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Adaptive optimization algorithms for scheduling multiple battery energy storage systems in complex grid scenarios

Author

Listed:
  • Ji, Fan
  • Zhu, Ziyu
  • Fan, Hongtao
  • Ma, Lei
  • Li, Xinran
  • Ji, Renyun
  • Yu, Zhuying
  • Fu, Shiyi
  • Sun, Yaojie

Abstract

The rapid proliferation of renewable energy sources has compounded the complexity of power grid management, particularly in scheduling multiple Battery Energy Storage Systems (BESS). Addressing this challenge, we present the Adaptive Optimization Energy Management System (AO-EMS) algorithm that significantly enhances the flexibility and reliability of power system dispatch in complex grid environments. Our algorithm adeptly manages multiple Points of Common Coupling (PoC) and Transformer Nodes (TNs), employing a priority-based capacity control mechanism alongside an integrated State of Charge (SoC) balancing strategy. These features ensure equitable utilization and extended longevity of storage resources. Adjustable priority settings enable precise attainment of control goals within defined constraints, optimizing resource allocation and bolstering system stability. Through theoretical analysis and practical implementation—including scenarios with intricate branch configurations—we demonstrate the algorithm’s effectiveness. Operational data from two real-world test sites and one transient simulation further demonstrate the stability and robustness of our adaptive optimization approach under diverse topologies, renewable variability, and load fluctuations.

Suggested Citation

  • Ji, Fan & Zhu, Ziyu & Fan, Hongtao & Ma, Lei & Li, Xinran & Ji, Renyun & Yu, Zhuying & Fu, Shiyi & Sun, Yaojie, 2026. "Adaptive optimization algorithms for scheduling multiple battery energy storage systems in complex grid scenarios," Applied Energy, Elsevier, vol. 405(C).
    • Handle: RePEc:eee:appene:v:405:y:2026:i:c:s0306261925018793
    DOI: 10.1016/j.apenergy.2025.127149
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