WO2022021957A1 - 运营商收益最大化的v2g二阶段随机规划调度模型 - Google Patents
运营商收益最大化的v2g二阶段随机规划调度模型 Download PDFInfo
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Definitions
- the invention relates to the field of energy management optimization models, in particular to a V2G scheduling method based on two-stage stochastic planning for maximizing operator benefits.
- V2G short for Vehicle-to-Grid
- V2G is designed for electric vehicles to interact with the grid, using the electric vehicle's battery as a buffer for the grid and renewable energy.
- electric vehicles EVs
- EVs electric vehicles
- EVs are gradually occupying more fuel vehicle markets due to their low operating costs and outstanding energy conservation and environmental protection effects.
- EVs as mobile energy storage, interact with the power grid through V2G, which can bring many auxiliary services to the power grid, including auxiliary peak regulation and auxiliary frequency regulation for the power grid.
- This model can realize auxiliary peak regulation, and can accurately control the charging and discharging state of EVs and the charging and discharging capacity of EVs, so that EVs can participate in grid operation regulation in an orderly manner.
- V2G operators dispenser center, AG
- the V2G operator is the main revenue body of the model, and its functions include: managing the charging and discharging of EVs within the agreement, providing power for EVs outside the agreement, operating the renewable energy power generation system in the region, providing power transfer for regional partial loads and carrying out regional surplus power go online.
- V2G scheduling resource randomness The problem of EVs participating in V2G charging and discharging scheduling is an optimal decision-making problem with multiple uncertainties. Uncertainty can be divided into V2G scheduling resource randomness and renewable energy generation randomness. In previous studies, it is difficult to comprehensively consider the multiple randomness of EVs participating in the V2G process, and the research on the combination of V2G scheduling resources and the randomness of renewable energy is not in-depth.
- the present invention provides a V2G scheduling method based on two-stage stochastic programming that maximizes the operator's income, and a V2G two-stage nonlinear stochastic programming model combining the randomness of V2G scheduling and the randomness of renewable energy generation. , which combines V2G scheduling resources with randomness at the level of renewable energy.
- a V2G dispatching two-stage stochastic planning method for maximizing the operator's income which is used in a system including at least electric vehicles, charging and discharging piles and a power grid, which includes the following steps:
- a random scenario set is constructed based on the day-ahead parameter set of electric vehicles in the operator's service area, the conditions of in-protocol electric vehicles and out-of-protocol electric vehicles, and the power generation of renewable energy. Under the random charging requirements of external electric vehicles, the charging and discharging optimization scheduling of electric vehicles within the agreement is carried out;
- a final random scenario is constructed by using the random scenario ensemble model, and a V2G two-stage nonlinear stochastic programming model under the final random scenario is constructed;
- the V2G two-stage nonlinear stochastic programming model is used to maximize the overall revenue of the V2G operator.
- the present invention has the following beneficial effects:
- V2G dispatching resources Fully considering the uncertainty of V2G dispatching resources and renewable energy generation, a two-stage stochastic programming model is established to maximize the operator's revenue, effectively improving the V2G dispatching process, clarifying and quantifying the revenue source of the V2G dispatching system, and comprehensively optimizing the electric power in the agreement.
- the operation status of vehicles participating in V2G scheduling provides theoretical and methodological support for the establishment of the optimal utilization of vehicle-network interaction resources.
- the scenario generation method for the randomness of V2G dispatching resources and the randomness of renewable energy generation is improved, so that the scenario set of the two-stage stochastic programming model fully reflects a variety of random factors. .
- FIG. 1 is a flowchart of a method for V2G scheduling in an embodiment of the present invention
- FIG. 2 is a benefit-cost relationship diagram of a V2G operator in an embodiment of the present invention
- FIG. 3 is a schematic diagram of a scenario generation process considering randomness V2G optimal scheduling model in an embodiment of the present invention
- FIG. 4 is a distribution diagram of V2G operator network nodes in an embodiment of the present invention.
- Fig. 5 is the EVs decision variable diagram in the embodiment of the present invention.
- FIG. 6 is a bar graph of the charging and discharging load of EVs in a scenario according to an embodiment of the present invention.
- the present invention may include the following steps:
- Step 1 V2G vehicle pile network resource monitoring statistics
- Vehicle-pile-network information interaction real-time data update, and access to the day-to-day parameters of vehicles participating in scheduling (model, battery capacity, battery power, parking location, charging and discharging climbing ability, etc.).
- EVs in the agreement The vehicles that have promised to participate in the dispatching are arranged at the charging and discharging stations managed by the operator according to the principle of distance optimization, connect to the grid before the specified time, and respond to the charging and discharging, and off-grid instructions of the dispatching center in real time.
