Optimization of Module Parameters for PV Power Estimation Using a Hybrid Algorithm

Abstract

This article proposes a novel method to estimate the optimal parameters and power outputs for photovoltaic (PV) power generation. Accurate estimation for PV power generation allows efficient scheduling to meet the load demand and reduces the effect of uncertainty for a microgrid. The parameters that are provided by the PV manufacturer have a nonlinear relationship with power output and may vary with the aging of the PV cells. To allow finer and more accurate estimation for PV power output, the parameters of the single-diode $R_{p}$ model are transformed into 13 parameters under various weather conditions. The principal component analysis (PCA) and an assessment index are used to delete the parameters that have little effect on the output. Using the actual input/output data, a hybrid charged system search (HCSS) algorithm is then used to estimate the optimal parameters. When the parameters are optimized, the estimation for PV power output can be produced as long as the inputs are given. The proposed method is tested on two different PV power generation systems. To verify the performance of the proposed method, the results are compared with the results for the application of the traditional differential evolution (DE) and particle swarm optimization (PSO) methods.