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Combining Energy Networks

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Abstract

Electricity markets depend on upstream energy markets to supply the fuels needed for generation. Since these markets rely on networks, congestion in one can quickly produce changes in another. In this paper we develop a combined partial equilibrium market model which includes the interactions of natural gas and electricity networks. We apply the model to a stylized representation of Europe’s electricity and natural gas markets to illustrate the upstream and downstream feedback effects which are not obvious on first sight. We find that both congestion and loop-flow effects in electricity markets impact prices and quantities in markets located far from the initial cause of the market changes.

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Notes

  1. We use the term “partial equilibrium” models in contrast to “general equilibrium” models which deal with the endogenous determination of final consumers’ income and maintain the circular flow of commodities and monetary values.

  2. Put differently, at the distribution stage pipeline flows are undirected. Thus, the decision to use compressors results in an integer decision over the direction of natural gas flowing in the pipeline.

  3. The introduction of a trader is based on Egging et al. (2008)

  4. The perpendicular sign \( \bot \) denotes complementarity. In the example of equation (1) the extended formulation would be \( c_g^{{gas}} + PC_g^{{gas}} \geqslant PS_g^{{gas}}{; }X_g^{{gas}} \geqslant 0;\left( {c_g^{{gas}} + PC_g^{{gas}} - PS_g^{{gas}}} \right)X_g^{{gas}} = 0{ } \). To avoid repeated statements of equations, we use the perpendicular sign in the optimization problem to associate dual multipliers with constraints and to represent the respective first order condition, i.e. we always solve optimization problems constraint by equations and never solve them under complementarity conditions.

  5. \( \tilde{g} \) always denotes the origin of the natural gas.

  6. For example, transporting electricity from the north of Germany to the south of Germany also leads to cross-border flows through the Benelux and France.

  7. The concept of introducing a hub price is based on Hobbs (2001).

  8. For the sake of simplicity, we assume that each plant type produces with only one fuel, i.e. for each (i,e) η fie is strictly positive for exactly one fuel f. Therefore, the efficiency parameter also serves to establish a mapping between the generation technology set I and the set of fuels F. Relaxing this assumption requires extending the range of the generation variable, i.e. introducing X fie el as the amount generated by plant i at node e using fuel f.

  9. We formulate the general model with an emission restriction at every node. However, the modification for allowing allowances trade between generators located at different nodes is straightforward by introducing a subset of the electricity node set e which determines generators allowed to trade. In turn, the emission price in the zero profit condition (18) is replaced by the price of the respective trading system.

  10. Note that this assumes that each electricity node is served by exactly one natural gas node such that the sum on the left side includes exactly one element.

  11. Mixed generation represents a large variety of multi-fuel engines operating on coal and/or gas and/or oil. We do not consider their demand as part of the natural gas demand.

  12. Coal prices and power plant efficiencies are not locational differentiated. Thus, the only cost difference between Polish and German coal production is its impact on the network.

References

  • An S, Li Q, Gedra TW (2003) Natural gas and electricity optimal power flow. IEEE PES Transmission and Distribution Conference and Exposition, Dallas, Texas, Sept. 7–12

  • Arnold M, Andersson G (2008) Decomposed electricity and natural gas optimal power flow. 16th PSCC, Glasgow, Scotland, July 14–18

  • Aune FR, Golombek R, Kittelsen SAC, Rosendahl KE, Wolfgang O (2001) LIBEMOD—LIBEralisation MODel for the European Energy Markets: a technical description. Ragnar Frisch Centre for Economic Research, Working Paper 1/2001. http://www.frisch.uio.no/pdf/arbnot01_01.pdf. Accessed 15.01.2010

  • Bauer N et al (2008) REMIND: the equations. Potsdam Institute for Climate Impact Research (PIK). http://www.pik-potsdam.de/research/research-domains/sustainable-solutions/esm-group/remind-code. Accessed 15.01.2010

  • Brooke A, Kendrick D, Meeraus A (2008) GAMS a user’s guide. GAMS Development Cooperation, Washington

    Google Scholar 

  • Capros P, Georgakopoulos P, Van Regemorter D, Proost S, Schmidt C (1997) The GEM-E3 general equilibrium of the European union. Economic and financial modeling. 21–160

  • Egging R, Gabriel SA, Holz F, Zhuang J (2008) A complementarity model for the European natural gas market. Energy Policy 36(7):2385–2414

    Article  Google Scholar 

  • Eurostat (2010) Energy statistics—supply, transformation, Consumption. http://epp.eurostat.ec.europa.eu/portal/page/portal/energy/data/database. Accessed 15.01.2010

  • Ferris MC, Munson TS (2000) Complementarity problems in GAMS and the path solver. J Econ Dyn Control 24(2):165–188

    Article  Google Scholar 

  • Gabriel SA, Zhuang J, Kiet S (2005) A large-scale linear complementarity model of the North American natural gas market. Energy Econ 27(4):639–665

    Article  Google Scholar 

  • Green R (2007) Nodal pricing of electricity: how much does it cost to get it wrong? J Regul Econ 31(2):125–149

    Article  Google Scholar 

  • Grübler A, Messner S (1998) Technological change and the timing of mitigation measures. Energy Econ 20(5–6):495–512