- V2G operators generate random scenarios through scenario generation-combination method, which are applied to the second-stage constraints of the V2G scheduling mathematical model. Under the condition of meeting the random charging requirements of EVs outside the agreement, the electric vehicles within the agreement are optimally scheduled for charging and discharging. 2-1. V2G Scheduling Resource Random Scenario
- the random scenarios of V2G scheduling resources mainly include random scenarios of vehicle initial SOC and random scenarios of V2G service station resources.
- the log-normal distribution model (1) of the daily driving distance of electric vehicles in the protocol is adopted, and the driving distance of EVs in the protocol before grid connection is obtained by the Monte Carlo method, which is regarded as the random driving distance.
- the corresponding generated random scenario set is SC D .
- the number of scenarios is reduced by simultaneous backward reduction, and finally the wind power output scenario SC WT is generated by the wind turbine power fitting model.
- photovoltaic power generation simulation one year's historical data of photovoltaic daily power generation is selected to establish a photovoltaic power generation scenario pool, random scenarios of photovoltaic power generation are obtained through random sampling, and random scenarios of photovoltaic power generation are generated by synchronous backward reduction method. Scenario SC PV .
- Equation (4) is used to calculate the probability of scenario combination SC F.
- P(sc D ) is 1/SC D , respectively
- P(sc Z ) is 1/SC Z , respectively.
- P(sc PV ) and P(sc WT ) are determined by the scenario reduction algorithm.
- Table 1-Table 3 lists the parameters and variables of the V2G two-stage nonlinear stochastic programming model, which are defined as follows.
- Equation F maximizes the overall revenue of the V2G operator, see equation (5).
- Equation (6) is the operator’s total revenue (Rev EV ) in which electric vehicles participate in dispatching
- Equation (7) is the operator’s total revenue (Rev AG ) from coordinating power supply to local loads and surplus power grid
- Equation (8) is The operator purchases the total cost of thermal power on the day before and on the current day (Cost B );
- Equation (9) is the total cost of renewable energy generation under the jurisdiction of the operator (Cost OM ).
- Equation (10) is the repulsion constraint of electric vehicle charging and discharging: the charging operation and discharging operation of the same electric vehicle cannot occur at the same time during the scheduling period;
- Equation (11-14) is the electric vehicle charging state constraint: the electric vehicle is connected to the distribution network to start charging within the t period, in order to limit the shortest charging time and the shortest idle time, so as to avoid frequent switching between charging, discharging and idle states, resulting in Damaged EV batteries and higher costs of switching services; of which for the shortest charging time, is the shortest idle time;
- Equation (19-21) Constraints on the maximum switching times of electric vehicle charging and discharging: the maximum number of electric vehicle charging and discharging switching times in a day is limited, and the maximum number of electric vehicle switching states in a day can be limited, which can effectively avoid excessive state switching of electric vehicles frequently; of which are the upper limit of the charging and discharging times of a single electric vehicle in the V2G scheduling plan, respectively, and V i is the upper limit of the switching times of charging and discharging;
- Equation (22-23) The initial state constraints when electric vehicles are connected to the grid: Equation (22) calculates the initial power when the vehicle is connected to the grid through the travel distance before the electric vehicle is connected to the grid, and Equation (23) calculates the initial SOC of EV i , driving The randomness of the distance causes the initial SOC of the electric vehicle cluster to be random; where is a random parameter of EV i ’s driving distance before grid connection in the protocol;
- Equation (24-26) is the constraint on the maximum number of services with V2G nodes: due to the limitation of V2G service station capacity and transformer power, the number of vehicles that can be charged and discharged at the same node is limited; Equation (24) limits the maximum number of nodes that can be charged at the same time. Equation (25) limits the maximum number of electric vehicles that can be discharged at the same time at node m, and Equation (26) limits the number of electric vehicles that can be charged and discharged at the same time at node m less than the number of charging and discharging piles; where ⁇ m , ⁇ m are each The maximum number of vehicles that can be charged and discharged during the V2G service station period;
- Equations (27-28) are the electric vehicle charging and discharging energy constraints: during the charging and discharging process of the electric vehicle, the actual chargeable and dischargeable amount is limited by the real-time SOC; among them: when and When both are 0, EV i is charged at time period t and discharge capacity is constrained to 0; when or , the charging capacity of EV i and discharge capacity Respectively subject to the maximum value of the schedulable capacity of the battery and constraint;
- Equation (29-30) is the state-of-charge constraint of the electric vehicle: the variation range of the battery state of charge of the electric vehicle in the scheduling protocol is given; Equation (29) is the optimal battery operating condition range when the vehicle participates in V2G; Equation (30) It means that the state of charge SOC of the electric vehicle needs to meet the user's expectation after the scheduling, and the charging and discharging scheduling is carried out on the premise of meeting the user's future travel needs; where T end is set as the scheduling end time.
