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
针对锂离子电池在串并联使用过程中出现的电压、容量、内阻等不一致性问题,对传统双层Buck-Boost均衡电路模组内进行改进,解决了传统均衡电路不能均衡相邻单体电池的问题并且提升了均衡速率。在此基础上,设计一种新型锂电池组分层均衡...针对锂离子电池在串并联使用过程中出现的电压、容量、内阻等不一致性问题,对传统双层Buck-Boost均衡电路模组内进行改进,解决了传统均衡电路不能均衡相邻单体电池的问题并且提升了均衡速率。在此基础上,设计一种新型锂电池组分层均衡拓扑电路。均衡策略分别采用模糊控制与传统的均值控制算法,以各电池的荷电状态(state of charge, SOC)作为电池组的均衡控制目标,电池组组内使用改进型的Buck-Boost电路、组间使用双向反激式电路,在Matlab/Simulink中搭建模型并进行仿真分析。对比结果表明,采用模糊控制方法比传统均值控制方法缩短了13.8%的均衡时间,能更快的实现锂电池单体之间的SOC均衡,验证了所提方案的可行性。展开更多
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje...To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.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。
文摘针对锂离子电池在串并联使用过程中出现的电压、容量、内阻等不一致性问题,对传统双层Buck-Boost均衡电路模组内进行改进,解决了传统均衡电路不能均衡相邻单体电池的问题并且提升了均衡速率。在此基础上,设计一种新型锂电池组分层均衡拓扑电路。均衡策略分别采用模糊控制与传统的均值控制算法,以各电池的荷电状态(state of charge, SOC)作为电池组的均衡控制目标,电池组组内使用改进型的Buck-Boost电路、组间使用双向反激式电路,在Matlab/Simulink中搭建模型并进行仿真分析。对比结果表明,采用模糊控制方法比传统均值控制方法缩短了13.8%的均衡时间,能更快的实现锂电池单体之间的SOC均衡,验证了所提方案的可行性。
基金Supported by State Grid Corporation of China Science and Technology Project:Research on Key Technologies for Intelligent Carbon Metrology in Vehicle-to-Grid Interaction(Project Number:B3018524000Q).
文摘To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.
