A power distribution network joint operation method based on meeting electric vehicle charging demand

Abstract

The application discloses a power distribution network joint operation method based on meeting electric vehicle charging demand, comprising the following steps: dividing urban functional areas, performing Monte Carlo simulation on electric vehicle operation and charging behavior, and calculating charging data of each area; continuously operating for multiple days, superimposing daily charging load curves, taking average values, finding values when average values of single-day charging power and single-day load peak values tend to be stable, and taking the values as prediction values; performing functional partition on load nodes of a 24-node system of the power distribution network, and performing joint planning on the partitioned power distribution network system and the electric vehicle charging station with energy storage; connecting electric vehicles with charging demand in each area to the charging station with energy storage for operation, and introducing a time-of-use electricity price mechanism for orderly charging guidance. The application meets user charging demand, reduces charging load peak values of the charging station, and reduces the impact caused by large-scale charging load.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network technology, and in particular relates to a method for joint operation of power distribution networks based on meeting the charging needs of electric vehicles. Background Technology

[0002] With the depletion of fossil fuels, governments and markets are vigorously promoting the widespread adoption of electric vehicles.

[0003] However, with the increase in the number of electric vehicles, the charging needs of a large number of electric vehicle users and the impact of charging load on the power distribution network have gradually become prominent. Especially in 2022, when the number of electric vehicles exceeded 10 million, electric vehicles entered a large-scale era. In the future, the number of electric vehicles will continue to grow rapidly, and the above problems are even more urgent. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a distribution network joint operation method based on meeting the charging needs of electric vehicles. While satisfying the charging needs of electric vehicle users in various regions, it reduces the peak charging load of charging stations, mitigates the impact of large-scale charging loads, meets the conditions for safe operation of the distribution network, and effectively reduces the charging costs for electric vehicle users.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for joint operation of a power distribution network based on meeting the charging needs of electric vehicles, comprising the following steps:

[0006] S100 divides urban functional areas, performs Monte Carlo simulation of electric vehicle operation and charging behavior, and calculates electric vehicle charging data in each area; after electric vehicles run continuously for several days, the daily charging load curves are superimposed and the average value is taken to find the average value of the daily charging amount and daily load peak of electric vehicles when they tend to stabilize, and this value is used as the prediction value.

[0007] S200 divides the load nodes of the 24-node distribution network system into functional zones, and conducts joint planning for the partitioned distribution network system and electric vehicle charging stations with energy storage, including the expansion planning of the traditional distribution network and the configuration of charging stations and energy storage.

[0008] The S300 system connects electric vehicles with charging needs in various regions to charging stations with energy storage, and introduces a time-of-use pricing mechanism to guide orderly charging.

[0009] Furthermore, in step S100, the urban area is divided into work areas, residential areas and leisure and entertainment areas. Taking into account factors affecting the charging load of electric vehicles, including electric vehicle travel time, initial state of charge, travel mileage, destination and length of stay in various places, the survey data is fitted to obtain the probability density function of relevant parameters. Combined with the electric vehicle travel chain model, the travel and charging behavior of electric vehicles is simulated, and the Monte Carlo method is used to calculate the charging load of electric vehicles in each area.

[0010] To reduce the impact of the randomness of the initial charge value of electric vehicles on the accuracy of charging load prediction results, based on the random sampling of the initial charge state of electric vehicles, the electric vehicles are processed for multiple days of operation in combination with the travel chain model. The daily charging load curves are superimposed and the average value is taken. When the peak value of the load curve and the peak value of the charging power tend to stabilize, the number of days after stabilization is taken as the final value of the above two parameters and used as the prediction result.

[0011] Furthermore, step S100 includes the following steps:

[0012] S101, Fitting the factors affecting the charging load of electric vehicles includes the following steps:

[0013] S1011, Electric Vehicle Trip Start Time: The travel times of car users in the survey data were fitted to the data, and its distribution approximates a normal distribution. The probability density function is as follows:

[0014]

[0015] In the formula, t represents the start time of the private car owner's daily trip; f s (t) represents the probability corresponding to t; μ s f s The mathematical expectation of (t); σ s f s The standard deviation of (t);

[0016] S1012, Electric vehicle mileage. The mileage data of car users in the survey was fitted to the data, and its distribution approximates a normal distribution. The probability density function is:

[0017]

[0018] In the formula, s represents the mileage length of each segment of the journey; f D (s) represents the probability corresponding to s; μ D f D The mathematical expectation of (s); σ D f D The standard deviation of (s);

[0019] S1013, the initial state of charge of the electric vehicle, is treated as a random function of rand(1), and this value is guaranteed to be no less than the power consumption of the first leg of the trip.

