Optimal Bidding Framework for Integrated Renewable-Storage Plant in High-Dimensional Real-Time Markets

Yuhao Song, Shaowei Huang, Laijun Chen, Sen Cui and Shengwei Mei ()
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Yuhao Song: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Shaowei Huang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Laijun Chen: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Sen Cui: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Shengwei Mei: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Sustainability, 2025, vol. 17, issue 18, 1-23

Abstract: With the development of electricity spot markets, the integrated renewable-storage plant (IRSP) has emerged as a crucial entity in real-time energy markets due to its flexible regulation capability. However, traditional methods face computational inefficiency in high-dimensional bidding scenarios caused by expansive decision spaces, limiting online generation of multi-segment optimal quotation curves. This paper proposes a policy migration-based optimization framework for high-dimensional IRSP bidding: First, a real-time market clearing model with IRSP participation and an operational constraint-integrated bidding model are established. Second, we rigorously prove the monotonic mapping relationship between the cleared output and the real-time locational marginal price (LMP) under the market clearing condition and establish mathematical foundations for migrating the self-dispatch policy to the quotation curve based on value function concavity theory. Finally, a generalized inverse construction method is proposed to decompose the high-dimensional quotation curve optimization into optimal power response subproblems within price parameter space, substantially reducing decision space dimensionality. The case study validates the framework effectiveness through performance evaluation of policy migration for a wind-dual energy storage plant, demonstrating that the proposed method achieves 90% of the ideal revenue with a 5% prediction error and enables reinforcement learning algorithms to increase their performance from 65.1% to 84.2% of the optimal revenue. The research provides theoretical support for resolving the “dimensionality–efficiency–revenue” dilemma in high-dimensional bidding and expands policy possibilities for IRSP participation in real-time markets.

Keywords: integrated renewable-storage plant; high-dimensional markets; optimal bidding framework; sustainable operation; optimized operation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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