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ADAPT: A Price-Stabilizing Compliance Policy for Renewable Energy Certificates: The Case of SREC Markets

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  • Javad Khazaei

    (Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08540)

  • Michael Coulon

    (Department of Business and Management, University of Sussex, Brighton BN1 9SL, UK)

  • Warren B. Powell

    (Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08540)

Abstract

Currently, most Renewable Energy Certificate (REC) markets are defined based on targets that create an artificial step demand function resembling a cliff. This target policy produces volatile prices that can make investing in renewables a risky proposition. In this paper, we propose an alternative policy called Adjustable Dynamic Assignment of Penalties and Targets (ADAPT) that uses a sloped compliance penalty and a self-regulating requirement schedule, both designed to stabilize REC prices, helping to alleviate a common weakness of environmental markets. To capture market behavior, we model the market as a stochastic dynamic programming problem to understand how the market might balance the decision to use a REC now versus holding it for future periods (in the face of uncertain new supply). Then, we present and prove some of the properties of this market, and finally we show that this mechanism reduces the volatility of REC prices, which should stabilize the market and encourage long-term investment in renewables.

Suggested Citation

  • Javad Khazaei & Michael Coulon & Warren B. Powell, 2017. "ADAPT: A Price-Stabilizing Compliance Policy for Renewable Energy Certificates: The Case of SREC Markets," Operations Research, INFORMS, vol. 65(6), pages 1429-1445, December.
    • Handle: RePEc:inm:oropre:v:65:y:2017:i:6:p:1429-1445
    DOI: 10.1287/opre.2017.1641
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    Cited by:

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    2. Arvind Shrivats & Sebastian Jaimungal, 2019. "Optimal Behaviour in Solar Renewable Energy Certificate (SREC) Markets," Papers 1904.06337, arXiv.org, revised Apr 2020.
    3. Baamonde-Seoane, María A. & Carmen Calvo-Garrido, María del & Coulon, Michael & Vázquez, Carlos, 2021. "Numerical solution of a nonlinear PDE model for pricing Renewable Energy Certificates (RECs)," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    4. Hao, Peng & Guo, Jun-Peng & Chen, Yihsu & Xie, Bai-Chen, 2020. "Does a combined strategy outperform independent policies? Impact of incentive policies on renewable power generation," Omega, Elsevier, vol. 97(C).
    5. 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.
    6. Nguyen, Hieu T. & Felder, Frank A., 2020. "Generation expansion planning with renewable energy credit markets: A bilevel programming approach," Applied Energy, Elsevier, vol. 276(C).
    7. Thomas D. Jeitschko & Pallavi Pal, 2021. "Curbing Price Fluctuations in Cap-and-Trade Auctions," CESifo Working Paper Series 9266, CESifo.
    8. Dena Firoozi & Arvind V Shrivats & Sebastian Jaimungal, 2021. "Principal agent mean field games in REC markets," Papers 2112.11963, arXiv.org, revised Jun 2022.
    9. Zhang, Yanfang & Gao, Qi & Li, Hao & Shi, Xunpeng & Zhou, Dequn, 2024. "Navigating the energy transition with the Carbon-Energy-Green-Electricity scheme: An industrial chain-based approach for China's carbon neutrality," Energy Economics, Elsevier, vol. 140(C).
    10. Jiang, Zhong-Zhong & He, Na & Huang, Song, 2021. "Government penalty provision and contracting with asymmetric quality information in a bioenergy supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    11. Steven Campbell & Yichao Chen & Arvind Shrivats & Sebastian Jaimungal, 2021. "Deep Learning for Principal-Agent Mean Field Games," Papers 2110.01127, arXiv.org.

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