技术领域technical field
本发明涉及电动汽车充电技术领域，具体地涉及一种电动汽车群的充放电优化控制方法。The invention relates to the technical field of electric vehicle charging, in particular to a charging and discharging optimization control method for electric vehicle groups.
背景技术Background technique
电动汽车可以减少人类对石油资源的依赖，随着全球石油资源日渐枯竭，与传统的燃油汽车相比，电动汽车备受青睐。随着电动汽车技术的快速发展，其由于污染少、运行成本低而受到越来越多人的欢迎，电动汽车市场占有率越来越大。随着电动汽车的发展，电动汽车充电站也在逐步建设，这将对充电设施发展和电网运行带来新的负荷增长。Electric vehicles can reduce human's dependence on oil resources. With the depletion of global oil resources, electric vehicles are favored compared with traditional fuel vehicles. With the rapid development of electric vehicle technology, it is welcomed by more and more people due to its less pollution and low operating cost, and the market share of electric vehicles is increasing. With the development of electric vehicles, electric vehicle charging stations are gradually being built, which will bring new load growth to the development of charging facilities and the operation of the power grid.
多辆汽车在各个充电站采用固定充电模式进行充电时，例如采用恒电流-恒电压-涓流模式充电时，电网系统负荷波动增强，现有的电压控制措施难以满足用户对电压限制的要求；并且强随机性负荷集中用电导致系统的负荷峰值增高，导致电力系统的装机容量不足。When multiple vehicles are charged in a fixed charging mode at each charging station, for example, when charging in a constant current-constant voltage-trickle mode, the load fluctuation of the grid system will increase, and the existing voltage control measures are difficult to meet the user's requirements for voltage limitation; Moreover, the concentrated power consumption of strong random loads leads to an increase in the peak load of the system, resulting in insufficient installed capacity of the power system.
发明内容Contents of the invention
本发明的目的在于提供一种实现电动汽车效益优化和系统负荷优化的电动汽车群的充放电优化控制方法。The object of the present invention is to provide a charge and discharge optimization control method for electric vehicle groups that realizes optimization of electric vehicle benefits and system load.
本发明提供的电动汽车群的充放电优化控制方法包括获取每台参与充放电车辆的次日充放电时间段数据；将次日时间离散为多个时隙，根据所有车辆的次日充放电时间段数据生成时隙集合；获取时隙集合中每个时隙的电力潮流数据和关于电力潮流的灵敏度数据；根据灵敏度数据采用内点法计算每个时隙中每台车辆的充放电功率。The charging and discharging optimization control method of the electric vehicle group provided by the present invention includes obtaining the next day's charging and discharging time period data of each vehicle participating in charging and discharging; discretizing the next day's time into multiple time slots, according to the next day's charging and discharging time of all vehicles Segment data to generate a time slot set; obtain the power flow data and sensitivity data about the power flow in each time slot set in the time slot set; calculate the charging and discharging power of each vehicle in each time slot by using the interior point method according to the sensitivity data.
由上述方案可见，先根据所有用户输入的次日充放电时间段数据组成时隙集合，对每个时隙的电力潮流和灵敏度进行计算，再根据灵敏度利用内点法计算出每个时隙中每台车辆的最优充放电功率，充放电优化控制方法减轻电压波动，降低负荷峰谷差，减少配电网网损，增加电动汽车用户的经济收益。It can be seen from the above scheme that the time slot set is formed based on the next day’s charging and discharging time period data input by all users, and the power flow and sensitivity of each time slot are calculated, and then the power flow and sensitivity in each time slot are calculated by using the interior point method according to the sensitivity. The optimal charging and discharging power of each vehicle, the charging and discharging optimization control method reduces voltage fluctuations, reduces load peak-to-valley differences, reduces distribution network losses, and increases the economic benefits of electric vehicle users.
进一步的方案是，根据灵敏度数据采用内点法计算每个时隙中每台车辆的充放电功率后，判断车辆的实际充放电时间段数据是否与已获取的该车辆的次日充放电时间段数据匹配，若否，根据实际充放电时间段数据和已获取的灵敏度数据采用内点法计算后续的每个时隙中每台车辆的充放电功率。A further solution is to use the interior point method to calculate the charging and discharging power of each vehicle in each time slot according to the sensitivity data, and then judge whether the actual charging and discharging time period data of the vehicle is consistent with the acquired charging and discharging time period of the vehicle for the next day. The data match, if not, calculate the charging and discharging power of each vehicle in each subsequent time slot by using the interior point method according to the actual charging and discharging time period data and the obtained sensitivity data.
