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Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics

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  • Coninx, Kristof
  • Deconinck, Geert
  • Holvoet, Tom

Abstract

Maintaining a real time balance between energy consumption and production is challenging when faced with increasing penetration of Renewable Energy Sources (RES) because of the increased variability in generation output. Demand-Side Management (DSM) techniques address this issue by steering consumers’ energy off-take, thereby enabling further penetration of RES. Present paper addresses the problem of overproduction from distribution grid connected wind generation. We present and analyze two business cases in the Belgian-European energy landscape for using upward consumption flexibility to deal with excessive wind power injection. We focus on the perspective of the flexibility providers and the strategic choice they face in choosing the business partner that maximizes their expected financial compensation. Evolutionary game theory is used to model this strategic choice and to provide a framework for quantifying realistic financial compensation bounds based on real world market and wind production data for multiple locations in Belgium. Results show that in a competitive market setting compensation payments for flexible power consumption are higher when dealing with higher wind forecast error levels. These results validate the economic benefits of having accurate wind production forecasts.

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  • Coninx, Kristof & Deconinck, Geert & Holvoet, Tom, 2018. "Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics," Applied Energy, Elsevier, vol. 218(C), pages 104-113.
    • Handle: RePEc:eee:appene:v:218:y:2018:i:c:p:104-113
    DOI: 10.1016/j.apenergy.2018.02.098
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    1. Hiroux, C. & Saguan, M., 2010. "Large-scale wind power in European electricity markets: Time for revisiting support schemes and market designs?," Energy Policy, Elsevier, vol. 38(7), pages 3135-3145, July.
    2. Daniel Friedman, 1998. "On economic applications of evolutionary game theory," Journal of Evolutionary Economics, Springer, vol. 8(1), pages 15-43.
    3. Timur Kuran & William H. Sandholm, 2008. "Cultural Integration and Its Discontents," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(1), pages 201-228.
    4. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar, 2015. "Analyzing major challenges of wind and solar variability in power systems," Renewable Energy, Elsevier, vol. 81(C), pages 1-10.
    5. repec:hhs:iuiwop:487 is not listed on IDEAS
    6. Brijs, Tom & De Vos, Kristof & De Jonghe, Cedric & Belmans, Ronnie, 2015. "Statistical analysis of negative prices in European balancing markets," Renewable Energy, Elsevier, vol. 80(C), pages 53-60.
    7. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    8. Nachbar, J H, 1990. ""Evolutionary" Selection Dynamics in Games: Convergence and Limit Properties," International Journal of Game Theory, Springer;Game Theory Society, vol. 19(1), pages 59-89.
    9. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    10. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    11. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, December.
    12. Denholm, Paul & Hand, Maureen, 2011. "Grid flexibility and storage required to achieve very high penetration of variable renewable electricity," Energy Policy, Elsevier, vol. 39(3), pages 1817-1830, March.
    13. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
    14. Agastya, Murali, 2004. "Stochastic stability in a double auction," Games and Economic Behavior, Elsevier, vol. 48(2), pages 203-222, August.
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