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Computational modelling of price formation in the electricity pool of England and Wales

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  • Bunn, Derek W.
  • Day, Christopher J.

Abstract

This paper develops a detailed computational model of price formation in the England and Wales electricity pool, as it operated for 11 years from 1990 to 2001. It is clear that during this period, the repeated nature of the daily auction, between a small number of generators, with a substantial amount of information in common, gave rise to a continuous evolution of learning and gaming in practice with no evidence of convergence to a stationary Nash solution. In terms of representing reality, a computational approach inspired by evolutionary economics, can succeed in reflecting well the type of behaviour observed, to an extent that cannot be matched by alternative analytical models. Cycles of pricing appear in the model, apparently as they seem to do in practice, yet average behaviour has been validated against the theoretical supply function results for the more stylised circumstances where analytical results are possible. The paper therefore makes a methodological contribution in the development of a model of competitive electricity markets inspired by computational learning and gaming. It also makes an applied contribution by providing a more realistic basis for identifying whether high market prices can be ascribed to problems of market structure or market conduct.

Suggested Citation

  • Bunn, Derek W. & Day, Christopher J., 2009. "Computational modelling of price formation in the electricity pool of England and Wales," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 363-376, February.
    • Handle: RePEc:eee:dyncon:v:33:y:2009:i:2:p:363-376
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    References listed on IDEAS

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    Cited by:

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    2. Lin-Ju Chen & Lei Zhu & Ying Fan & Sheng-Hua Cai, 2013. "Long-Term Impacts of Carbon Tax and Feed-in Tariff Policies on China's Generating Portfolio and Carbon Emissions: A Multi-Agent-Based Analysis," Energy & Environment, , vol. 24(7-8), pages 1271-1293, December.
    3. Wu, Zhaoyuan & Zhou, Ming & Zhang, Ting & Li, Gengyin & Zhang, Yan & Liu, Xiaojuan, 2020. "Imbalance settlement evaluation for China's balancing market design via an agent-based model with a multiple criteria decision analysis method," Energy Policy, Elsevier, vol. 139(C).
    4. Bunn, Derek & Koc, Veli & Sapio, Alessandro, 2015. "Resource externalities and the persistence of heterogeneous pricing behavior in an energy commodity market," Energy Economics, Elsevier, vol. 48(C), pages 265-275.
    5. Blyth, William & Bunn, Derek & Chronopoulos, Michail & Munoz, Jose, 2014. "Systematic Analysis of the Evolution of Electricity and Carbon Markets under Deep Decarbonisation," Discussion Papers 2014/39, Norwegian School of Economics, Department of Business and Management Science.
    6. Bazmi, Aqeel Ahmed & Zahedi, Gholamreza, 2011. "Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3480-3500.
    7. 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.
    8. Banal-Estañol, Albert & Rupérez Micola, Augusto, 2011. "Behavioural simulations in spot electricity markets," European Journal of Operational Research, Elsevier, vol. 214(1), pages 147-159, October.
    9. Young, David & Poletti, Stephen & Browne, Oliver, 2014. "Can agent-based models forecast spot prices in electricity markets? Evidence from the New Zealand electricity market," Energy Economics, Elsevier, vol. 45(C), pages 419-434.

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