[0020] S1014, Parking Duration of Electric Vehicles in Various Locations: Data fitting was performed on the parking duration of car users in the survey data. The distribution in both work areas and leisure / recreation areas approximates a generalized extreme value distribution with different parameters. Probability density function:

[0021]

[0022]

[0023] In the formula, μ represents the position parameter, σ represents the scale parameter, k represents the shape parameter, and z represents the intermediate substitution variable;

[0024] S102, Establish charging conditions for electric vehicles. If the current state of charge of the electric vehicle is insufficient to reach the next destination, it needs to be charged. The formula for calculating the current state of charge is:

[0025]

[0026] In the formula, SOC ini This indicates the state of charge before the start of the journey; s m This indicates the maximum range that the electric vehicle can travel;

[0027] If charging is required, the charging time calculation formula is as follows:

[0028]

[0029] In the formula, SOC exp This indicates the battery state that the owner expects to achieve, where W represents the battery capacity and P represents the charging power.

[0030] S103, the travel chain model, establishes multiple travel chains for electric vehicles based on functional area division, and performs data fitting on the travel purposes in the survey data to obtain the probability of each of the above travel chains.

[0031] S104, The Monte Carlo method is used to simulate the data obtained above, including the following steps:

[0032] S1041, Set the number of Monte Carlo simulations N, and set n=1;

[0033] S1042, Extract the travel chain model of the nth electric vehicle;

[0034] S1043, randomly select the start time of the journey and the initial state of charge of the nth electric vehicle;

[0035] S1044, randomly select the mileage of the nth electric vehicle and set its travel speed;

[0036] S1045, randomly select the parking time of the nth electric vehicle based on the travel chain;

[0037] S1046, determine whether charging is required;

[0038] S1047: If charging is required, record the charging time, duration, and location, and update the next travel time and state of charge; if charging is not required, directly update the next travel time and state of charge.

[0039] S1048, Determine whether to end today's itinerary;

[0040] S1049, If the trip for today ends, record the final battery status and return the time; if the trip does not end, return to step S2044.

[0041] S105 records the current charging time, charging duration, charging amount, and charging location of each electric vehicle and plots the load curve for each area, recording the peak load.

[0042] S106: Use the final state of charge of the previous day as the starting state of charge of the next day. The travel chain, travel time, travel distance, and parking time are the same as the data of the previous day. Record the charging-related data of the day, add the charging load, divide by the total number of days, draw the load curve of each region, and record the load peak.

[0043] S107 plots the relationship between peak load and number of operating days to obtain the predicted peak load value for each region.

[0044] Furthermore, in step S200, the load nodes in the 24-node distribution network system are divided into working area nodes, residential area nodes, and recreational area nodes. The predicted values ​​obtained in step S100 are used to establish a joint planning model of the distribution network and electric vehicle charging stations with energy storage. The model includes two parts: objective function and constraint conditions.

[0045] The Yalmip toolbox was used on the MATLAB platform to solve the problem and obtain the planning results, distribution network line expansion, and the location and capacity of charging stations and energy storage construction.

[0046] Furthermore, an objective function is established, which includes investment and construction costs, operation and maintenance costs, power supply costs, network loss costs, and load shedding costs. Minimizing the sum of these costs is taken as the objective of the joint programming model, and the calculation formula is as follows:

[0047] c TPV =c I +c M +cE +c R +c U ;

[0048] In the formula, c TPV c represents the total cost. I c represents the investment and construction costs. M c represents the operating and maintenance cost. E c represents the cost of electricity supply. R c represents the cost of network loss. U This represents the cost of load loss.

[0049] Furthermore, the investment and construction costs include the investment and construction costs of equipment involved in the lines in traditional power distribution network planning, as well as the investment and construction costs of introduced electric vehicle charging stations and energy storage systems;

[0050] The operation and maintenance costs include the operation and maintenance costs of equipment involved in the lines in traditional distribution network planning, as well as the operation and maintenance costs of introduced electric vehicle charging stations and energy storage systems;

[0051] The power supply cost includes the power supply cost of all transformers in the system;

[0052] The network loss cost includes the current loss cost on all lines in the system and the loss cost on the transformer.

[0053] The loss of load cost includes the loss of load cost on all nodes in the system.

[0054] Furthermore, the constraints are established, including:

[0055] Distribution network safety constraints include: setting upper and lower limit constraints based on the safe operating conditions that each device in the distribution network should meet; and setting relevant constraints for distribution network power flow calculation.

[0056] The relevant constraints for meeting user charging needs are that the sum of the capacity of the charging stations and energy storage systems established in each regional node should be greater than the peak value of electric vehicle charging load in that region. Here, the peak value is taken as the predicted value in S100.

[0057] And radial constraints, meaning that each load node has one and only one line connected to it that can carry current.

[0058] Furthermore, the upper and lower limit constraints include: the maximum and minimum allowable values ​​of voltage at each node; the maximum allowable value of current at each line; the maximum allowable value of current at each transformer; the maximum allowable value of power at each electric vehicle charging station; and the maximum allowable value of power at each energy storage station.

[0059] Constraints related to power flow calculation in distribution networks include: Kirchhoff's voltage law, which states that the voltage difference between the two ends of each line is equal to the voltage drop across the line; and Kirchhoff's current law, which states that the power flowing into each node is equal to the power flowing out of the node.

