Optimal sizing and siting of energy storage systems based on power grid vulnerability analysis: a trilevel optimization model

The integration of high proportions of renewable energy reduces the reliability and flexibility of power systems. Coordinating the sizing and siting of battery energy storage systems (BESS) is crucial for mitigating grid vulnerability. To determine the optimal capacity and location of BESS in high-penetration renewable energy systems, this paper proposes a trilevel optimization model for BESS sizing and siting. The upper-level is a pre-optimization layer that uses global vulnerability index to filter out potential BESS siting schemes that cannot mitigate system vulnerability, thereby enhancing the efficiency of capacity allocation and siting. The middle-level employs an improved particle swarm optimization algorithm to determine the optimal capacity and power configuration of BESS, aiming to maximize its equivalent annual revenue. The lower-level uses an improved butterfly algorithm to maximize the expected revenue of daily operation scheduling. The improved algorithms not only enhance computational efficiency but also significantly improve accuracy. The proposed model is validated through case studies based on the extended IEEE 33-bus system. Case studies reveal that configuring BESS at critical nodes improves the global vulnerability index to 0.282, reflecting a significant enhancement in grid stability. Additionally, this configuration increases annual revenue by $14,000 compared to conventional strategies and shortens the investment payback period by 0.4 years. • Propose a trilevel optimization model for BESS sizing and siting. • Pre-optimization layer uses vulnerability index for efficiency. • Determine the optimal sizing and siting while enhancing economic benefits. • Improved optimization algorithm enhances sizing and siting efficiency.