Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

考虑负荷空间均衡和充电站合作博弈的快速充电定价机制

为了平衡快速充电负荷空间分布,提高电动汽车用户充电满意度,提出考虑负荷空间均衡和充电站合作博弈的快速充电动态定价机制。首先,通过实际订单数据逆地理编码得到出行概率转移矩阵,结合路网模型和速度-流量模型建立电动汽车充电负荷时空分布模型;然后,考虑用户主观意愿,建立计及用户偏好的充电站选择决策模型,模拟用户对于充电站选择的理性决策行为;最后,以充电站节点负荷均衡性为目标,建立区域内快速充电站合作模式下的动态定价博弈模型,通过迭代算法求解均衡值,并利用Shapley值法对合作联盟所得剩余收益进行分配。仿真实例表明,所提快速充电站动态定价策略能有效降低充电站节点的负荷方差,提高充电桩利用率和充电站收益,并减小用户排队时间,提高用户满意度。

Battery electric buses charging schedule optimization considering time-of-use electricity price

Purpose–This paper aims to optimize the charging schedule for battery electric buses(BEBs)to minimize the charging cost considering the time-ofuse electricity price.Design/methodology/approach–The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model.The objective is to minimize the total charging cost of the BEB fleet.The charge decision of each BEB at the end of each trip is to be determined.Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.Findings–This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line.The results show that the total charge cost with the optimized charging schedule is 15.56%lower than the actual total charge cost under given conditions.The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent,which can provide a reference for planning the number of charging piles.Originality/value–Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

Hierarchical Mean-Field Type Control of Price Dynamics for Electricity in Smart Grid

This paper solves a mean-field type hierarchical optimal control problem in electricity market.The authors consider n-1 prosumers and one producer.The ith prosumer,for 1<i<n,is a leader of the(i-1)th prosumer and is a follower of the(i+1)th prosumer.The first player(agent)is the follower at the bottom whereas the nth is the leader at the top.The problem is described by a linear jump-diffusion system of conditional mean-field type,where the conditioning is with respect to common noise,and a quadratic cost functional involving,the square of the conditional expectation of the controls of the agents.The authors provide a semi-explicit solution of the corresponding meanfield-type hierarchical control problem with common noise.Finally,the authors illustrate the obtained result via a numerical example with two different scenarios.

计及动态电价的电动汽车参与微电网调度双层优化策略

电动汽车(electric vehicle,EV)充电负荷变化量受微电网爬坡性能限制,因此文中考虑微电网机组爬坡特性,提出一种计及动态电价的EV参与微电网调度双层优化策略。上层为EV负荷模型,分析不同类型EV快/慢充特性,考虑微电网电价对EV充电需求的引导,建立以用户满意度最大为目标的EV负荷模型。下层为多微电网运行模型,根据微电网净负荷大小制定动态电价策略,考虑EV充电负荷对微电网新能源的消纳及电源爬坡的需求,优化各区域动态电价,并以微电网净负荷波动及运行成本最小为目标,建立多微电网区域运行模型。最后对某城市区域微电网及EV充电需求算例进行分析和验证,结果表明:与固定电价及峰谷分时电价相比,所提方法实现了EV负荷在微电网区域有序充电、平抑净负荷波动的效果,能有效降低充电行为对微电网安全经济运行的影响。

考虑EV充放电意愿的园区综合能源系统双层优化调度

随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。

融合路网-电网信息的电动汽车充放电行为引导与调控策略

在智慧城市理念快速发展的背景下,电动汽车(EV)无引导的出行与无序的充放电行为影响着电力-交通耦合网络的安全高效运行。针对此问题,提出一种融合路网-电网信息的EV充放电行为引导与调控两阶段优化策略。首先,分析电力-交通耦合网络的交互影响因素和方式,提出EV充放电出行路径引导和调控架构;其次,提出融合路网-电网信息的EV充放电出行决策电价动态更新策略,并建立计及用户时间-经济成本的EV充放电出行路径决策优化模型;然后,兼顾多方主体利益,建立EV充放电调控模型;最后,仿真结果表明,所提策略能够综合考虑路网和电网实时运行状态,充分挖掘EV在路径引导与充放电调控过程中的可调潜力,实现系统协调运行和多方共赢。

