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.