- Equation (31) is the electric vehicle power balance constraint: the electric power of EV i in the t period is equal to the remaining electric power in the t-1 period plus the difference between the charge and discharge in the t period;
- Equation (32-33) Electric vehicle charging and discharging climbing constraints: the charging and discharging climbing ability of electric vehicles is affected by the rated power of the charging and discharging piles and the charging mode. climb Avoid aggravated capacity loss caused by over-charging and discharging of batteries; if and only after the vehicle accepts the first-stage scheduling plan, the charge-discharge climbing constraint will take effect in the second stage; The maximum hill-climbing capability for charging is, The maximum ramping capability for discharge is;
- Equations (34-35) are the maximum service capacity constraints of V2G nodes: Equation (35) calculates the total amount of charging demand generated by electric vehicles that arrive randomly outside the protocol; Equation (34) The capacity of electric vehicles in the protocol that can participate in charging scheduling, this part The amount of electricity is affected by the number of electric vehicles and the amount of charging outside the agreement of formula (35) and is random; among them is the random arrival number of electric vehicles outside the agreement, The rated charging capacity that node m can provide;
- Equations (36-37) are the network node capacity and balance constraints: the model builds an energy transmission network, and the node power balance of the network satisfies Kirchhoff’s law; Equation (36) limits the maximum capacity of the bidirectional energy flow, and stipulates that the power transmission is in the standard inside; formula (37) is introduced in After describing the randomness of wind and solar power generation, construct an energy balance constraint for each node to ensure that the total inflow of the node is equal to the total outflow;
- equation (27) is transformed into equations (38)-(40), and equation (28) is transformed into equations (41)-(43) in the same way, In order to improve the quality and speed of the model solution set, where,
- the two-stage stochastic optimization model of wind-solar power generation randomness and V2G resource randomness is constructed as follows.
- the standard distribution network topology of IEEE-33 nodes is selected, and some nodes are pre-installed with wind turbines and photovoltaic power generation systems.
- the topology is shown in Figure 4. .
- Nodes 20 and 11 are respectively equipped with a single GE1.5-77 wind turbine and a GE1.7-100 high-power wind turbine, and other V2G service sites are equipped with small wind power photovoltaic systems. parameter.
- the set target SOC for the end of scheduling is 0.8.
- the charging power of EVs arriving randomly outside the protocol is set to be 40kw.
- Multivariate joint scenarios were generated, setting SC D to 4, SC Z to 5, SC WT to 5, and SC PV to 2.
- SCF 200 final scenarios were generated by scenario combination.
- the branch and bound algorithm of the Gurobi solver is called to solve the model.
- Figure 5 shows the EVs charging and discharging decision diagram under the constraints of the shortest charging and discharging time. It shows that almost all EVs in the protocol participate in the charging and discharging day scheduling, and the way of participating in the scheduling obeys the model constraints.
- the proportion of the dispatched time period for EVs clusters is close to 100% of the total time period, which proves that the model can effectively control the charging and discharging status of electric vehicles in the protocol, and under the goal of maximizing the revenue of the dispatch center, the EVs clusters in the protocol need to be on standby all day and keep connected state.
- the period from 0:00 to 2:00 in the morning is the concentrated discharge period of EVs. Because the discharge price during this period is relatively low, EVs are discharged in the dispatching agreement of the dispatch center to supply other loads to maximize dispatching revenue.
- Figure 6 proves the random parameters of EVs driving distance before grid connection Influence on charge and discharge load.
- the optimization results of the objective function in Table 6 show that when 100 EVs are dispatched, the expected revenue of the dispatch center on this dispatch day is 69,323.4 yuan.
- the charging income of dispatching EVs is 12,359.5 yuan
- the discharging cost of dispatching EVs is 3,388.9 yuan
- the net income of dispatching EVs is 8,970.6 yuan.
- the profit from dispatching EVs accounts for 13% of the total profit.
- the biggest profit of the dispatch center comes from the local load power consumption. Through the local consumption of renewable energy to supply the regional power load and electric vehicles, the dispatch center can obtain considerable benefits.
