Enhancing Power Systems Transmission Adequacy via Optimal BESS Siting and Sizing using Benders Decomposition with Feasibility Cuts

TL;DR

This paper introduces a comprehensive framework for optimally siting and sizing battery energy storage systems in power transmission networks to improve resource adequacy, using advanced decomposition techniques and heuristics for complex large-scale problems.

Contribution

It develops a novel mixed-integer second-order cone programming approach combined with Benders Decomposition and feasibility cuts for efficient BESS placement and sizing in large-scale power systems.

Findings

Effective handling of high temporal resolution network constraints

Parallelizable formulation with strong convergence guarantees

Improved resource adequacy through optimized BESS deployment

Abstract

This work presents a general framework for the operationally driven optimal siting and sizing of battery energy storage systems in power transmission networks, aimed at enhancing their resource adequacy. The approach considers multi-period planning horizons, enforces network constraints at high temporal resolution, and targets large-scale meshed systems. The resulting computationally complex mixed-integer non-linear programming problem is reformulated as a mixed-integer second-order cone programming problem and solved via Generalized Benders Decomposition, with feasibility cuts enabling congestion management and voltage regulation under binding network limits. A tailored heuristic recovers an alternating-current power-flow-feasible operating point from the relaxed solution. The proposed formulation is parallelizable, yielding excellent computational performance, while featuring rigorous guarantees of convergence.