Integrated Stochastic Optimal Self-Scheduling for Two-Settlement Electricity Markets

Kai Pan () and Yongpei Guan ()
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Kai Pan: Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Yongpei Guan: Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611

INFORMS Journal on Computing, 2022, vol. 34, issue 3, 1819-1840

Abstract: The complexity of current electricity wholesale markets and the increased volatility of electricity prices because of the intermittent nature of renewable generation make independent power producers (IPPs) face significant challenges to submit offers. This challenge increases for those owning traditional coal-fired thermal generators and renewable generation. In this paper, an integrated stochastic optimal strategy is proposed for an IPP using the self-scheduling approach through its participation in both day-ahead and real-time markets (i.e., two-settlement electricity markets) as a price taker. In the proposed approach, the IPP submits an offer for all periods to the day-ahead market for which a multistage stochastic programming setting is explored for providing real-time market offers for each period as a recourse. This strategy has the advantage of achieving overall maximum profits for both markets in the given operational time horizon. Such a strategy is theoretically proved to be more profitable than alternative self-scheduling strategies as it takes advantage of the continuously realized scenario information of the renewable energy output and real-time prices over time. To improve computational efficiency, we explore polyhedral structures to derive strong valid inequalities, including convex hull descriptions for certain special cases, thus strengthening the formulation of our proposed model. Polynomial-time separation algorithms are then established for the derived exponential-sized inequalities to speed up the branch-and-cut process. Finally, both numerical and real case studies demonstrate the potential of the proposed strategy. Summary of Contribution: This paper develops innovative models and methods to study a family of practically important problems via the interactions of operations research and computing. Specifically, this paper provides in-depth analyses of innovative stochastic optimization modeling approaches and develops computationally efficient polyhedral results. The paper also verifies the effectiveness of proposed analyses via extensive computing experiments.

Keywords: innovative formulation; renewable generation; self-scheduling; stochastic optimization (search for similar items in EconPapers)
Date: 2022
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