A novel energy management framework for retired battery-integrated microgrid with peak shaving and frequency regulation

Yuyang Wan, Hancheng Zhang, Yuanyuan Hu, Yanbo Wang, Xueshan Liu, Qun Zhou and Zhe Chen

Energy, 2024, vol. 313, issue C

Abstract: As intermittent renewable energy sources (RESs) increasingly become integral to the power grid, the imperative to ensure frequency stability of power grid has emerged as a critical challenge. Addressing this, this paper proposes a novel energy management framework in retired battery-integrated microgrid with grid frequency regulation (FR) and peak shaving. The EV battery can be hierarchically utilized by the two-stage control framework to improve economic efficiency. In the first stage energy management, a novel heuristic algorithm called the walrus optimization algorithm (WaOA) is employed to implement the optimal energy scheduling of microgrid for minimizing operating costs. In the second stage control strategy, a deep deterministic policy gradient (DDPG) agent is applied to dynamically adjust the power sharing of energy storage station according to the states of retired batteries. Furthermore, a comprehensive retired battery aging model is incorporated into the proposed strategy to reduce total battery capacity loss. The simulation results verify the superior performance of the proposed energy management framework under various microgrid scenarios. This work provides a practical approach to the cascaded utilization of EV batteries, which further improves the sustainability and economics of EV batteries.

Keywords: Renewable energy sources; Microgrid; Frequency regulation; Retired battery; Heuristic algorithm; Deep reinforcement learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036855

DOI: 10.1016/j.energy.2024.133907

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