Microgrid capacity configuration method and device and electronic equipment

Abstract

The invention provides a micro-grid capacity configuration method and device and electronic equipment, and is applied to the technical field of micro-grid planning, and the method comprises the steps: obtaining a charging time characteristic parameter of load equipment and a charging space characteristic parameter of the load equipment; determining a configuration capacity value of the charging equipment according to the charging time characteristic parameter and the charging space characteristic parameter; and carrying out capacity configuration on the microgrid according to the configuration capacity value of the charging equipment. When an electric vehicle and other load devices are connected to the micro-grid, the capacity configuration of the charging device can be reasonably planned and the capacity resource of the micro-grid can be flexibly adjusted by considering the charging characteristics of the load devices, so that the resource utilization rate can be improved.

Description

Technical Field

[0001] This application relates to the field of microgrid planning technology, and in particular to a microgrid capacity configuration method, apparatus and electronic equipment. Background Technology

[0002] With growing environmental awareness, the demand for electric vehicles is increasing. However, the integration of distributed power sources and electric vehicles into microgrids also presents new challenges. Electric vehicles generally require significant charging power. When a large number of electric vehicles are connected to a microgrid without proper coordination, peak charging periods can overlap with peak electricity consumption periods. This overlap can lead to overload of microgrid components, increased line loss costs, and three-phase imbalance, thereby threatening the safety of the microgrid.

[0003] The transportation sector still accounts for approximately 10% of carbon emissions, making it the third largest carbon-emitting sector in my country, after the energy industry and manufacturing and construction industries, indicating a huge need for carbon reduction. Among these, road transportation carbon emissions mainly come from vehicles, accounting for 80% of the total emissions in the transportation sector. However, currently, electricity accounts for only 5% of the energy consumption structure in transportation. Under the dual-carbon context, the transportation industry urgently needs to accelerate its green transformation.

[0004] The construction of charging stations is gradually catching up with the development of electric vehicles, but there is still room for improvement to achieve a lower vehicle-to-charging-station ratio. The unreasonable layout of new energy vehicle infrastructure, especially slow-charging stations, results in low overall utilization and resource waste. Summary of the Invention

[0005] This application provides a microgrid capacity configuration method and apparatus to solve the problem of low resource utilization in the prior art.

[0006] To solve the above-mentioned technical problems, this application is implemented as follows:

[0007] In a first aspect, embodiments of this application provide a microgrid capacity configuration method, the method comprising:

[0008] Obtain the charging time characteristic parameters and the charging space characteristic parameters of the load equipment;

[0009] The configuration capacity value of the charging device is determined based on the charging time characteristic parameters and the charging space characteristic parameters;

[0010] The capacity of the microgrid is configured according to the configuration capacity value of the charging equipment.

[0011] Optionally, determining the configuration capacity value of the charging device based on the charging time characteristic parameter and the charging space characteristic parameter includes:

[0012] The charging time characteristic parameters and the charging space characteristic parameters are input into a pre-built microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment;

[0013] The microgrid capacity configuration model includes an objective function and objective constraints. The objective function is constructed based on at least one of a first objective function aimed at minimizing peak-shaving pressure, a second objective function aimed at minimizing energy costs, and a third objective function aimed at maximizing the greenness of energy. The objective constraints include the power constraints of the charging equipment and the charging constraints of the load equipment.

[0014] Optionally, the objective function is constructed based on the first objective function, the second objective function, the third objective function, the first weight parameter of the first objective function, the second weight parameter of the second objective function, and the third weight parameter of the third objective function;

[0015] The step of inputting the charging time characteristic parameters and the charging space characteristic parameters into a pre-constructed microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment includes:

[0016] The charging time characteristic parameters, the charging space characteristic parameters, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function are input into the microgrid capacity configuration model to obtain the capacity value of the charging equipment.

[0017] Optionally, before inputting the charging time characteristic parameters, the charging space characteristic parameters, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function into the microgrid capacity configuration model, the method further includes:

[0018] The first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function are determined using the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method.