Abstract
一种最大化运营商收益的V2G调度二阶段随机规划方法，涉及能源管理优化模型领域，该方法针对电动汽车充放电调度问题，基于分布式可再生能源-储-EVs充放电的电力系统，建立一种结合V2G调度随机性和可再生能源发电随机性的V2G二阶段非线性随机规划模型。通过约束线性化将其转化为混合整数线性规划模型(MILP)。另外，为使随机情景能全面覆盖多种不确定性因素，设计了一种情景生成和组合的方法将V2G调度资源和可再生能源层面的随机性结合。V2G二阶段随机规划模型求解探寻适应V2G调度层面随机性和可再生能源随机性的电动汽车最优充放电计划，并提升其参与电力辅助服务的收益。
Description
本发明涉及能源管理优化模型领域，具体地说是一种最大化运营商收益的基于二阶段随机规划的V2G调度方法。
V2G，是Vehicle-to-Grid的简称，它的目的是电动汽车与电网互动，利用电动车的电池作为电网和可再生能源的缓冲。在节能减排和化石能源紧缺的外部大环境下，电动汽车(EVs)凭借其运行成本低廉，节能环保效应突出的特点逐渐占据更多燃油车的市场。除了节能减排以外，EVs作为移动储能，通过V2G方式与电网互动可以为电网带来很多辅助服务，其中包括为电网辅助调峰和辅助调频。本模型可实现辅助调峰，可准确控制EVs的充放电状态以及EVs充放电电量,让EVs有序参与电网运行调控。在EVs参与电网运行调控中，V2G运营商(调度中心，AG)的集中调度的作用不可或缺。
V2G运营商为模型的收益主体，其职能包括：管理协议内EVs充放电，为协议外EVs提供电力，运营区域内的可再生能源发电系统，为区域部分负荷提供电力中转以及进行区域的余电上网。
EVs参与V2G充放电调度的问题是具有多种不确定性的最优化决策问题。不确定性可以分为V2G调度资源随机性和可再生能源发电随机性。在以往的研究中，EVs参与V2G过程中的多种随机性难以得到全面的考虑，而且在V2G调度资源和可再生能源随机性的结合研究并不深入。
发明内容
针对现有技术中的不足，本发明提供一种最大化运营商收益的基于二阶段随机规划的V2G调度方法，结合V2G调度随机性和可再生能源发电随机性的V2G二阶段非线性随机规划模型，将V2G调度资源和可再生能源层面的随机性结合。
为实现上述目的，本发明的技术方案如下：
一种最大化运营商收益的V2G调度二阶段随机规划方法，用于至少包括电动汽车、充放电桩和电网构成的系统，其包括以下步骤：
获取运营商服务区域内的电动汽车的日前参数集，同时，向运营商服务区域内的电动汽 车发出调度邀约协议，将同意调度邀约协议的电动汽车归类为协议内电动汽车，将无响应及拒绝调度邀约协议的电动汽车视为协议外电动汽车；
基于运营商服务区域内的电动汽车的日前参数集、协议内电动汽车和协议外电动汽车的情况，以及可再生能源的发电情况构建随机情景集合，所述构建随机情景集合在满足设定的协议外电动汽车随机充电需求下，对协议内电动汽车进行充放电优化调度；
考虑各个随机因素相互独立，利用随机情景集合模型构建最终随机情景，构建在所述最终随机情景下的V2G二阶段非线性随机规划模型；
利用所述V2G二阶段非线性随机规划模型实现最大化V2G运营商总体收益。
本发明与现有技术相比，其有益效果在于：
充分考虑V2G调度资源和可再生能源发电不确定性，建立了最大化运营商收益的二阶段随机规划模型，有效完善V2G调度的过程，明确及量化V2G调度系统的收益来源，全面优化协议内电动汽车参与V2G调度的操作状态，为车网互动资源优化利用建模建立提供理论与方法的支撑。
在充分考虑电动汽车受多种随机性影响的情况下，改进了V2G调度资源随机性与可再生能源发电随机性的情景生成方法，从而使得二阶段随机规划模型的情景集全面体现多种随机因素。
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例中所需要使用的附图进行简单的介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例中V2G调度的方法流程图；
图2为本发明实施例中V2G运营商的收益成本关系图；
图3为本发明实施例中考虑随机性V2G优化调度模型情景生成过程原理图；
图4为本发明实施例中V2G运营商网络节点分布图；
图5为本发明实施例中EVs决策变量图；
图6为本发明实施例中情景EVs充放电负荷的柱状图。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整的描 述，显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
实施例：
