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

• Published in: Applied energy Vol. 292; p. 116882
• Main Authors: Al-Lawati, Razan A.H., Crespo-Vazquez, Jose L., Faiz, Tasnim Ibn, Fang, Xin, Noor-E-Alam, Md
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.06.2021
• Subjects: Data uncertainty; Data-driven scenarios; Sequential energy market; Two-stage stochastic optimization; Variable renewable resource generator; Wind energy; Variable renewable resource generator; Wind energy; Data-driven scenarios; Sequential energy market; Data uncertainty; Two-stage stochastic optimization
• DOI: 10.1016/j.apenergy.2021.116882
• ISSN: 0306-2619

•Considered simultaneously day-ahead, intraday, reserve, and balancing markets.•Decision-making frameworks are presented for wind and storage power plants.•The sequential framework involves a series of stochastic optimization models.•The proposed models are validated with a real-world case study. 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.