly), 1 or less (yearly or less);
• Optimization goal of the tool: which can be optimization of the operation of the system, optimization of investment in the system, optimization of both operation and investment or simulation of the operation
of pre-determined energy systems only.
17
Table 2.1: Modeling tools analyzed
Model
Date
Optimization goal
Time horizon
Time step
(years)
(per year)
AEOLIUS [34]
2005
Simulation
≤2
≥8760
AIM [35]
1996
Investment
>30
≤1
BALMOREL [36]
2000 Operation+investment >1 and ≤30
≥8760
BCHP Screening Tool [37]
2003
Operation
≤2
≥8760
CGEN [38]
2007
Operation
≤2
≥8760
CHP capacity optimizer [39] 2005 Operation+investment >2 and ≤30
≥8760
CHPSizer [40]
2001
Operation
>30
>1 and <8760
COMPETES [41]
2004
Operation
≤2
>1 and <8760
2008 Operation+investment
>30
≥8760
COSMEE [43]
1998
Operation
≤2
≥8760
DEARS [44]
2005
Investment
>30
≤1
≤2
≥8760
COMPOSE [42]
DER-CAM[45]
DIME [46]
2007 Operation+investment >2 and ≤30 >1 and <8760
DNE21 [47]
1999
Investment
DTI [48]
1999
Investment
E2M2s [49]
>30
≤1
>2 and ≤30 >1 and <8760
E3ME / E3MG / MDM [50] 1996
Investment
>30
≤1
Investment
>2 and ≤30
≤1
E4cast [51]
2000
EGEAS [52]
≥8760
Elfin [53]
1991
Operation
≤2
≥8760
EMCAS [54]
2002
Investment
>30
≥8760
EMELIE [55]
2003
Simulation
>30
>1 and <8760
EMINENT [56]
2005
Simulation
≤2
≥8760
EMPS [57]
1975
Operation
>2 and ≤30 >1 and <8760
Energy 2020 [58]
1972
Simulation
>2 and ≤30
≤1
EnergyPLAN [21]
≤2
≥8760
energyPRO [59]
>30
≥8760
ENPEP [60]
1985
Simulation
>30
≤1
ERIS [61]
1996
Investment
>30
≤1
ESPAUT [62]
2005
Investment
≤2
≥8760
Continued on Next Page. . .
18
Table 2.1 – Continued
Model
Date
Optimization goal
Time horizon
Time step
(years)
(per year)
FOSSIL2 [63]
1972
Investment
>30
>1 and <8760
genEris [64]
2005
Investment
>30
≤1
GTMax [65]
1995
Operation
≤2
≥8760
≤2
≥8760
2000
>30
≥8760
≤2
≥8760
Simulation
≤2
>1 and <8760
H2A analysis [66]
H2RES [67]
HOMER [20]
Operation
HUD CHP Screening Tool [68] 2002
HYDROGEMS [69]
1995
Simulation
≤2
≥8760
HYPRO [70]
2005
Simulation
>2 and ≤30
≤1
IKARUS [71]
1991
Investment
>30
≤1
>30
>1 and <8760
INFORSE [73]
2002
Simulation
>30
≤1
Invert [74]
2003
Investment
>2 and ≤30
≤1
IMAGE-TIMER [72]
IPM [75]
LEAP [19]
MARKAL/TIMES [76, 5]
1980
Simulation
≥8760
>30
≤1
>30
>1 and <8760
MELP [77]
1997
Investment
>2 and ≤30
≤1
MERGE [78]
1994
Simulation
>30
≤1
Mesap PlaNet [79]
1997
Simulation
>30
≥8760
MESSAGE [80]
>30
≤1
MiniCAM [81]
1982
Simulation
>30
≤1
MTSIM [82]
2009
Operation
≤2
≥8760
NARE [83]
NEMS [84]
1993
Simulation
>2 and ≤30
NEWAVE [85]
1992
Operation
>2 and ≤30 >1 and <8760
ORCED [86]
≤1
≤2
≥8760
OVER/UNDER [87]
1977
Investment
>2 and ≤30
≤1
PERSEUS [34]
1996
Investment
>30
>1 and <8760
PLEXOS [88]
≥8760
POLES [89]
1993
Simulation
>30
≤1
POWERS [90]
2001
Simulation
>30
>1 and <8760
Continued on Next Page. . .
19
Table 2.1 – Continued
Model
Date
Optimization goal
Time step
(years)
(per year)
>30
≤1
PRIMES [91]
1994
ProdRisk [92]
PROMETHEUS [93]
PROVIEW [94]
2007
Simulation
Time horizon
Simulation
>30
≥8760
≤1
≥8760
≥8760
RAMSES [95]
1988
Operation
>2 and ≤30
Ready Reckoner [96]
1997
Simulation
>2 and ≤30 >1 and <8760
REMARK [62]
2006
Operation
≤2
≥8760
RETScreen [97]
1996
Investment
>30
>1 and <8760
ROM [98]
2007
Operation
≤2
≥8760
RSTEM [99]
2005
Operation
SAFIRE [100]
1997
Investment
>30
≤1
SAGE [101]
2002
Investment
>2 and ≤30
≤1
SAMLAST [102]
1993
Operation
≤2
>1 and <8760
SESAM [103]
1987
Simulation
SimREN [104]
1999
Simulation
≤2
≥8760
SIVAEL [105]
1990
Simulation
≤2
≥8760
SMART [106]
>30
≥8760
SPSEK [107]
STREAM [108]
2004
Simulation
System optimizer [109]
2004
Investment
>2 and ≤30 >1 and <8760
>2 and ≤30 >1 and <8760
≤2
≤1
≥8760
>2 and ≤30 >1 and <8760
TRNSYS16 [110]
>30
≥8760
UniSyD3.0 [111]
2003
Simulation
>30
>1 and <8760
UPLAN [112]
1988
Operation
>2 and ≤30
≥8760
UREM [113]
2007
Investment
>30
≤1
VTT-EMM [114]
2007
Operation
>2 and ≤30 >1 and <8760
WASP [112]
1972
Investment
>2 and ≤30 >1 and <8760
WEM [115]
1993
Investment
>2 and ≤30
≤1
WILMAR Planning Tool [116] 2006
Operation
≤2
≥8760
Investment
>30
≤1
WITCH [117]
2005
20
A description of some of the most used energy models [32] is given below.
2.1.1
LEAP
The development of Long-range Energy Alternatives Planning System (LEAP)
was initiated in 1997 by SEI-Boston (Boston Center of the Stockholm Environment Institute) [19] and other five institutions: EDRC (South Africa),
ENDA (West Africa), ETC(Europe), FAO-RWEDP (Asia), IDEE (Latin
America). Since then it has been used for many different purposes by organizations from more than 150 countries.
LEAP is designed as an energy-environment modeling tool based on
long-range scenario analysis. These scenarios are alternatives of how the
energy system will evolve throughout the years and are developed using a
comprehensive accounting of how energy is acquired, converted and used
in a given region or economy under a range of alternative assumptions on
population, economic development, technology, price and so on. LEAP,
unlike macroeconomic models, does not attempt to estimate the impact of
energy policies on employment or GDP. It does not automatically generate
optimum or market-equilibrium scenarios, but it can be used to identify
least-cost scenarios.
Since LEAP is a tool intended for medium- to long-term planning, most
of its calculations occur on an annual time-step. However, some calculations
can be done with higher temporal resolution. The electric demand can be
modeled using three alternative methods for describing electric system load.
The first one is using a load duration curve (LDC) for the entire system,
which is exogenously determined. The second is using seasonal/time of day
load shape for each electric demand technology separately, with the overall
system load shape being calculated internally by summing across the various
load shapes. The third option is to use seasonal/time of day load shape for
the system as a whole.
Regarding the supply side, the technologies can be specified by the user
and the installed capacity can either be inserted exogenously, where values
are explicitly entered by the user reflecting existing capacity, or endogenously, where values are calculated internally by LEAP in order to maintain
a minimum planning reserve margin. Besides the capacity, the capacity fac-
21
tor or the maximum availability is also introduced by the user. The balance
between demand and supply is done using a set of dispatch rules.
Some examples of uses of LEAP are: in China, the Chinese Energy Research Institute (ERI) has used LEAP to explore how China could achieve
its development goals whilst also reducing its carbon intensity; in the U.S., a
prominent Non-Governmental Organization, the Natural Resources Defense
Council (NRDC) uses LEAP to analyze national fuel economy standards
and advocate for policies that encourage clean vehicles and fuels; in Rhode
Island, LEAP has been the main organizational tool for analyzing and monitoring the States award-winning GHG mitigation process, in which multiple
stakeholders are guiding the States efforts to meet its GHG emission reduction goals; in the Philippines, LEAP is used by the Department of Energy
to help develop its National Energy Plans.
2.1.2
NEMS
The National Energy Modeling System (NEMS) was developed by Energy
Information Administration of the Department of Energy of the USA [84].
It models the energy system of the United States and is mainly used to
produce the Annual Energy Outlook.
NEMS is an energy-economic model of US energy markets for the midterm
period through 2025. It projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomic
and financial factors, world energy markets, resource availability and costs,
behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics. As an annual model,
NEMS can also provide the impacts of transitions to new energy programs
and policies.
NEMS is divided into several modules: four supply modules (oil and gas,
natural gas transmission and distribution, coal, and renewable fuels); two
conversion modules (electricity and petroleum refineries); four end-use demand modules (residential, commercial, transportation, and industrial); one
module to simulate energy/economy interactions (macroeconomic activity);
one module to simulate world oil markets (international energy activity);
and one module that provides the mechanism to achieve a general market
22
equilibrium among all the other modules (integrating module).
Since energy resources and prices, the demand for specific energy services, and other characteristics of energy markets vary widely across the
United States, NEMS is a regional model. Depending on data availability
and other factors, different modules can have different regional disaggregation. For example, the demand modules (e.g., residential, commercial,
industrial and transportation) use the nine Census divisions, the Electricity
Market Module uses 15 supply regions based on the North American Electric Reliability Council (NERC) regions, the Oil and Gas Supply Module
uses 7 onshore and 3 offshore supply regions based on geologic breakdowns,
and the Petroleum Market Module uses 3 regions based on combinations of
the five Petroleum Administration for Defense Districts.
2.1.3
MARKAL
The development of the MARKet ALlocation (MARKAL) started at the
Brook-haven National Laboratory in the 70s [76]. Later on, the coordination
and further development of the model was transferred to ETSAP (Energy
Technology System Analysis Program). ETSAP has individual national
teams (in over 35 countries) with a common, comparable and combinable
methodology, mainly based on the MARKAL model, permitting in-depth
national and multi-country evaluations.
MARKAL is used to do long-term energy studies at the world, national,
regional and state/province level. It is used with time-periods that are
usually 5 or 10 years long, but allows some resolution as it recognizes three
seasons (Winter, Summer and Intermediate) and two diurnal divisions (Day
and Night).
The model is built using as inputs data regarding end-use energy services
demand for a base year, a set of existing and future technologies for energy
production and consumption, and trends for future energy demands. The
model then optimizes the investment and operation of existing and new
energy production facilities, by minimizing the total energy system cost.
MARKAL represents the energy system throughout all energy conversions, from different sources to end-use. Therefore, there are 5 different
classes in which energy can flow: resources, stocks, processes (from energy
23
carrier to energy carrier), generation (from energy carrier to electricity and
heat) and end-use devices. The demand of energy by the end-use devices is
calculated using efficiencies as MARKAL is driven by the demand of energy
services (instead of energy itself).
Several different models have been created using MARKAL as a basis, as
exemplified in Table 2.2 [23], using different modeling methods such as linear
programming (LP), nonlinear programming (NLP), stochastic programming
(SP) or mixed integer programming (MIP).
Table 2.2: Overview of the MARKAL family of models
Member/version Type of model Short description
MARKAL
LP
Standard model. Exogenous energy demand.
MARKAL-MACRO
NLP
Coupling to macro-economic mode, energy
demand endogenous.