需要说明的是，本发明实施例的术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
在一个具体实施例中，本发明可以包括如下步骤：
步骤1、V2G车桩网资源监测统计
对V2G运营商服务区域内EVs与服务站容量进行统计与分析，用于构建随机情景集合：
1.车-桩-网信息交互，实时数据更新，获得车辆参与调度的日前参数(车型，电池容量，电池电量，停靠位置，充放电爬坡能力等)。
2.根据用户响应电网V2G调度邀约的结果，将同意参与日前调度的EVs归类为协议内EVs，无响应及拒绝邀约的电动汽车视为协议外电动汽车。
3.协议内EVs：日前承诺参与调度的车辆，以距离优选原则安排于运营商管理的各充放电站，在规定时间前并网，实时响应调度中心充放电、并离网指令。
4.协议外EVs：随机产生充电需求，并被调度中心优先满足，此类充电需求不受调度中心控制。
步骤2、随机情景生成-组合
V2G运营商通过情景生成-组合的方法生成随机情景，应用于V2G调度数学模型的第二阶段约束。在满足一定协议外EVs随机充电需求下，对协议内电动汽车进行充放电优化调度。2-1.V2G调度资源随机情景
V2G调度资源随机情景主要包括车辆初始SOC的随机情景和V2G服务站资源随机情景。
为展现协议内EVs参与调度的SOC随机性，采用协议内电动汽车日前行驶距离对数正态分布模型(1)，通过蒙特卡洛方法获取协议内EVs并网前的行驶距离，作为行驶距离随机参数
相应生成随机情景集为SC
D。
为描述EVs充放电站可调度资源(可调度容量)随机性。选择平均达到率为恒定的齐次泊松模型描述协议外电动汽车随机到达数量(2)，通过蒙特卡洛方法获取充放电站随机到达的协议外电动汽车数量，作为到达数量随机参数
再相应生成随机情景集SC
Z。
2-2.可再生能源发电随机情景
根据风力发电维布分布(Weibull distribution)式(3)，通过拉丁超立方抽样(Latin hypercube sampling,LHS)获取风速随机参数。
通过同步回代情景削减法(simultaneous backward reduction)减少情景数量，最后通过风机功率拟合模型生成风电出力情景SC
WT。
在光伏发电模拟方面，选取一年的光伏日度发电历史数据，建立光伏发电情景池(scenario pool)，通过随机抽样获取光伏发电随机情景，通过同步回代情景削减法(simultaneous backward reduction)生成随机情景SC
PV。
2-3.随机情景的组合
考虑各个随机因素相互独立，将以上四类随机情景(SC
D，SC
Z，SC
WT，SC
PV)组合计算，根据式(4)，将上述各随机情景交叉组合，生成模型最终随机情景SC
F。式(4)用于计算情景组合SC
F的概率。
其中P(sc
D)分别为1/SC
D，P(sc
Z)分别为1/SC
Z。P(sc
PV)以及P(sc
WT)通过情景削减算法后确定。
步骤3、V2G二阶段非线性随机规划模型
先对电动汽车参与调度的过程进行变量定义，对区域微电网的能量供给进行变量定义。再对V2G运营商的收益框架进行搭建，之后建立二阶段约束并进行约束的线性化。
表1-表3分别列出了V2G二阶段非线性随机规划模型的参数及变量，定义如下。
表1 索引与集合
表2 调度模型参数
表3 模型变量
3-1.目标函数
目标方程F最大化V2G运营商总体收益，见式(5)。其中，式(6)为电动汽车参与调度的运营商总收益(Rev
EV)；式(7)为运营商协调电力供给当地负荷和余电上网的总收益(Rev
AG)；式(8)为运营商在日前和当日购买火电总成本(Cost
B)；式(9)为运营商管辖的可再生能源发电总成本(Cost
OM)。
式中：
第一阶段约束条件:
式(10)为电动汽车充放电排斥性约束：调度时段内同一辆电动汽车充电操作和放电操作不能同时发生；
式(11-14)为电动汽车充电状态约束：t时段内电动汽车接入配电网开始充电，为限制最短充电时长和最短空闲时长，以避免在充电、放电、空闲状态间频繁切换，造成电动汽车电池受损以及切换服务的成本抬高；其中
为最短充电时长，
为最短空闲时长；
式(15-18)电动汽车放电状态约束：用于限制最短放电时长以及最短空闲时长；其中
为最短放电时长；
式(19-21)电动汽车充放电最大切换次数约束：对一日内最大的电动汽车充放电切换次数进行限制，限制电动汽车一天内可切换状态的最大次数，可有效避免出现电动汽车状态切 换过度频繁；其中
分别为V2G调度计划内的单辆电动汽车充电、放电次数上限，V
i为充放电切换次数上限；
第二阶段约束条件：
