Capacity Optimization of Marine Power Generation System Based on Adaptive Particle Swarm Algorithm

To optimize the utilization of renewable energy and enhance the cost-effectiveness of a ship's photovoltaic-diesel-storage complementary power generation system, it is crucial to determine the optimal installed capacity for each power source. The process begins by creating separate mathematical models for the output of photovoltaic array, batteries and diesel generators, combining hourly meteorological data for an entire year with the ship's corresponding load requirements. Subsequently, the Adaptive particle swarm algorithm is employed to optimize the capacities of these power sources, aiming to minimize the system's total lifecycle cost. The algorithm employs asynchronous learning factors with asymmetric values and dynamically adjusts inertia weights to assist particles in escaping local optima, thereby enhancing convergence accuracy.