WO2021098352A1 - 一种考虑电动汽车充电站选址定容的主动配电网规划模型的建立方法 - Google Patents
一种考虑电动汽车充电站选址定容的主动配电网规划模型的建立方法 Download PDFInfo
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- the invention relates to the technical field of power grids, in particular to a method for establishing an active distribution network planning model considering the location and capacity of electric vehicle charging stations.
- DG Distributed Generation
- EV Electric Vehicles
- the power planning content of the distribution network has become more complex, and more considerations need to be taken in terms of access costs and comprehensive benefits. the elements of.
- DG output and load demand have significantly different timing characteristics.
- Reasonable access to energy storage equipment can store energy during low power consumption periods and release energy during peak power consumption periods to achieve complementarity between DG and load demand and reduce The overall cost of the planning scheme.
- the access of EV charging stations will bring about charging load, and its location and capacity issues will also affect the stability of the distribution network and the convenience of EV travel. Therefore, the establishment of an effective planning model including wind and solar storage and charging stations is of great significance to the active distribution network.
- the purpose of the present invention is to provide a method for establishing an active distribution network planning model considering the location and capacity of electric vehicle charging stations. Under the new situation, the results of the distribution network planning can be more adapted to the energy development trend under the new situation.
- the invention adopts the following scheme to realize: a method for establishing an active distribution network planning model considering the location and capacity of electric vehicle charging stations, including the following steps:
- Step S1 Establish a transportation network, based on the M/M/s queuing model and the closure location model, establish a transportation network with electric vehicle charging stations;
- Step S2 Establish an active distribution network model, establish a time series model of distributed power sources and loads based on the time sequence method; establish an energy storage model based on the principle of equivalent load;
- Step S3 By converting the electric vehicle traffic flow into the equivalent load of the equivalent charging station, the transportation network and the power grid are coupled together, and an active distribution network embedded planning model considering the transportation network is established.
- step S1 is:
- Step S11 Calculate the EV charging power demand
- N B is the demand for a battery pack per day
- N EVi is the ownership of the i-th EV
- L di respectively, and L 0i EV i-th and the average distance traveled per day once a full charge can travel distance
- a i, B i, and K EVdi respectively attendance EV i-th battery replacement ratio and the number of each vehicle equipped with the battery pack;
- the charging power requirement is:
- ⁇ tran is the efficiency of the transformer
- ⁇ charge is the efficiency of the charger
- P c is the average charging power
- t charge is the charging time
- Step S12 Establish a charging station location and capacity model
- the traffic flow intercepted by a single charging station is calculated according to the following formula:
- Equation (3) Represents the unit value of the one-way traffic demand of the shortest path k at time period t; ⁇ ko and ⁇ kd are the traffic demand weights of the start and end points of the path k, respectively, to indicate the busyness of each traffic node; D k is the path The unit value of k length; ⁇ t and ⁇ RH are the travel proportions of EV users in time t and peak time h respectively; ⁇ od is the shortest path set from any starting point o to any end point d in the transportation network, calculated by Floyd algorithm ; T is the time period collection; A binary variable that indicates whether the flow on path k can be intercepted by the charging station. If path k passes through the charging station, the variable is 1, otherwise it is 0;
- the calculation method is as follows:
- Is the traffic flow intercepted by node i in time period t; Is the binary variable of whether path k passes through node i; Is the binary variable of whether to build a charging station at node i; ⁇ is the set of network nodes; ⁇ i,t is the number of electric vehicles that arrive at the charging station at node i to receive charging services at time t; in formula (6) Is the charging power of node i at time t; ⁇ i RH is the average arrival rate of vehicles to be charged at node i during peak traffic hours, that is, the number of EVs that arrive at the charging station to receive charging services per unit time; It is the total daily charging frequency demand of the electric vehicle charging station, which needs to be calculated according to W B in formula (2); W B /pre-set maximum capacity of the battery pack; W B is the charging power; in formula (7), p CS is the charging power of a single charging device; ⁇ is the average service rate of a single device, in units
- Step S13 Based on the M/M/s queue model, the constant capacity problem of the charging station is transformed into the following nonlinear integer programming problem:
- the number of charging devices configured for node i, multiplied by p CS is the capacity of the charging station at node i;
- W i RH and W allowed are the average waiting time and threshold for receiving charging services during peak traffic hours;
- P i non is The probability that all the charging station equipment at node i is idle; Is the average utilization rate of equipment at node i during peak traffic hours.
