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Two-stage stochastic optimization frameworks to aid in decision-making under uncertainty for variable resource generators participating in a sequential energy market

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  • Al-Lawati, Razan A.H.
  • Crespo-Vazquez, Jose L.
  • Faiz, Tasnim Ibn
  • Fang, Xin
  • Noor-E-Alam, Md.

Abstract

Decisions for a variable renewable resource generator’s commitment in the energy market are typically made in advance when little information is obtainable about wind availability and market prices. Much research has been published recommending various frameworks for addressing this issue. However, these frameworks are limited as they do not consider all markets a producer can participate in. Moreover, current stochastic programming models do not allow for uncertainty data to be updated as more accurate information becomes available. This work proposes two decision-making frameworks for a wind energy generator participating in day-ahead, intraday, reserve, and balancing markets. The first framework is a two-stage stochastic convex optimization approach, where both scenario-independent and scenario-dependent decisions are made concurrently. The second framework is a series of four two-stage stochastic optimization models wherein the results from each model feed into each subsequent model allowing for scenarios to be updated as more information becomes available to the decision-maker. In the simulation experiments, the multi-phase framework performs better than the single-phase in every run, and results in an average profit increase of 7%. The proposed optimization frameworks aid in better decision-making while addressing uncertainty related to variable resource generators and maximize the return on investment.

Suggested Citation

  • Al-Lawati, Razan A.H. & Crespo-Vazquez, Jose L. & Faiz, Tasnim Ibn & Fang, Xin & Noor-E-Alam, Md., 2021. "Two-stage stochastic optimization frameworks to aid in decision-making under uncertainty for variable resource generators participating in a sequential energy market," Applied Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:appene:v:292:y:2021:i:c:s0306261921003706
    DOI: 10.1016/j.apenergy.2021.116882
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    References listed on IDEAS

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    2. Hohl, Cody & Lo Prete, Chiara & Radhakrishnan, Ashish & Webster, Mort, 2023. "Intraday markets, wind integration and uplift payments in a regional U.S. power system," Energy Policy, Elsevier, vol. 175(C).
    3. Silva-Rodriguez, Lina & Sanjab, Anibal & Fumagalli, Elena & Gibescu, Madeleine, 2024. "Light robust co-optimization of energy and reserves in the day-ahead electricity market," Applied Energy, Elsevier, vol. 353(PA).
    4. Xiong, Houbo & Zhou, Yue & Guo, Chuangxin & Ding, Yi & Luo, Fengji, 2023. "Multi-stage risk-based assessment for wind energy accommodation capability: A robust and non-anticipative method," Applied Energy, Elsevier, vol. 350(C).
    5. Fusco, Andrea & Gioffrè, Domenico & Francesco Castelli, Alessandro & Bovo, Cristian & Martelli, Emanuele, 2023. "A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets," Applied Energy, Elsevier, vol. 336(C).
    6. Rujie Zhu & Kaushik Das & Poul Ejnar Sørensen & Anca Daniela Hansen, 2023. "Optimal Participation of Co-Located Wind–Battery Plants in Sequential Electricity Markets," Energies, MDPI, vol. 16(15), pages 1-17, July.
    7. Weigang Jin & Peihua Wang & Jiaxin Yuan, 2024. "Key Role and Optimization Dispatch Research of Technical Virtual Power Plants in the New Energy Era," Energies, MDPI, vol. 17(22), pages 1-24, November.
    8. Lindberg, O. & Zhu, R. & Widén, J., 2024. "Quantifying the value of probabilistic forecasts when trading renewable hybrid power parks in day-ahead markets: A Nordic case study," Renewable Energy, Elsevier, vol. 237(PB).
    9. Cao, Jiaxin & Yang, Bo & Zhu, Shanying & Chung, Chi Yung & Guan, Xinping, 2022. "Multi-level coordinated energy management for energy hub in hybrid markets with distributionally robust scheduling," Applied Energy, Elsevier, vol. 311(C).

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