A method and system for optimizing reactive power configuration of distribution network considering overcharging load characteristics

Abstract

The present invention discloses a method and system for optimizing reactive power configuration of a distribution network considering the characteristics of overcharging load. The method comprises the following steps: collecting the number of electric vehicles and regular parking positions, as well as the number of grid-connected nodes and various types of charging piles of each supercharging station; calculating the number of electric vehicles that each supercharging station must serve; simulating the daily starting state of charge and daily driving distance of each electric vehicle; simulating the daily multiple travel behaviors of each electric vehicle; calculating the parking time and parking duration of each electric vehicle; judging the charging demand and type of each electric vehicle each time it stops; calculating the time domain load of each charging pile; using a pre-constructed reactive power optimization configuration model of a distribution network considering the constraints of reactive facility site selection and fixed capacity, obtaining a construction plan for reactive power optimization configuration of a distribution network, and implementing the plan to complete the reactive power optimization configuration of a distribution network. The present invention can ensure that the voltage of the distribution network does not exceed the lower limit due to the overcharging pulse load, while reducing the construction cost of reactive power optimization configuration.

Description

Technical Field

[0001] The present invention relates to the field of voltage and reactive power optimization of distribution networks, and in particular to a distribution network reactive power optimization configuration method and system taking into account overcharge load characteristics. Background Art

[0002] Electric vehicles have developed rapidly due to their clean energy and environmentally friendly advantages. However, compared with fuel vehicles, electric vehicles currently have a longer time to replenish electricity and a shorter endurance time, which affects the further promotion and development of electric vehicles. The existing plan for the construction of ultra-fast charging piles aims to shorten the charging time of electric vehicles and bring a more convenient electric vehicle experience. However, the access to a large number of high-power (minimum of about 350kWh) ultra-fast charging piles will bring challenges to the voltage quality of the distribution network operation, especially because short-term high loads will cause the voltage of some nodes to exceed the lower limit. Therefore, in the scenario of supercharging access, studying the changes in electric vehicle charging behavior and load characteristics, and optimizing the configuration of reactive resources in the distribution network will become a very important research content in the field of distribution network planning.

[0003] Under the background of the rapid popularization and promotion of supercharging station construction, the existing distribution network reactive power optimization configuration scheme for electric vehicle load has limitations, specifically the following two points: First, the popularization of supercharging piles will change the charging habits of electric vehicles. Supercharging can allow electric vehicles to complete charging within 10 minutes, so that short parking time during the day can be used for charging. The original load distribution of daytime charging trough and nighttime charging peak will change to a dispersed distribution at night and daytime (Wang Min, Lv Lin, Xiang Yue. Electric vehicle peak shaving coordinated dispatch strategy considering V2G price incentives [J]. Electric Power Automation Equipment, 2022, 42(04): 27-33+85.); Second, the current distribution network reactive power optimization configuration is mostly for slow charging piles and fast charging piles. The load characteristics are that there is a load of less than 50kW for a period of time, which lasts for a long time but has a small peak value, while the supercharging pile load is pulsed, with a short duration but a large peak value. The existing configuration scheme is difficult to solve the overload problem of the distribution network (Hu Daner, Li Zichen, Peng Yonggang, et al. Deep reinforcement learning active-reactive coordinated voltage control strategy for distribution network with electric vehicle charging piles [J]. Power System Technology, 2023, 47(12): 4985-4996.). Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing reactive power configuration of a distribution network taking into account overcharge load characteristics.

[0005] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0006] A method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics comprises the following steps:

[0007] S1. Collect the number of electric vehicles and their regular parking locations in the medium-voltage distribution network, as well as the number of grid-connected nodes of each supercharging station and various types of charging piles;

[0008] S2. According to the distance between the regular parking location of the electric vehicle and the grid connection node of the supercharging station, the number of electric vehicles that each supercharging station needs to serve is obtained;

[0009] S3, simulating the daily starting state of charge and daily driving distance of each electric vehicle in the medium voltage distribution network;

[0010] S4, using the improved Monte Carlo method to simulate the daily multiple travel behaviors of each electric vehicle in the medium voltage distribution network;

[0011] S5. Calculate the parking time and parking duration of each trip of the electric vehicle;

[0012] S6. According to the parking time and parking duration of each electric vehicle trip, the charging demand and type of each electric vehicle when parking each time are determined to obtain the charging behavior of the electric vehicle;

[0013] S7. Calculate the time domain load of each charging pile according to the charging behavior of the electric vehicle;

[0014] S8. Use a pre-built distribution network reactive power optimization configuration model that takes into account reactive power facility site selection and capacity constraints to obtain a distribution network reactive power optimization configuration construction plan, and implement the plan to complete the distribution network reactive power optimization configuration.

[0015] Further, in step S1, the regular parking position of the electric vehicle is the position where the electric vehicle is parked for the longest time in a day;

[0016] The number of various types of charging piles at each supercharging station includes supercharging piles, fast charging piles, and slow charging piles.

[0017] Furthermore, step S2 specifically includes the following steps:

[0018] S2.1. Count the distances from the regular parking locations of electric vehicles to the grid-connected nodes of each supercharging station to obtain the distance matrix :

[0019]

[0020] in, The first Electric cars to The distance between the grid connection nodes of each supercharging station;

[0021] S2.2. Select the grid connection node of the supercharging station closest to each electric vehicle, as follows:

[0022]

[0023] in, The first Electric vehicles and their nearest supercharging stations The distance between

[0024] S2.3. Electric vehicles are given priority to go to the nearest supercharging station for charging, and the number of electric vehicles that each supercharging station needs to serve is counted:

[0025]

[0026] in, For the An initial value of the number of electric vehicles that each supercharging station must serve; For the The number of electric vehicles that a supercharging station must serve.