[0060] Furthermore, in step S300, electric vehicles with charging needs in various areas are connected to charging stations with energy storage, and a time-of-use pricing mechanism is introduced to guide orderly charging. The steps include:

[0061] S301, Input system load margin and electricity price information. The load margin is obtained by the difference between the conventional load curve and the charging station and energy storage capacity. The charging station and energy storage capacity are taken from the charging station and energy storage capacity of each area solved in S200. Obtain the charging demand of electric vehicle users. The charging station obtains the battery capacity, current state of charge, expected dwell time and the state of charge level of the electric vehicle after charging. The above parameters are taken from the relevant parameters after the operation of each vehicle in step S100.

[0062] S302, dynamically set time-of-use electricity prices. Electric vehicle charging stations aim to achieve peak shaving and valley filling of load, and prioritize charging users during periods with lower load. Under the constraints of ensuring that the charging station transformer does not overload and meeting the charging needs of electric vehicles, a time-of-use charging price is dynamically set for each electric vehicle user.

[0063] Furthermore, in step S302, time-of-use pricing is dynamically determined. Electric vehicle charging stations aim to achieve peak shaving and valley filling of load, prioritizing charging during periods of lower load for users. This is constrained by ensuring the charging station transformer does not overload and meeting the charging needs of electric vehicles. A time-of-use charging price is dynamically determined for each electric vehicle user. The steps include:

[0064] S3021, Calculate the number of time periods required for electric vehicle charging:

[0065]

[0066] In the formula, Δt represents the length of a time period, which is 15 minutes; η represents the charging efficiency of the charging pile. Represents the smallest integer whose value is not less than x; This represents the user's desired state of charge. P represents the state of charge of an electric vehicle when it enters a charging station. i B represents the charging power of the charging station. i Indicates the battery capacity of an electric vehicle;

[0067] S3022, Calculate the number of parking time periods:

[0068]

[0069] In the formula, t i T represents the parking duration of the i-th electric vehicle; i Indicates the number of parking time periods; Represents the largest integer not greater than x;

[0070] S3023, determine whether the transformer load margin meets the user's charging needs; if the transformer load margin is sufficient, select the J with the largest margin during the shutdown period. i The suggested time period is defined as a time period; if the transformer load margin is insufficient, it is determined whether the energy storage system load margin meets the user's charging needs.

[0071] S3024, if the energy storage system load margin is sufficient, J with the largest margin will be selected during the shutdown period. i The system provides a suggested time period; if the energy storage system's load margin is insufficient, the calculation system can ensure the battery charging level reaches its maximum when the user leaves, and will then alert the user.

[0072] S3025, User Response: Determine the charging plan based on the user's response. If the user accepts the suggestion, off-peak electricity pricing will be used. If the user does not accept the suggestion, charging will be arranged for the user during a charging period immediately, and charging will be charged according to peak electricity pricing. At the same time, the charging load margin for each period will be updated.

[0073] S3026 determines whether the energy storage capacity is sufficient. If not, it exits the energy storage and charges the energy storage during the off-peak period with the largest load margin at night, while updating the load margin.

[0074] The beneficial effects of adopting this technical solution are:

[0075] This invention divides cities into functional zones: work areas, residential areas, and leisure and entertainment areas. It obtains probabilistic models of electric vehicle (EV) travel time, initial state of charge, and travel distance by fitting survey data. These models, combined with a travel chain model, simulate the travel and charging behavior of EV users, using the peak charging load after EV operation stabilizes as the predicted value. Building upon traditional distribution network expansion planning, it introduces EV charging station and energy storage models, using distribution network safety operation conditions and meeting EV charging demand as constraints for joint planning. By incorporating load forecast results, it verifies the operation of EV charging stations and introduces time-of-use pricing to guide orderly charging, further reducing peak loads. This invention's solution, while meeting the charging needs of EV users in each area, reduces peak charging loads at charging stations, mitigates the impact of large-scale charging loads, meets the conditions for safe distribution network operation, and effectively reduces charging costs for EV users. Attached Figure Description

[0076] Figure 1This is a schematic diagram of a distribution network joint operation method based on meeting the charging needs of electric vehicles according to the present invention.

[0077] Figure 2 This is a schematic diagram illustrating the relationship between the peak charging load of 2000 electric vehicles in each region and the number of operating days in an embodiment of the present invention.

[0078] Figure 3 This is a diagram showing the joint planning results in an embodiment of the present invention;

[0079] Figure 4 This is a diagram showing the ordered and disordered charging load results of the charging station in the work area in an embodiment of the present invention;

[0080] Figure 5 This is a diagram showing the results of ordered and disordered charging loads at residential charging stations in an embodiment of the present invention.

[0081] Figure 6 This is a diagram showing the charging load results of residential charging stations with and without energy storage in an embodiment of the present invention;

[0082] Figure 7 This is a schematic diagram of the peak load of a residential charging station under different user response ratios (a) in an embodiment of the present invention;

[0083] Figure 8 This is a diagram showing the ordered and disordered charging load results of the charging station in the leisure and entertainment area in an embodiment of the present invention;

[0084] Figure 9 This is a diagram showing the charging load results of the recreational area charging station with and without energy storage in an embodiment of the present invention. Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings.