- step S2 specifically includes the following content:
- P Li (t) and P DGi (t) represent the load value of node i at time t and the DG output value
- the energy storage element adjustment strategy is as follows:
- step S3 specifically includes the following content:
- the objective function of the embedded planning model is:
- f 1 represents the economic cost, including construction cost C inv and operating cost C ope ; r is the discount rate, ⁇ is the investment period; f 2 is the voltage quality index; Is the voltage quality evaluation function value of node i under scenario s; n is the total number of nodes in the network; ⁇ represents the set of nodes; ⁇ S is the set of scenarios; f 3 represents the traffic network satisfaction index; ⁇ od is any starting point in the traffic network o The set of shortest paths to any end point d; Represents the unit value of the one-way traffic flow demand of the shortest path k at time period t; A binary variable that indicates whether the flow on path k can be intercepted by the charging station; T is the time period set; p DG is the unit capacity of DG; Is a binary variable for whether to build a charging station at node i; with ⁇ PV and ⁇ WG are the set of nodes installed photovoltaic and wind turbines respectively; N j is
- the constraints of the embedded planning model include traffic network constraints and power grid constraints.
- the traffic network constraints are as follows:
- the number of charging devices configured for node i, which is multiplied by p CS is the charging power of a single charging device at node i;
- ⁇ i RH is the average arrival rate of the vehicles to be charged at node i during the peak traffic period, which refers to the unit time The number of EVs arriving at the charging station to receive charging services;
- ⁇ od is the set of shortest paths from any starting point o to any end point d in the traffic network;
- W i RH and W allowed are the charging services during peak traffic hours.
- the present invention has the following beneficial effects:
- the present invention can reduce the construction cost of electric vehicle charging stations to the greatest extent and reduce the cost of distribution network planning.
- Voltage stability is one of the planning goals of the present invention. Therefore, the present invention can reduce the voltage fluctuation degree of the distribution network to the greatest extent, and make the voltage distribution of the distribution network more uniform.
- the present invention couples the electric vehicle network into the power network, fully considers the impact of electric vehicle charging on the distribution network, and combines electric vehicle charging to formulate the optimal optimal dispatching strategy of the grid, and optimize the electric vehicle access to the distribution network Scheduling has very significant practical significance.
- Fig. 1 is a diagram of wind power output according to an embodiment of the present invention.
- Fig. 2 is a graph of photovoltaic power output according to an embodiment of the present invention.
- Fig. 3 is a resident load power diagram according to an embodiment of the present invention.
- Fig. 4 is a commercial load power diagram according to an embodiment of the present invention.
- Fig. 5 is an industrial load power diagram according to an embodiment of the present invention.
- Fig. 6 is a topological diagram of a coupling network of a distribution network and a transportation network according to an embodiment of the invention.
- FIG. 7 is a flowchart of solving a planning model according to an embodiment of the present invention.
- Fig. 8 is a graph of load timing characteristics before and after EV charging in Example 19 of the present invention.
- Fig. 9 is a graph of load power before and after EV charging in the spring peak period according to an embodiment of the present invention.
- Fig. 10 is a graph of node voltage amplitudes before and after EV charging in the spring peak period according to an embodiment of the present invention.
- This embodiment provides a method for establishing an active distribution network planning model considering the location and capacity of electric vehicle charging stations, which couples the power network and the transportation network. While considering the equipment components such as wind and solar storage in the active distribution network, through The traffic flow of electric vehicles in the transportation network is converted into the electric load required for electric vehicle charging, which realizes the location and capacity of electric vehicle charging stations. It includes the following steps:
- Step S1 Establish a transportation network, based on the M/M/s queuing model and the interception location model in the transportation field, establish a transportation network with electric vehicle charging stations;
- Step S2 Establish an active distribution network model, establish a time series model of distributed power (including wind power and photovoltaic) and load based on the time sequence method; establish an energy storage model based on the principle of equivalent load;
- Step S3 By converting the electric vehicle traffic flow into the equivalent load of the equivalent charging station, the transportation network and the power grid are coupled together, and an active distribution network embedded planning model considering the transportation network is established.
