Genetic Algorithm-Based Techno-Economic Optimization of a PV–Wind–Wave–Battery Hybrid Microgrid for the Island of Lampedusa

The decarbonization of isolated island power systems is hindered by dependence on diesel generation, limited operational flexibility, and pronounced seasonal demand variability. Lampedusa constitutes a representative Mediterranean case study, characterized by an isolated diesel-based power system with limited renewable energy integration and significant tourism-driven load fluctuations. This study proposes an integrated techno-economic optimization framework for the design and operation of a hybrid island microgrid combining photovoltaic, wind, and wave energy converters with battery energy storage systems (BESSs) and existing diesel generation. The analysis is based on a deterministic hourly resolution dispatch model implemented in MATLAB 2025a and coordinated by an energy management system (EMS), accounting for operational constraints, diesel minimum-load requirements, renewable technology diversification constraints, and battery storage dynamics. The optimal system configuration is determined using a genetic algorithm that minimizes the levelized cost of energy (LCOE) over a discrete design space. Under an annual electricity demand of 33.8 GWh, the optimal configuration achieves a renewable penetration of approximately 65% with a minimum LCOE of 0.1719 €/kWh, requiring 6.0 MW photovoltaic capacity, 3.0 MW wind capacity, 0.5 MW wave energy capacity, and 5.2 MWh BESS energy capacity. Renewable penetration targets exceeding 70% lead to infeasible operating solutions under the imposed system constraints.