[0086] In this embodiment, see Figure 1 As shown, this invention proposes a method for joint operation of a distribution network based on meeting the charging needs of electric vehicles, including the following steps:

[0087] S100 divides urban functional areas, performs Monte Carlo simulation of electric vehicle operation and charging behavior, and calculates electric vehicle charging data in each area; after electric vehicles run continuously for several days, the daily charging load curves are superimposed and the average value is taken to find the average value of the daily charging amount and daily load peak of electric vehicles when they tend to stabilize, and this value is used as the prediction value.

[0088] S200 divides the load nodes of the 24-node distribution network system into functional zones, and conducts joint planning for the partitioned distribution network system and electric vehicle charging stations with energy storage, including the expansion planning of the traditional distribution network and the configuration of charging stations and energy storage.

[0089] The S300 system connects electric vehicles with charging needs in various regions to charging stations with energy storage, and introduces a time-of-use pricing mechanism to guide orderly charging.

[0090] As an optimization of the above embodiment, in step S100, the urban area is divided into work area, residential area and leisure and entertainment area. Taking into account the factors affecting the electric vehicle charging load, including electric vehicle travel time, initial state of charge, travel mileage, destination and length of stay in various places, the survey data is fitted to obtain the probability density function of relevant parameters. Combined with the electric vehicle travel chain model, the electric vehicle travel and charging behavior is simulated, and the Monte Carlo method is used to calculate the electric vehicle charging load in each area.

[0091] To reduce the impact of the randomness of the initial charge value of electric vehicles on the accuracy of charging load prediction results, based on the random sampling of the initial charge state of electric vehicles, the electric vehicles are processed for multiple days of operation in combination with the travel chain model. The daily charging load curves are superimposed and the average value is taken. When the peak value of the load curve and the peak value of the charging power tend to stabilize, the number of days after stabilization is taken as the final value of the above two parameters and used as the prediction result.

[0092] In specific implementation, step S100 may include the following steps:

[0093] S101, Fitting the factors affecting the charging load of electric vehicles includes the following steps:

[0094] S1011, Electric Vehicle Trip Start Time: The travel times of car users in the survey data were fitted to the data, and its distribution approximates a normal distribution. The probability density function is as follows:

[0095]

[0096] In the formula, t represents the start time of the private car owner's daily trip; f s (t) represents the probability corresponding to t; μ s f s The mathematical expectation of (t); σ s f s The standard deviation of (t);

[0097] S1012, Electric vehicle mileage. The mileage data of car users in the survey was fitted to the data, and its distribution approximates a normal distribution. The probability density function is:

[0098]

[0099] In the formula, s represents the mileage length of each segment of the journey; f D (s) represents the probability corresponding to s; μ D f D The mathematical expectation of (s); σ D f D The standard deviation of (s);

[0100] S1013, the initial state of charge of the electric vehicle, is treated as a random function of rand(1), and this value is guaranteed to be no less than the power consumption of the first leg of the trip.

[0101] S1014, Parking Duration of Electric Vehicles in Various Locations: Data fitting was performed on the parking duration of car users in the survey data. The distribution in both work areas and leisure / recreation areas approximates a generalized extreme value distribution with different parameters. Probability density function:

[0102]

[0103]

[0104] In the formula, μ represents the position parameter, σ represents the scale parameter, k represents the shape parameter, and z represents the intermediate substitution variable;

[0105] S102, Establish charging conditions for electric vehicles. If the current state of charge of the electric vehicle is insufficient to reach the next destination, it needs to be charged. The formula for calculating the current state of charge is:

[0106]

[0107] In the formula, SOC ini This indicates the state of charge before the start of the journey; s m This indicates the maximum range that the electric vehicle can travel;

[0108] If charging is required, the charging time calculation formula is as follows:

[0109]

[0110] In the formula, SOC exp This indicates the battery state that the owner expects to achieve, where W represents the battery capacity and P represents the charging power.

[0111] S103, the travel chain model, establishes multiple travel chains for electric vehicles based on functional area division, and performs data fitting on the travel purposes in the survey data to obtain the probability of each of the above travel chains.

[0112] S104, The Monte Carlo method is used to simulate the data obtained above, including the following steps:

[0113] S1041, Set the number of Monte Carlo simulations N, and set n=1;

[0114] S1042, Extract the travel chain model of the nth electric vehicle;

[0115] S1043, randomly select the start time of the journey and the initial state of charge of the nth electric vehicle;

[0116] S1044, randomly select the mileage of the nth electric vehicle and set its travel speed;

[0117] S1045, randomly select the parking time of the nth electric vehicle based on the travel chain;

[0118] S1046, determine whether charging is required;

[0119] S1047: If charging is required, record the charging time, duration, and location, and update the next travel time and state of charge; if charging is not required, directly update the next travel time and state of charge.

[0120] S1048, Determine whether to end today's itinerary;

[0121] S1049, If the trip for today ends, record the final battery status and return the time; if the trip does not end, return to step S2044.

[0122] S105 records the current charging time, charging duration, charging amount, and charging location of each electric vehicle and plots the load curve for each area, recording the peak load.