- the location and capacity model includes the MMs queuing model and the interception location model, and the specific content of the step S1 is:
- Step S11 Calculate the EV charging power demand
- EV generally provides electric energy by several battery packs, and each battery pack is composed of several batteries in series and parallel [12] . Therefore, the demand for EV charging can be analyzed with the help of the intermediate variable of the number of battery packs.
- EVs can be divided into buses, official vehicles, taxis, private cars and other vehicles (sanitation vehicles, postal vehicles, etc.).
- the demand for the number of battery packs is mainly related to information such as the number of EVs of each model, the daily mileage, the attendance rate, and the ratio of battery replacement. Therefore, this article calculates the number of battery packs, as shown in equation (1).
- N B is the demand for a battery pack per day
- N EVi is the ownership of the i-th EV
- L di respectively, and L 0i EV i-th and the average distance traveled per day once a full charge can travel distance
- a i, B i, and K EVdi respectively attendance EV i-th battery replacement ratio and the number of each vehicle equipped with the battery pack;
- the EV adopts a unified standard lithium-ion battery pack, and each group is formed by connecting 15 single cells (with a rated voltage of 3.65V and a rated capacity of 6A ⁇ h) in series.
- the charging process can be approximately constant power charging, and the charging power P C of a single battery pack is approximately 1660 W, and the required duration t charge is 2.5 h.
- EV charging stations are equipped with a large number of charging equipment, a set of equipment is composed of a transformer and several attached chargers. For the convenience of analysis, assuming that the charging station distributes power according to the maximum demand of the EV battery pack, the charging power demand at this time is:
- ⁇ tran is the efficiency of the transformer
- ⁇ charge is the efficiency of the charger
- P c is the average charging power
- t charge is the charging time
- Step S12 Establish a charging station location and capacity model
- the gravity space interaction model is used in combination with the Floyd algorithm to calculate the annual traffic flow F CS intercepted by the charging stations of the whole system.
- the calculation formula is as follows:
- the traffic flow intercepted by a single charging station is calculated according to the following formula:
- Equation (3) Represents the unit value of the one-way traffic demand of the shortest path k at time period t; ⁇ ko and ⁇ kd are the traffic demand weights of the start and end points of the path k, respectively, to indicate the busyness of each traffic node; D k is the path The unit value of the length of k; ⁇ t and ⁇ RH are the travel proportions of EV users in time t and peak time h, respectively; ⁇ od is the shortest path (OD path) collection from any starting point o to any end point d in the transportation network, by Calculated by Floyd algorithm; T is the set of time periods; A binary variable that indicates whether the flow on path k can be intercepted by the charging station. If path k passes through the charging station, the variable is 1, otherwise it is 0;
- the traffic flow intercepted by the system throughout the year can be used as one of the criteria for evaluating traffic network satisfaction. Simultaneously, It will also affect the average arrival rate ⁇ i,t and charging power of the vehicle to be charged at node i in each period And the number of charging devices that need to be configured at the charging station. It is assumed that ⁇ i, t are respectively proportional to the traffic flow intercepted by the charging station and the proportion of EV trips.
- the equivalent charging load is calculated.
- the calculation method is as follows:
- Step S13 For the charging station, the level of service depends to a large extent on the average charging waiting time of car owners during peak traffic hours.
- the queuing problem of charging stations can be alleviated, and the waiting time will be shortened, but it also increases the investment cost of the entire system. Therefore, the number of devices in each charging station can be configured by setting the threshold of the average charging waiting time and establishing relevant constraints to achieve the optimal investment in charging equipment. It is assumed that the arrival process and charging service duration of the vehicle to be charged at the charging station are simulated by Poisson distribution and negative exponential distribution, respectively. Based on the M/M/s queue model, the constant capacity problem of the charging station is transformed into the following nonlinear integer programming problem:
- the number of charging devices configured for node i, multiplied by p CS is the capacity of the charging station at node i;
- W i RH and W allowed are the average waiting time and threshold for receiving charging services during peak traffic hours;
- P i non is The probability that all the charging station equipment at node i is idle; Is the average utilization rate of equipment at node i during peak traffic hours.