[0027] Furthermore, step S3 specifically includes the following steps:

[0028] S3.1. Obtain the probability density function of the daily starting state of charge of electric vehicles , the specific calculation is as follows:

[0029]

[0030] in, is the function's independent variable; It is the natural index base; is the average value of the daily starting state of charge; is the standard deviation of the daily starting state of charge;

[0031] S3.2. Obtaining the probability density function of the daily driving distance of electric vehicles , the specific calculation is as follows:

[0032]

[0033] in, is the function's independent variable; is the average daily driving distance; is the standard deviation of daily driving distance;

[0034] S3.3. Calculate the cumulative probability distribution function by integrating the probability density function, as follows:

[0035]

[0036] in, is the cumulative probability distribution function, is the inverse cumulative probability distribution function, that is, Substitute the value of Calculation can be obtained Corresponding independent variable; is the probability density function; for the moment; and Substitute , and obtain the inverse function of the cumulative probability distribution of the initial state of charge And the inverse function of the cumulative probability distribution of driving distance ;

[0037] S3.4. Inverse function of cumulative probability distribution at initial state of charge And the inverse function of the cumulative probability distribution of driving distance The daily starting state of charge and daily driving distance of each electric vehicle are obtained by sampling:

[0038]

[0039] in, , The first The daily starting state of charge and daily driving distance of electric vehicles; , Respectively The probability density function value of the electric vehicle's initial state of charge every day The probability density function value of the daily driving distance The sampling results in ; for Mapping on the inverse function of the cumulative probability distribution of the initial state of charge; for Mapping on the inverse function of the cumulative probability distribution of driving distances.

[0040] Furthermore, step S4 specifically includes the following steps:

[0041] S4.1. Calculate the probability density function of electric vehicle travel time , as follows:

[0042]

[0043] in, is the function's independent variable; It is the natural index base; is the average of the first travel peak time; is the average of the second peak travel time; is the standard deviation of the first travel peak time; is the standard deviation of the second peak travel time;

[0044] S4.2. Calculate the cumulative probability distribution function of travel time by integrating the probability density function , as follows:

[0045]

[0046] in, for the moment;

[0047] S4.3. Use the improved Monte Carlo method to simulate the travel time of electric vehicles, as follows:

[0048] First, randomly select the first sampling value in the set interval ,turn up The corresponding horizontal coordinate , recorded as the first trip time of the electric vehicle on that day;

[0049] Then, randomly select the second sampling value in the set interval ,turn up Corresponding horizontal axis ,like , then the electric car only Go out once and stop sampling; if , then remember The second trip time of the electric vehicle on the same day, and the sampling continues until the Sampling results The corresponding horizontal coordinate , then the number of electric vehicle trips on that day Stop sampling.

[0050] Furthermore, step S5 specifically includes the following steps:

[0051] S5.1. Calculate the parking time of each trip of an electric vehicle, that is, the time when each trip ends. This can be simulated by sampling the travel time of two adjacent trips, as follows:

[0052]

[0053] in, Respectively Electric car The arrival and departure times of the trips; For A random variable with values ​​between ;

[0054] S5.2. For The last trip of an electric vehicle per day is calculated by adding the last trip time to the trip time. The way to simulate:

[0055]

[0056] The travel time The lognormal distribution is as follows:

[0057]

[0058] in, is the travel time probability density function; is the function's independent variable; is the average travel time; is the standard deviation of travel time;

[0059] S5.3. Calculate the rest time of each stop based on the stop time and the next travel time:

[0060]

[0061] in, For the Electric car The rest time after the first trip; after the last trip, the electric car returns to the dock and there is no need to calculate the rest time.

[0062] Furthermore, step S6 specifically includes the following steps:

[0063] S6.1. Determine whether there is a need for charging each time the electric vehicle stops. The criteria are as follows:

[0064]

[0065] in, For the Electric car The amount of electricity after the first trip; is the battery capacity; The amount of electricity consumed per kilometer; For the Electric car The distance of the trip;

[0066] S6.2. According to the charging demand for each parking, select the charging method according to the parking time of the electric vehicle, as follows:

[0067] When charging an electric vehicle after its last trip, select a slow charging pile; Electric car Rest time per trip When, electric vehicles choose fast charging piles; when When charging, electric vehicles choose super charging piles.

[0068] Furthermore, step S7 specifically includes the following steps:

[0069] S7.1. Calculate the charging pile state matrix at each moment according to the charging demand of each electric vehicle:

[0070]

[0071] in, For the The supercharging station corresponding to the grid-connected node Charging pile status matrix at the moment; _1, _2. _3 are the maximum number of super charging piles, fast charging piles, and slow charging piles. In order to make the matrix Length alignment, set non-existent elements to 0; , , Respectively Grid-connected nodes in The moment _1 super charging pile, _2 fast charging piles, _The charging status of the 3 slow charging piles. If there is an electric car charging at the charging pile, the charging status of the charging pile is 1, otherwise it is 0. The matrix Initially set to 0;

[0072] S7.2. Calculate the time domain load of each charging pile as follows:

[0073]

[0074] in, for Moment Charging pile load at each grid-connected node; Number the charging piles in the supercharging station; , , They are the charging power of a single supercharging pile, fast charging pile, and slow charging pile respectively.