MARKAL-MICRO
NLP
Coupling to micro-economic model, energy
demand endogenous, responsive to price
changes.
MARKAL-ED (MED) LP
As MARKAL-MICRO but with step-wise
linear representation of demand function.
MARKAL with
NLP
multiple regions
Linkage of multiple country specific
MARKAL-ED and MARKAL-MACRO,
including trade of emissions permit.
MARKAL with
LP
material flows
Besides energy flow (electricity, heat),
material flows and recycling of materials can
be modelled in the RES.
MARKAL with
SP
uncertainties
MARKAL-ETL
Stochastic Programming. Only with standard
model.
MIP
Endogenous technology learning based on
learning by doing curve. Specific cost
decreases as function of cumulative experience.
Some of the examples of the use of MARKAL [118] are the global models
maintained by International Energy Agency (ETP - Energy Technology Perspectives MARKAL Model) and by the Energy Information Administration
of the Department of Energy of the USA (SAGE - System for the Analysis
of Global Energy markets), and the national model of China maintained by
the Tsinghua University in Beijing (China Tsinghua).
24
2.1.4
TIMES - The Integrated Markal-Efom System
The Integrated MARKAL-EFOM System (TIMES), introduced in 1999, is
the latest development of the MARKAL framework maintained by the IEA’s
Energy Technology System Analysis Program [5, 119].
TIMES is a tool used to estimate energy dynamics in local, national
or multi-regional energy systems (Germany, SADC, World) [120, 121, 122]
over a long-term, multi-period time horizon. The model is built through a
detailed description of technologies and commodities that characterize the
energy system.
The main advantage that TIMES has regarding its predecessors MARKAL
and EFOM is its flexibility. With TIMES, it is possible to sub-divide the
year in several time periods with different lengths (defined by the user),
which is useful for more detailed studies that need to incorporate more dynamics in either supply or demand. However, given that the model uses an
optimization algorithm and has a very large technology base, the number of
time periods will have a large impact on the computational complexity of
the model. Other advantages are the possibility to have different levels of
disaggregation for different sectors and the option of making investment in
blocks.
TIMES can be described as a dynamic partial equilibrium optimization
model as it finds the minimum cost solution to provide the modeled energy
demands through several time periods by making decisions on equipment
investment and operation, primary energy supply and energy trades.
To build the model, the user must provide information regarding end-use
energy service demands for a reference case, estimates of existing stock of
energy related equipment in all sectors, the characteristics of available future
technologies and also present and future sources of primary energy supply
and their potentials. Using an optimization algorithm, the model determines
the best solution such that the quantities and prices in each time period are
such that the suppliers produce exactly the quantities demanded by the
consumers, which means that the total surplus is maximized. The dispatch
of electricity and any other commodities is done by an ascending order of
cost subject to any constraint the user defines (for example a limitation on
renewable energy shares). The model can also incorporate environmental
25
issues, making it suitable to analyze the impact of energy and environmental
policies.
TIMES has been applied in a large number of projects, with different
scopes. These range from the modeling of the energy choices in a typical
(non-electrified) rural village up in South Africa up to 2017 [123], to the
modeling of the World to study the impact of carbon taxes on the energy
sector of each region and the emissions associated with it [124, 125].
The African village application [123] presents a model of energy system
dynamics for a low-income rural community in South Africa. The authors
analyze future consumption of energy services in the village and the impacts
of gaining access to grid electricity. One of the main points of interest of this
work is the introduction of load curves in TIMES. The authors divide the
year in 4 seasons, and for each season they introduce six 4-hours time-slices.
This means that for each season they have different consumption according
to the hour of the day.
One example of a national model is the TIMES PT model [126, 127].
This TIMES model was developed within the EU FP7 research project
NEEDS and was calibrated and validated to model the Portuguese system
from 2000 to 2050. The yearly time steps are divided into 4 seasons, which
allows for some resolution on medium-term dynamics (such as hydro availability). TIMES PT has been used to study the evolution of the Portuguese
energy system under climate change scenarios and the European Emissions
Trading Schemes.
The World-TIMES model [124, 125] divides the world into 15 large regions, with the energy demand being characterized in 42 segments, and
determines energy dynamics for 100 years. The authors analyze the penetration level of nuclear energy under contrasted sets of assumptions on
technology parameters and exogenous constraints on nuclear energy development to reflect some negative social perceptions of nuclear power.
Recently, TIMES has been used in combination with other models such
as EMPS [128] and GEMINI-E3 [129] to develop more accurate estimations
of the evolution of energy systems. This is due to the increased flexibility
of TIMES in defining time periods and processes, which makes it easy to
adapt it and combine it with other models.
26
2.1.5
HOMER
HOMER started being developed in 1993 by the National Renewable Energy
Laboratory. It is a micro-power optimization model, indented to support
design of off-grid and grid-connected power systems [20, 130].
The main goal of HOMER is to help design power systems by answering
questions such as: What components does it make sense to include in the
system design? How many and what size of each component should you
use? HOMER uses simulation and sensitivity analysis algorithms that allow
to evaluate the economic and technical feasibility of a large number of technology options and to account for uncertainty in technology costs, energy
resource availability, and other variables.
To do this, Homer simulates the operation of the power system at a given
site by making energy balance calculations for each of the 8760 hours in a
year. For each hour, HOMER compares the electric and thermal load (space
heating, crop drying) to the energy that the system can supply in that hour
and computes the flows of energy between each component of the system.
HOMER performs these energy balance calculations for each system configuration that the user wants to test. It then determines whether a configuration is feasible, i.e., whether it can meet the electric demand under
the specified conditions, and estimates the cost of installing and operating
the system over the lifetime of the project. The system cost calculations
account for costs such as capital, replacement, operation and maintenance,
fuel, and interest.
Although being commonly referred to as an optimization tool, HOMER
is more like a simulation tool. After the user defines the set of configurations that he wants to test, HOMER simulates them all and then sorts them
by net present cost, allowing all the system design options to be compared.
Furthermore, HOMER allows the user to define sensitivity variables as inputs. For each sensitivity variable, HOMER analyzes the different system
configurations.
Some examples of applications of HOMER [131] are the Very Large-Scale
Deployment of Grid-Connected Solar Photovoltaics in the United States:
Challenges and Opportunities, and Advancing Clean Energy Use in Mexico.
27
2.1.6
DER-CAM
The Distributed Energy Resources Customer Adoption Model (DER-CAM)
was developed in 2000 at Lawrence Berkeley National Laboratory [45]. It
is an economic model of customer Distributed Energy Resources (DER)
adoption implemented in the General Algebraic Modeling System (GAMS)
optimization software and CPLEX solver.
The main goal of the model is to minimize the cost of supplying electric
and heat loads of a specific customer site by optimizing the installation and
operation of distributed generation, combined heat and power, and thermally activated cooling equipment. To do this, the model must receive as
inputs the costumer’s end-use hourly load profiles (for space heat, hot water, gas, cooling and electricity), the costumer’s default electricity tariff,
fuel prices and other relevant price data, the capital, operating and maintenance (O&M), the interest rate on customer investment and the basic
physical characteristics of alternative generating, heat recovery and cooling
technologies, including the thermal-electric ratio that determines how much
residual heat is available as a function of generator electric output.
The outputs of the model are the capacities of distributed generation
and combined heat and power technology or combination of technologies to
be installed (if any), when and how much of the capacity installed will be
running and the total cost of supplying the electric and heat loads.
DER-CAM can be used for many different applications. It can be used to
guide choices of equipment at specific sites, or provide general solutions for
example sites and propose good choices for sites with similar circumstances.
It can additionally provide the basis for the operations of installed on-site
generation. Also, it can be used to assess the market potential of technologies
by anticipating which kinds of customers might find various technologies
attractive.
Some examples of applications of DER-CAM [132] are the Assessment
of Distributed Energy Adoption in Commercial Buildings, The Value of
Distributed Generation under Different Tariff Structures and Distributed
Generation Investment by a Microgrid Under Uncertainty.
28
2.1.7
EnergyPLAN
EnergyPLAN was developed in 1999 at the Aalborg University, Denmark, to
simulate energy systems and provide information for the design of national
or regional energy planning strategies [21]. It optimizes the operation of
an energy system for a year using hourly resolution, including heat and
electricity supplies and the transport and industrial sectors, by following a
cost minimization strategy. It can be used for different analysis of energy
systems: technical analysis, market exchange analysis and feasibility studies.
The use of hourly resolution enables the model to consider the temporal dynamics of energy demand, the variability of RES, minimum technical
constraints on electricity production for grid stabilization and optimization
of the use of energy storage units. EnergyPLAN has been used to develop
several studies that include optimizing the combination of RES [133], management of surplus electricity [134], the effect of energy storage [135].
2.2
Evolution of energy models
Due to the increased awareness regarding the importance of security of supply, economically competitive electricity systems and the potential impacts
of GHG emissions to the environment, the number of tools developed has
been increasing significantly. Figure 2.2 shows the distribution of the modeling tools that were analyzed since 1970 until 2010, in periods of 5 years.
Figure 2.2: Development of new modeling tools since 1970
29
As shown by the figure, the number of energy modeling tools has increased significantly in the last 20 years. It should be noted that the number
of models developed in the period 2006-2010 might be significantly higher,
with their applications not having yet been published.
The tools mentioned above were divided based on their time horizon
and temporal resolution, as well as their mode of operation, as shown in
Figure 2.3. From the analysis it is possible to see that there are two main
types of models:
• Long-term energy systems planning tools with temporal resolution of
a year or less that are used to study the evolution of a system over a
time period of over 30 years (tools for simulation and optimization of
investment);
• Short-term tools of a year or less with hourly or higher temporal resolution that are capable of analyzing with detail the operation of a
system (tools for optimization of operation and optimization of both
operation and investment).
Figure 2.3: Overview of existing modeling tools based on their time horizon
and the number of time periods per year they consider
On the one hand, long-term modeling tools are characterized as being
capable of optimizing the investment in new generation capacity over a large
number of years or simulating pre-determined energy systems to see how well
they are able to address the yearly growth of electricity demand. This is due
to their low temporal resolution, which does not allow them to capture the
30
necessary dynamics for performing the optimization of operating the system.
On the other hand, short-term tools are generally capable of optimizing
the operation of a system, and sometimes are even able to optimize the
investment in new plants taking into account only one year. This reflects
their ability to model hourly dynamics of electricity supply and demand,
which makes them suitable for analyzing how a system should be operated.
The analysis of the models developed during different time periods shows
that not only has the number of models increased significantly in recent
times, but the scope of the models has also been changing, as shown in
Figure 2.4 for the time periods of: before 1980, from 1981 to 1990, from
1991 to 2000 and from 2001 to 2010.
Figure 2.4: Evolution of the number of energy models, temporal resolution
and time horizon from before 1980 until 2010
This evolution throughout the years is a response to the evolution of
electricity systems, which have been conditioned by several events such as
the oil crisis of the 1970’s, the explosion of the nuclear power plant in Chernobyl in 1986 and the signing of the Kyoto Protocol in 1997. These events
mark clear changes in the mix of electricity systems, as shown in Figure 2.5
for a combination of countries
1
1
[136].
The countries accounted for were Canada, United States, Japan, Australia, New
31
Figure 2.5: Evolution of the electricity generation mix from 1970 until 2007
for a combination of countries
The following sections establish a relation between the evolution of the
electricity systems and the development of new modeling methodologies.
2.2.1
Before 1980 - The energy crisis and the push towards
nuclear
The decade of 1970’s was characterized by the first worldwide energy crisis
due to the shortage of petroleum in the major industrial countries. The
rising oil prices, combined with geopolitical factors such as the Arab Oil
Embargo of OAPEC and the Iranian Revolution, raised for the first time
the concerns of energy dependency on oil and security of supply. To mitigate
the potential impacts of future increases of oil prices, other solutions started
to be analyzed with greater emphasis, such as the substitution of oil by
other thermal energy sources, the large scale investment in nuclear energy
and the impact of energy efficiency on reducing energy demand. These three
topics were the driving forces for the models that were developed during this
period.
