Collaborative dispatch of microgrid clusters under multi-strategy for electric vehicles based on improved BES algorithm

Abstract A cluster system consisting of interconnected microgrids represents a novel form of smart grid architecture. However, the uncontrolled access of a large number of electric vehicles (EVs) poses challenges to system operation, including increased operational costs and threats to safe and stable grid performance. To address these issues, this paper proposes a Multi-Factor Oriented Model strategy (MOM strategy) and improved bald eagle search (BES) algorithm to optimize the multi-objective day-ahead scheduling of microgrid clusters. Firstly, building upon an established disordered charging model, roulette strategy is employed to integrate photovoltaic redundancy and time-of-use tariffs, thereby constructing an orderly charging and discharging model for EVs. Secondly, a multi-objective day-ahead scheduling model for microgrid cluster with multiple charging and discharging strategies for electric vehicles is established. The multi-microgrid scheduling problem is solved within the proposed optimization framework using an improved bald eagle search algorithm that integrates the Levy flight strategy and the golden sine strategy through an updating factor. Finally, through case study of a park-based microgrid cluster, the scheduling results under various scenarios and EV strategies are analyzed. The results demonstrate that the proposed improved algorithm outperforms other algorithms in practical applications. The multi-factor oriented ordinal model strategy effectively reduces operational costs, minimizes power fluctuations in the tie lines between microgrids, and mitigates the adverse effects of large-scale EV integration on the economic and stable operation of the cluster system. This study provides a scientific reference for addressing the scheduling challenges of EVs at different scales in day-ahead.