摘要
随着电动汽车的规模化应用,为了缓解电网的压力,越来越多的分布式能源被接入。然而,电动汽车充电站的可再生能源供给一般小于电动汽车充电负荷,须与大电网联合运行。针对分布式能源与电网之间的协同增效利用,提出了微电网内的充电站储能系统与电网相互协调的策略。在建立电动汽车负荷模型的基础上,根据微网内可再生能源实时出力与负荷需求的求解不平衡率,使充电站储能系统和大电网根据不平衡率按一定比例协同运行。当电动汽车接入电网充电时,首次利用强化学习算法建立电价控制模型,实现两者的能源协调控制。以某地区的微网为例进行仿真分析,通过对比不同用户响应度的配电网负荷和充电站储能系统,验证该策略在微电网与大电网协同运行优化的有效性。该策略发挥了充电站储能系统和电网的联合运行优势,减小了电网负荷峰谷差,优化了电力负荷曲线,对解决如何有效地结合分布式能源和大电网对电动汽车进行充电等问题具有一定的实际意义和价值。
With the large-scale application of electric vehicles,more and more distributed energy sources are connected in order to relieve the pressure on the power grid.However,the renewable energy supply of EV charging stations is generally smaller than the EV charging load and must be operated jointly with the large power grid.Therefore,aiming at the problem of synergistic utilization with distributed energy and power grid,this paper proposes a strategy of coordination between storage system with charging station energy and power grid in micro grid.Firstly,the electric vehicle load model is established.Secondly,according to the real-time energy output and load of renewable energy in the micro grid,the unbalance rate is obtained.Then the energy storage system of the charging station and the large power grid operate in a certain proportion on the basis of the unbalance rate.Thirdly,when the electric vehicle is connected to the power grid for charging,the intensive learning algorithm is used for the first time to establish the electricity price control model and realize the energy coordination control of both.Finally,the simulation analysis is carried out on the micro grid in a certain region.The results of the calculation show that the effectiveness of this strategy in the coordinated operation optimization of micro grid and large grid is verified by comparing the load of distribution network and the energy storage system of charging station with different user responsiveness.This strategy gives full play to the combined operation advantages of charging station energy storage system and power grid,reduces the peak and valley load difference of power grid,optimizes the power load curve,and has significant practical significance and value for solving problems such as how to effectively combine distributed energy sources and large power grid to charge electric vehicles.
作者
牛亚琳
李华玥
李松岭
杨涌文
Niu Yalin;Li Huayue;Li Songling;Yang Yongwen(Energy and Mechanical Engineering College,Shanghai Electric Power University;Energy and Mechanical Engineering College,Science and Technology Center Laboratory,Shanghai Electric Power University)
出处
《上海节能》
2019年第2期97-101,共5页
Shanghai Energy Saving
基金
上海市科学技术委员会科研计划项目(18DZ1203403)
关键词
分布式能源
电动汽车
实时电价
强化学习
Distributed Energy
Electric Vehicle
Real-time Electricity Price
Reinforcement Learning