- the first constraint of equation (9) can set the threshold as 0.1h.
- the second constraint of equation (9) must be set.
- the core decision variables of the charging station location and capacity model considering traffic flow are The optimization process not only directly affects the satisfaction index of the transportation network, but also affects the charging power and the investment cost of charging equipment. Therefore, the model can better link the traffic demand with the power load, and further reflects the interlocking and mutual influence relationship between the distribution network and the traffic network.
- the distributed power sources are obviously intermittent and random, and are greatly restricted by meteorological conditions.
- their output also has certain characteristics. Regularity. In terms of seasonal characteristics, WG output reaches its maximum in winter and minimum in summer, while PV is the opposite. In terms of timing characteristics, WG reaches its maximum in the evening, while PV has a greater output at noon.
- Resident load, commercial load and industrial load in daily life also have similar time series rules, but the three types of main loads are different in nature and their changing rules are not the same.
- the step S2 specifically includes the following content:
- storage batteries are used to store energy, and a coordinated optimization strategy for energy storage based on equivalent load is proposed. It is assumed that the output power of wind power and photovoltaic power generation is constant in each scenario, and a typical day in the time series characteristics is used as a cycle for research.
- P Li (t) and P DGi (t) represent the load value of node i at time t and the DG output value.
- step S3 specifically includes the following content:
- the objective function of the embedded planning model is:
- f 1 represents the economic cost, including construction cost C inv and operating cost C ope ; r is the discount rate, ⁇ is the investment period; f 2 is the voltage quality index; Is the voltage quality evaluation function value of node i in scenario s; n is the total number of nodes in the network; ⁇ represents the set of nodes; ⁇ S is the set of scenarios; f 3 represents the traffic network satisfaction index; ⁇ od is any starting point in the traffic network o Set of shortest paths to any end point d; Represents the unit value of the one-way traffic flow demand of the shortest path k at time period t; A binary variable that indicates whether the flow on path k can be intercepted by the charging station; T is the time period set; p DG is the unit capacity of DG; Is a binary variable for whether to build a charging station at node i; with ⁇ PV and ⁇ WG are the set of nodes installed photovoltaic and wind turbines respectively; N j is the
- the constraints of the embedded planning model include traffic network constraints and power grid constraints.
- the traffic network constraints are as follows:
- the number of charging devices configured for node i, which is multiplied by p CS is the charging power of a single charging device for node i;
- ⁇ i RH is the average arrival rate of the vehicles to be charged at node i during peak traffic hours, that is, the arrival rate per unit time
- ⁇ od is the set of shortest paths from any starting point o to any end point d in the traffic network;
- W i RH and W allowed are the charging services during peak traffic hours.
- this embodiment adopts the transportation network-distribution network coupling topology structure shown in FIG. 6 to simulate and verify the model of the present invention.
- the unit capacity of the DG node is set to 0.1MW in combination with the actual project, the number of installations is limited to 20, the maximum capacity of energy storage is 3MWh, and the maximum power of a single charge and discharge of the battery is 0.3MW; node voltage The amplitude constraint range is 0.95 ⁇ 1.05pu; the number of charging stations is limited to 8.
- the NSGA-II algorithm is used to solve the simulation example of this embodiment. Since the NSGAII algorithm is an existing very mature solution algorithm, the detailed calculation method of the algorithm is not described in detail in this application. Suppose the maximum number of iterations of the NSGAII algorithm is 50, the population size is 150, the crossover rate is 0.9, the mutation rate is 0.1, and the polynomial mutation index is 20. The parameters related to economic costs, the weight of traffic flow at traffic nodes, and the proportion of EV trips in each period are shown in Table 1 to Table 3.
- the relevant information of the electric vehicle that simulates this system is shown in Table 4.
- the transformer efficiency and charger efficiency used in the charging station in this paper are 95% and 90% respectively.
- the total daily demand for battery packs in this area is 2,284, and the daily average demand for charging power is 11,086.1kWh.
