摘要
持续高温天气易导致电网供需平衡紧张,功率大、时空分布随机性强的电动汽车充电负荷可能进一步加剧电力负荷尖峰,但同时也具备成为优质调峰资源的潜力。面向高温天气下的城市电网,文中研究计及用户意愿的城市充电负荷模拟和空间引导方法。首先,基于计划行为理论分析了车辆出行行为的形成过程,构建了基于马尔可夫链的电动汽车出行模型,用于分析不同场景下车辆的出行轨迹;然后,建立基于出行里程及时长的车辆能耗计算方法和考虑用户意愿的充电选择模型,实现电动汽车充电负荷时空分布的精细化模拟;最后,提出了一种基于低谷充电折扣的城市充电负荷空间引导策略,并采用粒子群优化算法对充电折扣进行优化求解。算例仿真表明,高温天气下用户出行意愿出现规律性变化,考虑高温天气和用户意愿影响后城市充电负荷的模拟结果更符合实际情况;此外,实施低谷充电折扣后充电负荷的时空转移可有效降低城市电网负荷峰谷差,并提高电动汽车使用和电力系统运行的经济性。
Continuous high-temperature weather is prone to cause tension in the supply-demand balance of the power grid.Besides,charging loads of electric vehicles(EVs)may further aggravate the peak load due to the large charging power and high spatialtemporal distribution randomness.However,EVs also have the potential to provide high-quality peak-shaving resources.For the urban power grid in the high-temperature weather,this paper studies the simulation and the spatial guidance methods of urban charging loads considering user willingness.First,the formation process of vehicle travel behaviors is analyzed based on the planned behavior theory,and a travel model of EVs based on the Markov chain is constructed to analyze travel trajectories in different scenarios.Besides,a calculation method of vehicle energy consumption based on travel mileage and time duration is proposed,and a charging option model considering user willingness is established.The proposed models are adopted to finely simulate the spatial-temporal distribution of charging loads.Finally,a spatial guidance strategy for urban charging loads based on low-valley charging discounts is proposed,and the particle swarm optimization algorithm is used to optimally solve the charging discounts.The simulation results show that the user travel willingness changes regularly in the high-temperature weather,and the simulation results of urban charging loads considering the effects of high-temperature weather and user willingness are more in line with reality.Moreover,the spatial-temporal shifting of charging loads after conducting the low-valley charging discount can effectively alleviate the load difference between the peak and valley of the urban power grid.Besides,it can also improve the economy of vehicle usage and power system operation.
作者
韩林阳
叶承晋
朱超
高强
于海跃
HAN Linyang;YE Chengjin;ZHU Chao;GAO Qiang;YU Haiyue(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Economic and Technical Research Institute of State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310002,China;State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310007,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2024年第10期139-150,共12页
Automation of Electric Power Systems
基金
国家电网公司总部科技项目(5400-202219411A-2-0-ZN)。
关键词
电动汽车
充电负荷
高温天气
出行意愿
充电引导
electric vehicle(EV)
charging load
high-temperature weather
travel willingness
charging guidance
