Open Access

ARTICLE

Second-Life Battery Energy Storage System Capacity Planning and Power Dispatch via Model-Free Adaptive Control-Embedded Heuristic Optimization

Chuan Yuan¹, Chang Liu^2,3, Shijun Chen¹, Weiting Xu^2,3, Jing Gou¹, Ke Xu^2,3, Zhengbo Li^4,*, Youbo Liu⁴

1 State Grid Sichuan Electric Power Company, Chengdu, 610041, China
2 State Grid Sichuan Electric Power Company Economic and Technological Research Institute, Chengdu, 610041, China
3 Sichuan New Power System Research Institute Co., Ltd., Chengdu, 610041, China
4 The College of Electric Engineering, Sichuan University, Chengdu, 610065, China

* Corresponding Author: Zhengbo Li. Email:

(This article belongs to the Special Issue: Construction and Control Technologies of Renewable Power Systems Based on Grid-Forming Energy Storage)

Energy Engineering 2025, 122(9), 3573-3593. https://doi.org/10.32604/ee.2025.067785

Received 12 May 2025; Accepted 25 June 2025; Issue published 26 August 2025

Abstract

The increasing penetration of second-life battery energy storage systems (SLBESS) in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries. This paper presents a novel model-free adaptive voltage control-embedded dung beetle-inspired heuristic optimization algorithm for optimal SLBESS capacity configuration and power dispatch. To simultaneously address the computational complexity and ensure system stability, this paper develops a comprehensive bilevel optimization framework. At the upper level, a dung beetle optimization algorithm determines the optimal SLBESS capacity configuration by minimizing total lifecycle costs while incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy. At the lower level, a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states, state-of-health variations, and degradation constraints. The proposed model-free approach circumvents the need for complex battery charging/discharging power control models and extensive historical data requirements while maintaining system stability through adaptive control mechanisms. A novel cycle life degradation model is developed to quantify the relationship between remaining useful life, depth of discharge, and operational patterns. The integrated framework enables simultaneous strategic planning and operational control, ensuring both economic efficiency and extended battery lifespan. The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems, demonstrating superior computational efficiency, robust performance across different network configurations, and significant improvements in battery utilization compared to conventional approaches.

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Keywords

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