由上可见，根据实际充放电时间段对后续的每个时隙中每台车辆的充放电功率进行实施计算和调控，系统的最优决策方案根据实际数据进行适时调整。It can be seen from the above that the charging and discharging power of each vehicle in each subsequent time slot is calculated and regulated according to the actual charging and discharging time period, and the optimal decision-making scheme of the system is timely adjusted according to the actual data.
进一步的方案是，将次日时间离散为多个时隙，根据所有车辆的次日充放电时间段数据生成时隙集合中，根据车辆的次日充放电时间数据的并集生成时隙集合。A further solution is to discretize the time of the next day into multiple time slots, generate a time slot set based on the next day's charging and discharging time period data of all vehicles, and generate a time slot set based on the union of the next day's charging and discharging time data of vehicles.
进一步的方案是，获取每台参与充放电车辆的次日充放电时间段数据前，根据申请充放电的车辆的电池状态与违约率信息获取参与充放电的车辆数据。A further solution is to obtain the data of vehicles participating in charging and discharging according to the battery status and default rate information of the vehicles applying for charging and discharging before obtaining the data of the next day's charging and discharging time period for each vehicle participating in charging and discharging.
由上可见，分析申请加入电网充放电服务电动汽车的电池状态、历史违约率等信息，筛选出未来可参与电动汽车充放电服务的电动汽车。It can be seen from the above that the battery status, historical default rate and other information of electric vehicles applying for grid charging and discharging services are analyzed, and electric vehicles that can participate in electric vehicle charging and discharging services in the future are screened out.
进一步的方案是，根据所有车辆的次日充放电时间段数据生成时隙集合中，次日充放电时间段根据用户输入的次日充放电起始时间、充放电结束时间和中断时间生成。A further solution is to generate time slot sets based on the next day's charging and discharging time period data of all vehicles, and the next day's charging and discharging time period is generated according to the next day's charging and discharging start time, charging and discharging end time and interruption time input by the user.
由上可见，次日充放电时间段数据根据用户预先输入的时间数据生成，关于时隙集合的数据更为准确。It can be seen from the above that the next day's charging and discharging time period data is generated according to the time data input by the user in advance, and the data about the time slot set is more accurate.
进一步的方案是，根据所有车辆的次日充放电时间段数据生成时隙集合中，次日充放电时间段根据车辆历史时间内的充放电起始时间、充放电结束时间和中断时间的平均值生成。A further solution is to generate a time slot set based on the next day’s charging and discharging time period data of all vehicles, and the next day’s charging and discharging time period is based on the average value of the charging and discharging start time, charging and discharging end time and interruption time in the vehicle’s historical time period generate.
由上可见，当用户没有输入时间数据时，系统根据该用户的历史充放电时间记录而分析获取该用户车辆的次日充放电时间段数据。It can be seen from the above that when the user does not input time data, the system analyzes and obtains the next day's charging and discharging time period data of the user's vehicle according to the user's historical charging and discharging time records.
进一步的方案是，根据所有车辆的次日充放电时间段数据生成时隙集合中，根据车辆的需充电量和该车辆的最大充电功率生成该车辆充电所需的时隙数量，将该车辆的时隙数量分配至时隙集合中。进一步的方案是，根据车辆的需充电量和该车辆的充电功率生成该车辆充电所需的时隙数量中，根据该车辆当天的电池驱动比例、行走距离和全电动可行距离生成该车辆的需充电量。A further solution is to generate the time slot set according to the charging and discharging time period data of all vehicles on the next day, generate the number of time slots required for charging the vehicle according to the required charging capacity of the vehicle and the maximum charging power of the vehicle, and then generate the number of time slots required for charging the vehicle. The number of slots is allocated into slot sets. A further solution is to generate the number of time slots required for charging the vehicle according to the required charging capacity of the vehicle and the charging power of the vehicle, and generate the required time slots for the vehicle according to the battery driving ratio, walking distance and all-electric feasible distance of the vehicle that day. charging capacity.
由上可见，根据该车辆当天的电池驱动比例、行走距离和全电动可行距离生成该车辆的需充电量，从而更准确地计算出该车辆充电所需的时隙数量。It can be seen from the above that the required charging capacity of the vehicle is generated according to the battery driving ratio, travel distance and all-electric feasible distance of the vehicle on the day, so as to more accurately calculate the number of time slots required for charging the vehicle.
