基于数据驱动的高渗透率电动汽车充电规划与优化OA
Data driven planning and optimization of high penetration electric vehicle charging
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.
In the context of"double high"penetration of renewable energy and electric vehicles,the uncertainty of power grid supply and demand has significantly increased,urgently demanding planning and scheduling strategies to ensure stable operation.To address this,a data-driven multi-source fusion method is proposed to construct a charging demand prediction model,achieving joint optimization of facility layout and dynamic charging and discharging strategies.The Open Distribution System Simulator(OpenDSS)platform is used as a carrier to model and simulate a typical distribution network.results show that the proposed method can effectively reduce the peak-valley difference of the power grid,enhance the stability of power grid operation and the utilization rate of charging facilities,reduce user charging waiting time.
戚成飞;王亚超;李文文;张炜;赵鹏
国网冀北电力有限公司计量中心,北京 100052国网冀北电力有限公司计量中心,北京 100052国网冀北电力有限公司计量中心,北京 100052国网冀北电力有限公司计量中心,北京 100052输配电装备及系统安全与新技术国家重点实验室(重庆大学),重庆 400042
电动汽车高渗透率充电需求预测
electric vehicleshigh penetration ratecharging demand forecast
《中国电力》 2026 (2)
104-113,10
This work is supported by National Natural Science Foundation of China(No.52077012). 国家自然科学基金资助项目(52077012).
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