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Agent-based simulation of electricity markets: a literature review

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  • Sensfuß, Frank
  • Ragwitz, Mario
  • Genoese, Massimo
  • Möst, Dominik

Abstract

Liberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets.

Suggested Citation

  • Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
  • Handle: RePEc:zbw:fisisi:s52007
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Koppelaar, Rembrandt H.E.M. & Keirstead, James & Shah, Nilay & Woods, Jeremy, 2016. "A review of policy analysis purpose and capabilities of electricity system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1531-1544.
    2. Safarzynska, Karolina & van den Bergh, Jeroen C.J.M., 2011. "Industry evolution, rational agents and the transition to sustainable electricity production," Energy Policy, Elsevier, vol. 39(10), pages 6440-6452, October.
    3. repec:eee:tefoso:v:122:y:2017:i:c:p:71-90 is not listed on IDEAS
    4. Milad Maralani & Milad Maralani & Basil Sharp & Golbon Zakeri, 2016. "The Potential Impact of Industrial Energy Savings on The New Zealand Economy," EcoMod2016 9308, EcoMod.
    5. Herrmann, J.K. & Savin, I., 2017. "Optimal policy identification: Insights from the German electricity market," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 71-90.
    6. Broeer, Torsten & Fuller, Jason & Tuffner, Francis & Chassin, David & Djilali, Ned, 2014. "Modeling framework and validation of a smart grid and demand response system for wind power integration," Applied Energy, Elsevier, vol. 113(C), pages 199-207.
    7. Arango, Santiago & Larsen, Erik, 2011. "Cycles in deregulated electricity markets: Empirical evidence from two decades," Energy Policy, Elsevier, vol. 39(5), pages 2457-2466, May.
    8. Weiss, Olga & Bogdanov, Dmitry & Salovaara, Kaisa & Honkapuro, Samuli, 2017. "Market designs for a 100% renewable energy system: Case isolated power system of Israel," Energy, Elsevier, vol. 119(C), pages 266-277.
    9. Bunn, Derek & Yusupov, Tim, 2015. "The progressive inefficiency of replacing renewable obligation certificates with contracts-for-differences in the UK electricity market," Energy Policy, Elsevier, vol. 82(C), pages 298-309.
    10. Haghnevis, Moeed & Askin, Ronald G. & Armbruster, Dieter, 2016. "An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems," Socio-Economic Planning Sciences, Elsevier, vol. 56(C), pages 67-87.
    11. Rosen, Christiane & Madlener, Reinhard, 2012. "Auction Design for Local Reserve Energy Markets," FCN Working Papers 7/2012, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Mar 2013.
    12. repec:spr:annopr:v:258:y:2017:i:2:d:10.1007_s10479-016-2222-4 is not listed on IDEAS
    13. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.

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