[0075] Furthermore, in step S8, the objective function of the pre-constructed distribution network reactive power optimization configuration model considering the reactive power facility location and capacity constraints is as follows:

[0076]

[0077] in, is the annualized investment cost of the distribution network; is the annual line loss cost of the distribution network; The annualized investment cost for reactive power configuration;

[0078] and The specific calculation is as follows:

[0079]

[0080] in, For the on-grid electricity price; For branch exist The current at the moment; For branch The resistance of is the total number of distribution network branches; the annualized investment cost of reactive configuration includes the annualized investment cost of static var generator (SVG) Annual investment cost of capacitors ; Including SVG annual construction cost SVG annual maintenance cost and SVG annual active power loss cost , Including the annual construction cost of capacitors , capacitor switching cost and capacitor annual active loss cost , the specific calculation is as follows:

[0081]

[0082] in, , They are the construction investment costs of SVG and capacitor respectively; is the annualized investment coefficient; The total number of nodes configured for building reactive power; , Respectively Reactive capacity of SVG and capacitor at each grid-connected node; For the The number of SVGs installed at each grid-connected node, with a value in {0,1}; The annual maintenance cost of each SVG; is the single switching cost of the capacitor; For the The number of times the capacitor at a grid-connected node is switched on and off in a year; , They are the compensation efficiencies of SVG and capacitor respectively; , are the average access ratios of SVG and capacitor respectively;

[0083] The pre-built distribution network reactive power optimization configuration model considering the constraints of reactive power facility location and capacity includes optimal power flow constraints and reactive power facility location and capacity constraints.

[0084] The optimal power flow constraints include node active power balance constraints, node reactive power balance constraints, node voltage upper and lower limit constraints, branch voltage drop constraints and second-order cone relaxation constraints, among which the second-order cone relaxation constraints are variables that make the node voltage and branch current square terms equivalent to first-order terms; the reactive facility location and capacity constraints include SVG (Static VarGenerator) access upper and lower limit constraints, reactive facility location quantity constraints, capacitor reactive capacity constraints and capacitor gear constraints, as well as M- Linearization constraints;

[0085] The optimal power flow constraints are as follows:

[0086]

[0087] in, , They are Time Branch From Active power flowing into and out of each grid-connected node; , They are The generator sends the The active power of the grid-connected node, The load power of each grid-connected node; , They are Time branch From Reactive power flowing into and out of each grid-connected node; , , They are When SVG flows into The reactive power of the grid-connected node and the capacitor flowing into the The reactive power of the grid-connected nodes, The reactive power of the load at each grid-connected node; , , They are Moment The voltage level of each grid-connected node, the lower and upper limits of the node voltage; , for Time Branch The voltage of the first and last grid-connected nodes; , They are Time branch Line current , No. The node voltage of the grid-connected node The second-order cone relaxation variable of ;

[0088] The constraints for reactive power facility location and capacity are as follows:

[0089]

[0090] in, For the The SVG capacity built at each grid-connected node; The total number of SVGs built in the distribution network; is the minimum value; Maximum value; for Moment Reactive power compensation capacity of capacitors at each grid-connected node; express Moment The reactive power compensation capacity of a single capacitor at each grid-connected node; for Moment The number of capacitors switched at each grid-connected node; For the The total number of capacitors available for switching at each grid-connected node.

[0091] Furthermore, in step S8, the number of grid-connected nodes and charging piles of each supercharging station, the number of electric vehicles that each supercharging station needs to serve, the time domain load of each charging pile, and the topological structure parameters of the medium-voltage distribution network are input into a pre-constructed distribution network reactive power optimization configuration model that considers the constraints on the site selection and capacity of reactive facilities. The site selection and capacity of the output capacitor and SVG are used as a construction plan for the reactive power optimization configuration of the distribution network, and the plan is implemented to complete the reactive power optimization configuration of the distribution network.

[0092] A reactive power optimization configuration system for a distribution network considering overcharging load characteristics includes the following functional modules:

[0093] The first functional module: obtains the number of electric vehicles in the medium-voltage distribution network, the common parking locations, and the data on the grid-connected nodes and the number of charging piles of each supercharging station, and calculates and outputs the number of electric vehicles that each supercharging station needs to serve;

[0094] The second functional module: Based on the data of the number of electric vehicles in the medium-voltage distribution network, the Monte Carlo method is used to simulate the initial state of charge and daily driving distance of electric vehicles in the medium-voltage distribution network, and the improved Monte Carlo method is used to simulate the daily multiple travel behaviors of electric vehicles in the medium-voltage distribution network, and the behavior data of each electric vehicle is output;

[0095] The third functional module: according to the behavior data of each electric vehicle, calculate the parking time and parking duration of each electric vehicle trip, judge and output the charging demand and type of each electric vehicle when parking each time;

[0096] The fourth functional module: calculates and outputs the time domain load of each supercharging pile according to the number of electric vehicles that each supercharging station needs to serve and the charging requirements and types of each electric vehicle each time it stops;

[0097] The fifth functional module: According to the number of grid-connected nodes and charging piles of each supercharging station, the number of electric vehicles that each supercharging station needs to serve, the time domain load of each charging pile, and the topological structure parameters of the medium-voltage distribution network, a pre-built distribution network reactive power optimization configuration model that considers the siting and sizing constraints of reactive facilities is used to obtain a distribution network reactive power optimization configuration plan that considers the supercharging load characteristics, and the plan is implemented to complete the distribution network reactive power optimization configuration.