[0123] S106: Use the final state of charge of the previous day as the starting state of charge of the next day. The travel chain, travel time, travel distance, and parking time are the same as the data of the previous day. Record the charging-related data of the day, add the charging load, divide by the total number of days, draw the load curve of each region, and record the load peak.

[0124] S107 plots the relationship between peak load and number of operating days, such as... Figure 2 As shown in Table 1, the peak load forecast values ​​for each region are obtained.

[0125] Table 1. Peak Electric Vehicle Load in Each Region (19,700 vehicles)

[0126]

[0127] As an optimization of the above embodiment, in step S200, the load nodes in the 24-node distribution network system are divided into working area nodes, residential area nodes and recreational area nodes. The predicted values ​​obtained in step S100 are used to establish a joint planning model of the distribution network and electric vehicle charging stations with energy storage. The model includes two parts: objective function and constraint conditions.

[0128] The solution was obtained using the Yalmip toolbox on the MATLAB platform, and the resulting planning results were obtained (e.g.) Figure 3 The locations and capacities of the power distribution network (as shown in Table 2) and the construction of charging stations and energy storage facilities (as shown in Table 3) are as follows.

[0129] Table 2 Distribution Network Expansion Planning Scheme

[0130]

[0131]

[0132] Table 3 Charging Station and Energy Storage Planning Scheme

[0133]

[0134] The objective function is established, which includes investment and construction costs, operation and maintenance costs, power supply costs, network loss costs, and load shedding costs. Minimizing the sum of these costs is taken as the objective of the joint programming model, and the calculation formula is as follows:

[0135] c TPV =c I +c M +c E +c R +c U ;

[0136] In the formula, c TPV c represents the total cost. I c represents the investment and construction costs. M c represents the operating and maintenance cost. E c represents the cost of electricity supply. R c represents the cost of network loss. U This represents the cost of load loss.

[0137] Preferably, the investment and construction costs include the investment and construction costs of equipment involved in the lines in traditional power distribution network planning, as well as the investment and construction costs of the introduced electric vehicle charging stations and energy storage systems;

[0138] The operation and maintenance costs include the operation and maintenance costs of equipment involved in the lines in traditional distribution network planning, as well as the operation and maintenance costs of introduced electric vehicle charging stations and energy storage systems;

[0139] The power supply cost includes the power supply cost of all transformers in the system;

[0140] The network loss cost includes the current loss cost on all lines in the system and the loss cost on the transformer.

[0141] The loss of load cost includes the loss of load cost on all nodes in the system.

[0142] The establishment of the constraints includes:

[0143] Distribution network safety constraints include: setting upper and lower limit constraints based on the safe operating conditions that each device in the distribution network should meet; and setting relevant constraints for distribution network power flow calculation.

[0144] The relevant constraints for meeting user charging needs are that the sum of the capacity of the charging stations and energy storage systems established in each regional node should be greater than the peak value of electric vehicle charging load in that region. Here, the peak value is taken as the predicted value in S100.

[0145] And radial constraints, meaning that each load node has one and only one line connected to it that can carry current.

[0146] Preferably, the upper and lower limit constraints include: the maximum and minimum allowable values ​​of voltage at each node; the maximum allowable value of current at each line; the maximum allowable value of current at each transformer; the maximum allowable value of power at each electric vehicle charging station; and the maximum allowable value of power at each energy storage station.

[0147] Constraints related to power flow calculation in distribution networks include: Kirchhoff's voltage law, which states that the voltage difference between the two ends of each line is equal to the voltage drop across the line; and Kirchhoff's current law, which states that the power flowing into each node is equal to the power flowing out of the node.

[0148] As an optimization of the above embodiment, in step S300, electric vehicles with charging needs in each area are connected to charging stations with energy storage, and a time-of-use pricing mechanism is introduced to guide orderly charging. The steps include:

[0149] S301, Input system load margin and electricity price information. The load margin is obtained by the difference between the conventional load curve and the charging station and energy storage capacity. The charging station and energy storage capacity are taken from the charging station and energy storage capacity of each area solved in S200. Obtain the charging demand of electric vehicle users. The charging station obtains the battery capacity, current state of charge, expected dwell time and the state of charge level of the electric vehicle after charging. The above parameters are taken from the relevant parameters after the operation of each vehicle in step S100.

[0150] S302, dynamically set time-of-use electricity prices. Electric vehicle charging stations aim to achieve peak shaving and valley filling of load, and prioritize charging users during periods with lower load. Under the constraints of ensuring that the charging station transformer does not overload and meeting the charging needs of electric vehicles, a time-of-use charging price is dynamically set for each electric vehicle user.

