Stochastic Optimal Harmonic Suppression with Permissible Photovoltaic Penetration Level for Grid-Linked Systems using Monte Carlo-Based Hybrid NSGA2-MOPSO

Abstract

Abstract Integrating a high Penetration level of Solar Photovoltaic (SPV) power in the electricity grid could enhance the system’s sustainability, reliability, and affordability. Nevertheless, at higher Penetrations, the intermittency, non-dispatchable nature of the SPV generation, and the extensive utilization of inverter-based interfaces generate excessive harmonic distortions that damage power system devices and interrupt the smooth operation of the power system. Thus, the severity of the harmonic distortion impacts varies as a function of the degree of the SPV Penetration level in the grid-connected system. Though the problem is highly nonlinear stochastic programming with multiple conflicting power quality criteria, no existing study holistically captures the randomness, the contradictory nature of the objectives, and the grid’s technical limitations simultaneously. This study proposes a novel Monte-Carlo-based Hybrid multi-objective methodology to scale up the Photovoltaic Penetration level with a minimum Total Harmonic Distortion (THD) for multilevel SPV inverters in grid-connected systems without violating the system’s standard operational limitations. Six state-of-the-art Multi-Objective Evolutionary algorithms were implemented and compared using hypervolume indica- tor, execution time, and parametric statistical analysis to obtain a quality solution. The results showed that the Hybrid NSGAII-MOPSO outflanked the rest in terms of convergence, diversity, and execution time. It could be inferred that even under variable weather conditions, this harmonic suppression design approach could accurately optimize the SPV Penetration level and mitigate the THD without degrading the grid’s standard operational constraints. In comparison, the stochastic design technique creates a far more reliable SPV grid-connected system than the deterministic approach.