进一步的方案是，获取时隙集合中每个时隙的电力潮流数据和灵敏度数据中，根据每个时隙中配电网拓扑结构参数、预测用户负荷和车辆充放电功率数据生成电力潮流数据。A further solution is to obtain the power flow data and sensitivity data of each time slot in the time slot set, and generate power flow data according to the distribution network topology parameters, predicted user load and vehicle charging and discharging power data in each time slot.
更进一步的方案是，根据每个时隙中配电网拓扑结构参数、预测用户负荷和车辆充放电功率数据生成电力潮流数据中，通过采集监控SCADA系统获取配电网拓扑结构参数。A further solution is to generate power flow data based on distribution network topology parameters, predicted user loads, and vehicle charging and discharging power data in each time slot, and obtain distribution network topology parameters through the acquisition and monitoring SCADA system.
由上可见，通过采集监控SCADA系统获取配电网拓扑结构参数并根据每个时隙中配电网拓扑结构参数、预测用户负荷和车辆充放电功率数据生成电力潮流数据，可保证每个时隙中电力潮流数据的准确性。It can be seen from the above that by collecting and monitoring the SCADA system to obtain the topological parameters of the distribution network and generating power flow data according to the topological parameters of the distribution network in each time slot, the predicted user load and the vehicle charging and discharging power data, it can ensure that each time slot Accuracy of power flow data in China.
附图说明Description of drawings
图1为本发明电动汽车群的充放电优化控制方法实施例中电动汽车群的充放电优化控制系统的结构框图。Fig. 1 is a structural block diagram of the charging and discharging optimization control system of the electric vehicle group in the embodiment of the charging and discharging optimization control method of the electric vehicle group in the present invention.
图2为本发明电动汽车群的充放电优化控制方法实施例的流程图。以下结合附图及实施例对本发明作进一步说明。Fig. 2 is a flow chart of an embodiment of the charging and discharging optimization control method for an electric vehicle group according to the present invention. The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
具体实施方式Detailed ways
图1为本发明电动汽车群的充放电优化控制方法实施例中电动汽车群的充放电优化控制系统的结构框图。本发明提供的电动汽车群的充放电优化控制方法基于电动汽车群的充放电优化控制系统实现，电动汽车群的充放电优化控制系统包括远程抄表系统1、采集监控SCADA系统2、数据输入模块3、负荷预测模块4、电力潮流计算模块5、汽车充放电统计模块6、汽车充放电优化模块7和汽车充放电设备8。Fig. 1 is a structural block diagram of the charging and discharging optimization control system of the electric vehicle group in the embodiment of the charging and discharging optimization control method of the electric vehicle group in the present invention. The charging and discharging optimization control method of the electric vehicle group provided by the present invention is realized based on the charging and discharging optimization control system of the electric vehicle group, and the charging and discharging optimization control system of the electric vehicle group includes a remote meter reading system 1, an acquisition and monitoring SCADA system 2, and a data input module 3. Load forecasting module 4, power flow calculation module 5, vehicle charge and discharge statistics module 6, vehicle charge and discharge optimization module 7 and vehicle charge and discharge equipment 8.