[0098] Compared with the prior art, the advantages of the present invention are:

[0099] The present invention can obtain the construction plan of reactive optimization configuration of distribution network by using the reactive optimization configuration model of distribution network that considers the constraints of reactive facility site selection and capacity, based on the number of grid-connected nodes and charging piles of each supercharging station, the number of electric vehicles that each supercharging station needs to serve, the time domain load of each charging pile, and the topological structure parameters of the medium-voltage distribution network. The present invention can ensure that the voltage of the distribution network does not exceed the lower limit due to the supercharging pulse load, while reducing the construction cost of reactive optimization configuration. The present invention takes into account the impact of the introduction of supercharging load on the voltage safety of the distribution network, and can improve the economy of reactive optimization configuration while effectively solving the problems of supercharging overload and overvoltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 The present invention is a flowchart of a method for optimizing reactive power configuration of a distribution network taking into account overcharging load characteristics in an embodiment of the present invention.

[0101] Figure 2 Schematic diagram of the connection topology of a 62-node medium-voltage distribution network in an embodiment of the present invention.

[0102] Figure 3 Schematic diagram of time domain load distribution of fast charging piles in an embodiment of the present invention.

[0103] Figure 4 Schematic diagram of the time domain load distribution of the supercharging pile in an embodiment of the present invention.

[0104] Figure 5 Schematic diagram of voltage curves of nodes before using the present invention in the embodiment.

[0105] Figure 6Schematic diagram of voltage curves of nodes after using the present invention in an embodiment. DETAILED DESCRIPTION

[0106] The following will be combined with the accompanying drawings and the results of the invention embodiments to provide a detailed and complete description of the objectives, technical paths and practical benefits of the embodiments of the present invention, wherein the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by other technicians in the field without creative work are within the scope of protection of the present invention.

[0107] In one embodiment, a 10kV distribution network base state model of a city in the south is selected for simulation analysis. The topology is as follows: Figure 2 As shown, nodes 1 to 33 constitute the main line, and other nodes constitute the distribution network branches. The UFC label in the figure represents a supercharging station. Supercharging stations are installed at nodes 8, 19, and 28, totaling 3. Each supercharging station includes 2 supercharging piles and 7 fast charging piles. Each supercharging station is responsible for the charging needs of 100 electric vehicles per day. The charging time scale is 1min, the rated capacity is 10MVA, the voltage of the first node is 10kV, the line adopts the LGJ-240 model, the initial load power factor is 0.9, the electric vehicle load power factor is 0.95, the initial average load rate is 0.5, each supercharging power is 350kW, the fast charging is 35kW, and the slow charging is 10kW. Each supercharging station supplies 100 electric vehicles in the area.

[0108] The time domain distribution of fast charging load is as follows: Figure 3 As shown in Figure 2, the time domain distribution of the supercharging charging load is as follows: Figure 4 As shown in the figure, the peak value of fast charging load is small, and the charging load is small during the day. The charging load gradually increases from the evening and reaches its peak after 8 o'clock in the evening; the peak value of supercharging load is large, about twice the peak value of fast charging load, and is distributed in a pulsed manner. The load distribution during the day and at night is relatively uniform, so the original reactive power configuration scheme for fast and slow charging has limitations in the scenario of supercharging access.

[0109] Different reactive power optimization models are used to solve the heavy overload problem that still exists in supercharging access scenarios. The traditional model does not consider the frequency constraints of capacitor shifting and dynamic and static simultaneous regulation. The annual investment cost of the distribution network The simulation results are shown in Table 1.

[0110] Table 1 Annualized investment cost of distribution network under different configuration schemes

[0111]

[0112] It can be seen from the above table that as the number of nodes equipped with reactive facilities increases, the annual investment cost of the distribution network increases. That is, for overcharge loads, the economy of building large-capacity reactive facilities at a single node is better than building small-capacity reactive facilities at multiple nodes.

[0113] In the comparison of reactive power optimization methods, the invention of reactive power optimization model requires , Even less, in In the scenario of 28,089 yuan. Firstly, the model adds capacitor gear adjustment frequency constraints, which reduces the problem of increased switching costs caused by the high frequency of UFC large load fluctuations. Secondly, the invented model simultaneously considers dynamic and static reactive power compensation to participate in the optimization, which avoids the high cost of SVG and the high cost caused by the inflexible capacitor gear adjustment.

[0114] A reactive power optimization configuration method for distribution network considering overcharge load characteristics, such as Figure 1 As shown, the following steps are included:

[0115] S1. Collect the number of electric vehicles and their regular parking locations in the medium-voltage distribution network, as well as the number of grid-connected nodes of each supercharging station and various types of charging piles;

[0116] The regular parking location of an electric vehicle is the location where the electric vehicle is parked for the longest time in a day;

[0117] The number of various types of charging piles at each supercharging station includes supercharging piles, fast charging piles, and slow charging piles.

