Using ELMOD to identify country-specific factors for an optimized
grid performance
by
Andras Herczeg
An Abstract of a Thesis Submitted to the Graduate
Faculty of Rensselaer Polytechnic Institute
in Partial Fulfillment of the
Requirements for the degree of
MASTER OF SCIENCE
Major Subject: Engineering Science
The original of the complete thesis is on file
In the Rensselaer Polytechnic Institute Library
Approved:
Professor Ernesto Gutierrez-Miravete, Thesis Adviser
Rensselaer Polytechnic Institute
Troy, New York
February, 2015
(For Graduation May 2015)
ABSTRACT
In accordance with the European Union (EU) 20-20-20 targets, the share of the
renewable energy generation increases rapidly in the EU Member States. While many
forms of alternative generation are promoted, the majority of the green energy
investments are intended into the wind or solar based power generation. As the
popularity of the renewables has risen, the reliability standards for the transmission
systems have received greater attention, especially regarding grid and peak power
generation development. The paper reviews the recent wind developments and their
effect on the transmission grid stability and reliability. After the overview of the featured
optimization models, we identify the key country-specific factors that are needed to be
considered to maintain an optimal grid performance.
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ACRONYMS
The following is a list of acronyms and abbreviations that are used throughout this paper.
ELMOD is a bottom-up model of the European electricity
market including both generation and the physical
ELMOD
transmission network (DC Load Flow approach), which
combines electrical engineering and economics: its objective
function is welfare maximization, subject to line flow,
energy balance, and generation constraints.
The European Network of Transmission System Operators
for Electricity (ENTSO-E) is an association of Europe's
transmission system operators (TSOs) for electricity. It is a
ENTSO-E
successor
of
ETSO,
the
association
of
European
transmission system operators founded in 1999 in response
to the emergence of the internal electricity market within the
European Union.
The Price Coupling of Regions (PCR) is initiative of seven
European Power Exchanges, to develop a single price
coupling solution to be used to calculate electricity prices
PCR
across Europe, and allocate cross border capacity on a dayahead basis. This is crucial to achieve the overall EU target
of a harmonized European electricity market. PCR is based
on three main principles: a single algorithm, robust operation
and individual Power Exchange accountability.
A transmission system operator (TSO) is an entity entrusted
TSO
with transporting energy (electrical power or natural gas) on
a national or regional level, using fixed infrastructure.
2
1. Introduction
1.1 Background
The European Union aims to create a single, integrated European energy (including
electricity) market, which became the driving force of the regional market coupling
initiatives. These smaller-scale integrations ensuring the preparation for the European
Price Coupling and will ultimately lead into the creation of the European Internal Energy
Market by standardizing the systems and promoting cooperation between the given
countries.
The first pioneers of these models are in the stage of expansion: for instance the CZ-SKHU-RO Market Coupling was successfully launched on 19 November 2014, integrating
the Czech, Slovak, Hungarian and Romanian day-ahead electricity markets and
replacing CZ-SK-HU Market Coupling.1 Market coupling requires a close collaboration
by the transmission system operators (TSOs)2 of each country together with power
exchanges3 supported by national energy regulators4 in order to develop and implement
all necessary solutions which ensure technical and procedural compatibility with the
target European solution5 which is already implemented in other coupled European
regions.
Overall, market coupling allows higher efficiency of trading and capacity allocation,
which should lead to higher security of supply, higher liquidity and lower price
volatility.
1
For more information see
http://www.mavir.hu/documents/10262/199492726/20141911_PRess+Release_succesful+golive.pdf/92fdcaff-1196-47af-947a-23077588ab55
2
CZ, SK, HU, RO electricity TSOs: ČEPS, SEPS, MAVIR and Transelectrica
3
CZ, SK, HU, RO power exchanges: OTE, OKTE, HUPX and OPCOM
4
CZ, SK, HU, RO power exchanges: ERÚ, ÚRSO, MEKH and ANRE
5
Price Coupling of Regions (PCR), is the initiative of seven European Power Exchanges, to develop a
single price coupling solution to be used to calculate electricity prices across Europe, and allocate cross
border capacity on a day-ahead basis. For more information see: http://www.nordpoolspot.com/How-doesit-work/European-Integration/Price-coupling-of-regions/
3
1.2 Problem description
It should be noted though that the integration also reveals several challenges:
