Microgrid sizing via profit maximization: a population based optimization approach

In this paper we present a computational intelligence approach to solve the optimal sizing problem of grid connected microgrid (MG) components. A simulation model has been built for the MG and comprises households, solar photovoltaic (PV) plants, wind turbines (WT) and energy storage (ES) systems. The goal is the maximization of the long term economic benefits for the community being served by the MG. We choose to optimize the net present value (NPV) of the whole investment in a cost benefits analysis (CBA) scenario. In particular, due to the complexity and the high-dimensionality of the problem, we solved it using population based optimization techniques. We tested four different algorithms in their basic form, i.e. artificial bee colonies, particle swarm optimization, genetic algorithm and gravitational search algorithm, comparing their performances. The effectiveness of the approach is tested in a case study where the optimal ratings for the PVs, WTs and ESs are determined using real weather and electrical demand data in the central east part of Italy.