在可再生能源和电动汽车高渗透率“双高”背景下,电网供需不确定性显著上升,亟须新的规划与调度策略以保障运行稳定。为此提出一种基于数据驱动的多源融合方法,构建充电需求预测模型,实现充电设施布局与动态充放电策略的联合优化。以Ope...在可再生能源和电动汽车高渗透率“双高”背景下,电网供需不确定性显著上升,亟须新的规划与调度策略以保障运行稳定。为此提出一种基于数据驱动的多源融合方法,构建充电需求预测模型,实现充电设施布局与动态充放电策略的联合优化。以Open配电系统仿真器(Open distribution system simulator,OpenDSS)平台为载体,对一个典型配电网络进行建模与仿真。研究结果表明,所提方法能够有效降低电网峰谷差,提升电网运行稳定性及充电设施利用率,并降低用户充电等待时间。展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
文摘在可再生能源和电动汽车高渗透率“双高”背景下,电网供需不确定性显著上升,亟须新的规划与调度策略以保障运行稳定。为此提出一种基于数据驱动的多源融合方法,构建充电需求预测模型,实现充电设施布局与动态充放电策略的联合优化。以Open配电系统仿真器(Open distribution system simulator,OpenDSS)平台为载体,对一个典型配电网络进行建模与仿真。研究结果表明,所提方法能够有效降低电网峰谷差,提升电网运行稳定性及充电设施利用率,并降低用户充电等待时间。
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
