Multi-Objective Evolutionary Programming for Optimal Grid-Connected Photovoltaic System Design.

This paper presents a multi-objective-evolutionary programming (MOEP) algorithm for sizing grid-connected photovoltaic (GCPV) system. Unlike previous studies which focused on the sizing of GCPV system based on a single objective optimization problem, the sizing of GCPV system in this study was initially converted into a three different bi-objective optimization problems. The objectives were formulated based on the aim to maximize the expected technical and economic performance indicators for the system design. Apart from that, in each bi-objective optimization problem, the proposed MOEP was used to select the optimal combinations of photovoltaic (PV) module and inverter such that the optimal values of the two performance indicators could be achieved in a single run. The performance of the proposed MOEP-based sizing algorithm was later compared with the performance of the weighted sum method (WSM)- based sizing algorithm. In addition, when illustrating the effectiveness of the proposed MOEP in approximating the Pareto front, the non-dominated solutions from MOEP were compared with a benchmark algorithm which produced all possible design solutions. The proposed MOEP-based algorithm was found to outperform WSM in providing faster computations and better approximation of the Pareto front. [ABSTRACT FROM AUTHOR]