Optimized Strategies for Peak Shaving and BESS Efficiency Enhancement through Cycle-Based Control and Cluster-Level Power Allocation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917923238445056 |
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| author | Gan, Guo Junhui, Li Gang, Mu Gangui, Yan |
| author_facet | Gan, Guo Junhui, Li Gang, Mu Gangui, Yan |
| contents | Battery Energy Storage Systems (BESS) are essential for peak shaving, balancing power supply and demand while enhancing grid efficiency. This study proposes a cycle-based control strategy for charging and discharging, which optimizes capture rate (CR), release rate (RR), and capacity utilization rate (CUR), improving BESS performance. Compared to traditional day-ahead methods, the cycle-based approach enhances operational accuracy and reduces capacity waste, achieving a CUR increase from 75.1% to 79.9%. An innovative cluster-level power allocation method, leveraging an improved Particle Swarm Optimization (PSO) algorithm, is introduced. This strategy reduces daily energy loss by 174.21 kWh (3.7%) and increases BESS efficiency by 0.4%. Transient and steady-state energy loss components are analyzed, revealing that transient loss proportion decreases significantly as power depth increases, from 27.2% at 1 MW to 1.3% at 10 MW. Simulations based on a detailed Simulink/Simscape model validate these methods, demonstrating enhanced peak shaving effectiveness and prolonged BESS lifespan by reducing equivalent cycles. The study provides a robust framework for optimizing BESS performance and efficiency in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10268 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimized Strategies for Peak Shaving and BESS Efficiency Enhancement through Cycle-Based Control and Cluster-Level Power Allocation Gan, Guo Junhui, Li Gang, Mu Gangui, Yan Systems and Control Battery Energy Storage Systems (BESS) are essential for peak shaving, balancing power supply and demand while enhancing grid efficiency. This study proposes a cycle-based control strategy for charging and discharging, which optimizes capture rate (CR), release rate (RR), and capacity utilization rate (CUR), improving BESS performance. Compared to traditional day-ahead methods, the cycle-based approach enhances operational accuracy and reduces capacity waste, achieving a CUR increase from 75.1% to 79.9%. An innovative cluster-level power allocation method, leveraging an improved Particle Swarm Optimization (PSO) algorithm, is introduced. This strategy reduces daily energy loss by 174.21 kWh (3.7%) and increases BESS efficiency by 0.4%. Transient and steady-state energy loss components are analyzed, revealing that transient loss proportion decreases significantly as power depth increases, from 27.2% at 1 MW to 1.3% at 10 MW. Simulations based on a detailed Simulink/Simscape model validate these methods, demonstrating enhanced peak shaving effectiveness and prolonged BESS lifespan by reducing equivalent cycles. The study provides a robust framework for optimizing BESS performance and efficiency in real-world applications. |
| title | Optimized Strategies for Peak Shaving and BESS Efficiency Enhancement through Cycle-Based Control and Cluster-Level Power Allocation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2502.10268 |