[0019] Optionally, the target constraints may further include at least one of the following: power constraints of the transformer, output constraints of the new energy power generation equipment, power constraints of the flexible DC transmission equipment, capacity constraints of the energy storage equipment, and power constraints of the energy storage equipment.

[0020] The step of inputting the charging time characteristic parameters and the charging space characteristic parameters into a pre-constructed microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment includes:

[0021] The charging time characteristic parameters and the charging space characteristic parameters are input into a pre-built microgrid capacity configuration model to obtain the configuration capacity values ​​of the new energy power generation equipment, the flexible DC transmission equipment, the energy storage equipment, and the charging equipment.

[0022] The process of configuring the microgrid capacity based on the configured capacity value of the charging equipment includes:

[0023] The microgrid capacity is configured based on the configuration capacity values ​​of the charging equipment, the new energy power generation equipment, the flexible DC transmission equipment, and the energy storage equipment.

[0024] Optionally, the first objective function is:

[0025]

[0026] Where N represents the number of power supply areas, x1 represents the overall peak-shaving pressure of the power supply areas, g1 represents the peak-valley difference rate of the distribution transformer in the first power supply area, and g n Let be the peak-valley difference rate of the distribution transformer in the nth power supply area. This represents the average peak-to-valley difference rate of the distribution transformers in the power supply area.

[0027] And / or,

[0028] The second objective function is:

[0029]

[0030] Where x2 represents the difference between the cost of purchasing electricity and the revenue from selling electricity, S P,i S represents the configuration capacity value of new energy power generation equipment. s,i S represents the configured capacity value of the energy storage device. VL,i S represents the configured capacity value of flexible DC transmission equipment. Z,i C represents the configuration capacity value of the charging device. P C represents the manufacturing value of the new energy power generation equipment. s C represents the manufacturing value of the energy storage device. SV C represents the manufacturing value of the flexible DC transmission equipment. Z P represents the manufacturing value of the charging device. 1,i P represents the power output. 2,i R1 represents the on-grid power, R2 represents the on-grid electricity price, and R1 represents the on-grid electricity price.

[0031] And / or,

[0032] The third objective function is:

[0033]

[0034] Where x3 represents the degree of greenness of energy, P p P represents the power output of the new energy power generation equipment. t This indicates the power of the load equipment.

[0035] Optionally, the power constraint of the transformer includes:

[0036] P T ≤S T ;

[0037] Among them, P T S represents the downstream power of the transformer. T This indicates the rated capacity value of the transformer;

[0038] And / or,

[0039] The output constraints of the new energy power generation equipment include:

[0040] 0≤P P ≤S P ;

[0041] Among them, P P S represents the output power of the new energy power generation equipment. P This indicates the configuration capacity value of the new energy power generation equipment;

[0042] And / or,

[0043] The power constraints of the flexible DC transmission equipment include:

[0044] -S VL ≤P VL ≤S VL ;

[0045] Among them, P VL S represents the power of the flexible DC transmission equipment. VL This indicates the configured capacity value of the flexible DC transmission equipment;

[0046] And / or,

[0047] The capacity constraints and power constraints of the energy storage device include:

[0048] S i =S i-1 +P in(i-1) -P out(i-1)

[0049] 0≤S i ≤S S ,0≤Pin(i-1) ≤P SM ,0≤P out(i-1) ≤P SM ;

[0050] Among them, S i S represents the capacity value of the energy storage device at time i. S P represents the configured capacity value of the energy storage device. in P represents the charging power of the energy storage device. out P represents the discharge power of the energy storage device. SM This indicates the maximum charging power of the energy storage device or the maximum discharging power of the energy storage device.

[0051] And / or,

[0052] The power constraints of the charging device include:

[0053] P Ci ≤P zk ;

[0054]

[0055] Among them, P Ci Let P represent the charging power of vehicle i, I represent the maximum number of vehicles charging at a given moment, and P represent the charging power of vehicle i. z The rated power of the charging device is represented by K, the type of the charging device is represented by K, and the quantity of the k-th type of charging device is represented by J.