[0151] Preferably, in step S302, time-of-use pricing is dynamically determined. Electric vehicle charging stations aim to achieve peak shaving and valley filling of load, prioritizing charging during periods of lower load for users. This is constrained by ensuring the charging station transformer does not overload and meeting the charging needs of electric vehicles. A time-of-use charging price is dynamically determined for each electric vehicle user. The steps include:

[0152] S3021, Calculate the number of time periods required for electric vehicle charging:

[0153]

[0154] In the formula, Δt represents the length of a time period, which is 15 minutes; η represents the charging efficiency of the charging pile. Represents the smallest integer whose value is not less than x; This represents the user's desired state of charge. P represents the state of charge of an electric vehicle when it enters a charging station. i B represents the charging power of the charging station. i Indicates the battery capacity of an electric vehicle;

[0155] S3022, Calculate the number of parking time periods:

[0156]

[0157] In the formula, t i T represents the parking duration of the i-th electric vehicle; i Indicates the number of parking time periods; Represents the largest integer not greater than x;

[0158] S3023, determine whether the transformer load margin meets the user's charging needs; if the transformer load margin is sufficient, select the J with the largest margin during the shutdown period. i The suggested time period is defined as a time period; if the transformer load margin is insufficient, it is determined whether the energy storage system load margin meets the user's charging needs.

[0159] S3024, if the energy storage system load margin is sufficient, J with the largest margin will be selected during the shutdown period. i The system provides a suggested time period; if the energy storage system's load margin is insufficient, the calculation system can ensure the battery charging level reaches its maximum when the user leaves, and will then alert the user.

[0160] S3025, User Response: Determine the charging plan based on the user's response. If the user accepts the suggestion, off-peak electricity pricing will be used. If the user does not accept the suggestion, charging will be arranged for the user during a charging period immediately, and charging will be charged according to peak electricity pricing. At the same time, the charging load margin for each period will be updated.

[0161] S3026 determines whether the energy storage capacity is sufficient. If not, it exits the energy storage and charges the energy storage during the off-peak period with the largest load margin at night, while updating the load margin.

[0162] This invention discloses a method for joint operation of a distribution network based on meeting the charging needs of electric vehicles. The steps include: dividing the city into work areas, residential areas, and recreational areas; performing Monte Carlo simulations on electric vehicle travel and charging behavior; to reduce the impact of the randomness of the initial state of charge setting of electric vehicles on the accuracy of electric vehicle charging load prediction, the electric vehicles are run continuously, and the daily charging load curves are superimposed and averaged to find the number of days when the average daily charging amount and daily peak load of electric vehicles tend to stabilize, and this value is used as the final prediction value; dividing the 24-node distribution network system into node partitions, establishing a joint planning model of the distribution network and electric vehicle charging stations with energy storage, ensuring that the configuration capacity of electric vehicle charging stations and energy storage in each region meets the charging load demand of the corresponding region, i.e., the daily peak charging load; connecting users with charging needs in each region to the corresponding electric vehicle charging stations for operational verification, and introducing a time-of-use pricing mechanism to guide orderly charging; comparing parameters such as the distribution transformer load and user charging costs under orderly and disorderly charging, and with and without energy storage conditions, to verify the effectiveness of the method. The solution proposed in this invention not only meets the charging needs of electric vehicle users in various regions, but also reduces the peak charging load of charging stations, mitigates the impact of large-scale charging loads, meets the conditions for safe operation of the power distribution network, and effectively reduces the charging costs for electric vehicle users.

[0163] The effectiveness of this method is verified by comparing parameters such as the distribution transformer load and user charging costs under orderly and disorderly charging, energy storage and non-energy storage scenarios of electric vehicle charging stations.

[0164] The table compares parameters such as the distribution transformer load and user charging costs for electric vehicle charging stations under ordered and disordered charging scenarios, with and without energy storage conditions. Table 4 shows the peak load of substations and the average charging cost per vehicle in each region. The load curves for ordered and disordered charging at charging stations in work areas, residential areas, and recreational areas are shown below. Figure 4 , Figure 5 , Figure 8 As shown, the load curves of charging stations in residential and recreational areas with and without energy storage are as follows: Figure 6 , Figure 9As shown, when the orderly charging strategy response rate is 90%, the charging load in residential areas can meet the charging demand without energy storage, but it is highly dependent on the user response ratio. Therefore, the charging station load curves under different response ratios are plotted, as shown below. Figure 7 As shown.