Zealand, Austria, Belgium, Denmark, Finland, France, Germany, Greece, Hungary, Iceland Ireland, Italy, Luxembourg, Netherlands, Norway, Poland, Portugal, Spain, Sweden,
Switzerland, Turkey and United Kingdom. The limitation on the amount of countries
considered was due to data availability.
32
Models such as EMPS, IPM, LEAP and MARKAL were developed with
the goal of long-term planning of energy systems by comparing the traditional electricity production from oil with other thermal energy sources such
as coal and natural gas, as well as the already commonly used hydro power.
The main purpose of WASP, on the other hand, was analyzing the potential
and competitiveness of Nuclear energy and Hydro power, while the objective
behind the development of Fossil 2 and Energy 2020 (its successor) was to
understand the impact of energy efficiency on investment decisions in future
generation capacity.
Given the long-term impacts of the topics under study, the modeling
platforms that were developed during this period had the common characteristic of performing multi-year analysis. Most of these models have been
further developed in order to take into account other issues that appeared
in following decades, such as MARKAL/TIMES and LEAP. To this date,
these two modeling tools are still continuously updated and are widely used
by the modeling community, whether it is at the academic or the policy
making level.
Examples of the studies performed using these tools include the modeling of oil and gas supply in long-range forecasting using LEAP [137], a
comparison of the energy systems of the United Kingdom, the federal Republic of Germany and Belgium using MARKAL [138] and a survey of the
market for nuclear power in developing countries using WASP [139].
2.2.2
1981-1990 - The diversification of alternative energy
sources
Following the oil crisis in the 1970’s, the consumption of petroleum products
decreased considerably during the 1980’s which led to the decrease in oil
prices. This volatility resulted in an increased interest in the diversification
of energy sources, mainly nuclear energy.
However, in April 1986, an explosion at the nuclear power plant in Chernobyl, Ukraine, created a plume of highly radioactive smoke fallout which
spread over large parts of the Western Soviet Union and Europe. This disaster raised the issue of safety concerning nuclear power plants and public fear
forced several countries to refrain from building new nuclear plants. To this
33
day, this disaster still influences the public’s perception of nuclear energy.
The desire to reduce the dependency on oil and the disinvestment in nuclear
energy, which was considered to be a strong contender for the replacement
of conventional thermal generation, opened the opportunity for the use of
alternative energy sources such as wind and solar.
This led to the development of new modeling methodologies that had
the objective of comparing different energy sources for electricity generation
under competitive market structures while taking into account the impacts
on the environment, such as EnergyPRO, ENPEP, SPSEK and others. It is
particularly interesting to note that models such as RAMSES, SIVAEL and
SESAM were also used to design district heating systems.
Generally, the tools developed during the 1980’s were designed to look
into the long-term evolution of energy systems, following the trend of the
past decade. The only identified tool that had a different focus was SIVAEL,
which is able to model energy systems with hourly temporal resolution but
only for 1 year.
Some of the studies performed during these years include an evaluation
of the environmental impacts of the energy system using ENPEP [140] and
the planning of energy systems while taking into account air quality using
UPLAN [141].
2.2.3
1991-2000 - Renewable energy sources as a response to
environmental concerns
One of the major global discussion points in the 1990’s was the potential
environmental impacts of the high increase of energy demand. Following the
Vienna Convention for the Protection of the Ozone Layer, and its successor
Montreal Protocol on Substances That Deplete the Ozone Layer, an international agreement for the ”stabilization of greenhouse gas concentrations
in the atmosphere at a level that would prevent dangerous anthropogenic
interference with the climate system” was reached in Kyoto, Japan, in 1997.
In what concerns energy systems, this increased awareness of the impact
of human activities on the environment provided a decisive boost for the
large-scale investment in RES, as their emission coefficients of GHG is much
lower than that of conventional fuels.
34
In the electricity sector, two important key questions needed to be answered: were the existing renewable energy technologies economically viable (and if not, when would they be), and what were the economic and
technological impacts on the system of large-scale deployment of these technologies. While the first question regards the evolution of energy systems
through time, the second question concerns the day-to-day and hour-to-hour
operation of the system. To address these issues, two main trends for the
development of new energy models stood out, as shown in Figure 2.4:
• Long-term energy models capable of simulating and optimizing the
investment in new generation capacity with low temporal resolution,
such as ERIS, MERGE and NEMS;
• Short-term energy models that calculate the energy balances of supply
and demand and the electricity spot prices with hourly resolution (or
higher) for a one or two years time horizon, such as EnergyPLAN,
HOMER and DER-CAM.
Long-term analysis performed during this period include an analysis on
emission reduction strategies using MERGE [142], the modeling of energy
storage systems using batteries with NEMS [143], estimating the environmental and economic impacts of the large-scale introduction of PV in the
residential sector using NEMS [144] and a post-Kyoto analysis at a global
scale using ERIS [145].
Examples of analysis performed using short-term energy models include
hybrid systems for a village in Chile using HOMER [146], the value of wind
power using ELFIN [147], a simulation of the California power market using
ELFIN [148] and the impact of carbon taxes or allowances on the electricity
generation market in Ohio using ORCED [149].
2.2.4
2001-2010 - The emergence of sustainable energy systems
The 2000’s saw a significant shift on how the evolution of energy systems
was perceived. Energy efficiency, smart grids, dynamic demand and EVs
became the most common discussion topics to address a continuous increase
of energy demand in the electricity sector and others and a new steep increase
35
in energy costs, mainly due to geopolitical factors such as the instability in
the Middle East.
Energy efficiency has been identified as a cost-effective solution to reduce
greenhouse gases emissions and fossil fuel usage, as well as increase the
economic competitiveness of companies. Smart grids and dynamic demand
provided a way to better manage the necessary balancing between electricity
supply and demand, which can help increase the penetration of RES and
delay the investment in new generation capacity by lowering peak electricity
demand. EVs are considered an opportunity for reducing GHG emissions
in the road transportation sector, by using electricity produced from RES,
and for performing supply and demand balancing for grid stability. The
possibility of distributed energy storage using the batteries of the vehicles
is also something that is being studied in detail.
Due to the impacts of these emergent technologies on the operation of
the energy systems, the models developed during this decade are generally
characterized by having a higher temporal resolution, with most of them
being able to model every hour of the year or even more. Examples of these
types of models include AEOLIUS, CGEN, COMPOSE and PLEXOS.
Some of the studies that were performed using these tools include analyses of the gas and electricity network of GB using CGEN [38], of different
options for dealing with the intermittency of wind power in a large-scale
deployment on the West Danish energy system using COMPOSE [150], of
the benefits of energy storage systems and relocation options in renewable
energy systems using COMPOSE [151] and of the impact of wind power on
the operation costs in Germany and Scandinavia using Willmar [152].
2.3
Hybrid methodologies
While the more common approaches to energy systems modeling consist
on using just one modeling tool, frameworks that combine the capabilities
of two or more models have been developed in recent years to more accurately represent the complexity of the existing energy systems, as shown in
Figure 2.6.
The combination of different models in one framework allows taking advantage of the strengths of those models without having the large computa36
tional requirements that a single large modeling tool would require. These
frameworks are developed according to the desired goal and can combine
large macro-economic models with long-term energy systems planning models for economically robust analysis [153, 154], long-term energy systems
planning models with short-term energy balance models for optimizing the
evolution of energy systems [128, 34] or even combine short-term energy
balance models with grid operation models to guarantee the technical feasibility of a given system. Furthermore, more than two types of models
can be combined in complex frameworks to achieve more than one objective
[155, 156, 157].
Figure 2.6: Development of hybrid modeling frameworks and their general
purpose depending on the types of energy modeling approaches that are
combined
The next sections describe some examples of hybrid methodologies.
2.3.1
PERSEUS and AEOLIUS
With the purpose of analyzing the effects of having large-scale wind power
production in a system, the French-German Institute for Environmental
Research of the University of Karlsruhe in Germany, developed a framework
that couples the long- and short-term modeling approaches [34]. The models
37
considered in this framework are PERSEUS for the long-term modeling of
the system and AEOLIUS for the short-term modeling.
The PERSEUS model is a multi-period bottom-up model that is used
to determine the evolution of the electricity system of a region for a time
horizon of around 20 years, taking into account the potential and cost data
for different renewable energy technologies. Due to the large size of the
model in terms of regions and time frame, each year is only divided in 36
time slots, which results from considering 24 hours in 8 typical days. While
this temporal resolution is sufficient for systems with low shares of wind
energy (or other intermittent RES), a higher one is necessary to analyze
systems with high shares [34].
To account for the high variability of the wind resource, PERSEUS was
coupled with the AEOLIUS model, which is a power plant scheduling simulation model with high temporal resolution. AEOLIUS is able to simulate
the hourly operation of an electricity system for a period of 1 year. The
scheduling of power plants is done using a day-ahead approach, as occurs
with the current market regulations in Germany and other countries, with
the model also allowing the introduction of short-term, intra-day forecasts.
As such, the model is capable of covering in detail the provision of stand-by
capacities and control power from renewable and conventional capacities, as
well as intermediate storage [158].
The PERSEUS and AEOLIUS models are used in an iterative process in
which the investment decisions made by PERSEUS are used in AEOLIUS
as inputs, as shown in Figure 2.7 [158]. AEOLIUS is then used to test the
feasibility of the system and produces a number of restrictions (regarding
capacities, reserve requirements and efficiency losses) that are then included
in PERSEUS for a new iteration. With this iterative process it is possible
to design the expansion of an existing energy system for a long-term period
while ensuring an economically and ecologically optimized integration of
fluctuating renewable power generation.
This hybrid methodology has already been applied to analyze the largescale introduction of wind energy in the energy systems of Germany and
Spain [34, 158] and the interactions between RES and emissions trading in
the European electricity system [159].
38
Figure 2.7: Framework developed to combine the models PERSEUS and
AEOLIUS
2.3.2
IPAC
The Integrated Policy Assessment model for China (IPAC) was based on
work developed by the Energy Research Institute (ERI) since 1992 [160].
The main goal of IPAC is to develop scenarios for the evolution of the energy
mix of China, as well as for GHG emissions. IPAC is a complex framework
that consists of 12 different models (IPAC-SGM, IPAC-AIM/CGE, IPACTIMES, IPAC-Emission, IPAC/Tech, IPAC/SE, IPAC/EAlarm, IPAC/AIMLocal, IPAC/Gains-Asia, IPAC-health, AIM-air and IPAC-AIM/tech), as
shown in Figure 2.8 [155]. The development of IPAC had benefited from
several international collaboration using individual modeling tools such as
AIM (developed with the National Institute for Environment Studies), SGM
(developed with the Pacific Northwest National Laboratory) and TIMER
(developed with RIVM).
To be able to provide support to several different audiences, IPAC includes bottom-up and top-down models. Each model can be used separately
and studies can be done using combinations of only some of the models.
Some of the main models are:
• The IPAC-AIM/technology model is a bottom-up technology assess-
39
ment tool that analyzes detailed policy options on energy and environment. It is a single region model for China and has three modules:
energy service demand projection, energy efficiency estimation and
technology selection.
• The IPAC-SGM model is a computational general equilibrium topdown model that links economic activities with energy and environment. This model is an extension of the original SGM model and
includes data for China. It considers 9 production sectors and eleven
consuming sectors.
• The IPAC-Emission model is a global model, for analyzing scenarios
on energy and GHG emissions. It divides the World in 9 regions
and considers the major emission sources, such as energy activities,
industries, land use, agriculture, and forests.
• The IPAC-AIM/Local model is used to perform regional analysis covering various sectors and taking into account detailed technologies.
Figure 2.8: Framework of the Integrated Policy Assessment model for China
(IPAC)
40
IPAC has been used in a number of studies such as the design of a
low carbon scenario up to 2050 for China [155], analyzing scenarios and
developing policies for energy demand and emissions in buildings in China
[161], analyzing industry growth and energy use [162], assessing fiscal and
financing mechanisms for energy systems in China [162] and assessing clean
coal as an option for China [162].
