Installed capacity optimization of hybrid energy generation system

In this study, the installed capacity selection of a hybrid energy generation system (HEGS) based on the algorithm of improved particle swarm optimization (IPSO) with dynamically changing inertia weight and acceleration coefficients is presented. In the IPSO, the penalty technique is used to solve the optimization problem with equality and inequality constraints for updating the particle´s position and its global best position. The studied HEGS, which includes wind power, photovoltaic (PV), and fuel cells (FC), aims to suppress the penalty bill caused by exceeding the contract power capacity with the power company and to supply the backup emergent power. In order to enable each energy source for making the best contribution in the system and satisfying the required load demand at minimal installation cost and shortening the payback period, an optimal objective function by considering the installation cost and cost recovery is formulated, and the optimal ratio of the installed capacity of the HEGS can be obtained by calculating the minimum value of the objective function. The proposed IPSO algorithm has been examined, tested and compared with other methods on the optimization problem, and proven to be more efficient in searching the global solution through numerical simulations of a real case.