[0165] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for joint operation of distribution networks based on meeting the charging needs of electric vehicles, characterized in that, Including the following steps: S100 divides urban functional areas, performs Monte Carlo simulation of electric vehicle operation and charging behavior, and calculates electric vehicle charging data in each area; after electric vehicles run continuously for several days, the daily charging load curves are superimposed and the average value is taken to find the average value of the daily charging amount and daily load peak of electric vehicles when they tend to stabilize, and this value is used as the prediction value. S200 divides the load nodes of the 24-node distribution network system into functional zones, and conducts joint planning for the partitioned distribution network system and electric vehicle charging stations with energy storage, including the expansion planning of the traditional distribution network and the configuration of charging stations and energy storage. The S300 connects electric vehicles with charging needs in various regions to charging stations with energy storage, and introduces a time-of-use pricing mechanism to guide orderly charging. In step S100, the urban area is divided into work area, residential area and leisure and entertainment area. Taking into account factors affecting electric vehicle charging load, including electric vehicle travel time, initial state of charge, travel mileage, destination and length of stay in various places, the survey data is fitted to obtain the probability density function of relevant parameters. Combined with the electric vehicle travel chain model, the travel and charging behavior of electric vehicles is simulated, and the Monte Carlo method is used to calculate the charging load of electric vehicles in each area. To reduce the impact of the randomness of the initial charge value of electric vehicles on the accuracy of charging load prediction results, based on the random sampling of the initial charge state of electric vehicles, the electric vehicles are processed for multiple days of operation in combination with the travel chain model. The daily charging load curves are superimposed and the average value is taken. When the peak value of the load curve and the peak value of the charging power tend to stabilize, the number of days after stabilization is taken as the final value of the above two parameters and used as the prediction result. Step S100 includes the following steps: S101, Fitting the factors affecting the charging load of electric vehicles includes the following steps: S1011, Electric Vehicle Trip Start Time: The travel times of car users in the survey data were fitted to the data, and its distribution approximates a normal distribution. The probability density function is as follows: ； In the formula, t represents the start time of the private car owner's daily trip; f s (t) represents the probability corresponding to t; μ s f s The mathematical expectation of (t); σ s f s The standard deviation of (t); S1012, Electric vehicle mileage. The mileage data of car users in the survey was fitted to the data, and its distribution approximates a normal distribution. The probability density function is: ； In the formula, s represents the mileage length of each segment of the journey; f D (s) represents the probability corresponding to s; μ D f represents D (s) Mathematical expectation; σ D f D The standard deviation of (s); S1013, the initial state of charge of the electric vehicle, is treated as a random function of rand(1), and this value is guaranteed to be no less than the power consumption of the first leg of the journey. S1014, Parking Duration of Electric Vehicles in Various Locations: Data fitting was performed on the parking duration of car users in the survey data. The distribution in both work areas and leisure / recreation areas approximates a generalized extreme value distribution with different parameters. Probability density function: ； In the formula, μ represents the position parameter, σ represents the scale parameter, k represents the shape parameter, and z represents the intermediate substitution variable; S102, Establish charging conditions for electric vehicles. If the current state of charge of the electric vehicle is insufficient to reach the next destination, it needs to be charged. The formula for calculating the current state of charge is: ； In the formula, SOC ini This indicates the state of charge before the start of the journey; s m This indicates the maximum range that the electric vehicle can travel; If charging is required, the charging time calculation formula is as follows: ； In the formula, SOC exp This indicates the battery state that the owner expects to achieve, where W represents the battery capacity and P represents the charging power. S103, the travel chain model, establishes multiple travel chains for electric vehicles based on functional area division, and performs data fitting on the travel purposes in the survey data to obtain the probability of each of the above travel chains. S104, The Monte Carlo method is used to simulate the data obtained above, including the following steps: S1041, Set the number of Monte Carlo simulations N, and set n=1; S1042, Extract the travel chain model of the nth electric vehicle; S1043, randomly select the start time of the journey and the initial state of charge of the nth electric vehicle; S1044, randomly select the mileage of the nth electric vehicle and set its travel speed; S1045, randomly select the parking time of the nth electric vehicle based on the travel chain; S1046, determine whether charging is required; S1047: If charging is required, record the charging time, duration, and location, and update the next travel time and state of charge; if charging is not required, directly update the next travel time and state of charge. S1048, Determine whether to end today's itinerary; S1049, If the trip for today ends, record the final battery status and return the time; if the trip does not end, return to step S2044. S105 records the current charging time, charging duration, charging amount, and charging location of each electric vehicle and plots the load curve for each area, recording the peak load. S106: Use the final state of charge of the previous day as the starting state of charge of the next day. The travel chain, travel time, travel distance, and parking time are the same as the data of the previous day. Record the charging-related data of the day, add the charging load, divide by the total number of days, draw the load curve of each region, and record the load peak. S107 plots the relationship between peak load and number of operating days to obtain the predicted peak load value for each region.

2. The method for joint operation of a distribution network based on meeting the charging needs of electric vehicles according to claim 1, characterized in that, In step S200, the load nodes in the 24-node distribution network system are divided into working area nodes, residential area nodes and recreational area nodes. The predicted values ​​obtained in step S100 are used to establish a joint planning model of the distribution network and electric vehicle charging stations with energy storage. The model includes two parts: objective function and constraint conditions. The Yalmip toolbox was used on the MATLAB platform to solve the problem and obtain the planning results, distribution network line expansion, and the location and capacity of charging stations and energy storage construction.

3. The method for joint operation of a distribution network based on meeting the charging needs of electric vehicles according to claim 2, characterized in that, The objective function is established, which includes investment and construction costs, operation and maintenance costs, power supply costs, network loss costs, and load shedding costs. Minimizing the sum of these costs is taken as the objective of the joint programming model, and the calculation formula is as follows: ； In the formula, c TPV c represents the total cost. I c represents the investment and construction costs. M c represents the operating and maintenance cost. E c represents the cost of electricity supply. R c represents the cost of network loss. U This represents the cost of load loss.

