Integrating photovoltaic systems and smart microgrid optimization using non-dominated sorting genetic Algorithm II

This study explores photovoltaic (PV) system integration and optimization for grid and microgrid applications, emphasizing energy storage and control techniques to improve reliability and efficiency. Two PV systems (250 kW and 1 MW) were analyzed using PI, sliding mode, and MPC control methods. Sliding mode control achieved higher output power levels, while energy storage devices managed power fluctuations effectively. An NSGA-II optimization framework reduced costs, emissions, and energy consumption, validated by MATLAB simulations. A critical challenge in PV system integration lies in the dynamic and static performance of inverter control methods. Ensuring efficient and reliable power delivery under varying conditions necessitates evaluating different control techniques. Comprehensive analysis of PI, sliding mode, and MPC control methods for grid-connected PV systems. Integration of energy storage to address solar variability and peak demands. The development of an NSGA-II optimization framework minimizing costs, PAR, and CO2 emissions while improving energy management. Sliding mode control demonstrated superior output power levels. Optimization reduced electricity costs by 19.0%, PAR by 30.7%, and CO2 emissions by 21.7%. PV systems, particularly in grid-connected configurations, enhance energy reliability and reduce dependency on non-renewable sources. A 250kW system was analyzed using PI, sliding mode, and MPC techniques. Sliding mode control showed higher power output and smoother performance. Similarly, a 1 MW microgrid-connected system tested these methods, with comparable results. The integration of energy storage devices like batteries and DC-DC converters improved system stability, especially under variable solar irradiation and sudden load changes. Energy management strategies allowed batteries to discharge during low solar availability and recharge when supply exceeded demand. Simulink models evaluated a 200 kW PV array with a 380 V grid and 150 kW battery, showing effective grid integration and addressing power shortages. Boost and three-phase inverters enabled efficient power delivery to the grid. An NSGA-II-based optimization methodology for microgrids targeted economic, technological, and environmental goals. By modeling wind speed and solar radiation with beta probability density functions, the approach scheduled power allocation efficiently for residential loads. The framework reduced costs, emissions, PAR, and user discomfort. Three scenarios demonstrated significant improvements in operational metrics, with scenario-3 achieving the best results. MATLAB simulations validated these findings, showcasing reductions in electricity costs, peak-to-average ratios, and carbon emissions. And finally this study underscores the importance of integrating advanced control techniques, energy storage, and optimization algorithms in PV systems. Sliding mode control and NSGA-II optimization significantly enhance system efficiency and sustainability, offering viable solutions for meeting growing energy demands.

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