2.3.3
TIMES and EMPS
The Institute for Energy Technology (IFE) in Norway has developed a modeling framework that uses TIMES to model the evolution of energy systems
and EMPS to model the electricity market [128].
A TIMES model with higher than usual temporal resolution was developed to simulate electricity production in Norway. The model was developed
using several previous MARKAL models (National and some regional models) and is capable of simulating end use options more accurately. This model
divides the country in 7 regions, with each year being divided into 260 periods (every week, 5 time periods per week), and demand being characterized
by 25 segments.
The EMPS model is a stochastic model that optimizes the operation
of hydro-thermal systems and has been widely used by stakeholders in the
Nordic market for the last two decades [57]. The model is typically used for
simulating the operation for a 3-5 years time horizon.
The main goal of the work done by IFE is the interaction between TIMES
and the EMPS model to model the power market of Norway while taking into
account the evolution of the energy system and the operational and market
dynamics of running the system. TIMES is used to provide the electricity
demand, which is then used as input in EMPS. EMPS then calculates the
electricity price for each time period. After the electricity price is computed
in EMPS, this is introduced in TIMES in order to obtain the new energy
system and demand. Both models are used in an iterative process to obtain
a more realistic solution, as shown in Figure 2.9 [163].
While this methodology is still under development and has only been
applied to single regions, the initial results showed a good agreement with the
expected load curves and price development during the year. Furthermore,
41
the methodology allowed improving the modeling of hydropower storage,
which is important in systems with high penetration of RES and water
storage systems. The authors expect to improve the methodology and apply
it to all seven regions. Open research questions include the linking of regions
to allow grid exchanges and the use of Mixed Integer Programming due to
the small size of the regions.
Figure 2.9: Modeling framework using EMPS and TIMES
2.3.4
GEMINI-E3 and TIAM
To produce better descriptions of the world energy system, a new modeling
framework combining the ETSAP-TIAM energy model and the GEMINIE3 general equilibrium model was developed [154]. This methodology enables using the detailed technological representation of ETSAP-TIAM for
the computation of energy flows and prices and the general equilibrium effects of GEMINI-E3 to include the main economy factors and their impacts
on energy demand.
GEMINI-E3 is a general equilibrium model that considers 24 regions in
the World. It is able to analyze 14 different sectors and account for CO2 and
GHG emissions, with a time period of 2001-2050. The model uses recursive
dynamics, the expectations of agents are based on adaptive rules and the
model does not presume perfect foresight.
The ETSAP-TIAM is a model developed by members from ETSAP using
TIMES [119]. TIAM (TIMES Integrated Assessment Model) is a global
model with 15 regions covering the entire planet and a time horizon until
42
2050. TIAM is a technology rich, dynamic inter-temporal partial equilibrium
model, which maximizes total surplus using linear programming. The model
is driven by end-use demands and considers price elasticity. It divides the
World in 15 regions and considers 9 energy commodity trades, as well as
CO2 and GHG emissions.
The algorithm used in the framework developed is the Gauss-Seidel
Method, where the method searches for a fixed point for the useful demand by using an iterative procedure, as shown in Figure 2.10 [154]. TIAM
is run to produce a fuel mix, a technological progress for each sector and
the energy prices. This is then used as input in GEMINI-E3 to calculate
the GDP and the industrial outputs, which are then used to build a new
demand function to be used in TIAM. This procedure continues until the
demand stabilizes.
Figure 2.10: Coupling framework using GEMINI-E3 and TIAM
2.3.5
The Altos Integrated Market Model Suite
To help Government agencies, corporations and national energy companies
deal with the complexities of energy systems, the Altos Management Partners Inc have developed a fully integrated suite of models [156]. This can
be used by companies engaged in asset, operation, trading, fuel acquisition
and other businesses as well as by agencies that engage in regulatory, policy
and other activities.
The Altos Integrated Market Model Suite is an agent-based, interconnected suite of models that considers all fuels and regions of North America.
43
For natural gas and oil, the World market is considered. An example of
the application of the Altos Integrated Market Model Suite is shown in Figure 2.11 [156].
In total, the Altos Integrated Market Model Suite consists of 6 different
models:
• The Altos North American Regional Gas (NARG) Model is a simulation model that considers the regional interactions of supply, transportation and demand of natural gas in North America.
• The Altos World Gas Trade Model (WGTM) is a global model for
analyzing the supply, transportation, shipping, liquefaction, regasification, infrastructure and demand of natural gas.
• The Altos North American Regional Electric (NARE) Model is used
to analyze fuel substition, generation investment, operation and retirement, market price competition, net energy for load and ancillary
services, outbound/inbound transmissions and transmission capacity
expansion in the North America electric market.
• The Altos North American Regional Coal (NARC) Model simulates
the mining, supply, transformation and consumption of coal in North
America. It determines market clearing prices, coal reserve additions,
flowing volumes, inventory additions and withdrawals and new transportation entry and exit.
• The Altos ”Cap and Trade” Tradable Emissions Model analyzes the
effects of cap and trade environmental markets on energy markets, and
vice versa. The model is applied to the North American market and
considers a 30 years time horizon.
• The Altos World Oil Model (WOM) is fundamental economic supplydemand model of World oil markets.
The interface developed by Altos integrates all models in a single analysis
model. The models are run together and interact with each other to obtain
more accurate analysis of the North America energy systems.
44
Figure 2.11: Application of the Altos Integrated Market Model Suite
2.4
Key findings
The key findings after analyzing the current state-of-the-art of energy systems modeling are:
• The development of new modeling methodologies, and improvement
of previously existing ones, is pushed by the need to provide answers
to the challenges energy systems face.
• Currently, there are two main types of modeling tools: long-term energy systems planning tool with temporal resolution of a year or less
that are used to study the evolution of a system over a time period
of over 30 years (tools for simulation and optimization of investment)
and short-term tools of a year or less with hourly or higher temporal
resolution that are capable of analyzing with detail the operation of a
system (tools for optimization of operation and optimization of both
operation and investment).
• There is a lack of modeling tools capable of addressing the expected
large-scale deployment of RES, the development of new technologies
45
such as EVs and demand response and the wide spread introduction
of DSM policies, which require energy models to be able to analyze
the long-term evolution of energy systems while taking into account
the short-term dynamics of electricity supply and demand.
• A new set of energy modeling methodologies that link two or more
specific energy modeling tools and are capable of producing more detailed representations of energy systems have started to be developed
in the last years. However, the application of these hybrid modeling
methodologies to develop systems with high penetrations of RES while
considering technologies such as energy storage systems still need to
be further researched.
46
Chapter 3
Case studies
For the work developed in this thesis, 3 different case studies were used:
2 islands of the Azores archipelago (São Miguel and Flores) and mainland
Portugal.
3.1
The Azores islands
The energy systems of the nine islands of Azores are naturally isolated autonomous energy systems with limited prospects of inter-connection due to
the depth of the sea and the large distance between each island. At the
present moment, most of the energy systems are largely dependent on imported fuels, diesel and heavy oil, for electricity production and transports,
exposing the region to the economic burden of fluctuating global oil prices
and to weather enforced limitations on the normal operation of the logistic chains. In 2007 in the Azores, fossil fuels accounted for around 88%
of all primary energy consumption, with the electricity production (around
38%) and transportation (around 34%) sectors being the main fossil fuels
consumers [164].
The Government of Azores has an ambitious energy strategy that aims
to achieve 50% of renewable electricity production in 2013 and 75% in 2018.
This is in accordance with Electricity of the Azores (EDA) strategy to increase renewable penetration, which includes several investments in geothermal power plants in the major islands, like São Miguel, and several wind
47
farms and hydro stations in the smaller islands, such as Flores. Another policy goal of the Government is to increase the renewable energy penetration
in the primary energy up to 40%, which is an extremely difficult target to
achieve as all sectors of energy consumption must be addressed (electricity,
transportation, domestic water heating and cooking, among others).
3.1.1
São Miguel
Electricity supply in São Miguel consists mainly of geothermal energy, hydro power and thermal engines using heavy oil, with renewable electricity
generation experiencing a large increase in the period 1994 to 2009, as illustrated in Figure 3.1 [164], as a result of a major policy of the regional
government to increase the use of endogenous energy resources. The main
increases in renewable electricity are due to investments in new geothermal
generation capacity. Concerning the further investment in RES, the main
sources available are geothermal and wind energy with a 9 MW wind park
currently under construction.
Figure 3.1: Electricity production in São Miguel, by generation type, from
1994 until 2009
Currently, the system is composed of 8 fuel oil thermal generation units
totaling 98 MW, 2 geothermal units with a total installed capacity of approximately 27 MW and 7 run-of-river hydro systems with a total capacity
of 5 MW [165], as shown in Table 3.1.
48
Table 3.1: Installed generation capacity in São Miguel
Plant
Energy source Year Capacity
(MW)
Caldeirão
Fuel oil
1987 98.064
Tambores
Hydro
1909 0.094
Fábrica Nova
Hydro
1927 0.608
Canrio
Hydro
1990 0.400
Foz da Ribeira
Hydro
1990 0.800
Ribeira da Praia
Hydro
1991 0.800
Salto do Cabrito
Hydro
1997 0.670
Túneis
Hydro
2000 1.658
Ribeira Grande Geothermal 1994 14.800
Pico Vermelho Geothermal 2007 13.000
Electricity consumption has grown considerably between 1994 and 2009,
and the main consumers are the Domestic and the Commerce and Services
sectors which represent around 70% of total electricity consumed in the
island. Figure 3.2 shows the evolution of electricity consumption in the 5
main sectors: Domestic, Commerce and Services, Public Services, Industry
and Public Lighting. The evolution presented in Figure 3.2 shows that after
a high growth period, the last 3 years have shown a much smaller increase
of electricity consumption.
Figure 3.2: Electricity consumption in São Miguel, by sector, from 1994
until 2009
49
3.1.2
Flores
Flores is one of the most isolated islands of the Azores, with an area of
around 141 km2 , and around 4117 inhabitants [166]. It is the island with
the largest penetration of renewable energy in electricity production in the
archipelago, as around 54% of all electricity produced in 2009 was based
in either hydro or wind power. In fact, renewable energies have provided a
large part of the electricity needs of the island for many years now, as shown
in Figure 3.3 [164].
Figure 3.3: Electricity production in Flores, by generation type, from 1994
until 2009
Currently, the electricity system in Flores is a combination of wind turbines, hydropower and diesel engines, aided by a flywheel energy storage
system [167]. The system is composed of 4 hydropower generators, 2 wind
turbines and 4 diesel engines. Table 3.2 shows the year of installation and
the capacity of each generator [168].
In order to be able to use the total installed power of wind and hydro, a
flywheel energy storage system was installed in the island. This system helps
maintain frequency and voltage stability, which were the main problems for
the wind-hydro-diesel system, and enables the island to have periods of 100%
renewable based electricity, if the conditions are favorable.
Being a small, services driven economy, the main electricity consumption sectors in Flores are Domestic and Commerce and services. Together
50
Table 3.2: Installed generation capacity in Flores
Plant
Energy source Year Capacity
(KW)
Além-Fazenda
Hydro
1966 1400
Além-Fazenda
Diesel
1991 2310
Boca da Vereda
Wind
2002 600
they are responsible for more than 75% of all electricity consumed in the
island, with the Domestic sector representing around 40%. However, the
relative weight of each sector has been changing throughout the years, as
electricity consumption has been growing at a higher rate in the Commerce
and services sector than in the Domestic sector. Figure 3.4 [164] shows the
evolution of electricity consumption in Flores, divided in 5 different sectors:
Domestic, Commerce and services (private), Public services, Industries and
Public lighting.