- each EV has a single charging capacity of 30kWh and the charging power of a single charging device is 60kW
- the total daily charging frequency of all charging stations is 370
- the average service rate of a single device is 0.5.
- the simulation example of this embodiment involves two parts of the power distribution system and the transportation network, and the solution process is shown in FIG. 7.
- the economic cost corresponding to the optimal solution is 14.114 million U.S. dollars, the voltage quality index is 0.061 pu, and the traffic satisfaction index is 1.3 ⁇ 10 -6 pu, which means that the traffic flow value intercepted throughout the year is 7.68 ⁇ 10 5 pu.
- the site construction node is generally selected in a location with a higher traffic flow weight (such as nodes 3, 6, 7) or The hub location of the transportation network (such as nodes 19, 20, 26, etc.).
- the total traffic flow weight of the site selection node in Table 6 is 8.57, accounting for 39.6% of the weight of the entire transportation network.
- these charging stations can capture 190.54pu of traffic flow during the daily rush hour, accounting for 90.9% of the total traffic flow during rush hour. It can be seen that the satisfaction index constructed in the model can effectively help build a site to capture as much traffic flow as possible, and can provide charging services for more EV users without changing the original driving route.
- EV charging will increase the charging load on the site-building node and change the load timing characteristics of the node.
- node 19 as an example, consider the load timing curve before and after EV charging as shown in FIG. 8.
- the location and capacity planning of the EV charging station will not only change the load sequence characteristics of the station node, but also change the load level of the entire distribution network node, thereby affecting the voltage quality of the node.
- the peak period in spring as an example, consider the system load level before and after EV charging as shown in FIG. 9, and the node voltage amplitude distribution of the corresponding scene is as shown in FIG. 10.
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Claims (4)
- 一种考虑电动汽车充电站选址定容的主动配电网规划模型的建立方法,其特征在于:包括以下步骤:步骤S1:建立交通网络,基于M/M/s排队模型和截流选址模型,建立含电动汽车充电站的交通网络;步骤S2:建立主动配电网模型,基于时序法,建立分布式电源和负荷的时序模型;基于等效负荷原理,建立储能模型;步骤S3:通过将电动汽车交通流量转换成等效充电站的等效负荷,将交通网和电网耦合在一起,建立考虑交通网络的主动配电网嵌层规划模型。