远程抄表系统1将获取的车辆充放电时间段数据、车辆充放电功率数据发送至数据输入模块3，采集监控SCADA系统2将获取的负荷功率和配电网拓扑结构参数发送至数据输入模块3，数据输入模块3将获取的配电网拓扑结构参数、预测用户负荷和车辆充放电功率数据发送至电力潮流计算模块5、负荷预测模块4和汽车充放电统计模块6，从而对电力潮流、电力潮流灵敏度和每辆车辆的充放电功率进行计算，最后通过汽车充放电优化模块7和汽车充放电设备8实现充放电功率调节。其中，汽车充放电统计模块6负责及时保存、管理全部电动汽车历史数据，计算过往一段历史时间电动汽车的充电起始时间、充电结束时间和中断外出时间的平均值，负荷预测模块4负责及时保存、管理全部负荷历史数据，根据负荷历史数据预测第二天24小时负荷，而电力潮流计算模块5根据预测的第二天24小时负荷和初始化的电动汽车充放电功率计算第二天24小时配电网三相电力潮流。The remote meter reading system 1 sends the acquired vehicle charging and discharging time period data and vehicle charging and discharging power data to the data input module 3, and the acquisition and monitoring SCADA system 2 sends the acquired load power and distribution network topology parameters to the data input module 3 , the data input module 3 sends the obtained distribution network topology parameters, predicted user load and vehicle charging and discharging power data to the power flow calculation module 5, load forecasting module 4 and vehicle charging and discharging statistics module 6, so as to analyze the power flow, power The power flow sensitivity and the charging and discharging power of each vehicle are calculated, and finally the charging and discharging power adjustment is realized through the vehicle charging and discharging optimization module 7 and the vehicle charging and discharging equipment 8 . Among them, the vehicle charging and discharging statistical module 6 is responsible for timely saving and managing all the historical data of electric vehicles, and calculates the average value of the charging start time, charging end time and interrupted going out time of the electric vehicle in a period of history in the past, and the load forecasting module 4 is responsible for timely saving , Manage all historical load data, predict the next day’s 24-hour load according to the load historical data, and the power flow calculation module 5 calculates the next day’s 24-hour power distribution based on the predicted next day’s 24-hour load and the initialized electric vehicle charging and discharging power Network three-phase power flow.
结合图2，图2为本发明电动汽车群的充放电优化控制方法实施例的流程图。首先以24小时为周期启动上述电动汽车群的充放电优化控制系统进行工作。系统首先执行步骤S1，获取每台参与充放电车辆的次日充放电时间段数据。汽车充放电的用户通过申请而加入到汽车群中，而系统则可通过申请信息数据中获取申请充放电的车辆的电池状态与违约率信息，并根据该车辆的电池状态与违约率信息筛选出符合条件，在未来参加车辆充放电服务的车辆群。而获取每台参与充放电车辆的次日充放电时间段数据的方式则有以下两项：1、用户可登入系统，并在系统中输入预约信息，预约信息包括次日的充放电起始时间、充放电结束时间和中断时间等。2、若用户没有在系统中输入预约信息，系统则对该车辆历史时间内的充放电起始时间、充放电结束时间和中断时间的平均值进行分析计算而生成该车辆的次日充放电时间段数据。优选地，用户输入的预约信息中，还包括该车辆当天的电池驱动比例、行走距离和全电动可行距离和车辆的充电功率等。With reference to FIG. 2 , FIG. 2 is a flow chart of an embodiment of the charging and discharging optimization control method for an electric vehicle group according to the present invention. Firstly, start the charging and discharging optimization control system of the above-mentioned electric vehicle group in a cycle of 24 hours to work. The system first executes step S1 to obtain the next day's charging and discharging time period data of each vehicle participating in charging and discharging. Users of car charging and discharging join the car group through application, and the system can obtain the battery status and default rate information of the vehicle applying for charging and discharging from the application information data, and filter out the information based on the battery status and default rate information of the vehicle. The group of vehicles that meet the conditions and participate in the vehicle charging and discharging service in the future. There are two ways to obtain the next day’s charging and discharging time period data for each vehicle participating in charging and discharging: 1. The user can log in to the system and enter reservation information in the system. The reservation information includes the starting time of charging and discharging for the next day , charging and discharging end time and interruption time, etc. 2. If the user does not enter the reservation information in the system, the system will analyze and calculate the average value of the charging and discharging start time, charging and discharging end time and interruption time of the vehicle within the historical time to generate the charging and discharging time of the vehicle for the next day segment data. Preferably, the appointment information input by the user also includes the vehicle's battery drive ratio, walking distance, all-electric feasible distance, and vehicle charging power of the vehicle on that day.