Figure 3.4: Electricity consumption in Flores, by sector, from 1994 until
2009
3.2
Mainland Portugal
In the last decade, Portugal has followed an ambitious energy policy with the
goals of reducing the dependence on imports from fossil fuels and the sub51
stitution of oil for cleaner energy sources such as renewable energy sources
and natural gas. To this end, the investment in renewable energy sources
have been promoted through feed-in tariffs while several agreements have
been established in order to decrease the import costs of natural gas as well
as to diversify the origin of the imports.
In this work, the Portuguese electricity system was considered as isolated
and is analyzed considering a diverse portfolio for investment in generation
capacity from renewable and non-renewable energy sources. The possible
investment in nuclear energy was not considered as the focus of the work
was to develop modeling methodologies for systems with high penetrations
of RES.
Current system
The electricity generation system in Portugal has changed significantly in the
last five years with large increases of the installed capacities of natural gas
and wind energy and the introduction of solar and wave energy, as shown
in Table 3.3 [169]. The term status producers reflects the special status
that some energy sources, such as cogeneration and biomass (thermal status
producers), smaller hydro, wind, solar and wave, benefit from and that
enables them to not be a part of the electricity market and have priority in
dispatch. The large hydro system also include a 1036 MW storage capacity
which is used to maximize the electricity generation from renewable energy
sources and take advantage of the low market prices of electricity during the
night period.
Coal
Oil
Natural Gas
Large Hydro
Thermal Status Producers
Hydro Status Producers
Wind Status Producers
Solar Status Producers
Wave Status Producers
2005
1776
1909
2166
4582
1166
333
891
0
0
2006
1776
1909
2166
4582
1295
365
1515
0
0
2007
1776
1877
2166
4578
1365
374
2048
13
0
2008
1776
1877
2166
4578
1424
385
2662
53
2
2009
1756
1878
2992
4578
1610
395
3357
95
2
2010
1756
1822
3829
4578
1698
410
3705
122
2
Table 3.3: Generation capacity (MW) in the Portuguese electricity system
52
Expected system
Following the European Directive 2009/28/EC of the European Parliament
and of the Council, Portugal designed a National Action Plan for Renewable
Energies [170] that establishes the targets for the use of renewable energy
sources in the country for the time period of 2011-2020. In what concerns
the electricity system, a summary of the targets for investment in generation
capacity from renewable energy sources is shown in Table 3.4 [170]. The
main investments will be made in large hydro systems and onshore wind
energy, with the investment in other energy sources being less significant.
The storage capacity is expected to increase from the current 1036 MW to
4302 MW in this period.
2011 2012 2013 2014 2015 2016 2017 2018 2019
Large Hydro 4524 5231 5231 5476 6467 7489 8394 8712 8798
Small Hydro
457 503 503 550 550 600 650 650 700
Wind onshore 4928 5600 5600 5600 6100 6100 6100 6600 6800
Wind offshore
0
0
0
0
25
25
25
25
25
Solar
258 340 465 590 720 860 1005 1160 1325
Wave
5
5
10
35
60
75 100 125 175
2020
8798
750
6800
75
1500
250
Table 3.4: Total installed capacity (MW) of renewable energy sources proposed by the Portuguese Government
While offshore wind, solar and wave energy are still considered expensive
technologies, the decision to invest in these systems reflects the importance
of having a diversified energy system and takes into account potential benefits of the development of local businesses and competences.
53
54
Chapter 4
High-temporal resolution
modeling of the dynamics of
energy systems
The capability that different modeling tools have of incorporating electricity
supply and demand dynamics depends largely on the temporal resolution of
the model and has a clear impact on the model results. In this section, a
TIMES model with 288 time periods per year that is able to consider some
of the dynamics of RES and electricity demand and making investment
decisions for a multi-year period is presented. The model is applied to São
Miguel Island, Azores, and different temporal resolutions are compared.
The proposed methodology is also compared with 2 other modeling
methodologies with different temporal resolution, LEAP with 9 time periods per year and EnergyPLAN which considers all hours of the year, using
Flores Island, Azores, as case study.
4.1
Modeling hourly electricity dynamics for policy making in long-term scenarios
This work is presented in detail in Paper I - ”Modeling hourly electricity
dynamics for policy making in long-term scenarios” by André Pina, Carlos
Silva and Paulo Ferrão published in the journal Energy Policy [9].
55
4.1.1
Aim of the study
The paper presents an extension of the TIMES energy planning tool for
investment decisions in electricity production that considers seasonal, daily
and hourly supply and demand dynamics by dividing each year in 4 seasons,
3 days per season (weekday, Saturday and Sunday) and 24 hours per day
(totaling 288 periods per year). The inclusion of these dynamics enables
the model to produce more accurate results in what concerns the impact
of introducing energy efficiency policies and the increased use of renewable
energies.
A major feature of TIMES consists on its flexibility in what concerns
the time resolution, which can be defined by the user with as much or as
little as desired. A typical TIMES model for analyzing the evolution of the
electricity system of a region can be schematically represented as shown in
Figure 4.1, with the inputs and outputs that are different due to the increase
in time resolution being represented in dashed lines. This increase in time
resolution needs electricity consumption profiles for each sector and profiles
for the availability of renewable energies to be assumed for the different days
and seasons.
Figure 4.1: Main inputs and outputs of a typical TIMES model for electricity
systems
The model was validated in São Miguel (Azores, Portugal) for the years
2006-2009, which has already a considerable penetration of RES. In 2007,
RES in São Miguel accounted for 18.8% of the primary energy supply and
56
46.7% of the electricity production, where geothermal was the main renewable energy source, representing 41.4% of the electricity produced, while
hydro represented 5.3%. In the same year, 40% of primary energy was used
for electricity production and 36% for transportation [164]. To increase the
share of RES in the electricity sector, Electricidade dos Açores (EDA) is
also planning to install wind turbines in the island, totaling an installed
capacity of 9 MW by 2014. For the model used in this work, the range of
capacity factors for the RES considered are presented in Table 4.1, having
been defined based on historical information provided by EDA [171].
Hourly
Min Max
Geothermal
Hydro
Wind
0% 100%
Resource
Seasonal
Min Max
36% 62%
14% 39%
Yearly
Min Max
55% 73%
40% 53%
25% 28%
Table 4.1: Range of capacity factors in each temporal level, for each resource
The benefits of using a model with increased time resolution were analyzed for two different situations. First, TIMES models using the same data
but with different time resolutions of 1, 2, 3, 4, 6 and 8 hours per day (which
corresponds to having 24, 12, 8, 6, 4 and 3 time periods of equal length per
day) were run for the same time period (2005 - 2025) and analyzed in terms
of the production of electricity from wind energy in order to quantify the
impact of using models with lower temporal resolution. Second, the more
accurate modeling of energy efficiency policies is studied in terms of the impact it can have on decisions concerning the investment in new generation
capacity.
4.1.2
Main results
Comparison of different temporal resolutions
The amount of electricity produced from wind energy from the models with
different time resolution was analyzed for 3 different time periods (20102015, 2010-2020 and 2010-2025). Assuming that the values obtained by the
1 hour (24 periods) time resolution model are closer to reality due to the
higher level of detail, the values obtained from the different time resolution
57
models are normalized to the values obtained from the 1 hour time resolution
model.
The results, represented in Figure 4.2, demonstrate that the use of lower
time resolutions can lead to overestimations of the renewable electricity absorbed by the system. This effect is particularly significant for the first
years being analyzed (2010-2015), when the installed capacity is lower and
the amount of electricity produced is also smaller, with an overestimation
of around 3 times the amount of electricity from wind energy that can be
absorbed by the models with low time resolution. This overestimation was
due to the models with lower resolution installing more wind generation capacity in the first years than the 1 hour resolution model, which might not
be economically viable as the system might not be able to absorb as much
electricity as expected and therefore generate lower revenues.
Figure 4.2: Comparison of electricity production from wind energy from
models with different time resolution, for the time periods of 2010-2015,
2010-2020 and 2010-2025
Importance of correctly modeling electricity demand
The elimination of standby power can have a significant impact on the planning of electricity systems as it will lower the consumption during the night
period, which is typically characterized by low consumption. The impact of
correctly modeling the elimination of standby power on the investment in
new generation capacity from RES is analyzed for the São Miguel case study
for the time period of 2005 - 2025. Three different situations were tested:
58
• No efficiency scenario in which no standby power is eliminated;
• Low detail scenario in which the consumption from the residential sector was reduced by 5.2% every hour, which corresponds to the standby
power [172];
• High detail scenario in which the overall reduction is the same but
the reductions are considered to take place mostly in the night period
(1:00 to 7:00) and during the working period (7:00 to 17:00), with no
significant gains occurring during peak hours.
The amount of wind energy generation capacity that is economically
feasible in each scenario is significantly different, as shown in Figure 4.3,
with the amount of wind capacity suggested by the model to be installed
in the High detail scenario being much smaller than the one in the Low
detail scenario for most of the time period considered (around 50% in 2020).
Furthermore, the installation of 5 MW of wind energy occurs in 2015 for the
No efficiency scenario, in 2016 for the Low detail scenario and only in 2018 for
the High detail scenario, while, by 2025, there is not much difference between
the installed capacity in all the scenarios. This is due to two effects: increase
in overall electricity demand and increase in fossil fuel prices through the
years, which benefits the installation of wind energy even if they cannot be
used at the maximum possible capacity factor.
Figure 4.3: Wind energy generation capacity for the different scenarios
59
4.2
The relevance of the energy resource dynamics
in the mid/long-term energy planning models
This work is presented in detail in Paper II - ”The relevance of the energy
resource dynamics in the mid/long-term energy planning models” by Gustavo Haydt, Vı́tor Leal, André Pina and Carlos A. Silva published in the
journal Renewable Energy [10].
4.2.1
Aim of the study
The paper presents an analysis of different modeling methodologies for balancing electricity supply sources and demand in systems with high penetration of intermittent renewable energy sources, such as wind and run-of-river
hydro and explores the reasons and circumstances where common balance
approaches used by mid- and long-term energy models show significant differences in the amount of electricity from RES that is dispatched.
Renewable energy sources vary in different timescales, that range from
seconds to decades and with different relevance depending on the resource,
as shown in Table 4.2.
Resource/ Timescale Hour Day Seasonal Year Decades
Hydro (Dam)
O
+
+
+
+
Hydro (Run-of-river) +
+
+
+
+
Solar
O
+
+
+
O
Wind
+
+
+
+
O
Geothermal
O
Biomass
O
O
O
Tidal
+
+
+
O
O
Wave
+
+
+
O
O
Table 4.2: Qualitative characterization of renewable resource dynamics: +
(important), O (mild), - (not important)
Due to the time-dynamics of demand and supply, one cannot simply
assume that the production from RES can keep growing indefinitely with
all the electricity that they could potentially produce being used by the
energy system, as there will be some periods in which there is excess of
available electricity and others in which there is not enough. This is particularly important as the wind, solar, run-of-river hydro, waves and other
non-controllable RES become more representative in power generation.
60
In this work, three different balance methods are compared to analyze
how different temporal resolutions in each year influence the amount of RES
that the model assumes that can be used. The three methods are:
• Integral, which consists in representing the electricity demand in a
load duration curve (LDC) and matching the needs represented by
the curve with the available supply options, following a dispatch rule,
with each year divided in a small number of time-slices (varying, in
the majority of the cases, from 5 to 10 time-slices);
• Semi-dynamic, which tries to capture some of the electricity supply
and demand dynamics by using only selected typical days to represent
the power variations along the year instead of using all days in a year
(as used for the dynamic method);
• Dynamic, which consists in representing the electricity demand in an
hourly load curve for a period, which can range from a day to a few
years, where all the hours are represented in the same sequence as they
happen.
The integral, semi-dynamic and dynamic balancing methods are modeled
here in LEAP, TIMES and EnergyPLAN respectively. LEAP was used
with 9 time-slices for the construction of the LDC. In TIMES, each year
was divided into 4 seasons, 3 days per season (Wednesday, Saturday and
Sunday), and 24h in each day (total of 288 time periods per year). In
EnergyPLAN, every hour of the year was modeled.