    Article  Google Scholar 

  • Hobbs BF (2001) Linear complementarity models of Nash–Cournot competition in Bilateral and POOLCO power markets. IEEE Trans Power Syst 16(2):194–202

    Article  Google Scholar 

  • Holz F (2009) Modeling the European natural gas market—static and dynamic perspectives of an oligopolistic market. Dissertation, TU Berlin

  • IPCC (2006) 2006 IPCC guidelines for national greenhouse gas inventories. In: Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds). Institute for global environmental strategy

  • Kouvaritakis N, Soria A, Isoard S (2000) Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching. Int J Global Energy Issues 14(1–2):104–115

    Google Scholar 

  • Leuthold F, Weigt H, von Hirschhausen C (2008) ELMOD—a model of the European electricity market. Dresden University of Technology Electricity Market Working Papers WP-EM-00

  • Lochner S, Bothe D (2007) Nord stream-gas, quo vadis? Analyse der Ostseepipeline mit dem TIGER-Modell. Energiewirtsch Tagesfragen 57(11):18–23

    Google Scholar 

  • Loulou R, Goldstein G, Noble K et al (2004) Documentation for the MARKAL family of models

  • Mathiesen L, Roland K, Thonstad K (1987) The European natural gas market: degrees of market power on the selling side. In: Golombek R, Hoel M, Vislie J (eds), Natural gas markets and contracts, contributions to economic analysis. North-Holland, 27–58

  • Möst D, Perlwitz H (2009) Prospects of gas supply until 2020 in Europe and its relevance for the power sector in the context of emission trading. Energy 34(10):1510–1522

    Article  Google Scholar 

  • Neuhoff K, Barquin J, Boots MG, Ehrenmann A, Hobbs BF, Rijkers AM, Vásquez M (2005) Network-constrained cournot models of liberalized electricity markets: the devil is in the details. Energy Econ 27(3):495–525

    Article  Google Scholar 

  • Neumann A, Viehrig N, Weigt H (2009) InTraGas—a stylized model of the European natural gas network. Dresden University of Technology resource markets working paper WP-RM-16

  • O’Neil RP, Sotkiewicz PM, Hobbs BF, Rothkopf MH, Stewart WR Jr (2005) Efficient market-clearing prices in markets with nonconvexities. Eur J Oper Res 164:296–285

    Google Scholar 

  • Paltsev S, Reilly JM, Jacoby HD, Eckaus RS, McFarland J, Sarofim M, Asadoorian M, Babiker M (2005) The MIT Emission Prediction and Policy Analysis (EPPA) model: version 4. MIT Joint program on the science and policy of global change report 125

  • Perner J, Seeliger A (2004) Prospects of gas supplies to the European market until 2030—results from the simulation model EUGAS. Util Policy 12(4):291–302

    Article  Google Scholar 

  • Rüster S (2010) Recent dynamics in the global liquefied natural gas industry. Dresden university of technology resource markets working papers WP-RM-19

  • Rutherford TF (1995) Extension of GAMS for complementarity problems arising in applied economic analysis. J Econ Dyn Control 19(8):1299–1324

    Article  Google Scholar 

  • Smeers Y (1997) Computable equilibrium models and the restructuring of the European electricity and gas markets. Energy J 18(4):1–31

    Google Scholar 

  • Stigler H, Todem C (2005) Optimization of the Austrian electricity sector (Control Zone of VERBUND APG) under the constraints of network capacities by nodal pricing. Cent Eur J Oper Res 13:105–125

    Google Scholar 

  • UCTE (2007) System adequacy forecast, SAF 2006–2015: scenarios. http://www.entsoe.eu/fileadmin/user_upload/_library/publications/ce/systemadequacy/saf/UCTE_SAF_2006-2015_scenarios.zip. Accessed 15.01.2010

  • Unsihuay C, Lima JWM, de Souza ACZ (2007) Modeling the integrated natural gas and electricity optimal power flow. IEEE Power Engineering Society General Meeting, Tampa, Florida, June 24–28

  • Ventosa M, Baíllo Á, Ramos A, Rivier M (2005) Electricity market modeling trends. Energy Policy 33(7):897–913

    Article  Google Scholar 

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Acknowledgments

We thank Christian von Hirschhausen, Sophia Rüster, and participants of the YEEES meeting April 2010 in Cambridge, UK, and the editor and referees for their comments and suggestions.

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Authors and Affiliations

  1. Dresden University of Technology, Faculty of Business and Economics, Chair of Energy Economics and Public Sector Management, 01069, Dresden, Germany

    Jan Abrell & Hannes Weigt

Authors
  1. Jan Abrell
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  2. Hannes Weigt
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Correspondence to Hannes Weigt.

Appendix

Appendix

Table 4 Electricity generation specifications
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Table 5 Dataset
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Fig. 3
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Base case, natural gas market, prices and congestion

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Base case, electricity market, prices and congestion

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Fig. 5
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Russian case, natural gas market, prices and congestion

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Fig. 6
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Russian case, electricity market, prices and congestion

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Fig. 7
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Emission case, natural gas market, prices and congestion

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Fig. 8
figure 8

Emission case, electricity market, prices and congestion

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Abrell, J., Weigt, H. Combining Energy Networks. Netw Spat Econ 12, 377–401 (2012). https://doi.org/10.1007/s11067-011-9160-0

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