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.

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.

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.

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.

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。

基于动态电价机制的配网运营商与用户储能商双层协调多目标优化策略

为在电力市场和调度运用中发挥用户侧分布式储能的价值,充分利用电价的优化调节能力,提出考虑负荷需求响应的多目标双层动态电价模型。其中,上层以区域配网代理运营商运营成本和平均负荷波动率最小为目标,下层考虑储能商负荷需求响应,以各储能商运营成本最小为目标。此外,采用改进优劣解距离法对模型求得的Pareto最优解集进行多属性决策,以避免传统优劣解距离法中人为赋权的主观性。最后,决策出最优的内部电价、充放电计划和负荷安排。算例表明,该策略可以降低储能商的运行成本,提高区域配网运营商的收益与区域供电可靠性,同时有利于平滑负荷曲线。

考虑动态电碳排放因子的新型电力系统电碳综合需求响应调度方法

为进一步促进用户侧用电行为低碳化,以动态电碳排放因子为信号、以碳税为手段,结合电价建立一种新的用户侧电碳综合需求响应。根据平均电碳排放因子的度电排放表征特性,提出能够反映不同时段用电碳排放差异的动态电碳排放因子计算方法。在现有发电计划安排能够保证用户碳税利益需求的前提下,将动态电碳排放因子与碳税结合,以碳税作为经济手段引导用户关注各时段用电产生的碳排放差异。将动态电碳排放因子和电价的特点相结合,提出一种考虑动态电碳排放因子的新型电力系统电碳综合需求响应调度方法,用户能够根据各时段电价和电碳排放因子信息调整自身用电行为,以期降低负荷峰谷差、缓解系统调峰压力的同时,加强用户侧用电低碳性。通过仿真分析,验证了所提方法能够在降低峰谷差的基础上,进一步发挥用户侧低碳用电方面的作用,有助于提高电力系统经济性和低碳性。

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。

考虑众包场景的电动车动态需求车辆路径问题

针对企业自有车辆和社会车辆共同取送货的场景,以及国家节能环保的政策背景,考虑分时电价、部分充电、软时间窗、以及动态需求等因素,以最小化配送总成本为目标,建立考虑众包场景的电动车动态需求车辆路径问题(EDDVRP-CD)的两阶段整数规划模型。考虑动态需求的时效性,设计了启发式算法——改进的禁忌自适应大规模邻域搜索算法(IALNS-TS),增加了新的删除算子和修复算子,同时提出了加速策略。分别与两种算法——自适应大规模邻域搜索算法(ALNS)以及禁忌搜索算法(TS)进行对比,通过大量算例验证了IALNS-TS算法能够快速响应动态需求,并有效降低总配送费用。

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%。

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。

考虑接入电动汽车充电站的虚拟电厂低碳经济调度

风光出力不确定背景下,为解决电动汽车充电站接入虚拟电厂后两者之间的利益协调问题,构建以虚拟电厂运营商为上层,电动汽车充电站为下层的双层规划模型。首先,结合氢能系统以及电热负荷需求响应特性,设计一种源荷协调响应机制,提升系统整体灵活运行的能力。其次,结合虚拟电厂能源-负荷分布情况、碳排放情况以及电动汽车充电站运行计划等因素,制定一种动态定价策略,引导下层电动汽车充电站参与虚拟电厂调度,优先消纳新能源,协调上下层之间的利益关系。此外,引入阶梯型碳交易机制和碳捕集设备,进一步提升系统低碳运行能力。最后,采用CPLEX商用求解器对双层规划模型迭代求解,同时引入联合最优判别机制获取联合最优解,算例结果验证了所提模型及方法在协调不同利益主体的利益关系和降低系统碳排方面的有效性。

EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

The increasingly large number of electric vehicles(EVs)has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks.To address this issue,an EV charging station load predictionmethod is proposed in coupled urban transportation and distribution networks.Firstly,a finer dynamic urban transportation network model is formulated considering both nodal and path resistance.Then,a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature.Thirdly,the Monte Carlo method is applied to predict the distribution of EVcharging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model.Moreover,a dynamic charging pricing scheme for EVs is devised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks.Finally,the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model.The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87%to 37.21%compared to the BPR model.Additionally,the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from27.2 to 31.49MWh by considering the influence of ambient temperature and speed on power energy consumption.

基于动态分时电价引导的电动汽车需求侧响应

为激励电动汽车(electric vehicles,EVs)参与需求侧响应以减小电网负荷峰谷差,同时提高电动汽车用电经济性,首先,利用蒙特卡罗法模拟出电动汽车无序充电负荷,再根据电动汽车是否受电网调控或者受价格信号引导,将电动汽车分为3类进行处理;然后,以电网峰谷差均方值最小和用户充放电费用最小为目标,搭建基于电价需求弹性矩阵的动态分时电价(time-of-use,TOU)需求响应模型;最后,根据湖南某地区历史负荷数据对一天的电价进行分段,再通过仿真分析,验证考虑实时负荷反馈的动态分时电价可以有效地应对负荷波动,并且对于减小峰谷差和降低用户用电成本的效果更明显。

计及动态电价的电动汽车定制化充电策略

为应对电动汽车(EV)充电负荷给电网带来的冲击,平衡电网、充电站和用户三方的利益,提出计及动态电价的电动汽车定制化充电策略。首先根据三方利益需求明确充电站动态电价机制;接着建立以用户充电费用最低和台区负荷波动率最小为目标的EV充电过程优化模型;随后在人工蜂群(ABC)算法的基础上,引入自适应正态衰减系数形成自适应ABC算法,并用其对模型进行求解,得到EV定制化的有序充电计划;进一步结合动态电价机制和EV定制化充电方法优化每辆EV的充电过程;最后基于蒙特卡洛方法模拟不同数量EV在不同充电方式下的充电情况。仿真结果表明,所提方法可以大幅提高台区负荷指标、保证站方收益和降低用户充电费用,实现电网、充电站和用户三方利益共赢。