Benchmarking Classical Metaheuristic Algorithms for Techno-Economic Optimisation of PV Battery Renewable Energy Systems

Optimising grid-connected photovoltaic (PV)-battery renewable energy systems involves balancing economic performance, renewable energy use, and grid reliance. While HOMER Grid offers dependable optimisation, running multiple simulations for benchmarking algorithms is computationally costly. Surrogate-assisted optimisation presents a more efficient alternative for testing several algorithms under consistent conditions. This research evaluated six classic metaheuristics, Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimiser (GWO), Whale Optimisation Algorithm (WOA), and Stochastic Fractal Search Algorithm (SFSA), to find the most cost-effective techno-economic sizing of a PV battery system. Using a dataset of 272 core HOMER Grid cases, with 258 cases used for extended economic indicators, a distance-weighted k-nearest neighbour surrogate model was developed. All algorithms ran with the same population sizes, iteration limits, and independent repetitions. Their performance was assessed through solution quality, convergence behaviour, runtime, Friedman ranking, and Holm-adjusted Wilcoxon tests. The best design included a 7.799 kW PV array, 23 batteries, and a 2.560 kW converter, with a levelised cost of energy (LCOE) of R0.6149/kWh, a net present cost (NPC) of R52,401.45, a renewable fraction of 97.25%, and annual grid energy purchases of 216.26 kWh. Differential Evolution (DE) delivered the top average performance; SFSA was statistically comparable to DE (Holm-adjusted p = 0.999945), and PSO showed the fastest convergence with consistently near-optimal results. Overall, DE, SFSA, and PSO proved highly robust and effective, establishing a useful reference point for evaluating alternative surrogate-assisted PV battery optimisation approaches.