To test the results of different balancing methods with different renewable energy levels, four alternative scenarios for the installed capacity of
RES were considered for Flores Island, Azores, for the year 2020, as seen in
Table 4.3.
Resource/Technology Existing Scenario A Scenario B Scenario C Scenario D
Installed
2008
2020
2020
2020
2020
Diesel
2700
2700
2700
2700
2700
Hydro
1480
1480
2080
2680
2680
Wind
600
1100
1600
2100
2800
Total
4780
5280
6380
7480
8180
Table 4.3: Generation capacity (MW) considered in each scenario
61
4.2.2
Main results
The outputs of each model consisted on the amount of electricity produced
from each source (hydro, wind and thermal) in each of the scenarios that
were considered. The penetration of RES achieved in each scenario by each
method is shown in Table 4.4, as well as indicators comparing the generation
capacity from RES and peak and valley electricity demands.
Scenario Renewables installed/ Renewables installed/
Demand peak
Demand valley
A
0.79
1.86
B
1.12
2.66
C
1.46
3.45
D
1.67
3.96
Share of renewable electricity
Integral Semi-dynamic Dynamic
33.9%
34.0%
33.9%
48.6%
48.6%
48.0%
63.0%
62.6%
58.9%
72.9%
71.1%
63.9%
Table 4.4: RES results using the integral, semi-dynamic and dynamic balance methods for all scenarios
Comparing the results from the integral balance method in LEAP, the
semi-dynamic balance method in TIMES and the dynamic balance method
in EnergyPLAN, it is possible to observe that in situations where the installed capacity from RES is similar to the demand peak, or in other perspective is below to three times the demand valley (scenarios A and B), there
is no relevant difference between the results of the different balance methods. However, as the generation capacity from RES increases, the share
of renewable electricity estimated by the semi-dynamic and the dynamic
methods becomes significantly lower than the one estimated by the integral method with the difference between the semi-dynamic and the integral
methods being of 1.8 decimal points and between the dynamic and integral
methods of 9 decimal points. This difference can have a significant impact
on achieving (or not) a preset target.
The lower penetrations obtained by the higher temporal resolution models are because they are more prepared to identify the mismatches that
sometimes exist between the amount of electricity from RES that can be
generated and the amount of electricity that the system needs. An example
of this mismatch using the dynamic balance method in scenario D is shown
in Figure 4.4 where there are a large number of hours in which the electricity
that can be generated from RES far exceeds the demand.
62
Figure 4.4: Hourly load curve for 13 days for January 2020 for Flores, supply
scenario D, dynamic balance method in EnergyPLAN
4.3
Key findings
The key findings from the high-temporal resolution modeling of the dynamics of energy systems are:
• The increase in time resolution of the models built in TIMES is crucial to achieve better estimates for the potential of RES in electricity
systems and to allow designing more robust investment plans for the
evolution of electricity systems.
• While the proposed temporal resolution for TIMES (4 seasons, 3 days
per season and 24 hours per day for each year) can help improve the
analysis of the investment in RES, its results still have a significant
difference when compared to the modeling with hourly resolution.
• The use of low temporal resolution models can lead to the overestimation of the amount of electricity from RES that a system can use
and the overinvestment in new generation capacity, which results in
a reduced cost-effectiveness of the system and an underestimation of
the CO2 emissions to be expected from the system.
• The modeling of electricity demand dynamics must be taken into account as they can have a significant impact on the optimization of the
new generation capacity from RES, depending on the penetration rate
of RES.
63
64
Chapter 5
Modeling the dynamics of
electricity demand
The introduction of new technologies and policies in an energy system can
have a clear impact on the dynamics of electricity demand by leading to
the decrease or increase of consumption. Without proper planning, these
changes in the electricity demand sector can lower the economic competitiveness of the electricity supply structure by making already existing capacities
unnecessary or by requiring the investment in additional capacity that will
be used only in short periods of time.
In this chapter, the TIMES model previously presented is used to analyze
the impact of different DSM strategies on the penetration of RES in the
electricity sector. A study concerning EVs is also performed in order to
identify the potential benefits of adopting an intelligent time of charging
strategy.
5.1
The impact of demand side management
strategies in the penetration of renewable electricity
This work is presented in detail in Paper III - ”The impact of demand side
management strategies in the penetration of renewable electricity” by André
Pina, Carlos Silva and Paulo Ferrão published in the journal Energy [11].
65
5.1.1
Aim of the study
The paper presents an analysis on the impact of DSM strategies in the evolution of the electricity mix of Flores Island, Azores, which is characterized
by high shares of RES in the electricity sector. The introduction of DSM
strategies in the island needs to be well planned in order to enable a better management of the existing resources by reducing investment needs and
increasing the use of RES.
The TIMES methodology presented previously is used to model the electricity system of Flores and optimize the investment and operation of wind
and hydro plants until 2020 based on scenarios for demand growth (General
efficiency scenarios), promotion of behavioral changes to eliminate standby
power (No standby power) and deployment of demand response technologies in the domestic sector (Dynamic demand). The different DSM strategies
were considered in the model as follows:
• ”General efficiency” is the option to invest in energy efficiency programs across all sectors, starting in 2011, which have the effect of
reducing demand growth per year to only 50% of the one estimated
with the linear regressions for each sector.
• ”No standby power” is the option to gradually eliminate the standby
power consumption of the Domestic sector by educating the population
or the introduction of new technologies that do not have this. Standby
power is estimated as being responsible for around 5% of the electricity
consumed by the sector in Portugal [172]. If this option is chosen,
the model gradually eliminates standby power using a linear decrease
in each year, starting in 2011 and disappearing completely in 2015.
This scenario is primarily intended to test the evolution of the energy
infrastructure for lower demand in low consumption hours.
• Finally, ”Dynamic demand” is the option to gradually enable the washing, dryer and dish washing machines to be operated remotely by the
grid operator when it is more convenient. If chosen, the automation of
these machines begins in 2013, with all of them having this capability
in 2018.
66
Based on whether these options are considered or not, 8 different scenarios were designed, as shown in Figure 5.1.
Figure 5.1: Scenarios description according to the options for DSM
5.1.2
Main results
The results showed that the scenarios with higher demand growth (scenarios
1 to 4) installed all the potential generation capacity from RES and achieved
higher shares of electricity from those sources than the other scenarios. Furthermore, the scenarios that considered the introduction of dynamic demand
capabilities showed a delay of 3 to 4 years in the installation of new generation capacity. This was due to the dynamic demand allowing a better
management of the generation capacity that was installed, as shown in Figure 5.2 for the hours of the average day of 2020 in which the scenarios 2,
4, 6 and 8 have higher utilization rates (electricity produced divided by the
potential to produce it) during the night and early morning period.
(a)
(b)
Figure 5.2: Utilization rates of renewable electricity generators for each hour
of the average day in 2020 for scenarios a) without standby power elimination
b) with standby power elimination
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The higher utilization rates will improve the cost-effectiveness of the installed generation capacity by increasing the potential revenues from selling
electricity. Due to having lower installed generation capacities, the scenarios with ”General Efficiency” are able to achieve higher utilization rates
throughout the day.
In the model that was developed, the optimal load pattern for the domestic machines is calculated in order to obtain the best economic performance
of the electricity generation system. Figure 5.3 shows the fraction of loads
that are shifted in each of the scenarios that have this option enabled, as
well as the maximum fraction of load that can be shifted according to the assumed technology deployment (starting in 2013 and growing gradually until
all machines have this possibility in 2018).
Figure 5.3: Fraction of possible shiftable loads that is shifted in each year
for the different scenarios
The results show that all scenarios have a similar behavior, in which
there is first an increase of the fraction of loads that are shifted, followed by
a stabilization of this value. The use of this technology seems to stabilize
at around 40% for scenarios 4, 6 and 8, and at around 35% for scenario 2.
This means that even if all machines have this technology, the loads will be
shifted around 40% of the time on a per year basis or that, if the penetration
of this technology is lower, the machines that do have it will have their loads
shifted a larger fraction of the time.
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5.2
Modeling the introduction of electric vehicles
in an island
The introduction of EVs has been identified as one of the key issues for
the next years due to the potential gains that can be achieved, such as the
reduction of the consumption of fossil fuels in the transportation sector, the
reduction of imports of fossil fuels and the maximization of the utilization of
RES. On the long-run, the possibility of having EVs exchanging electricity
with the grid from the vehicle to the grid (V2G) can provide significant
benefits for grid stabilization and by serving as distributed storage systems.
However, the impact on the life cycle of the bateries would also have to be
accounted for.
Electric vehicles can also present some challenges on the electricity system as the expansion of the systems needs to be addressed and, if not properly managed, the vehicles could be charged using electricity produced from
fossil fuels if no other alternative is available. In this case, EVs would only be
shifting the consumption of fossil fuels from the roads to the power plants.
Therefore, it is important to understand how the benefits of EVs can be
maximized.
This work analyses the introduction of EVs in the island of Flores,
Azores, and how different penetrations of EVs influence the investment in
new RES generation capacity and the share of RES in the electricity produced for the EVs. The possibility of optimizing the time of charging of
EVs in order to increase the use of RES is also studied. For the modeling
performed it was considered that the EVs system was not able to be used
for energy storage.
5.2.1
Modeling methodology
To improve the modeling of electricity supply and demand dynamics, as well
as the dynamics of the charging of EVs, a two step modeling approach was
used, as shown in Figure 5.4. The framework used in this work consists on
using first a medium-term energy model (energy systems planning model)
to optimize the investment in new generation capacity from RES by taking
into account the evolution of electricity demand and fuel prices through the
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years. The output from this model is the installed capacity in each year,
which is then used as input for a short-term. The short-term model (energy
systems balancing model) is a model with one year time horizon and hourly
temporal resolution that is able to optimize the balancing between electricity
production from the different energy sources and electricity demand.
The medium-term model used in this work is the same as the one used
in Section 5.1 and PAPER III. The model is capable of analyzing the Flores
electricity system in the time horizon of 2010-2030 and optimizes the investment in new hydro generation capacity taking into account the projected
growth for the island and the penetration of EVs. The possibilities for the
installation of new hydro plants were one plant of 1600 kW and another of
1040 kW that can be built from 2011 onward.
The short-term model is a MATLAB model that had been developed for
another work, Section 6.1 and PAPER IV, which was expanded in order to
include the additional electricity demand from EVs and the capability to
optimize the time of charging of the vehicles. The main constraint that was
added to model concerns the charging necessities of the EVs, which were
assumed to be the same every day of the year. As such, a new constraint
that requires that the same amount of electricity is produced in each day
for EVs was introduced in the model.
Figure 5.4: Framework considered for modeling the introduction of EVs
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5.2.2
Scenarios
To study the potential benefits of the introduction of EVs, four different
scenarios were designed taking into account different penetration rates of
EVs and different time of charging strategies, as shown in Table 5.1. These
are two decisive factors on how the introduction of EVs will impact the
electricity production system of a region and which energy sources can be
used to charge the vehicles.
Scenario
Scenario
Scenario
Scenario
1
2
3
4
Penetration rate of EVs Time of charging
Low
Fixed
Low
Flexible
High
Fixed
High
Flexible
Table 5.1: Scenarios for the penetration of EVs and time of charging strategies
Regarding the penetration rates of EVs, two different situations were
considered, as shown in Figure 5.5. The Low scenario considers the introduction of EVs from 2011 and a slow growth of the electricity consumed by
EVs until it reaches around 305 MWh per year by 2030. The High scenario
considers a faster growth but also a delay in the introduction of EVs, starting only by 2015. In this scenario, the electricity consumption of EVs is
around 1188 MWh per year by 2030.
Figure 5.5: Electricity consumption from EVs in the scenarios considered
Two different time of charging strategies were considered: fixed and
flexible. The fixed time of charging considers the situation in which people
71
connect their vehicles to the grid and charge them right after they arrive
home. The profile of the charging of EVs, shown in Figure 5.6, was based on
a study concerning the mobility patterns in Portugal and the hour at which
the vehicles arrive home from their last daily journey [173].