随后系统执行步骤S2，将次日时间离散为多个时隙，所有车辆的次日充放电时间段数据离散成对应时隙，将所有车辆计算获得的时隙数量进行并集，从而生成次日的时隙集合。如次日时间总和为24小时，若将其离散为96个时隙，每个时隙则为15分钟。由于在步骤S1中已经获得每台参与充放电车辆的次日充放电时间段数据和该车辆当天的电池驱动比例、行走距离和全电动可行距离和车辆的最大充电功率，系统根据该车辆当天的电池驱动比例、行走距离和全电动可行距离计算生成该车辆的需充电量，并根据计算得出的需充电量和充电功率计算出该车辆所需的充电时长，随后根据该车辆的最大充电功率和需充电量生成该车辆需要充电的时隙数量，最后，在该车辆次日充放电时间段内最早时隙中分配给该车辆所需数量的时隙进行充电，形成初始充放电方案。Then the system executes step S2 to discretize the time of the next day into multiple time slots, discretize the charging and discharging time period data of all vehicles into corresponding time slots, and combine the number of time slots calculated by all vehicles to generate the next day set of time slots. If the sum of the next day's time is 24 hours, if it is discretized into 96 time slots, each time slot is 15 minutes. Since the next day’s charging and discharging time period data of each vehicle participating in charging and discharging has been obtained in step S1, as well as the vehicle’s battery drive ratio, walking distance, all-electric feasible distance, and the maximum charging power of the vehicle on the day, the system uses the vehicle’s current day’s The battery drive ratio, walking distance and all-electric feasible distance are calculated to generate the required charging capacity of the vehicle, and the required charging time of the vehicle is calculated according to the calculated required charging capacity and charging power, and then according to the maximum charging power of the vehicle The number of time slots that the vehicle needs to be charged is generated based on the required charging amount, and finally, the required number of time slots are allocated to the vehicle in the earliest time slot of the next day's charging and discharging time period for charging to form an initial charging and discharging scheme.
每台车辆需要充电的时隙数量根据以下公式组计算：The number of time slots that each vehicle needs to charge is calculated according to the following formula group:
其中和分别为n节点k相第台电动汽车的电池驱动比例、行走距离、全电动可行距离、初始充电状态、目标充电状态、容量、充电效率和最大充电功率，Δt为时隙长度。计算n节点k相第个电动汽车电池初始充电状态、需要充电量和最小充电时隙数量in and Respectively for the n-node k-phase The battery driving ratio, walking distance, fully electric feasible distance, initial charging state, target charging state, capacity, charging efficiency and maximum charging power of an electric vehicle, Δt is the time slot length. Calculate n node k phase The initial charging state and required charging capacity of an electric vehicle battery and the minimum number of charge slots
随后系统执行步骤S3，获取时隙集合中每个时隙的电力潮流数据和关于电力潮流的灵敏度数据。电流潮流为三相电流潮流，每个时隙的电力潮流数据可根据每个时隙中配电网拓扑结构参数、预测用户负荷和车辆充放电功率数据生成，配电网中的调压设备采用传统控制方式，估计远方节点电压，如果估计电压值越限调整有载调压变压器、调压器分接头位置和投切电容器。Then the system executes step S3 to acquire the power flow data and the sensitivity data about the power flow for each time slot in the time slot set. The current flow is a three-phase current flow. The power flow data of each time slot can be generated according to the topology parameters of the distribution network in each time slot, the predicted user load and the vehicle charging and discharging power data. The voltage regulation equipment in the distribution network adopts The traditional control method estimates the voltage of remote nodes. If the estimated voltage value exceeds the limit, adjust the position of the on-load tap changer, the tap of the voltage regulator, and the switching capacitor.
每个时隙三相电力潮流数据根据以下公式组计算获得：The three-phase power flow data of each time slot is calculated according to the following formula group:
式组中Im为节点m中a相、b相、c相和中性点N注入电流复向量，IRem、IImm分别为Im的实部和虚部，注入电流包括负荷注入电流(ZIP为恒定阻抗、恒定电流且恒定功率)和电动汽车充电负荷注入电流Vm为节点n中a相、b相、c相和中性点N电压复向量；VRen、VImn分别为Vm的实部和虚部，分别为节点mZIP负荷的有功和无功功率，由负荷预测和三相潮流计算模块短期负荷预测得到，为节点m电动汽车充放电功率，通过公式计算负荷注入电流和电动汽车充电负荷注入电流In the formula group, Im is the complex vector of injection current of phase a, phase b, phase c and neutral point N in node m, IRem and IImm are the real part and imaginary part of Im respectively, and the injection current includes the load injection current (ZIP is constant impedance, constant current and constant power) and EV charging load injection current Vm is the voltage complex vector of phase a, phase b, phase c and neutral point N in node n; VRen and VImn are the real part and imaginary part of Vm respectively, are the active and reactive power of theZIP load at node m, respectively, which are obtained from the load forecasting and short-term load forecasting of the three-phase power flow calculation module, For the charging and discharging power of the electric vehicle at node m, the load injection current is calculated by the formula and EV charging load injection current
随后根据牛顿法按照以下公式组计算三相潮流计算：The three-phase power flow calculation is then calculated according to the following formula group according to Newton's method:
Ymn为节点m和节点n之间的导纳矩阵，分别为Ymn中l∈a,b,c,N、k∈a,b,c,N之间的导纳、电导和电纳，K为配电网总节点数。Ymn is the admittance matrix between node m and node n, are the admittance, conductance and susceptance between l∈a,b,c,N, k∈a,b,c,N in Ymn respectively, and K is the total number of nodes in the distribution network.