- 根据权利要求1所述的一种考虑电动汽车充电站选址定容的主动配电网规划模型的建立方法,其特征在于:所述步骤S1的具体内容为:步骤S11:对EV充电电量需求进行计算;计算电池组的数量需求:式中:N B为每天电池组的需求量;N EVi为第i种EV的保有量;L di和L 0i分别为第i种EV平均每天的行驶路程以及完整充电一次能够行驶的路程;a i、b i和K EVdi分别为第i种EV的出勤率、更换电池比例以及每辆车装备的电池组数量;充电电量需求为:W B=N BP Ct charge/(η tranη charge) (2)式中:η t ran为变压器效率;η charge为充电机的效率;P c表示平均充电功率;t charge表示充电时间;步骤S12:建立充电站选址定容模型;利用重力空间互动模型,并结合Floyd算法,计算出全系统充电站每年截获的交通流量F CS;其计算式如下所示:单个充电站截获的交通流量按以下公式计算:式(3)中: 表示最短路径k于时段t的单向交通流量需求的标幺值;ω ko和ω kd分别为路径k的起点和终点的交通需求权重,用以表示各交通节点的繁忙程度;D k为路径k长度的标幺值;σ t和σ RH分别为EV用户于时段t和高峰时段h的出行比例;Ω od为交通网络中任意起点o到任意终点d的最短路径集合,由Floyd算法求得;T为时间段集合; 表示路径k上的流量能否被充电站截获的二值变量,若路径k有经过充电站,则变量为1,否则为0;根据单个充电站截获的交通流量,计算等效的充电负荷,计算方式如下;式(5)中, 为节点i于时段t截获的交通流量; 为路径k是否经过节点i的二值变量; 为节点i处是否建设充电站的二值变量;Ω为网络节点的集合;λ i,t为在t时刻到达位于节点i的充电站接受充电服务的电动汽车数量;式(6)中 为节点i在t时刻的充电功率; 为节点i于交通高峰时段待充电车辆的平均到达率即指单位时间内到达充电站接受充电服务的EV数量; 为电动汽车充电站每日充电总频次需求,需根据式(2)中的W B计算得到 /预先设定的电池组最大容量;W B为充电电量;式(7)中,p CS为单台充电设备的充电功率;μ为单台设备的平均服务率,单位为辆/小时;步骤S13:基于M/M/s队列模型,把充电站的定容问题转化为如下非线性整数规划问题:
- 根据权利要求1所述的一种考虑电动汽车充电站选址定容的主动配电网规划模型的建立方法,其特征在于:所述步骤S2具体包括以下内容:建立主动配电网模型,基于时序法对包含风电和光伏的分布式电源典型出力和不同类型的典型负荷曲线进行抽样,得到节点i在时刻t的DG出力值P DGi(t)和负荷值P Li(t);基于等效负荷原理,建立储能模型,储能元件调节策略如下:计算节点i在时刻t的等效负荷P eqi和平均等效负荷P avi,P eqi(t)=P Li(t)-P DGi(t) (13)式中:P Li(t)和P DGi(t)表示节点i在时刻t的负荷值以及DG出力值;储能元件调节策略具体如下:当P eqi(t)+ΔP 1<<P avi时,蓄电池充电,ΔP 1为充电功率;若满足|P eqi(t)+ΔP 1-P avi|≤δP avi,则蓄电池充电;δ表示P eqi在其平均值附近的波动系数;当P eqi(t)-ΔP 2>>P avi时,蓄电池放电,ΔP 2为放电功率;若满足|P eqi(t)-ΔP-P avi|≤δP avi,则蓄电池放电。
- 根据权利要求1所述的一种考虑电动汽车充电站选址定容的主动配电网规划模型的建立方法,其特征在于:所述步骤S3具体包括以下内容:嵌层规划模型的目标函数为:式中:f 1表示经济成本,包括建设成本C inv和运行成本C ope;r为贴现率,η为投资年限;f 2表示电压质量指标; 为场景s下节点i的电压质量评估函数值;n为网络的节点总数;Ω表示节点集合;Ω S为场景的集合;f 3表示交通网络满意度指标;Ω od为交通网络中任意起点o到任意终点d的最短路径集合; 表示最短路径k于时段t的单向交通流量需求的标幺值; 表示路径k上的流量能否被充电站截获的二值变量;T为时间段集合;p DG为DG单位容量; 为节点i处是否建设充电站的二值变量; 和 分别为光伏和风力发电单位容量的投资成本;Ω PV和Ω WG分别为安装光伏和风机的节点集合;N j为第j个安装节点的DG个数;c CS为每台充电设备的投资成本; 为节点k是否投入储能装置的二值变量;Ω BS为安装储能的节点集合; 和 分别为储能装置的单位容量成本与充放电功率成本; 和 分别为储能最大容量和最大充放电功率; 和 分别为光伏和风力发电的单位运行费用;Δt s为场景s下配网年累计运行时间; 和 分别为场景s下的第j个PV或WG的出力;f e(s)和P em(s)分别为场景s下的电价和电功率需求; 和 为场景s下节点i的负荷功率和EV充电功率; 为场景s下的电能损耗;V s,i为场景s下节点i的电压幅值;V min和V max分别为节点电压的允许下限值和上限值;嵌层规划模型的约束条件包含交通网约束和电网约束,交通网约束如下:式中: 为节点i配置的充电设备数,其乘以p CS即为节点i处单台充电设备的充电功率; 为节点i于交通高峰时段待充电车辆的平均到达率即指单位时间内到达充电站接受充电服务的EV数量; 表示路径k上的流量能否被充电站截获的二值变量;Ω od为交通网络中任意起点o到任意终点d的最短路径集合; 和W allowed分别为交通高峰时段接受充电服务的平均等待时间及其阈值; 为节点i充电站设备全部空闲的概率; 为节点i于交通高峰时段的设备平均使用率;N CS为充电站最大建设数量;μ为单台设备的平均服务率;电网约束如下:配电网潮流约束:场景机会约束:DG安装容量约束:储能容量与充放电功率约束:
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