Optimised battery placement in distribution grids using quantum and quantum inspired QUBO solvers

The optimization of battery placement in distribution grids is a key challenge in the transition toward more reliable and resilient energy systems. This study evaluates the application of quantum and quantum-inspired solvers for addressing this problem in its Quadratic Unconstrained Binary Optimization (QUBO) formulations. The performance of various solvers, both quantum and quantum-inspired, is compared against traditional genetic algorithms. The analysis spans multiple test networks, including a large-scale real-world case study of the medium voltage grid in the Gipuzkoa region. Results indicate that quantum and quantum-inspired approaches can provide competitive or superior solutions balancing the three areas studied: investment costs, network reliability, and voltage control. Computational performance evaluations demonstrate that while simulated annealing remains the fastest among the tested approaches, hybrid quantum annealing successfully navigates complex optimization landscapes, reinforcing the feasibility of quantum computing in power grid applications.