灵敏度数据包括节点电压幅值、电池放电深度、电池充放电费用函数、电池放电损耗成本函数和负荷形状函数对于控制变量的灵敏度数据，灵敏度数据根据以下公式组计算获得：Sensitivity data includes node voltage amplitude, battery discharge depth, battery charge and discharge cost function, battery discharge loss cost function, and load shape function sensitivity data to control variables. The sensitivity data is calculated according to the following formula group:
首先采用公式计算出电动汽车充放电功率。设NPEVnk为n节点k相充放电电动汽车数目，为节点n的k相电动汽车充放电功率；代数变量节点n＝1,…,K；控制变量u由电动汽车充电功率和电动汽车放电功率构成节点n＝1,…,K；时隙t∈Γopt，其中Γopt为电动汽车群优化控制时隙集合，和分别为n节点k相电动汽车数目t时隙第个电动汽车充电功率和放电功率。First use the formula Calculate the charging and discharging power of electric vehicles. Let NPEVnk be the number of charge-discharge electric vehicles with n-node k-phase, Charge and discharge power for k-phase electric vehicle at node n; algebraic variable Node n=1,...,K; the control variable u is composed of electric vehicle charging power and electric vehicle discharging power Node n=1,...,K; time slot t∈Γopt , where Γopt is the set of electric vehicle group optimization control time slots, and Respectively, the number of electric vehicles with n nodes, k phases, and time slot t Charging power and discharging power of an electric vehicle.
将中实部虚部三相电力潮流展开得分别到公式组：Will The real part and imaginary part of the three-phase power flow are expanded to the formula group:
将上述两式写成一般形式F(x,u)＝0，随后根据公式得到代数变量x对控制变量u的灵敏度xu。Write the above two formulas in the general form F(x,u)=0, then according to the formula Get the sensitivity xu of the algebraic variable x to the control variable u .
根据公式和According to the formula and
计算得到节点电压幅值对代数变量x的灵敏度Vx，n＝1,...,K。随后根据公式Vu＝Vxxu得到节点电压幅值Calculate the node voltage amplitude Sensitivity Vx to algebraic variablex , n=1,...,K. Then get the node voltage amplitude according to the formula Vu = Vx xu
对控制变量u的灵敏度。 Sensitivity to the control variable u.
随后根据下式得到系统的有功功率网损Ploss：Then the active power loss Ploss of the system is obtained according to the following formula:
随后根据下式计算得到电动汽车群优化控制期间网损费用ZlossC：Then calculate the network loss cost ZlossC during the optimal control period of the electric vehicle group according to the following formula:
式中Ct为时隙t负荷电价，为时隙t电动汽车电池放电电价，ΩPEV为电动汽车所在节点集合。 In the formula, Ct is the load price of time slot t, is the electricity price of electric vehicle battery discharge in time slot t, and ΩPEV is the set of nodes where electric vehicles are located.
然后根据公式计算全部电动汽车总充放电费用ZLC。Then according to the formula Calculate the total charging and discharging cost ZLC of all electric vehicles.
随后根据公式和得到充放电费用ZLC对时隙节点i∈ΩPEV相k控制变量u的灵敏度ZLCu，ZLC对其它控制变量的灵敏度为0。Then according to the formula and Get charge and discharge charge ZLC pair time slot The sensitivity ZLCu of control variable u of node i∈ΩPEV phase k, and the sensitivity of ZLC to other control variables is 0.
然后根据公式得到t时隙n节点k相第个电动汽车电池放电深度随后根据公式得到全部电动汽车总的电池放电损耗成本函数ZCost。其中，分别为n节点k相第个电动汽车电池放电效率的倒数、自放电率、在时隙t的放电深度和电池价格。Then according to the formula Get t time slot n node k phase Depth of Discharge of Electric Vehicle Batteries Then according to the formula The total battery discharge loss cost function ZCost of all electric vehicles is obtained. in, Respectively for the n-node k-phase The reciprocal of the discharge efficiency of an electric vehicle battery, the self-discharge rate, the discharge depth at time slot t, and the battery price.
