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Optimal Placement and Capacity Dispatch Optimization of BESS for Transmission Congestion Mitigation in Deregulated Power Systems: A Salp Swarm Optimization Approach

Sep 2026 · Applied Sciences · 0 citations · 24 references

Abstract

Transmission congestion has become a major challenge in deregulated power systems (DPS). Although flexible AC transmission system (FACTS) devices are effective for congestion management, their high installation cost and complex control requirements limit widespread deployment. This paper proposes a battery energy storage system (BESS)-based congestion mitigation approach for DPS. The optimal locations of BESS units are identified using bus sensitivity factors (BSFs), while a weighted-sum scalarized single-objective optimization problem involving active power loss, voltage deviation, and system security margin is solved using salp swarm optimization (SSO). The main contribution of this work is the integrated BSF–SSO framework, which combines sensitivity-based placement with an efficient metaheuristic optimization technique. The proposed methodology is validated on the IEEE 30-bus test system. Results show that the optimally placed BESS units reduce active power losses by 31.7% relative to the base case, while it is reduced by 39.1% when using the post contingency of 25 MW as the baseline, voltage deviation by approximately 30.26%, and the congestion security index by 25.14%, thereby significantly alleviating transmission congestion. In addition, reactive power flow distribution and 24 h state-of-charge (SoC) dynamics are analyzed to provide a more comprehensive assessment of system performance. Compared with FACTS-based solutions, the proposed approach offers superior technical and economic benefits. A parametric sensitivity analysis further shows that SSO attains near-optimal convergence with as few as 15 agents, offering a favorable accuracy-versus-runtime trade-off for real-time DPS operation; the main results reported in this study, however, were generated using the more conservative configuration of n = 30 agents to maximize solution robustness.

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