1) The ongoing change impacts substantially both existing market players
(including the large incumbents) and new entrants in the short and medium term
as well. Long-term investment decisions can be challenging particularly, as the
regional prices most likely are going to differ after the integration from the
current ones.
2) The subsidy mechanism of the renewables (feed-in tariffs, green certificates,
etc.) in a given country – such as Germany – may have a long-lasting effect on
smaller markets (e.g. Central European countries). The political support of one
technology (e.g. large scale wind or solar) may prompt investors to delay much
needed investments into other (e.g. nuclear) capacities.
3) The location of capacities (especially the renewable ones) requires additional
grid development projects, which causes congestion in the present. Under
current network management methods this factor can be challenging to properly
taken into account, and we expect that the problem will exist at least until the
internal energy market is not completed with a more developed capacity
planning process.
The paper examines scenarios in the light of the current German investment plans
(focusing on the renewables, especially wind development) and their impact on the
prices and grid stability in the Central European countries.
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2. Theory and Methodology
2.1 Theoretical background
Ventosa et al. (2005) provide a detailed overview of market modeling tendencies. They
point out three trends: optimization models6, equilibrium models and simulation models.
Economic modeling of electricity markets not possible without accounting for technical
constraints (Huppmann and Kunz, 2011). Researchers of the electricity market
restructuring heavily rely on a model-based research, since it allows implementing a
complex approach of operations research, applied economics and engineering (Leuthold,
2010). The economic-engineering model-based approach is especially popular in the US
(Hogan, Hobbs, UC Berkeley, etc.); on the other hand, the available research for
Germany and Europe limited. The most known model for the European electricity
markets is the ELMOD.
2.2 ELMOD Analysis
ELMOD7 is a bottom-up model of the European electricity market including both
generation and the physical transmission network, which combines electrical
engineering and economics: to maximize welfare, subject to line flow, energy balance,
and generation constraints.
8
The scope of the (physical) model is the ENTSO-E9
countries, in particular Portugal, Spain, France, Netherlands, Belgium, Luxembourg,
Denmark, Germany, Switzerland, Austria, Italy, Poland, Hungary, Czech Republic,
Slovenia and Slovakia.
6
Optimization models can either apply a profit maximization of a single firm or a welfare maximization
approach under perfect competition.
7
A summary of ELMOD: <http://www.esa2.eu/documents/10157/17039/ELMOD.pdf>
8
The model was developed at the Chair of Energy Economics and Public Sector Management (EE2) at
Dresden University of Technology in order to analyze various questions on market design, congestion
management, and investment decisions, with a focus on Germany and Continental Europe (Leuthold,
2008).
9
41 TSOs from 34 countries are members of ENTSO-E. Source: <https://www.entsoe.eu/about-entsoe/inside-entso-e/member-companies/Pages/default.aspx>
5
The model provides simulations on an hourly basis, taking into account variable
demand, wind input, unit commitment, start-up costs, pump storage, and other details
(Leuthold, 2010).
ELMOD is a DC-Load Flow model of the European integrated transmission grid.
Generation and demand are localized at the nodal level to allow for a detailed
representation of different grid situations. Load at each node is modelled using the gross
value added of services and industries as well as the number of inhabitants and typical
load profiles. In different scenarios characteristic winter and summer workdays are
implemented on an hourly basis. Generation plants are represented with marginal costs
based on plant individual efficiency values and fuel and CO2-certificate-prices. The
model is implemented in GAMS. ELMOD is capable of different congestion
management (Kunz, 2013).
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3. Approach
Electricity is a special commodity due to several features: (a) non storable, (b) gridbound, (c) high fix cost ratio, (d) economies of scale in generation and transmission, (e)
daily and seasonal demand patterns, (f) power flows according to physical laws
(Kirchhoff). Power generation and the wholesale activities are unregulated in the EU;
however transmission and distribution remains a natural monopoly due to its
characteristics, similarly to other network industries (electronic communications, energy
and transport sectors).
The paper uses several assumptions when analyzing the different scenarios such as:

Perfect competition (no strategic players)

Perfect market bidding (marginal cost bids, no market power)

etc.
First we analyze the variation of ramping-costs and the variation of probabilities/wind
power generation of scenarios. Also, analyzing the impact of stochasticity on market
results (determinisitic vs. stochastic model setup) can be assessed. Based on the initial
results the analysis can be expanded on the effect of the wind generation capacity
expansion or carry out an investment analysis (policy evaluation) regarding installing
new power lines or endogenous pumped-hydro storage dispatch.
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4. Expected results and outcomes
We expect that the results show the importance of:

maintaining the existing peak capacities to balance demand

integrating the Central European grid network and constructing the required
cross border capacities
The final document will detail the various scenarios with the experienced issues.
Because of the public nature of all inputs and documents, the final report will be nonproprietary and will be able to be readily distributed.
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5. Milestones and resources
The schedule with key milestones has been made for execution of the project. Status
reports will be provided to the advisor.
Project development deadlines are the following:
1) Abstract (100 word writeup)
–
Feb 5, 2015
2) Tentative Project Proposal Draft extended (5-pages) abstract
–
Feb 9, 2015
3) Project Proposal
–
Feb 13, 2015
4) Collection of Industry Reference and Analysis
–
Feb 20, 2015
5) First Progress Report
–
Feb 27, 2015
6) Second Progress Report
–
Mar 20, 2015
7) Final Draft
–
Apr 3, 2015
8) Preliminary Final Report
–
Apr 17, 2015
9) Final Report
–
Apr 24, 2015
A number of industry resources are available for potential public event sourcing:
1. GAMS and ELMOD codes and documentations
2. ENTSO-E reports and database
3. EUROSTAT database
4. US EIA database, documents for counter check
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6. References
[1]
Gabriel, S.A. and F.U. Leuthold (2010): Solving discretely-constrained MPEC
problems with applications in electric power markets. Energy Economics, 32 (1),
p. 3-14.
[2]
Huppmann, D. and Kunz, F. (2011): Introduction to Electricity Network
Modelling; PhD Winterschool, Oppdal, March 2011
[3]
Kunz, F. (2013): Electricity Network Modelling: Basic Concepts and
Applications, PhD Winterschool, Tignes, April 2013
[4]
Leuthold, F.U. (2010): Economic Engineering Modeling of Liberalized
Electricity Markets: Approaches, Algorithms, and Applications in a European
Context, doctoral dissertation, Dresden University of Technology, January 8,
2010;
<http://tud.qucosa.de/fileadmin/data/qucosa/documents/2613/Dissertation_Leuth
old_FINAL_08012010.pdf>
[5]
Leuthold, F., Weigt, H. and C. von Hirschhausen. (2008): Efficient Pricing for
European Electricity Networks - The Theory of Nodal Pricing Applied to
Feeding-In Wind in Germany; Utilities Policy 16 (4), pp. 284-291.
[6]
Leuthold, F., Weigt, H. and C. von Hirschhausen. (2008): ELMOD - A Model of
the European Electricity Market, July 18, 2008; Dresden University of
Technology, Chair of Energy Economics and Public Sector Management,
Working Paper WP-EM-00.
[7]
Leuthold, F.U., Weigt, H. and C. von Hirschhausen (2010): A Large-Scale
Spatial Optimization Model of the European Electricity Market. Networks and
Spatial Economics, 2010
[8]
K. Neuhoff, J., Barquin, M.G. Boots, A. Ehrenmann, B.F. Hobbs, F.A. Rijkers
and M. Vázquez (2005): Network-constrained cournot models of liberalized
electricity markets: the devil is in the details. Energy Economics, 27(3):495 –
525, 2005.
[9]
Stigler H. and C. Todem (2005): Optimization of the Austrian Electricity Sector
(Control Zone of VERBUND APG) under the Constraints of Network Capacities
10
by Nodal Pricing. Central European Journal of Operations Research, 13 pp. 105–
125.
[10]
Ventosa, M., Baíllo, Á., Ramos, A. and M. Rivier (2005): Electricity Market
Modeling Trends; Energy Policy 33 (7) pp .897-913.
References for Electricity Data
[11]
European Network of Transmission System Operators for Electricity,
<https://www.entsoe.eu/resources/data-portal/>
[12]
EUROSTAT,
<http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/>
References for Wind Power Generation
[13]
Danish
Wind
Industry
Association,
<http://guidedtour.windpower.org,
http://www.talentfactory.dk/>
[14]
US
Department
of
Energy,
http://www.eere.energy.gov/>
11
<http://www.windpoweringamerica.gov/,
7. Appendices
This appendix provides a print out of example GAMS codes presented in the paper
related to the ELMOD.
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