根据公式和得到τ时隙n节点k相第个电动汽车电池放电深度对时隙n节点k相控制变量u的灵敏度对其它控制变量的灵敏度均为0。According to the formula and Get τ time slot n node k phase Depth of Discharge of Electric Vehicle Batteries right Sensitivity of control variable u at time slot n node k phase The sensitivity to other control variables is 0.
根据公式组According to the formula group
计算得到电池放电损耗成本函数ZCost对τ时隙n节点k相控制变量u的灵敏度ZCostu。The sensitivity ZCostu of the battery discharge loss cost function ZCost to the control variable u of node k in τ time slot n is calculated.
根据公式计算出时隙t节点n三相注入功率总和Pn(t)，随后根据公式计算得出负荷形状函数ZS，其中St为在时隙t除去电动汽车充放电外负荷归一化参数。According to the formula Calculate the sum Pn (t) of the three-phase injected power at node n of time slot t, and then according to the formula The load shape function ZS is calculated, where St is the normalized parameter of the load except for the charging and discharging of the electric vehicle at the time slot t.
根据公式和计算得到负荷形状函数ZS对时隙τ节点n相k控制变量u的灵敏度ZSu。According to the formula and Calculate the sensitivity ZSu of the load shape function ZS to the control variable u of the time slot τ node n phase k.
然后系统执行步骤S4，根据S3中计算获取的灵敏度数据采用内点法计算每个时隙中每台车辆的充放电功率。Then the system executes step S4, and calculates the charging and discharging power of each vehicle in each time slot by using the interior point method according to the sensitivity data obtained through calculation in S3.
目标函数Fun可以根据实际需要进行选择。其一，选择电动汽车用户费用最小化，Fun为全部电动汽车总的充放电费用ZLC与总的电池放电损耗成本函数ZCost之和，故有Fun＝ZLC+ZCost。其二，Fun为选择负荷形状函数ZS与总的电池放电损耗成本函数ZCost之和，故有Fun＝ZS+WgZCost，其中，Wg为权重系数。The objective function Fun can be selected according to actual needs. First, choose the electric vehicle user cost to be minimized, Fun is the sum of the total charging and discharging cost ZLC of all electric vehicles and the total battery discharge loss cost function ZCost , soFun = ZLC + ZCost . Second,Fun is the sum of the selected load shape function ZS and the total battery discharge loss cost function ZCost , soFun = ZS + Wg ZCost , where Wg is the weight coefficient.
以下公式组为电动汽车充放电的最优控制模型，采用内点法求解该最优控制模型：The following formula group is the optimal control model for electric vehicle charging and discharging, and the interior point method is used to solve the optimal control model:
最小化目标函数FunMinimize the objective function Fun
约束方程如下：The constraint equation is as follows:
ZLC(i+1)＝ZLC(i)+ZLCu(i)(u(i+1)-u(i))；ZLC(i+1) = ZLC(i) + ZLCu(i) (u(i+1) -u(i) );
ZCost(i+1)＝ZCost(i)+ZCostu(i)(u(i+1)-u(i))；ZCost(i+1) = ZCost(i) + ZCostu(i) (u(i+1) -u(i) );
ZS(i+1)＝ZS(i)+ZSu(i)(u(i+1)-u(i))；ZS(i+1) = ZS(i) + ZSu(i) (u(i+1) -u(i) );
Vmin≤V(i)+Vu(i)(u(i+1)-u(i))≤Vmax；Vmin ≤ V(i) + Vu(i) (u(i+1) -u(i) ) ≤ Vmax ;
n＝1,...,K；k＝a,b,c；n=1,...,K; k=a,b,c;
其中，和分别为n节点k相第个电动汽车电池充放电起始时隙、充放电结束时隙、充放电时隙集合、电池最大放电功率、t时隙末端电池中的储能、电池储能上限、电池储能下限、目标充电状态和单天最大允许放电深度，Γvalley为配电网负荷低谷时隙集合式，电动汽车充放电的最优控制模型。in, and Respectively for the n-node k-phase The charging and discharging start time slot of an electric vehicle battery, the charging and discharging end time slot, the collection of charging and discharging time slots, the maximum discharge power of the battery, the energy storage in the battery at the end of time slot t, the upper limit of battery energy storage, the lower limit of battery energy storage, and the target charging The state and the maximum allowable discharge depth in a single day, Γvalley is the distribution network load valley time slot set, the optimal control model for electric vehicle charging and discharging.