式(22-23)电动汽车并网时初始状态约束：式(22)通过电动汽车并网参与调度前的行驶距离计算车辆入网时的初始电量，式(23)计算EV
i的初始SOC，行驶距离的随机性造成电动汽车集群的初始SOC具有随机性；其中
为协议内EV
i并网前行驶距离的随机参数；
式(24-26)为拥有V2G节点最大服务数量约束：由于V2G服务站容量与变压器功率限制，在同一节点对充电和放电的车辆数量均有限制；式(24)限定节点m最大可同时充电的电动汽车数量，式(25)限定节点m最大可同时放电的电动汽车数量，式(26)限定节点m同时可充放电数量小于充放电桩的数量；其中α
m，β
m分别为每一个V2G服务站时段的最多可充，放电的车辆数量；
式(27-28)为电动汽车充放电能量约束：电动汽车充放电过程中，实际可充放电量受实时SOC的限制；其中：当
和
均为0时，EV
i在时段t充电量
和放电量
被约束为0；当
或
时，EV
i的充电量
和放电量
分别受电池可调度容量最大值
和
约束；
式(29-30)为电动汽车荷电状态约束：给出了调度协议内电动汽车电池荷电状态的变化范围；式(29)是车辆参与V2G时最佳电池工况范围；式(30)表示调度结束后电动汽车荷电状态SOC需满足用户期望值，在满足用户未来出行需求的前提下进行充放电调度；其中T
end设定为调度结束时间。
式(31)为电动汽车电量平衡约束:EV
i在t时段的电量等于t-1时段的剩余电量加上t时段充放电量的差值；
式(32-33)电动汽车充放电爬坡约束：电动汽车充放电的爬坡能力受充放电桩的额定功率以及充电方式影响，此约束限定每时段的电动车电池充放电量不大于充放电爬坡
避免电池充放电超限造成容量耗损加剧；当且仅当车辆接受第一阶段调度计划后，充放电爬坡约束才会在第二阶段生效；
为充电最大爬坡能力是，
为放电最大爬坡能力是；
式(34-35)为V2G节点最大服务容量约束：式(35)计算协议外随机到达的电动汽车产生充电需求总量；式(34)协议内的电动汽车可参与充电调度的容量，这个部分的电量受式(35)协议外电动汽车数量及充电量影响而具有随机性；其中
为协议外电动汽车随机到达数量，
节点m可提供的额定充电容量；
式中：
式(36-37)为网络节点容量与平衡约束：模型构建能量传输网络，网络的节点电量平衡满足基尔霍夫定律；式(36)限制了双向能流的最大容量，规定电力传输在标准内；式(37)在引入
描述风光发电随机性后，对每一节点构建能量平衡约束，保证节点总流入量等于总流出量；
非线性约束线性化：
由于约束条件式(27)和(28)均存在非线性项，将式(27)转化为式(38)-(40)，同理式(28)转化为式(41)-(43)，以提升模型解集质量与速度，其中，
基于以上目标和约束，构建风光发电随机性和V2G资源随机性的二阶段随机优化模型如下。
Maxmize F (5)
Subject to:
First-stage Constraints:(10)-(21)
充放电排斥性约束(10)
充电状态约束(11)-(14)
放电状态约束(15)-(18)
充放电最大切换次数约束(19)-(21)
Second-stage Constraints:(22)-(43)
EVs并网时初始状态约束(22)-(23)
V2G节点最大服务数量约束(24)-(26)
EVs充放电能量约束(27)-(28)
EVs充放电能量约束线性化(38)-(43)
EVs荷电状态约束(29)-(30)
EVs电量平衡约束(31)
EVs充放电爬坡约束(32)-(33)
V2G节点最大容量约束(34)-(35)
网络节点容量与平衡约束(36)-(37)
以下结合具体算例对模型优化结果进行详细说明：
以V2G运营商管理的区域结合可再生能源发电系统的交易机制为背景，选取IEEE-33节点的标准配电网拓扑，选取部分节点预装风力发电机和光伏发电系统，拓扑结构详见图4。
为验证模型的优化效果,先做以下参数设计，定8个V2G节点，调度协议内100辆EVs有序充放电。车型及部分参数如表4：
表4 参与调度EVs参数
节点20和节点11分别设置单台GE1.5-77风机和GE1.7-100型大功率风机，其他V2G服务站点配备小型的风电光伏系统，风机具体参数见于表5，通过步骤2生成风电随机参数。
表5 风机参数
对于协议内EVs，设定的调度结束的目标SOC为0.8。设定协议外随机到达的EVs的充电功率均为40kw。
生成多变量联合情景，设定SC
D为4、SC
Z为5、SC
WT为5、SC
PV为2。经过情景组合生成SC
F＝200个最终情景。在Python环境下调用Gurobi求解器的分支定界算法对模型进行求解计算。