Figure 5.6: Share of the daily demand of electricity by the EVs assumed for
each hour in the fixed charging strategy scenario
5.2.3
Main results
For all scenarios, the medium-term model decided to invest in the new hydro
plants as soon as it was possible. Due to the penetration of EVs being low
in both scenarios when compared to the total electricity demand (1.7% in
the low penetration rate scenario and 6.2% in the high penetration rate
scenario by 2030), the share of RES in the overall electricity production is
very similar in all scenarios reaching a share of 70% in 2011 and decreasing
to around 60% by 2030.
Regarding the share of RES on the additional electricity that is produced due to the demand by EVs, the situation is very different for the
four scenarios as shown in Figure 5.7. In all scenarios, the share of RES
decreases with the years due to the increasing demand and because no new
generation capacity from RES is installed after 2011. For scenarios 1 and 3,
the share of RES is the same until 2023 due to the low penetration of EVs.
After 2023, the share of RES in scenario 3 decreases faster in accordance
with the higher penetration rate of EVs. In the scenarios with flexible time
of charging, scenarios 2 and 4, the share of RES is around 2.5 times higher
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than the share of scenarios 1 and 3, respectively, due to some of the charging of EVs being shifted to periods in which there was excess of electricity
from RES. The share of RES decreases faster also in scenario 4 due to the
high penetration rate of EVs and the total amount of electricity that can be
shifted being limited by the excess of electricity from RES in each day.
Figure 5.7: Share of RES on the electricity that was produced for EVs
The charging of EVs with RES occurs only when there is excess of electricity from these resources when compared to the business as usual demand.
In this case, the RES used for electricity production in Flores are Hydro and
Wind which have lower capacity factors during the summer months (May
through August). Therefore, the share of RES in the electricity produced
for the charging of EVs is lower during these months, as shown in Figure 5.8.
Figure 5.8: Share of RES on the monthly electricity produced for EVs in
the year 2025
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As such, the goal of switching to EVs in order to reduce the consumption
of fossil fuels would not be accomplished during the summer months as the
electricity would have to be produced from fossil fuels. The use of flexible
time of charging strategies is not able to help increase the share of RES
during these months due to the low availability of the resources. In order
to maximize the benefits of the introduction of EVs, the electricity supply
sector needs to be planned accordingly to guarantee the existence of sufficient
generation capacity for the additional demand that will exist.
While the charging of EVs in the fixed scenario occurred primarily during
the evening period (18:00 to 24:00) in the fixed time of strategy scenarios,
Figure 5.6, the optimal time of charging distribution, Figure 5.9, showed
that to maximize the use of RES the charging should occur during the night
period (1:00 to 8:00). This is due to the lower electricity consumption during
the night hours, which increases the possibility of having excess of electricity
from RES during these hours.
Figure 5.9: Optimal average share of the daily demand of electricity by the
EVs in each hour for the scenarios with flexible charging in the year 2025
However, the charging patterns are not the same for all days as they
depend on the hours in which there is excess of electricity from RES. For
the days in which there was no excess of RES, the charging of EVs could be
performed as soon as people reached their homes. This explains why, in the
average day, the difference between electricity consumption during the night
period (1:00 to 8:00) would be only slightly higher than the consumption
verified during typical consumption hours (19:00 to 23:00).
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Therefore, in order to obtain the maximum benefits by optimizing the
time of charging of EVs, the system would require the automation of the
charging structure as to allow the EVs to charge whenever the system operator decided it was the optimal time for the charging. The implementation
of such a system would only be possible with the development of policies
that could convince the people to allow the time of charging of their vehicles
to be decided by the system operator.
5.3
Key findings
The key findings from the modeling of the dynamics of electricity demand
are:
• The modeling of electricity systems needs to include the modeling of
the DSM strategies that are being considered for the region, and their
dynamics, as they can change significantly the optimal investment plan
due to changes in the electricity demand patterns.
• The introduction of dynamic demand technologies can help delay the
investment in new generation capacity by reducing peak demand and
enabling a better management of the already existing capacity.
• The use of flexible time of charging strategies can help increase the
share of RES in the electricity produced for the charging of the EVs
by providing a better match between supply and demand.
• The environmental benefits from switching from conventional transportation technologies to EVs can change drastically from one month
to the other based on the RES that are used for the production of
electricity.
• Generally, the charging of EVs during the night period can increase
the cost-effectiveness of the EVs and increase the environmental benefits since that is where it is most likely to have excess of electricity
production from RES.
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76
Chapter 6
Hybrid modeling framework
for high penetrations of
renewable energy sources
The development of investment plans in new generation capacity using RES
requires the use of modeling tools capable of accounting for the long-term
evolutions of technology costs, fuel costs and demand and the short-term
dynamics of balancing electricity supply and demand in order to avoid over
investment and large periods of excess of RES. In this section, a hybrid
modeling framework that uses an energy system planning model and an
energy system balancing model is presented.
The modeling framework developed is applied to two different case studies: to the island of São Miguel, Azores, that can increase the penetration of
RES by using geothermal energy and wind energy and to mainland Portugal
which can invest in onshore and offshore wind, large hydro and run-of-river,
solar energy and wave energy.
6.1
Integrated Modeling Framework for Energy
Systems Planning
This work is presented in detail in Paper IV - ”Integrated Modeling Framework for Energy Systems Planning” by Carlos Silva, André Pina, Gonçalo
77
Pereira and Alexandra Moutinho published in Volume 3 of the Proceedings of the 23rd International Conference on Efficiency, Cost, Optimization,
Simulation and Environmental Impact of Energy Systems [12].
6.1.1
Aim of the study
The paper proposes an integrated modeling framework for energy systems
planning, where a TIMES model is used in combination with a half-anhour resolution model for the system operation developed in MATLAB, in
which the technical and economical feasibility of the long-term scenarios
considering the installation of renewable resources plants are tested and
validated at the operational level. These results are then feed back into the
TIMES model as parameters for resource availability and plant efficiency in
order to calibrate scenario design.
The method proposed consists of the use of both models in an iterative
cycle, in which the medium-term model is used to optimize the investment
in new generation capacity and the short-term model is used to calculate the
energy balances over one year with higher time resolution (hourly or less) by
optimizing the operational costs. The medium-term model provides to the
short-term model the installed capacities of each energy source that it has
to consider for the simulation of each year; the short-term model updates
the medium-term model constraints parameters regarding the operation of
the electricity generation facilities, such as the impossibility to start the
operation on a certain year or the limitation on the amount of electricity
that can be absorbed by the grid (effectively lowering the availability factor
in that year).
Generally, the framework works as shown in Figure 6.1. At the beginning
of each iteration, a run of the medium-term model provides the inputs for
the short-term model. Then, the short-term model runs for the first year
being considered. After this run, the outputs are analyzed and three things
may happen: (i) if the outputs show that some criteria are not met, then
there are restrictions that need to be introduced in the medium-term model
for that year; (ii) if the outputs show that the necessary criteria are met and
the end year has not been reached, then the short-term model is run for the
following year; (iii) if the outputs show that the necessary criteria are met
78
and the year being considered is the end year of the optimization, then the
process comes to an end.
The proposed methodology is tested in the design of the electric system
of the island of São Miguel in Azores - Portugal to determine when should
the investment in new geothermal capacity occur (one plant of 10 MW is
expected to start operations in 2013 and another plant of 10 MW can be
installed from that year onward) and what is the capacity of the wind-farms
that should be installed.
Figure 6.1: Proposed integrated modeling framework
6.1.2
Main results
When using only TIMES, the model decides to invest on the second 10 MW
geothermal unit comes into operation in the year 2013 together with the
already planned 10 MW plant which results in a large increase of the production from geothermal in that year, as shown in Figure 6.2. This is due to
the low cost of producing electricity from geothermal energy, which makes
it economically viable for the model to install even if generation had to be
stopped during the night periods, with the early investment in geothermal
delaying the investments in wind energy to 2017 and after.
79
Figure 6.2: Electricity production by source from TIMES initialization
The results obtained after the last iteration contrast heavily with when
only TIMES was used, where the early introduction of the geothermal plant
postponed the installation of wind power. After the final iteration (iteration
10), the results from the model showed that the second 10 MW geothermal
plant should be installed only in 2019 and that investment in wind energy
could begin by 2011. However, the maximum capacity that the model decided to install was only 9.9 MW whereas when using only TIMES the wind
power installed capacity reached 23 MW by 2020. The evolution of the
electricity production after the last iteration is shown in Figure 6.3.
Figure 6.3: Electricity production by source after the last iteration
The reduction of the installed capacity of wind power was due to the use
of the short-term model to evaluate the energy balance between supply and
demand and identify the excesses of electricity from wind energy that cannot
be used by the system. This allowed determining the real cost-effectiveness
80
of the wind turbines and, in this way, produce a more feasible solution for
the expansion of the electricity supply system.
6.2
High-resolution modeling framework for the
planning of electricity systems with high
penetration of renewables
This work is presented in detail in Paper V - ”High-resolution modeling
framework for the planning of electricity systems with high penetration of
renewables” by André Pina, Carlos Silva and Paulo Ferrão to be submitted
to the journal Applied Energy.
6.2.1
Aim of the study
The paper presents a modeling framework that is able to optimize the investment in new renewable generation capacity on the long-term while taking
into account the hourly dynamics of electricity supply and demand, by combining two models are used in an iterative cycle, with each providing input
for the other, as shown in Figure 6.4.
Figure 6.4: Modeling framework proposed
The framework uses two of the most used energy planning tools, EnergyPLAN which is able to model the hourly dynamics of RES and TIMES
which is able to optimize the investment in new generation capacity over
81
the long-term. While the investment optimization model (TIMES) provides
to the operation optimization model (EnergyPLAN) the installed capacities
of each energy source that it has to consider for the simulation of each year,
the results from the operation optimization model are used to update the investment optimization model constraints parameters regarding the amount
of new installed capacity that the system can handle for that year.
The algorithm is the same as the one presented in Section 6.1 and Figure 6.1. The criteria adopted to determine if the model needs to update
the constraints of the investment model consists on determining if all the
installed capacity from RES is able to produce at least 90% of the expected
yearly output (for example, if the expected capacity factor of wind is 25%,
the criteria are met if the real capacity factor is at least 22.5%). In the case
the criteria are not met, the constraints to be introduced on the installed
capacity from that iteration that maximize the production from renewable
energy sources and fulfils the criteria are calculated and added to the TIMES
model for the year being analyzed.
The framework was applied to continental Portugal for the time period of
2005-2050, in order to identify optimal investment plans in new renewable
and fossil generation capacity with the goal of achieving significant CO2
emissions reduction, under different scenarios. Two different scenarios were
considered in this work based on the capacity to pump water to reservoirs.
The expected capacity for storage is 4302 MW by 2020 due to the installation
of several reversible turbines in future investments of large hydro systems.
This situation is compared to the current 1036 MW of installed capacity,
with no new reversible system being installed in the future.
6.2.2
Main results
When using the TIMES model only, the investment decisions of the model
were the same for the two different pumped storage capacities (4302 MW
and 1036 MW), with the operation decisions being slightly different due
to the higher storage capacity of the 4302 MW scenario. However, when
applying the complete algorithm to both scenarios the results were very
different. While in the case of the 4302 MW the first result obtained from
TIMES verified the criteria in every year, the same did not happen for the
1036 MW case which needed 135 iterations. This is because the model did
82
not have any information on the amount of energy that can be stored at any
given period, which greatly benefits the scenario with high storage installed
capacity. As such, in spite of having a higher than usual time resolution,
the TIMES model is not able to fully account for the variability of RES.
The results obtained for before and after the iterative process are very
different in what concerns the investment in generation capacity using RES,
as shown in Figure 6.5, with the model deciding to install less generation
capacity using RES after the iterative process (in 2040 there was a decrease
of 14.5% from 27600 MW to 23600 MW and by 2050 the decrease was of
18.8% from 32000 MW to 26000 MW).
