@article{HAN2026, 
author = {Zifen HAN and Xiping MA and Yin MA and Yuanxing XIA and Ke WANG},
title = {Individual Benefit-Driven Bidding Strategy for Distributed Energy Storage Power Stations Aggregating Participation in Two-Stage Energy Markets},
year = {2026},
journal = {Distributed Energy},
volume = {11},
number = {2},
pages = {116-128},
keywords = {distributed energy storage, energy storage aggregation, day-ahead energy market, real-time energy market, bidding strategy},
url = {https://www.sciopen.com/article/10.16513/j.2096-2185.DE.26110133},
doi = {10.16513/j.2096-2185.DE.26110133},
abstract = {To address the challenges in aggregated participation of distributed energy storage stations in electricity energy markets, including insufficient consideration of individual benefits, misalignment between aggregated feasible regions and operational objectives, and weak coupling of bidding strategies in two-stage markets, this paper proposes an individual benefit-driven aggregation method and a two-stage market bidding strategy for distributed energy storage. Firstly, based on Karush-Kuhn-Tucker (KKT) conditions, an optimization model for the aggregated feasible region incorporating individual benefit constraints is constructed, enabling multi-agent resource integration while ensuring that the revenue of each energy storage station is no less than that achieved through independent market participation. Subsequently, a bi-level optimization model for energy storage aggregators participating in day-ahead and real-time two-stage energy markets is established, characterizing strategic bidding behavior in the day-ahead stage and constructing a power adjustment mechanism based on day-ahead schedule deviations in the real-time stage. Based on game theory, the existence and uniqueness of market equilibrium are analyzed, and a two-stage market clearing method is proposed by transforming the bi-level optimization into a single-level KKT system. Simulation results on the Roy Billinton test system demonstrate that the proposed aggregation method increases the total revenue of four aggregators by 15.5% compared to the Minkowski summation method, with revenue improvements reaching 16.7% for aggregators with higher heterogeneity. The total revenue in the two-stage market is 19.0% higher than that achieved by participating only in the day-ahead market. The proposed method achieves energy storage resource integration and revenue enhancement while ensuring individual rationality, effectively coupling day-ahead strategic bidding with real-time flexibility adjustments, thereby providing theoretical foundations and methodological support for distributed energy storage participation in electricity markets.}
}