考虑源荷互动的综合能源系统多目标双层规划

随着综合能源系统建设的不断推进,传统的“源随荷动”模式转向“源荷互动”已成为发展趋势。为此,提出了一种考虑源荷互动的综合能源系统多目标双层规划方法。首先,引入电–热–冷可中断负荷和可转移负荷参与综合需求响应,并制定了一种基于新能源发电与等效负荷相匹配的动态定价策略。然后,构建了含电动汽车充电站的综合能源系统多目标双层规划与运行模型,在上层规划模型中以经济成本、碳排放量、自主性为目标,在下层模型中以综合能源系统和电动汽车充电站的总运行成本最小为目标。最后,利用非支配排序遗传算法(non-dominated sorting genetic algorithm Ⅱ,NSGA-Ⅱ)算法和商业求解器CPLEX求解获得Pareto前沿解集,并利用Topsis法选择最优解。算例表明,该方法可以有效降低经济性成本、减少碳排放量、提高系统自主性。

计及电动汽车移动储能动态电价的微电网优化调度研究

针对大量电动汽车(electric vehicle,EV)接入微电网带来的稳定性及经济性问题,同时为充分发挥EV的移动储能特性,提出一种在动态电价机制下考虑EV动力电池移动储能特性的微电网调度模型。首先,建立含EV动力电池、风电、光伏、柴油发电机组的微电网数学模型;其次,在综合考虑分布式电源发电成本、EV用户成本、电池损失成本、环境成本的基础上,计及与主网电能交易、新能源补贴等多项收益,制定考虑新能源出力的EV动态电价机制;最后,以微电网运行成本、EV用户成本、环境成本最低为目标函数,建立计及EV移动储能动态电价机制的微电网调度模型。为验证所提方法的有效性,通过YALMIP和GUROBI建立并求解所提最优调度模型。仿真结果表明,相比传统控制方法,微电网总运行成本降低了16.96%。

分时电价下矿井多级接力式排水系统的优化策略

煤矿井下排水系统的工作效率直接影响到煤矿的生产安全和经济效益。现有矿井多级接力式排水系统对于电费波峰波谷特性和《煤矿防治水细则》要求的排水系统安全约束考虑不全面,难以实现整体系统的一体化安全经济运行。针对该问题,基于避峰就谷策略和动态规划方法,提出分时电价下矿井多级接力式排水系统的优化策略。通过考虑煤矿排水系统的多级串联结构、涌水量和水泵的排水能力,建立了煤矿多级接力式排水系统的数学模型。基于避峰就谷策略,以电费成本最低为目标函数,以水仓水位、水泵排水能力、煤矿安全要求等为约束条件,构建基于分时电价的多级接力式排水系统优化问题,并给出了基于动态规划方法的求解算法。以某矿井4级排水系统为例进行仿真分析,结果表明,该策略可有效控制井下水位,保证水位处于合理高度:当电价较高时,水泵开启的数量很少或为零,水仓处于高水位状态;当电价较低时,水泵开启的数量较多,水仓处于低水位状态。该策略能够在提升经济效益的同时,保障煤矿的生产效率和生产安全。

计及动态能源价格和共享储能电站的多主体综合能源系统双层优化调度策略

可再生能源不确定性背景下,为协调综合能源运营商、电热负荷聚集商及共享储能电站之间的利益关系,提出一种电热综合动态定价机制,基于综合需求响应特性,引导电热负荷聚集商参与综合能源系统运营商优化,促进储能高效应用和新能源就地消纳,实现多利益主体互利共赢。以综合能源系统运营商为主体,以电热负荷聚集商和共享储能运营商为从体,构建一主多从的综合能源多运营主体系统优化模型。充分挖掘共享储能运营商的运行模式,包括充电、放电和提供备用等模式。采用CPLEX对主从博弈模型迭代求解,算例结果验证了模型及方法的有效性。

Power Optimization Strategy Considering Electric Vehicle in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their household appliances （HA） for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system （EMS） and then propose a power optimization strategy based on household load model and electric vehicle （EV） model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price （TOUP）, the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.

动态分时电价机制下的电动汽车充放电调度策略研究

峰谷分时电价下会在基础负荷低时形成新的负荷峰。建立了一个以电动汽车充放电费用最小,电动汽车接入所引起的电网损失最小以及对电压稳定性的影响最小为优化目标的电动汽车充、放电调度数学模型,并通过凸优化算法解决了多变量、多目标和高维优化问题。考虑反复潮流计算的繁琐性,利用网络损耗灵敏度和L-P灵敏度来提升调度效率,并将电压稳定作为约束来保证调度过程中不会出现电压失稳的情况。最后在改进的IEEE33节点配电网系统中进行算例验证,表明本文所提方法可以降低充放电费用,满足用户对经济性的考虑,另外可以降低电动汽车充电负荷对系统的网络损耗和电压稳定的影响。

