Metaheuristics for renewable energy residential communities detection

Metaheuristics are high-level heuristic strategies that guide the behaviour of subordinated heuristics to solve optimization problems. This paper proposes a population-based metaheuristic approach for the detection of cost-efficient renewable-based residential energy communities. The problem is formulated as an optimization model minimizing total operational costs through coordinated photovoltaic generation and energy storage systems. Particle Swarm Optimization (PSO) is employed to efficiently explore the large combinatorial space of candidate communities. Results on a residential case study show that PSO achieves near-optimal solutions comparable to Monte Carlo-based benchmarks, while reducing computational time.