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Pricing of fluctuations in electricity markets

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  • Tsitsiklis, John N.
  • Xu, Yunjian

Abstract

In an electric power system, demand fluctuations may result in significant ancillary cost to suppliers. Furthermore, in the near future, deep penetration of volatile renewable electricity generation is expected to exacerbate the variability of demand on conventional thermal generating units. We address this issue by explicitly modeling the ancillary cost associated with demand variability. We argue that a time-varying price equal to the suppliers’ instantaneous marginal cost may not achieve social optimality, and that consumer demand fluctuations should be properly priced. We propose a dynamic pricing mechanism that explicitly encourages consumers to adapt their consumption so as to offset the variability of demand on conventional units. Through a dynamic game-theoretic formulation, we show that (under suitable convexity assumptions) the proposed pricing mechanism achieves social optimality asymptotically, as the number of consumers increases to infinity. Numerical results demonstrate that compared with marginal cost pricing, the proposed mechanism creates a stronger incentive for consumers to shift their peak load, and therefore has the potential to reduce the need for long-term investment in peaking plants.

Suggested Citation

  • Tsitsiklis, John N. & Xu, Yunjian, 2015. "Pricing of fluctuations in electricity markets," European Journal of Operational Research, Elsevier, vol. 246(1), pages 199-208.
  • Handle: RePEc:eee:ejores:v:246:y:2015:i:1:p:199-208
    DOI: 10.1016/j.ejor.2015.04.020
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    2. Durmaz, Tunç, 2016. "Precautionary Storage in Electricity Markets," Discussion Papers 2016/5, Norwegian School of Economics, Department of Business and Management Science.
    3. Clay Campaigne & Shmuel S. Oren, 2016. "Firming renewable power with demand response: an end-to-end aggregator business model," Journal of Regulatory Economics, Springer, vol. 50(1), pages 1-37, August.
    4. Tunç Durmaz, 2018. "Energy Storage and Renewable Energy: An Economic Approach," Yildiz Social Science Review, Yildiz Technical University, vol. 4(1), pages 15-38.
    5. Lester, Mason Scott & Bramstoft, Rasmus & Münster, Marie, 2020. "Analysis on Electrofuels in Future Energy Systems: A 2050 Case Study," Energy, Elsevier, vol. 199(C).
    6. Aussel, Didier & Brotcorne, Luce & Lepaul, Sébastien & von Niederhäusern, Léonard, 2020. "A trilevel model for best response in energy demand-side management," European Journal of Operational Research, Elsevier, vol. 281(2), pages 299-315.
    7. Moriarty, John & Palczewski, Jan, 2017. "Real option valuation for reserve capacity," European Journal of Operational Research, Elsevier, vol. 257(1), pages 251-260.
    8. Yang, Changhui & Meng, Chen & Zhou, Kaile, 2018. "Residential electricity pricing in China: The context of price-based demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2870-2878.
    9. Ren'e Aid & Dylan Possamai & Nizar Touzi, 2018. "Optimal electricity demand response contracting with responsiveness incentives," Papers 1810.09063, arXiv.org, revised May 2019.
    10. Tunç Durmaz, 2017. "Optimal storage under uncertainty: investigating the implications of frugality and prudence," Economics Bulletin, AccessEcon, vol. 37(3), pages 1624-1629.
    11. Moiseeva, Ekaterina & Wogrin, Sonja & Hesamzadeh, Mohammad Reza, 2017. "Generation flexibility in ramp rates: Strategic behavior and lessons for electricity market design," European Journal of Operational Research, Elsevier, vol. 261(2), pages 755-771.
    12. Xie Jiaping & Zhang Weisi & Xia Yu & Liang Ling & Kong Lingcheng, 2018. "Electricity Price of Hybrid Power System and Decision Making of Renewable Energy Investment Capacity," Journal of Systems Science and Information, De Gruyter, vol. 6(3), pages 193-213, June.
    13. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    14. Xiong Hu & Zhi-Wei Liu, 2017. "Distributed Optimal Day-Ahead Scheduling in a Smart Grid: A Trade-Off among Consumers, Power Suppliers, and Transmission Owners," Complexity, Hindawi, vol. 2017, pages 1-11, December.
    15. Szabó, Dávid Zoltán & Duck, Peter & Johnson, Paul, 2020. "Optimal trading of imbalance options for power systems using an energy storage device," European Journal of Operational Research, Elsevier, vol. 285(1), pages 3-22.

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