4. The method for joint operation of a distribution network based on meeting the charging needs of electric vehicles according to claim 3, characterized in that, The investment and construction costs include the investment and construction costs of equipment involved in the lines in traditional power distribution network planning, as well as the investment and construction costs of introduced electric vehicle charging stations and energy storage systems; The operation and maintenance costs include the operation and maintenance costs of equipment involved in the lines in traditional distribution network planning, as well as the operation and maintenance costs of introduced electric vehicle charging stations and energy storage systems; The power supply cost includes the power supply cost of all transformers in the system; The network loss cost includes the current loss cost on all lines in the system and the loss cost on the transformer. The loss-of-load cost includes the loss-of-load cost at all nodes in the system.

5. A method for joint operation of a distribution network based on meeting the charging needs of electric vehicles, as described in claim 2, is characterized in that... The constraints are established by: Distribution network safety constraints include: setting upper and lower limit constraints based on the safe operating conditions that each device in the distribution network should meet; and setting relevant constraints for distribution network power flow calculation. The relevant constraints for meeting user charging needs are that the sum of the capacity of the charging stations and energy storage systems established in each regional node should be greater than the peak value of electric vehicle charging load in that region. Here, the peak value is taken as the predicted value in S100. And radial constraints, meaning that each load node has one and only one line connected to it that can carry current.

6. A method for joint operation of a distribution network based on meeting the charging needs of electric vehicles, as described in claim 5, is characterized in that, The upper and lower limit constraints include: the maximum and minimum allowable values ​​of voltage at each node; the maximum allowable value of current at each line; the maximum allowable value of current at each transformer; the maximum allowable value of power at each electric vehicle charging station; and the maximum allowable value of power at each energy storage station. Constraints related to power flow calculation in distribution networks include: Kirchhoff's voltage law, which states that the voltage difference between the two ends of each line is equal to the voltage drop across the line; and Kirchhoff's current law, which states that the power flowing into each node is equal to the power flowing out of the node.

7. A method for joint operation of a distribution network based on meeting the charging needs of electric vehicles, as described in claim 1, is characterized in that, In step S300, electric vehicles with charging needs in various areas are connected to charging stations with energy storage, and a time-of-use pricing mechanism is introduced to guide orderly charging. The steps include: S301, Input system load margin and electricity price information. The load margin is obtained by the difference between the conventional load curve and the charging station and energy storage capacity. The charging station and energy storage capacity are taken from the charging station and energy storage capacity of each area solved in S200. Obtain the charging demand of electric vehicle users. The charging station obtains the battery capacity, current state of charge, expected dwell time and the state of charge level of the electric vehicle after charging. The above parameters are taken from the relevant parameters after the operation of each vehicle in step S100. S302, dynamically set time-of-use electricity prices. Electric vehicle charging stations aim to achieve peak shaving and valley filling of load, and prioritize charging users during periods with lower load. Under the constraints of ensuring that the charging station transformer does not overload and meeting the charging needs of electric vehicles, a time-of-use charging price is dynamically set for each electric vehicle user.

8. A method for joint operation of a distribution network based on meeting the charging needs of electric vehicles, as described in claim 7, is characterized in that, In step S302, time-of-use pricing is dynamically determined. Electric vehicle charging stations aim to achieve peak shaving and valley filling of load, prioritizing charging during periods of lower load for users. This is constrained by ensuring the charging station transformer does not overload and meeting the charging needs of electric vehicles. A time-of-use charging price is dynamically determined for each electric vehicle user. The steps include: S3021, Calculate the number of time periods required for electric vehicle charging: ； In the formula, ∆t represents the length of a time period, which is 15 minutes; η represents the charging efficiency of the charging pile. Represents the smallest integer whose value is not less than x; This represents the user's desired state of charge. P represents the state of charge of an electric vehicle when it enters a charging station. i Indicates the charging power of the charging station, BP i Indicates the battery capacity of an electric vehicle; S3022, Calculate the number of parking time periods: ； In the formula, t i Indicates the first i Parking duration of electric vehicles; T i Indicates the number of parking time periods; Indicates not greater than x The largest integer; S3023, determine whether the transformer load margin meets the user's charging needs; if the transformer load margin is sufficient, select the J with the largest margin during the shutdown period. i The suggested time period is defined as a time period; if the transformer load margin is insufficient, it is determined whether the energy storage system load margin meets the user's charging needs. S3024, if the energy storage system load margin is sufficient, J with the largest margin will be selected during the shutdown period. i The system provides a suggested time period; if the energy storage system's load margin is insufficient, the calculation system can ensure the battery charging level reaches its maximum when the user leaves, and will then alert the user. S3025, User Response: Determine the charging plan based on the user's response. If the user accepts the suggestion, off-peak electricity pricing will be used. If the user does not accept the suggestion, charging will be arranged for the user during a charging period immediately, and charging will be charged according to peak electricity pricing. At the same time, the charging load margin for each period will be updated. S3026 determines whether the energy storage capacity is sufficient. If not, it exits the energy storage and charges the energy storage during the off-peak period with the largest load margin at night, while updating the load margin.

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