[0118] S2. According to the distance between the regular parking location of the electric vehicle and the grid connection node of the supercharging station, the number of electric vehicles that each supercharging station needs to serve is obtained, which specifically includes the following steps:

[0119] S2.1. Count the distances from the regular parking locations of electric vehicles to the grid-connected nodes of each supercharging station to obtain the distance matrix :

[0120]

[0121] in, The first Electric cars to The distance between the grid connection nodes of each supercharging station;

[0122] S2.2. Select the grid connection node of the supercharging station closest to each electric vehicle, as follows:

[0123]

[0124] in, The first Electric vehicles and their nearest supercharging stations The distance between

[0125] S2.3. Electric vehicles are given priority to go to the nearest supercharging station for charging, and the number of electric vehicles that each supercharging station needs to serve is counted:

[0126]

[0127] in, For the The initial value of the number of electric vehicles that a supercharging station must serve is set to 0; For the The number of electric vehicles that a supercharging station must serve.

[0128] S3, simulating the daily starting state of charge and daily driving distance of each electric vehicle in the medium voltage distribution network, specifically including the following steps:

[0129] S3.1. Probability density function value of the daily starting state of charge of electric vehicles , the specific calculation is as follows:

[0130]

[0131] in, is the function's independent variable; It is the natural index base; is the average value of the daily starting state of charge; is the standard deviation of the daily starting state of charge; , ;

[0132] S3.2. Probability density function value of daily driving distance of electric vehicles , the specific calculation is as follows:

[0133]

[0134] in, is the function's independent variable; is the average daily driving distance; is the standard deviation of daily driving distance; , ;

[0135] S3.3. Calculate the cumulative probability distribution function by integrating the probability density function, as follows:

[0136]

[0137] in, is the cumulative probability distribution function, is the inverse cumulative probability distribution function, that is, Substitute the value of Calculation can be obtained Corresponding independent variable; is the probability density function; is the time, the maximum time in a day is 1440 minutes; and Substitute , and obtain the inverse function of the cumulative probability distribution of the initial state of charge And the inverse function of the cumulative probability distribution of driving distance ;

[0138] S3.4. Inverse function of cumulative probability distribution at initial state of charge And the inverse function of the cumulative probability distribution of driving distance The daily starting state of charge and daily driving distance of each electric vehicle are obtained by random sampling:

[0139]

[0140] in, , The first The daily starting state of charge and daily driving distance of electric vehicles; , Respectively The probability density function value of the electric vehicle's initial state of charge every day The probability density function value of the daily driving distance The sampling results in the interval Random value in for Mapping on the inverse function of the cumulative probability distribution of the initial state of charge; for Mapping on the inverse function of the cumulative probability distribution of driving distances.

[0141] S4. Using the improved Monte Carlo method to simulate the daily multiple travel behaviors of each electric vehicle in the medium voltage distribution network, specifically including the following steps:

[0142] S4.1. Calculate the probability density function of electric vehicle travel time , as follows:

[0143]

[0144] in, is the function's independent variable; It is the natural index base; is the average of the first travel peak time; is the average of the second peak travel time; is the standard deviation of the first travel peak time; is the standard deviation of the second peak travel time; ; ; ; ;

[0145] S4.2. Calculate the cumulative probability distribution function of travel time by integrating the probability density function , as follows:

[0146]

[0147] in, for the moment;

[0148] S4.3. Using the improved Monte Carlo method to simulate the travel time of each electric vehicle, in one embodiment, the details are as follows:

[0149] First, in the interval Randomly select the first sampling value from ,turn up The corresponding horizontal coordinate , recorded as the first trip time of the electric vehicle on that day;

[0150] Then, in the interval Randomly select the second sampling value from ,turn up Corresponding horizontal axis ,like , then the electric car only Go out once and stop sampling; if , then remember The second trip time of the electric vehicle on the same day, and the sampling continues until the Sampling results The corresponding horizontal coordinate , then the number of electric vehicle trips on that day Stop sampling.

[0151] S5. Calculating the parking time and parking duration of each trip of the electric vehicle, specifically including the following steps:

[0152] S5.1. Calculate the parking time of each trip of an electric vehicle, that is, the time when each trip ends. This can be simulated by sampling the travel time of two adjacent trips, as follows:

[0153]

[0154] in, Respectively Electric car The arrival and departure times of the trips; For A random variable with values ​​between ;

[0155] S5.2. For The last trip of an electric vehicle per day is calculated by adding the last trip time to the trip time. Simulate by:

[0156]

[0157] The travel time The lognormal distribution is as follows:

[0158]

[0159] in, is the travel time probability density function; is the function's independent variable; is the average travel time; is the standard deviation of travel time; ; ;

[0160] S5.3. Calculate the rest time of each stop based on the stop time and the next travel time:

[0161]

[0162] in, For the Electric car The rest time after the first trip; after the last trip, the electric car returns to the dock and there is no need to calculate the rest time.

[0163] S6, determining the charging demand and type of each electric vehicle each time it stops, and obtaining the charging behavior of the electric vehicle, specifically including the following steps:

[0164] S6.1. Determine whether there is a need for charging each time the electric vehicle stops. The criteria are as follows:

[0165]

[0166] in, For the Electric car The amount of electricity after the first trip, in kW·h; is the battery capacity, take 50kW·h; The power consumption per kilometer is 0.16 kW·h / km; For the Electric car The distance of the trip;

[0167] S6.2. According to the charging demand of each parking, the charging method is selected according to the parking time of the electric vehicle. The specific charging method selection is as follows:

[0168] When charging an electric vehicle after its last trip, select a slow charging pile; Electric car Rest time per trip When, electric vehicles choose fast charging piles; when When charging, electric vehicles choose super charging piles.

