Research on the Optimization Decision Method for Hydrogen Load Aggregators to Participate in Peak Shaving Market

This article takes the perspective of Hydrogen Load Aggregator (HLA) to optimize the declaration strategy of peak shaving market, improve the flexible regulation capability of power system and HLA economy as the research objectives, and proposes an optimization strategy method for HLA to participate in peak shaving market. Firstly, an improved Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM) time series prediction model is developed to address peak shaving demand uncertainty. Secondly, a bidding strategy model incorporating dynamic pricing is constructed by comprehensively considering electrolyzer regulation costs, market supply–demand relationships, and system constraints. Thirdly, a market clearing model for peak shaving markets with HLA participation is designed through analysis of capacity contribution and marginal costs among different regulation resources. Finally, the capacity allocation model is designed with the goal of minimizing the total cost of peak shaving among various stakeholders within HLA, and the capacity won by HLA in the peak shaving market is reasonably allocated. Simulations conducted on a Python3.12-based experimental platform demonstrate the following: the improved CNN-LSTM model exhibits strong adaptability and robustness, the bidding model effectively enhances HLA market competitiveness, and the clearing model reduces system operator costs by 5.64%.