(a)
(b)
Figure 6.5: Installed capacity from renewable energy sources a) before the
first iteration b) after the last iteration
Furthermore, after the iterative process, the model decides to invest 5
years sooner in Solar energy and 13 years sooner in Wave energy while
reducing the investment in Onshore and Offshore Wind energy. This is due
83
to the model being able to take into account that using different resources
with complementary variability guarantees that all installed capacity from
renewable energy sources is used efficiently, thereby reducing the investment
in generation capacity that would have low capacity factors.
The yearly penetration of RES is higher in the 4302 MW scenario (around
87% by 2050) when compared to the 1036 MW scenarios. Despite having the
same installed capacity, the 1036 MW scenario before the application of the
proposed methodology has a lower share of RES than the 4302 MW (84%
by 2050) because of the lower storage capacity which reduces the amount of
renewable energy that can be effectively stored in periods of excess. Due to
the lower installed generation capacity after the application of the proposed
methodology to the 1036 MW of storage capacity, the share of RES in the
electricity mix is only of around 72% by 2050, as shown in Figure 6.6.
(a)
(b)
Figure 6.6: Electricity mix in the different scenarios a) in the 1036 MW
scenario, iteration 1 b) in the 1036 MW scenario, last iteration
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The large scale deployment of electricity generation capacity from renewable energy sources can lead to excess production if the amount of electricity
that can be produced from these sources is higher than demand. This excess can create significant problems in the electricity grid as well as economic
problems due to low prices of electricity in the spot markets which reduces
the economic competitiveness of the installed generation capacity. The excess of electricity from renewable energy sources in the different scenarios is
shown in Figure 6.7 a) when considering all renewable energy sources and
in Figure 6.7 b) when considering only solar, wind offshore and wave, which
are less mature technologies and generally more expensive when compared
to wind onshore and hydro.
(a)
(b)
Figure 6.7: Percentage of excess electricity from renewable energy sources in
the different scenarios a) for all renewable energy sources and b) considering
only solar, wind offshore and wave energies
85
6.3
Key findings
The key findings from the development of a hybrid modeling framework for
systems with high penetrations of renewable energy sources are:
• The use of a hybrid modeling methodology that combines the longterm and short-term energy modeling approaches enables the optimization of investment in new generation capacity of RES while taking
into account the hourly dynamics of RES and of electricity demand.
• The increase in temporal resolution allows avoiding the overinvestment
in new generation capacity by identifying which investments will lead
to excesses in electricity production from RES that cannot be used by
the system.
• The modeling framework is able to optimize investments based on the
complementarities of the availability of RES which can lead to the
delay of investments for some RES and an anticipation for others.
• For systems with lower energy storage capacities there is a necessity
to model in detail the energy systems in order to develop investment
plans that consider accurate estimation of the electricity production
from each RES.
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Chapter 7
Conclusions and future work
The development of new modeling methodologies that combine the resolution of very detailed models with the long-term optimization capabilities of
broader models is crucial to the efficient planning of sustainable energy systems with large penetrations of RES and to support the design of efficient
energy policies.
7.1
State of the art in energy systems modeling
The development of new modeling methodologies, and improvement of previously existing ones, has occurred in order to build up the capability to
provide answers to the challenges energy systems face. This has been seen
since the beginning of the development of energy modeling tools in the 1970’s
with the scope of the modeling tools following the changes that occurred in
energy systems due to events with global impacts such as the energy crisis
in the 1970’s, the disaster in the nuclear plant of Chernobyl in 1986 and the
signing of the Kyoto Protocol in 1997.
Currently, there are two main types of modeling tools: Long-term energy systems planning tools with temporal resolution of a year or less and
Short-term tools of a year or less with hourly or higher temporal resolution.
Long-term modeling tools are characterized as being capable of optimizing
the investment in new generation capacity over a large number of years or
simulating pre-determined energy systems to see how well they are able to
address the yearly growth of electricity demand. However, they have low
87
temporal resolution, which does not allow them to capture the necessary
dynamics for performing the optimization of operating the system. Shortterm tools are generally capable of optimizing the operation of a system,
and sometimes are even able to optimize the investment in new plants, but
only taking into account one year. This reflects their ability to model hourly
dynamics of electricity supply and demand, which makes them suitable for
analyzing how a system should be operated.
The expected large-scale deployment of RES, the development of new
technologies such as EVs and demand response and the introduction of large
sets of DSM policies requires energy models to be able to analyze the longterm evolution of energy systems while taking into account the short-term
dynamics of electricity supply and demand. Energy models need to be able
to account for the changes of demand and of technology and fuel costs as
well as how the hourly match between electricity demand and electricity
production from RES to optimize the generation capacity mix and avoid the
investment in capacity that will lead to large periods of excess of electricity
from RES.
With this purpose, a new set of energy modeling methodologies that
link two or more specific energy modeling tools in order to produce a more
detailed representation of energy systems has started to be developed in the
last years. These new modeling methodologies can have different purposes
according to the modeling tools they include and can be used to perform
studies that combined different technical and economical aspects.
7.2
High-temporal resolution modeling of the dynamics of energy systems
The increase in time resolution of the models built in TIMES is crucial to
achieve better estimates for the potential of RES in electricity systems and to
allow designing more robust investment plans for the evolution of electricity
systems. The constraints and dynamics introduced by having higher time
resolution have a significant impact on the expected electricity production
from new RES generation units and also on the amount of new generation
capacity that can be installed, as lower time resolutions were shown to lead
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to overestimations of the amount of new renewable energy that could be
installed and absorbed by the system.
While the proposed temporal resolution for TIMES (4 seasons, 3 days
per season and 24 hours per day for each year) can help improve the analysis
of the investment in RES, its results still have a significant difference when
compared to the modeling with hourly resolution. Analyzing the results from
three different electricity demand and supply balance methods (i.e., integral,
semi-dynamic and dynamic methods) with different temporal resolutions it
becomes clear that the approach used in TIMES can help to identify the
existence of excesses of electricity from RES but not the total amount of
electricity that will not be used.
The use of low temporal resolution models can then lead to the overestimation of the amount of electricity from RES that a system can use and the
overinvestment in new generation capacity, which results in a reduced costeffectiveness of the system and an underestimation of the CO2 emissions to
be expected from the system. The results show that, if the proper hourly
dynamics are not included when modeling electricity supply and demand,
the information passed on to decision makers can lead to suboptimal energy
systems with reduced cost-effectiveness as some investments could be made
earlier than necessary. This could also lead the country or region to miss
out on a set target such as use of renewable energies or CO2 emissions caps.
The relevance of using energy models with high temporal resolution increases with the amount of generation capacity from RES that is installed
in the system. From the results obtained, differences in the dispatch of electricity from RES could appear when the installed capacities from RES is
slightly higher than the average power of the low consumption hours. Since
the variability of the resource plays an important role in the electricity available for use, differences can be hidden by when average availability factors
are used, which compensate situations where the resource is not available
to fulfill the demand with others when more resource is available than what
is needed by the demand. When the installed capacity from RES starts to
be significantly high when compared to demand, the possibility of leveling
the energy surplus with the energy shortfall from the resources ceases even
for energy models with lower temporal resolutions and differences start to
appear.
89
The modeling of electricity demand dynamics must also be taken into
account as they can have a significant impact on the optimization of the new
generation capacity from RES. The use of higher temporal resolution energy
system models is therefore crucial to assess the real impact of specific energy
policies and changes in demand patterns in the complete energy system as it
allows calculating the potential benefits and drawbacks of introducing them.
7.3
Modeling the dynamics of electricity demand
The introduction of demand management strategies, be it based on technological energy efficiency, consumer behavior changes or the introduction
of dynamic demand-side management technologies is crucial for the longterm sustainability of any region. These options will play a large role in
the transition to sustainable energy systems by keeping demand at levels
in which renewable energies can be used effectively to meet that demand.
Therefore, the modeling of electricity systems needs to include the modeling
of the DSM strategies that are being considered for the region, and their
dynamics, as they can change significantly the optimal investment plan due
to changes in the electricity demand patterns.
In particular, the introduction of dynamic demand technologies can help
delay the need for investment in new generation capacity by reducing peak
electricity demand and enabling a better management of the already existing capacity by shifting load to periods in which there would be excess of
production from RES. This benefits the system by enabling a phasing of the
investment and increasing the cost-effectiveness of the installed capacities.
Regarding EVs, the modeling of electricity demand dynamics enables
analyzing the impact on the electricity production system of using different
charging strategies. The results show that the use of flexible time of charging
strategies can help increase the share of RES in the electricity produced for
the charging of the EVs by providing a better match between supply and
demand. This can help double the share of RES used for charging the
EVs, which has a direct impact on the reductions in CO2 emissions of the
transportation sector. Generally, the results indicate that charging the EVs
during the night period can increase the cost-effectiveness of the EVs and
increase the environmental benefits since that is where it is most likely to
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have excess of electricity production from RES.
The environmental benefits of switching from conventional transportation technologies to EVs can also change drastically from one month to the
other based on the RES that are used for the production of electricity. A
high temporal resolution analysis allows identifying in which periods the
EVs will be charged with electricity produced from fossil fuels. This information can then be used to develop alternative strategies for these periods
that enable the increase of the environmental benefits of EVs.
7.4
Hybrid modeling framework for high penetrations of renewable energy sources
The development of hybrid modeling framework that uses already existing
energy modeling tools allows combining the strengths of different modeling
approaches and model with more detail the true complexity of electricity
systems. In the particular case analyzed in this thesis, the use of a hybrid
modeling methodology that combines the long-term and short-term energy
modeling approaches enables the optimization of investment in new generation capacity of RES while taking into account the hourly dynamics of RES
and of electricity demand.
The increase in temporal resolution to include every hour of the year
allows identifying which investments will lead to excesses in electricity production from RES that cannot be used by the system. With this information,
the model can then optimize investments in order to avoid the installation
of new generation capacity that will have low effective capacity factors.
Using an iterative process, the modeling framework is also able to optimize the generation capacity mix with the goal of reducing the amount of
excess electricity from RES by delaying the investment in some RES and
anticipating it for others, based on the complementarities of the availability
of RES. This combination of available RES allows reducing drastically the
amount of electricity from RES that would not be used while lowering only
slightly the penetration of RES in the electricity system. The analysis of
the complementaritities of resources and the matching of supply and demand also helps the model to avoid the trivial solution of investing only in
91
the cheapest technology available, as the investment in large quantities of the
same RES technology can lead to continuous decreases in the effective capacity factor of the marginal installed MW that reduce their cost-effectiveness
and make them more expensive than the other technologies.
Furthermore, the impact of having different energy storage capacities on
the planning of electricity systems can only be modeled in detail with the use
of very detailed models that allow the effective calculation of the amount
of energy that needs to be stored. The results show that the application
of the proposed modeling framework does not have a significant impact in
case a high storage capacity exists as the amount of excess electricity from
renewable energy sources is small precisely because of the possibility to
store a large part of it. However, in systems with low storage capacity the
application of the proposed modeling framework resulted in a significantly
different generation capacity mix.
7.5
Future work
The proposed methodology can also be applied to study the introduction
of different energy efficiency measures, electric vehicles or demand response
programs as it enables the model to take into account the simultaneous evolution of the electricity supply and demand sectors. Regarding the inclusion
of demand dynamics on the proposed modeling framework, it is possible to
include changes in the hourly load curves throughout the years from the evolution of different activity sectors due to economic objectives and different
growth rates as well as the short-term changes in electricity demand profiles
from the shifting of consumption from one hour to the other using EVs or
demand response.
However, the adaptation of the proposed methodology to account for
the shifting of loads requires the development of yearly models with hourly
resolution that are able to model electricity dispatch systems while considering EVs and demand response technologies. From the analysis performed
in this thesis, these models are currently lacking.
Addressing these issues will allow the design of comprehensive action
plans for achieving an economically and technically feasible pathway for
achieving sustainable energy systems.
92
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Supply and Demand Dynamics in Energy Systems Modeling

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