[0169] S7. Calculate the time domain load of each charging pile according to the charging behavior of the electric vehicle, which specifically includes the following steps:

[0170] S7.1. Calculate the charging pile state matrix at each moment according to the charging demand of each electric vehicle:

[0171]

[0172] in, For the The supercharging station corresponding to the grid-connected node Charging pile status matrix at the moment; _1, _2. _3 are the maximum number of super charging piles, fast charging piles, and slow charging piles. In order to make the matrix Length alignment, set non-existent elements to 0; , , Respectively Grid-connected nodes in The moment _1 super charging pile, _2 fast charging piles, _The charging status of the 3 slow charging piles. If there is an electric car charging at the charging pile, the charging status of the charging pile is 1, otherwise it is 0. The matrix Initially set to 0;

[0173] S7.2. Calculate the time domain load of each charging pile as follows:

[0174]

[0175] in, for Moment Charging pile load at each grid-connected node; Number the charging piles in the supercharging station; , , The charging powers of a single supercharging pile, fast charging pile, and slow charging pile are 350kWh, 35kWh, and 10kWh respectively.

[0176] S8. Use a pre-built distribution network reactive power optimization configuration model that takes into account reactive power facility site selection and capacity constraints to obtain a distribution network reactive power optimization configuration construction plan, and implement the plan to complete the distribution network reactive power optimization configuration.

[0177] The objective function of the pre-built distribution network reactive power optimization configuration model considering the constraints of reactive power facility location and capacity is as follows:

[0178]

[0179] in, is the annualized investment cost of the distribution network; is the annual line loss cost of the distribution network; The annualized investment cost for reactive power configuration;

[0180] and The specific calculation is as follows:

[0181]

[0182] in, The on-grid electricity price is 0.6 yuan / kWh; For branch exist The current at the moment; For branch The resistance of is the total number of distribution network branches; the annualized investment cost of reactive configuration includes the annualized investment cost of static var generator (SVG) Annual investment cost of capacitors ; Including SVG annual construction cost SVG annual maintenance cost and SVG annual active power loss cost , Including the annual construction cost of capacitors , capacitor switching cost and capacitor annual active loss cost , the specific calculation is as follows:

[0183]

[0184] in, , The construction investment costs of SVG and capacitor are 80,000 yuan / MVar and 20,000 yuan / MVar respectively; is the annualized investment factor (which can be calculated based on the discounted equipment life); The total number of nodes configured for building reactive power; , Respectively Reactive capacity of SVG and capacitor at each grid-connected node, in MVar; For the The number of SVGs installed at each grid-connected node, with a value in {0,1}; The annual maintenance cost of each SVG is 1,500 yuan per unit; The cost of switching on and off the capacitor is 500 yuan per time. For the The number of times the capacitor at each grid-connected node is switched on and off in one year; , are the compensation efficiencies of SVG and capacitor respectively, which are taken as 0.99, i.e. 99% of reactive capacity is used for compensation and the remaining 1% is active loss; , The average access ratios of SVG and capacitor are 0.97, which means that 97% of reactive capacity is accessed all day for 360 days a year on average.

[0185] The pre-built distribution network reactive power optimization configuration model considering the constraints of reactive power facility location and capacity includes optimal power flow constraints and reactive power facility location and capacity constraints.

[0186] Optimal power flow constraints (see: Wang Mingjuan, Liu Youbo, Gao Hongjun, et al. Two-stage stochastic model predictive control of active distribution network considering operation cost risk [J]. Power System and Clean Energy, 2020, 36(11):8-18.) include node active power balance constraints, node reactive power balance constraints, node voltage upper and lower limit constraints, branch voltage drop constraints and second-order cone relaxation constraints, where the second-order cone relaxation constraints are variables that equate the square terms of node voltage and branch current to linear terms; reactive facility location and capacity constraints include SVG (Static Var Generator) access upper and lower limit constraints, reactive facility location quantity constraints, capacitor reactive capacity constraints and capacitor gear constraints (see: Du Hongwei, Wei Tongzheng, Xia Dong, et al. Reactive voltage self-discipline-cooperative control of distribution network based on dynamic cluster division [J]. Automation of Electric Power Systems, 2024, 48(10):171-181.), and M- Linearization constraints;

[0187] The optimal power flow constraints are as follows:

[0188]

[0189] in, , They are Time branch From Active power flowing into and out of each grid-connected node; , They are The generator sends the The active power of the grid-connected node, The load power of each grid-connected node; , They are Time branch From Reactive power flowing into and out of each grid-connected node; , , They are When SVG flows into The reactive power of the grid-connected node and the capacitor flowing into the The reactive power of the grid-connected nodes, The reactive power of the load at each grid-connected node; , , They are Moment The voltage level of each grid-connected node, and the lower and upper limits of the node voltage; , for Time branch The voltage of the first and last grid-connected nodes; , They are Time branch Line current , No. The node voltage of the grid-connected node The second-order cone relaxation variable of ;

[0190] The constraints for reactive power facility location and capacity are as follows:

[0191]

[0192] in, For the The SVG capacity built at each grid-connected node; The total number of SVGs built in the distribution network; is the minimum value, take 0.001; The maximum value is 1000; for Moment Reactive power compensation capacity of capacitors at each grid-connected node; express Moment The reactive power compensation capacity of a single capacitor at each grid-connected node; for Moment The number of capacitors switched at each grid-connected node; For the The total number of capacitors available for switching at each grid-connected node.

