Optimizing Battery Energy Storage Systems for Grid Reliability: A Comparative Analysis of Sizing Methodologies

Abstract

Aim: The study aims to evaluate and compare optimization strategies for Battery Energy Storage System capacity planning and to determine an efficient and practical approach for enhancing power system stability and reliability in renewable energy dominated grids.

Methods: A systematic evaluation is conducted comparing Linear Programming, Particle Swarm Optimization, and rule based heuristic methods for BESS capacity planning. The analysis uses real operational data from the National Renewable Energy Laboratory, the California Independent System Operator, and standardized IEEE distribution networks. Performance is assessed based on computational requirements, solution accuracy, and feasibility of implementation. A hybrid two stage optimization framework is developed in which Linear Programming provides initial capacity estimates and Particle Swarm Optimization refines solutions under non linear operational constraints.

Results: Linear Programming achieves mathematically optimal solutions for linearized models and reduces total system costs by 15 to 20 percent compared to baseline scenarios. Particle Swarm Optimization performs effectively under non convex constraints and multiple competing objectives. Rule based methods deliver rapid results with computation times 10 to 100 times faster than optimization based techniques but with lower solution quality. The proposed hybrid framework produces solutions within 1.7 percent of the theoretical optimum while maintaining computational efficiency. Validation using California ISO market data shows that optimization based approaches capture 96 to 98 % of actual revenue opportunities.

Conclusion: The findings demonstrate that no single optimization method is universally optimal for BESS capacity planning. Linear Programming is suitable for linear formulations, Particle Swarm Optimization is effective for complex non linear problems, and rule based methods are useful for rapid decision making. Hybrid optimization approaches provide a balanced solution by combining high accuracy with practical computational performance.

Recommendation: System planners should adopt hybrid optimization strategies that integrate Linear Programming and Particle Swarm Optimization when addressing BESS planning problems involving both linear and non linear constraints. This approach ensures high solution quality while maintaining computational efficiency across diverse grid conditions and planning horizons.