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Optimal Generation and Trading in Solar Renewable Energy Certificate (SREC) Markets

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  • Arvind Shrivats
  • Sebastian Jaimungal

Abstract

SREC markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh. Firms offset these certificates against the floor and pay a penalty for any lacking certificates. Certificates are tradable assets, allowing firms to purchase/sell them freely. In this work, we formulate a stochastic control problem for generating and trading in SREC markets from a regulated firm’s perspective. We account for generation and trading costs, the impact both have on SREC prices, provide a characterization of the optimal strategy and develop a numerical algorithm to solve this control problem. Through numerical experiments, we explore how a firm who acts optimally behaves under various conditions. We find that an optimal firm’s generation and trading behaviour can be separated into various regimes, based on the marginal benefit of obtaining an additional SREC, and validate our theoretical characterization of the optimal strategy. We also conduct parameter sensitivity experiments.

Suggested Citation

  • Arvind Shrivats & Sebastian Jaimungal, 2020. "Optimal Generation and Trading in Solar Renewable Energy Certificate (SREC) Markets," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(1-2), pages 99-131, July.
    • Handle: RePEc:taf:apmtfi:v:27:y:2020:i:1-2:p:99-131
    DOI: 10.1080/1350486X.2020.1754260
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    Citations

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    Cited by:

    1. Masaaki Fujii & Akihiko Takahashi, 2021. "Equilibrium Price Formation with a Major Player and its Mean Field Limit," CARF F-Series CARF-F-509, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. René Carmona & Gökçe Dayanıklı & Mathieu Laurière, 2022. "Mean Field Models to Regulate Carbon Emissions in Electricity Production," Dynamic Games and Applications, Springer, vol. 12(3), pages 897-928, September.
    3. Cho, Sangmin & Kim, Jinsoo & Lim, Deokoh, 2024. "Optimal design of renewable energy certificate multipliers using an LCOE-Integrated AHP model: A case study of South Korea," Renewable Energy, Elsevier, vol. 226(C).
    4. David Evangelista & Yuri Saporito & Yuri Thamsten, 2022. "Price formation in financial markets: a game-theoretic perspective," Papers 2202.11416, arXiv.org.
    5. Dena Firoozi & Arvind V Shrivats & Sebastian Jaimungal, 2021. "Principal agent mean field games in REC markets," Papers 2112.11963, arXiv.org, revised Jun 2022.
    6. Hui, Wang & Xin-gang, Zhao & Ling-zhi, Ren & Fan, Lu, 2021. "An agent-based modeling approach for analyzing the influence of market participants’ strategic behavior on green certificate trading," Energy, Elsevier, vol. 218(C).
    7. Olivier Féron & Peter Tankov & Laura Tinsi, 2020. "Price Formation and Optimal Trading in Intraday Electricity Markets with a Major Player," Risks, MDPI, vol. 8(4), pages 1-21, December.
    8. Masaaki Fujii & Akihiko Takahashi, 2021. "``Equilibrium Price Formation with a Major Player and its Mean Field Limit''," CIRJE F-Series CIRJE-F-1162, CIRJE, Faculty of Economics, University of Tokyo.
    9. Liam Welsh & Sebastian Jaimungal, 2024. "Nash Equilibria in Greenhouse Gas Offset Credit Markets," Papers 2401.01427, arXiv.org, revised Jun 2024.
    10. Arvind V. Shrivats & Dena Firoozi & Sebastian Jaimungal, 2022. "A mean‐field game approach to equilibrium pricing in solar renewable energy certificate markets," Mathematical Finance, Wiley Blackwell, vol. 32(3), pages 779-824, July.
    11. Zhao, Chuandang & Xu, Jiuping & Wang, Fengjuan & Xie, Guo & Tan, Cheng, 2024. "Economic–environmental trade-offs based support policy towards optimal planning of wastewater heat recovery," Applied Energy, Elsevier, vol. 364(C).
    12. Masaaki Fujii & Akihiko Takahashi, 2021. "Equilibrium Price Formation with a Major Player and its Mean Field Limit," Papers 2102.10756, arXiv.org, revised Feb 2022.
    13. Steven Campbell & Yichao Chen & Arvind Shrivats & Sebastian Jaimungal, 2021. "Deep Learning for Principal-Agent Mean Field Games," Papers 2110.01127, arXiv.org.
    14. Olivier F'eron & Peter Tankov & Laura Tinsi, 2020. "Price formation and optimal trading in intraday electricity markets with a major player," Papers 2011.07655, arXiv.org.

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