[0193] In one embodiment, the objective function and constraints of a pre-constructed distribution network reactive power optimization configuration model that considers the constraints on the site selection and capacity of reactive facilities, as well as the number of grid-connected nodes and charging piles of each supercharging station, the number of electric vehicles that each supercharging station needs to serve, the time domain load of each charging pile, and the topological structure parameters of the medium-voltage distribution network are input into the optimization solver to solve the site selection and capacity size of the output capacitor and SVG, and obtain the economically optimal reactive power configuration construction plan that ensures that the distribution network voltage does not exceed the limit in the supercharging access scenario.

[0194] In one embodiment, when the method of the present invention is not used, the voltage distribution of each node of the distribution network after supercharging is connected is as follows: Figure 5 As shown in the figure, it can be seen that the voltage of the distribution network is lower than the per-unit value of 0.93 many times in a day, that is, the voltage exceeds the lower limit many times, and the safe operation of the distribution network is seriously threatened. After using the method of the present invention, the voltage distribution of the distribution network is as follows Figure 6 As shown in the figure, the voltage of each node is above the voltage lower limit, and the lowest voltage value in a day can be maintained at around 0.94, and the risk of voltage exceeding the limit is greatly reduced. Therefore, the present invention can effectively solve the problem of voltage exceeding the lower limit in the supercharging access scenario.

[0195] A reactive power optimization configuration system for a distribution network considering overcharge load characteristics includes the following functional modules:

[0196] The first functional module: obtains the number of electric vehicles in the medium-voltage distribution network, the common parking locations, and the data on the grid-connected nodes and the number of charging piles of each supercharging station, and calculates and outputs the number of electric vehicles that each supercharging station needs to serve;

[0197] The second functional module: Based on the data of the number of electric vehicles in the medium-voltage distribution network, the Monte Carlo method is used to simulate the initial state of charge and daily driving distance of electric vehicles in the medium-voltage distribution network, and the improved Monte Carlo method is used to simulate the daily multiple travel behaviors of electric vehicles in the medium-voltage distribution network, and the behavior data of each electric vehicle is output;

[0198] The third functional module: according to the behavior data of each electric vehicle, calculate the parking time and parking duration of each electric vehicle trip, judge and output the charging demand and type of each electric vehicle when parking each time;

[0199] The fourth functional module: calculates and outputs the time domain load of each supercharging pile according to the number of electric vehicles that each supercharging station needs to serve and the charging requirements and types of each electric vehicle each time it stops;

[0200] The fifth functional module: According to the number of grid-connected nodes and charging piles of each supercharging station, the number of electric vehicles that each supercharging station needs to serve, the time domain load of each charging pile, and the topological structure parameters of the medium-voltage distribution network, a pre-built distribution network reactive power optimization configuration model that considers the siting and sizing constraints of reactive facilities is used to obtain a distribution network reactive power optimization configuration construction plan that considers the supercharging load characteristics, and the plan is implemented to complete the distribution network reactive power optimization configuration.

[0201] In some embodiments of the present invention, a computer device may also be provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the distribution network optimization configuration method provided in the aforementioned embodiments when executing the computer program.

[0202] In some embodiments of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the distribution network optimization configuration method provided in the embodiment is implemented.

[0203] It should be understood by those skilled in the art that the present application embodiments can be provided as methods, equipment, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

[0205] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics, characterized in that: The following steps are involved: S1. Collect the number of electric vehicles and their regular parking locations in the medium-voltage distribution network, as well as the number of grid-connected nodes of each supercharging station and various types of charging piles; S2. According to the distance between the regular parking location of the electric vehicle and the grid connection node of the supercharging station, the number of electric vehicles that each supercharging station needs to serve is obtained; S3, simulating the daily starting state of charge and daily driving distance of each electric vehicle in the medium voltage distribution network; specifically comprising the following steps: S3.

1. Obtain the probability density function of the daily starting state of charge of electric vehicles ; S3.

2. Obtaining the probability density function of the daily driving distance of electric vehicles ; S3.

3. Calculate the cumulative probability distribution function by integrating the probability density function, according to and Get the inverse function of the cumulative probability distribution of the initial state of charge And the inverse function of the cumulative probability distribution of driving distance ; S3.

4. Inverse function of cumulative probability distribution at initial state of charge And the inverse function of the cumulative probability distribution of driving distance The daily starting state of charge and daily driving distance of each electric vehicle are obtained by sampling; S4. Using the improved Monte Carlo method to simulate the daily multiple travel behaviors of each electric vehicle in the medium voltage distribution network; specifically including the following steps: S4.

1. Obtain the probability density function of electric vehicle travel time; S4.

2. Calculate the cumulative probability distribution function of travel time by integrating the probability density function ; S4.