考虑车主多模式需求响应模糊意愿的优化调度策略

现代电动汽车(electrical vehicle, EV)用户需求响应具有多样性和意愿模糊性的特点,当实施单一激励政策时,EV响应将达不到预期效果。为此,提出了考虑车主多模式需求响应及其模糊意愿的含EV微电网的主从博弈优化调度策略。微电网主体针对净负荷制定多模式动态电价激励政策,引导EV在多模式电价中做出选择,促进EV有序充放电,实现其净负荷均方差和运行成本最小。车主从体基于模糊逻辑推理意愿决策,响应多模式动态电价,极小化车主成本。采用非支配排序遗传算法(NSGA-Ⅱ)求解优化模型,获得最优多模式动态电价和EV充放电策略。仿真结果验证了所提方法的有效性。

Vehicle-to-grid power system services with electric and plug - in vehicles based on flexibility in unidirectional charging

With proper power scheduling and dynamic pricing,a unidirectional charger can provide benefits and regulation services to the electricity grid,at a level approaching that of bidirectional charging.Power scheduling and schedule flexibility of electric and plug-in hybrid vehicles are addressed.The use of electric vehicles(EVs)as flexibility resources and associated unidirectional vehicle-to-grid benefits are investigated.Power can be scheduled with the EV charger in control of charging or via control by a utility or an aggregator.Charging cost functions suitable for charger-and utility-controlled power scheduling are presented.Ancillary service levels possible with unidirectional vehicle-to-grid are quantified using sample charging scenarios from published data.Impacts of various power schedules and vehicle participation as a flexibility resource on electricity locational prices are evaluated.These include benefits to both owners and load-serving entities.Frequency regulation is considered in the context of unidirectional charging.

动态电价机制下电动汽车竞争性充电的阻塞管理模型

针对动态电价机制下大规模电动汽车竞争性充电行为引发的电网安全问题,提出了考虑电网安全约束的电动汽车聚合商阻塞管理模型。首先,分别建立了配电网的无损线性化模型和电动汽车聚合商的集群模型;其次,在动态电价机制下,建立了考虑电网安全约束的电动汽车聚合商竞争性博弈模型;然后,基于变分不等式理论,证明了博弈模型均衡解的存在性和变分解的唯一性;最后,利用梯度下降算法和近端分解算法对博弈模型进行了分布式求解。算例结果表明,该模型可以有效地保障大规模电动汽车竞争性充电时的电网安全。

基于动态电价机制的用户侧分布式储能电能交易策略

用户侧分布式储能具备优化用户电力负荷曲线和协调消纳系统侧可再生能源发电的能力。提出一种基于动态电价机制的配电系统用户侧分布式储能电能交易策略。首先,该策略在充分考虑新能源出力和用户电能需求不确定性的基础上,构建用户侧分布式储能和配电系统的日前电能调度模型;然后,根据动态电价机制对用户侧分布式储能调度计划和系统侧可再生能源消纳水平的影响,建立基于主从博弈的配电网运营商和用户侧储能运营商的电能交易模型,并采用启发式算法获取博弈模型的均衡解;最后,通过算例验证了所提电能交易策略可有效提升配电网运营商的经济效益与新能源消纳水平,并降低用户侧的运行成本。

考虑辅助服务收益的储能与火电机组灵活性改造协调规划方法

可再生能源飞速发展对电力系统灵活性提出了较高要求。辅助服务收益有利于灵活性资源配置以及提高其参与调峰积极性。从火电机组深度调峰阶梯式补偿与储能峰谷套利两方面分析入手,提出了考虑辅助服务收益的储能与火电机组灵活性改造协调规划方法。首先,分析了灵活性资源调峰机理,其次构建了灵活性资源调峰的系统收益指标和成本指标;然后,建立兼顾系统综合收益和弃风率的多目标源–储规划模型,并给出了模型的求解思路和求解步骤,最后,通过中国东北某风电基地外送输电工程系统的仿真,得到了兼顾系统经济性和风电接纳能力的源–储规划方案;同时在此基础上探究了动态分时电价机制对利用储能与火电机组灵活性改造提升系统调峰能力的影响。

电动汽车的有序充电管理及其对配电网的影响分析

电动汽车以无序充电方式接入配电网时与网内基础用电负荷叠加,会形成峰上加峰的现象,不利于配电网的稳定运行。针对上述问题,首先对私家车充电负荷进行建模,采用蒙特卡罗抽样模拟电动汽车无序行为下的充电负荷曲线。然后提出一种新型的多时段动态充电价格机制,引导车主有序充电,并以配电网负荷波动最小为目标函数,优化电动汽车充电行为。最后在IEEE33节点配电网中,分别分析有序和无序充电负荷并网时电动汽车充电费用、配电网电压偏移率及网损,结果表明所提策略可有效兼顾用户利益和配电网的稳定运行。