图5为受最短充放电时间制约下的EVs充放电决策图。其显示几乎所有的协议内EVs都参与了充放电日调度，参与调度方式服从模型约束。EVs集群受调度时段占总时段比例接近100％，证明模型可有效控制协议内电动汽车的充放电状态，且在最大化调度中心收益的目标下，协议内EVs集群需全天待命，保持接入状态。凌晨0:00-2:00时段为EVs放电集中时段，因为这一时段放电价格较低，调度中心调度协议内EVs放电以供给其他负荷需求，以最大化调度收益。
图6证明并网前EVs行驶距离随机参数
对充放电负荷的影响。协议内EVs集群受供需关系约束，凌晨0:00-5:00时段和早上8:00-10:00时段放电较为集中。随机参数
对凌晨0:00-5:00时段的放电负荷有所影响，日前行驶距离的均值越大，接入时的SOC越低，参与放电的负荷就越小。但是早上8:00-10:00的放电负荷不受随机参数
的影响，因为随机参数
只对EVs初始SOC有影响，经过凌晨5:00-7:00时段的充电后，随机参数
的影响已经消除。晚上17:00-23:00时段，为实现调度日结束时目标SOC＝0.8，EVs的充电负荷会显著增高。
表6目标函数优化结果显示，调度100辆EVs时，该调度日调度中心期望收益为69323.4元。其中调度EVs充电收入12359.5元，调度EVs放电成本为3388.9元，调度EVs净收益8970.6元。调度EVs的利润占总利润比重为13％。调度中心最大的利润来源于当地负荷用电，通过可再生能源的就地消纳以供给区域用电负荷和电动汽车，调度中心可以获得可观的收益。
表6 目标函数能量调度收益表
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
上述实施例只是为了说明本发明的技术构思及特点，其目的是在于让本领域内的普通技术人员能够了解本发明的内容并据以实施，并不能以此限制本发明的保护范围。凡是根据本发明内容的实质所做出的等效的变化或修饰，都应涵盖在本发明的保护范围内。
Claims (5)
- 一种最大化运营商收益的基于二阶段随机规划的V2G调度方法，用于至少包括电动汽车、充放电桩和电网构成的能源系统，其特征在于，包括以下步骤：获取运营商服务区域内的电动汽车的日前参数集，同时，向运营商服务区域内的电动汽车发出调度邀约协议，将同意调度邀约协议的电动汽车归类为协议内电动汽车，将无响应及拒绝调度邀约协议的电动汽车归类为协议外电动汽车；基于运营商服务区域内的电动汽车的日前参数集、协议内电动汽车和协议外电动汽车的情况，构建随机情景集合，所述构建随机情景集合在满足协议外电动汽车随机充电需求下，对协议内电动汽车进行充放电优化调度；考虑各个随机因素相互独立，组合生成V2G调度资源和可再生能源发电随机情景，构建最终随机情景，构建在所述最终随机情景下的V2G二阶段随机非线性规划调度模型；求解所述V2G二阶段随机非线性规划调度模型实现最大化V2G运营商总体收益。
- 根据权利要求1所述的最大化运营商收益的基于二阶段随机规划的V2G调度方法，其特征在于，所述构建随机情景集合包括V2G调度资源随机情景，所述V2G调度资源随机情景包括以下一种或多种随机情景，如初始SOC的随机情景、V2G服务站资源随机情景，电力供应侧或需求侧负荷不确定性的情景，其中，初始SOC的随机情景：为展现协议内电动汽车参与调度时SOC随机性，采用协议内电动汽车日前行驶距离对数正态分布模型(1)，通过蒙特卡洛方法获取协议内电动汽车并网前的行驶距离，作为行驶距离随机参数相应生成随机情景集为SC D；
V2G服务站资源随机情景：为展现V2G服务站内可用充放电桩资源的随机性，由于V2G服务站能同时为协议内与协议外电动汽车服务，采用平均达到率为恒定的齐次泊松模型描述协议外电动汽车随机到达数量(2)，通过蒙特卡洛方法获取充放电站随机到达的协议外电动汽车数量，作为到达数量随机参数再相应生成随机情景集SC Z。
- 根据权利要求2所述的最大化运营商收益的V2G调度二阶段随机规划方法，其特征在于，所述构建随机情景集合还包括风力发电随机情景和光伏发电随机情景，其中，在风力发电随机情景中：根据风力发电维布分布模型(3)，通过拉丁超立方抽样获取风 速随机参数；
通过同步回代情景削减法(simultaneous backward reduction)减少情景数量，最后通过风机功率拟合模型生成风电出力情景SC WT；在光伏发电随机情景中，选取一年的光伏每日发电历史数据，建立光伏发电情景池，通过随机抽样获取光伏发电随机情景，通过同步回代情景削减法生成随机情景SC PV。 - 根据权利要求3所述的最大化运营商收益的基于二阶段随机规划的V2G调度方法，其特征在于，考虑各个随机因素相互独立，为将以上四类随机情景SC D，SC Z，SC WT，SC PV组合计算，将上述各随机情景交叉组合，生成最终随机情景SC F；式(4)用于计算情景组合SC F的概率；