3. Use the improved Monte Carlo method to simulate the travel time of electric vehicles, as follows: First, randomly select the first sampling value in the set interval ,turn up The corresponding horizontal coordinate , recorded as the first trip time of the electric vehicle on that day; Then, randomly select the second sampling value in the set interval ,turn up Corresponding horizontal axis ,like , then the electric car only Go out once and stop sampling; if , then remember The second trip time of the electric vehicle on the same day, and the sampling continues until the Sampling results The corresponding horizontal coordinate , then the number of electric vehicle trips on that day times, stop sampling; S5. Calculate the parking time and parking duration of each trip of the electric vehicle; S6. According to the parking time and parking duration of each electric vehicle trip, the charging demand and type of each electric vehicle when parking each time are determined to obtain the charging behavior of the electric vehicle; S7. Calculate the time domain load of each charging pile according to the charging behavior of the electric vehicle; S8. Use the pre-built distribution network reactive power optimization configuration model that takes into account the reactive power facility site selection and capacity constraints to obtain the distribution network reactive power optimization configuration plan and complete the distribution network reactive power optimization configuration.

2. According to claim 1, a method for optimizing reactive power configuration of a distribution network taking into account overcharging load characteristics is characterized in that: In step S1, the regular parking position of the electric vehicle is the position where the electric vehicle is parked for the longest time in a day; The number of various types of charging piles at each supercharging station includes supercharging piles, fast charging piles, and slow charging piles.

3. The method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics according to claim 1, characterized in that: Step S2 specifically includes the following steps: S2.

1. Count the distances from the regular parking locations of electric vehicles to the grid-connected nodes of each supercharging station to obtain a distance matrix; S2.2, selecting the grid connection node of the supercharging station closest to each electric vehicle; S2.

3. Electric vehicles are given priority to go to the nearest supercharging station for charging, and the number of electric vehicles that each supercharging station needs to serve is counted.

4. The method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics according to claim 1, characterized in that: Step S5 specifically includes the following steps: S5.

1. Calculate the stopping time of each trip of the electric vehicle, that is, the time when each trip ends, by simulating by sampling the travel time of two adjacent trips; S5.

2. For The last trip of an electric car per day is simulated by adding the last trip time to the trip time; S5.

3. Calculate the rest time of each stop based on the parking time and the next trip time; after the last trip, the electric vehicle returns to the dock and there is no need to calculate the rest time.

5. The method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics according to claim 1, characterized in that: Step S6 specifically includes the following steps: S6.

1. Determine whether there is a need for charging each time the electric vehicle stops; S6.

2. Select the charging method based on the charging demand for each stop and the parking time of the electric vehicle.

6. The method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics according to claim 1, characterized in that: Step S7 specifically includes the following steps: S7.

1. Calculate the charging pile state matrix at each moment according to the charging demand of each electric vehicle; S7.

2. Calculate the time domain load of each charging pile.

7. The method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics according to claim 1, characterized in that: In step S8, the objective function of the pre-constructed distribution network reactive power optimization configuration model considering the reactive power facility location and capacity constraints is as follows: in, is the annualized investment cost of the distribution network; is the annual line loss cost of the distribution network; The annualized investment cost for reactive power configuration; The pre-built distribution network reactive power optimization configuration model considering the constraints of reactive power facility location and capacity includes optimal power flow constraints and reactive power facility location and capacity constraints. The optimal power flow constraints include node active power balance constraints, node reactive power balance constraints, node voltage upper and lower limit constraints, branch voltage drop constraints, and second-order cone relaxation constraints; the reactive facility location and capacity constraints include SVG access upper and lower limit constraints, reactive facility location quantity constraints, capacitor reactive capacity constraints and capacitor gear constraints, and M- Linearization constraints; The number of grid-connected nodes and charging piles of each supercharging station, the number of electric vehicles that each supercharging station needs to serve, the time domain load of each charging pile, and the topological structure parameters of the medium-voltage distribution network are input into a pre-constructed distribution network reactive power optimization configuration model that considers the constraints of reactive power facility site selection and capacity. The site selection and capacity of the output capacitor and SVG are used as the construction plan for the reactive power optimization configuration of the distribution network. The plan is implemented to complete the reactive power optimization configuration of the distribution network.

8. A system for implementing the method for optimizing reactive power configuration of a distribution network considering overcharging load characteristics as described in any one of claims 1 to 7, characterized in that: Includes the following modules: The first functional module: obtains the number of electric vehicles in the medium-voltage distribution network, the common parking locations, and the data on the grid-connected nodes and the number of charging piles of each supercharging station, and calculates and outputs the number of electric vehicles that each supercharging station needs to serve; The second functional module: Based on the data of the number of electric vehicles in the medium-voltage distribution network, the Monte Carlo method is used to simulate the initial state of charge and daily driving distance of electric vehicles in the medium-voltage distribution network, and the improved Monte Carlo method is used to simulate the daily multiple travel behaviors of electric vehicles in the medium-voltage distribution network, and the behavior data of each electric vehicle is output; The third functional module: according to the behavior data of each electric vehicle, calculate the parking time and parking duration of each electric vehicle trip, judge and output the charging demand and type of each electric vehicle when parking each time; The fourth functional module: calculates and outputs the time domain load of each supercharging pile according to the number of electric vehicles that each supercharging station needs to serve and the charging requirements and types of each electric vehicle each time it stops; The fifth functional module: According to the number of grid-connected nodes and charging piles of each supercharging station, the number of electric vehicles that each supercharging station needs to serve, the time domain load of each charging pile, and the topological structure parameters of the medium-voltage distribution network, the distribution network reactive power optimization configuration model that considers the constraints on the site selection and capacity of reactive facilities is used to obtain the distribution network reactive power optimization configuration plan and complete the distribution network reactive power optimization configuration.

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