The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
In the context of evolving energy needs and environmental concerns,efficient management of distributed energy resources within microgrids has gained prominence.This paper addresses the optimization of power flow manag...In the context of evolving energy needs and environmental concerns,efficient management of distributed energy resources within microgrids has gained prominence.This paper addresses the optimization of power flow management in a hybrid AC/DC microgrid through an energy management system driven by particle swarm optimization.Unlike traditional approaches that focus solely on active power distribution,our energy management system optimizes both active and reactive power allocation among sources.By leveraging 24-hour-ahead forecasting data encompassing load predictions,tariff rates and weather conditions,our strategy ensures an economically and environmentally optimized microgrid operation.Our proposed energy management system has dual objectives:minimizing costs and reducing greenhouse gas emissions.Through optimized operation of polluting sources and efficient utilization of the energy storage system,our approach achieved significant cost savings of~15%compared with the genetic algorithm coun-terpart.This was largely attributed to the streamlined operation of the gas turbine system,which reduced fuel consumption and associated expenses.Moreover,particle swarm optimization maintained the efficiency of the gas turbine by operating at~80%of its nominal power,effectively lowering greenhouse gas emissions.The effectiveness of our proposed strategy is validated through simu-lations conducted using the MATLAB®software environment.展开更多
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
文摘In the context of evolving energy needs and environmental concerns,efficient management of distributed energy resources within microgrids has gained prominence.This paper addresses the optimization of power flow management in a hybrid AC/DC microgrid through an energy management system driven by particle swarm optimization.Unlike traditional approaches that focus solely on active power distribution,our energy management system optimizes both active and reactive power allocation among sources.By leveraging 24-hour-ahead forecasting data encompassing load predictions,tariff rates and weather conditions,our strategy ensures an economically and environmentally optimized microgrid operation.Our proposed energy management system has dual objectives:minimizing costs and reducing greenhouse gas emissions.Through optimized operation of polluting sources and efficient utilization of the energy storage system,our approach achieved significant cost savings of~15%compared with the genetic algorithm coun-terpart.This was largely attributed to the streamlined operation of the gas turbine system,which reduced fuel consumption and associated expenses.Moreover,particle swarm optimization maintained the efficiency of the gas turbine by operating at~80%of its nominal power,effectively lowering greenhouse gas emissions.The effectiveness of our proposed strategy is validated through simu-lations conducted using the MATLAB®software environment.