其中P(sc D)分别为1/SC D，P(sc Z)分别为1/SC Z；P(sc PV)以及P(sc WT)通过情景削减算法后确定。 - 根据权利要求4所述的最大化运营商收益的基于二阶段随机规划的V2G调度方法，其特征在于，所述V2G二阶段随机非线性规划模型包括目标函数、第一阶段约束条件和第二阶段约束条件。目标函数：目标方程F最大化V2G运营商总体收益，见式(5)；其中，式(6)为电动汽车参与调度的运营商总收益(Rev EV)；式(7)为运营商协调电力供给当地负荷和余电上网的总收益(Rev AG)；式(8)为运营商在日前和当日购买火电总成本(Cost B)；式(9)为运营商管辖的可再生能源发电总成本(Cost OM)；
式中：
第一阶段约束条件:式(10)为电动汽车充放电排斥性约束：调度时段内同一辆电动汽车充电操作和放电操作不能同时发生；
式(11-14)为电动汽车充电状态约束：t时段内电动汽车接入配电网开始充电，为限制最短充电时长和最短空闲时长，以避免在充电、放电、空闲状态间频繁切换，造成电动汽车电池受损以及切换服务的成本抬高；其中为最短充电时长，
为最短空闲时长；
式(15-18)电动汽车放电状态约束：用于限制最短放电时长以及最短空闲时长；其中为最短放电时长；
式(19-21)电动汽车充放电最大切换次数约束：对一日内最大的电动汽车充放电切换次数进行限制，限制电动汽车一天内可切换状态的最大次数，可有效避免出现电动汽车状态切换过度频繁；其中分别为V2G调度计划内的单辆电动汽车充电、放电次数上限，V i为充放电切换次数上限；
第二阶段约束条件：式(22-23)电动汽车并网时初始状态约束：式(22)通过电动汽车并网参与调度前的行驶距离计算车辆入网时的初始电量，式(23)计算EV i的初始SOC，行驶距离的随机性造成电动汽车集群的初始SOC具有随机性；其中为协议内EV i并网前行驶距离的随机参数；
式(24-26)为拥有V2G节点最大服务数量约束：由于V2G服务站容量与变压器功率限制，在同一节点对充电和放电的车辆数量均有限制；式(24)限定节点m最大可同时充电的电动汽车数量，式(25)限定节点m最大可同时放电的电动汽车数量，式(26)限定节点m同时可充放电数量小于充放电桩的数量；其中α m，β m分别为每一个V2G服务站时段的最多可充，放电的车辆数量；
式(27-28)为电动汽车充放电能量约束：电动汽车充放电过程中，实际可充放电量受实时SOC的限制；其中：当和
均为0时，EV i在时段t充电量
和放电量
被约束为0；当
或
时，EV i的充电量
和放电量
分别受电池可调度容量最大值
和
约束；
式(29-30)为电动汽车荷电状态约束：给出了调度协议内电动汽车电池荷电状态的变化范围；式(29)是车辆参与V2G时最佳电池工况范围；式(30)表示调度结束后电动汽车荷电状态SOC需满足用户期望值，在满足用户未来出行需求的前提下进行充放电调度；其中T end设定为调度结束时间。
式(31)为电动汽车电量平衡约束:EV i在t时段的电量等于t-1时段的剩余电量加上t时段充放电量的差值；
式(32-33)电动汽车充放电爬坡约束：电动汽车充放电的爬坡能力受充放电桩的额定功率以及充电方式影响，此约束限定每时段的电动车电池充放电量不大于充放电爬坡避免电池充放电超限；当且仅当车辆接受第一阶段调度计划后，充放电爬坡约束才会在第二阶段生效；
为充电最大爬坡能力是，
为放电最大爬坡能力是；
式(34-35)为V2G节点最大服务容量约束：式(35)计算协议外随机到达的电动汽车产生充电需求总量；式(34)协议内的电动汽车可参与充电调度的容量，这个部分的电量受式(35)协议外电动汽车数量及充电量影响而具有随机性；其中为协议外电动汽车随机到达数量，
节点m可提供的额定充电容量；
式中：
式(36-37)为网络节点容量与平衡约束：模型构建能量传输网络，网络的节点电量平衡 满足基尔霍夫定律；式(36)限制了双向能流的最大容量，规定电力传输在标准内；式(37)在引入描述风光发电随机性后，对每一节点构建能量平衡约束，保证节点总流入量等于总流出量；
非线性约束线性化：由于约束条件式(27)和(28)均存在非线性项，将式(27)转化为式(38)-(40)，同理式(28)转化为式(41)-(43)，以提升模型解集质量与速度，其中，
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