最优控制模型各式中上标(i)、(i+1)表示第i、i+1次迭代数值；The superscripts (i) and (i+1) in the various formulas of the optimal control model represent the iterative values of the i and i+1 iterations;
式和为充放电功率上下限约束；Mode and is the upper and lower limit constraints of charging and discharging power;
式表示负荷低谷时段不能放电；Mode Indicates that the load cannot be discharged during the low load period;
式为充放电状态互斥约束，同一时刻只能为充电或放电状态的其中一种；Mode The state of charge and discharge is mutually exclusive, and it can only be in one of the states of charge or discharge at the same time;
式和Mode and
分别为起始时隙和后续时隙电池储能计算公式；Respectively, the battery energy storage calculation formulas for the initial time slot and subsequent time slots;
式为电池储能上下限约束；Mode It is the upper and lower limits of battery energy storage;
式为结束时隙最低电池储能约束；Mode Minimum battery energy storage constraint for the end time slot;
式为最大放电深度约束；Mode is the maximum discharge depth constraint;
式表示全部电动汽车电池充放电时隙集合的并集构成电动汽车群优化控制时隙集合Γopt。Mode Representing the union of all electric vehicle battery charging and discharging time slot sets constitutes the electric vehicle group optimal control time slot set Γopt .
可选的是，上述电动汽车充放电的最优控制模型中，可将负荷低谷时段放电功率从控制变量中去掉，并且从最优控制模型中去除Optionally, in the above-mentioned optimal control model for charging and discharging of electric vehicles, the discharge power during the low load period can be removed from the control variables, and removed from the optimal control model
通过对电动汽车充放电的最优控制模型进行求解，随后即可进一步计算出每个时隙中每台车辆的充放电功率，减轻电压波动，降低负荷峰谷差，减少配电网网损，增加电动汽车用户的经济收益，使更多的电动汽车用户参与控制。By solving the optimal control model of charging and discharging of electric vehicles, the charging and discharging power of each vehicle in each time slot can be further calculated to reduce voltage fluctuations, reduce load peak-to-valley differences, and reduce distribution network losses. Increase the economic benefits of electric vehicle users and make more electric vehicle users participate in the control.
在次日到来后，系统执行判断步骤S5，判断获取的车辆的实际充放电时间段数据是否与已获取的该车辆的次日充放电时间段数据匹配。若判断结果为是，系统则执行步骤S7，根据步骤S4中最优控制模型的计算结果(即首次计算结果)对每个时隙中每台车辆的充放电功率进行控制。After the arrival of the next day, the system executes the judgment step S5 to judge whether the acquired actual charging and discharging time period data of the vehicle matches the acquired charging and discharging time period data of the vehicle for the next day. If the judgment result is yes, the system executes step S7, and controls the charging and discharging power of each vehicle in each time slot according to the calculation result of the optimal control model in step S4 (ie, the first calculation result).
若判断步骤S5的结果为否，系统则执行步骤S6，根据实际充放电时间段数据和获取的灵敏度数据采用内点法计算后续的每个时隙中每台车辆的充放电功率，最后执行步骤S8，根据重新计算的结果对后续每个时隙中每台车辆执行充放电功率控制。If the result of judging step S5 is negative, the system executes step S6, uses the interior point method to calculate the charging and discharging power of each vehicle in each subsequent time slot according to the actual charging and discharging time period data and the obtained sensitivity data, and finally executes the step S8. Perform charge and discharge power control for each vehicle in each subsequent time slot according to the recalculated result.
最后需要强调的是，以上所述仅为本发明的优选实施例，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种变化和更改，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。Finally, it should be emphasized that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the principles and principles shall be included within the protection scope of the present invention.
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| CN201711267703.0ACN107776433A (en) | 2017-12-05 | 2017-12-05 | A kind of discharge and recharge optimal control method of electric automobile group |
| CN201811474751.1ACN109383323B (en) | 2017-12-05 | 2018-12-04 | An optimal control method for charging and discharging of electric vehicle groups |
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| CN201811474751.1AActiveCN109383323B (en) | 2017-12-05 | 2018-12-04 | An optimal control method for charging and discharging of electric vehicle groups |
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