[0056] And / or,

[0057] The charging constraints of the load equipment include:

[0058]

[0059] Among them, P Cit ΔS represents the charging power of car i at time t. im This represents the amount of electricity required to charge vehicle i.

[0060] Secondly, embodiments of this application also provide a microgrid capacity configuration device. This microgrid capacity configuration device includes:

[0061] The first acquisition module is used to acquire the charging time characteristic parameters of the load device and the charging space characteristic parameters of the load device;

[0062] The first determining module is used to determine the configuration capacity value of the charging device based on the charging time characteristic parameters and the charging space characteristic parameters;

[0063] The first configuration module is used to configure the capacity of the microgrid according to the configuration capacity value of the charging device.

[0064] Optionally, the first determining module includes:

[0065] The first input unit is used to input the charging time characteristic parameters and the charging space characteristic parameters into a pre-built microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment;

[0066] The microgrid capacity configuration model includes an objective function and objective constraints. The objective function is constructed based on at least one of a first objective function aimed at minimizing peak-shaving pressure, a second objective function aimed at minimizing energy costs, and a third objective function aimed at maximizing the greenness of energy. The objective constraints include the power constraints of the charging equipment and the charging constraints of the load equipment.

[0067] Optionally, the objective function is constructed based on the first objective function, the second objective function, the third objective function, the first weight parameter of the first objective function, the second weight parameter of the second objective function, and the third weight parameter of the third objective function;

[0068] The first power transmission unit includes:

[0069] The first input subunit is used to input the charging time characteristic parameters, the charging space characteristic parameters, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function into the microgrid capacity configuration model to obtain the capacity value of the charging equipment.

[0070] Optionally, the device further includes:

[0071] The second determining module is used to determine the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function using the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method.

[0072] Optionally, the target constraints may further include at least one of the following: power constraints of the transformer, output constraints of the new energy power generation equipment, power constraints of the flexible DC transmission equipment, capacity constraints of the energy storage equipment, and power constraints of the energy storage equipment.

[0073] The first input unit includes:

[0074] The second input subunit is used to input the charging time characteristic parameters and the charging space characteristic parameters into a pre-built microgrid capacity configuration model to obtain the configuration capacity values ​​of the new energy power generation equipment, the flexible DC transmission equipment, the energy storage equipment, and the charging equipment.

[0075] The first configuration module includes:

[0076] The first configuration unit is used to configure the capacity of the microgrid according to the configuration capacity values ​​of the charging equipment, the new energy power generation equipment, the flexible DC transmission equipment, and the energy storage equipment.

[0077] Optionally, the first objective function is:

[0078]

[0079] Where N represents the number of power supply areas, x1 represents the overall peak-shaving pressure of the power supply areas, g1 represents the peak-valley difference rate of the distribution transformer in the first power supply area, and g n Let g represent the peak-valley difference rate of the distribution transformer in the nth power supply area, and g represent the average peak-valley difference rate of the distribution transformer in the power supply area.

[0080] And / or,

[0081] The second objective function is:

[0082]

[0083] Where x2 represents the difference between the cost of purchasing electricity and the revenue from selling electricity, S P,i S represents the configuration capacity value of new energy power generation equipment. s,i S represents the configured capacity value of the energy storage device. VL,i S represents the configured capacity value of flexible DC transmission equipment. Z,i C represents the configuration capacity value of the charging device. P C represents the manufacturing value of the new energy power generation equipment. s C represents the manufacturing value of the energy storage device. SV C represents the manufacturing value of the flexible DC transmission equipment. Z P represents the manufacturing value of the charging device. 1,i P represents the power output. 2,i R1 represents the on-grid power, R2 represents the on-grid electricity price, and R1 represents the on-grid electricity price.

[0084] And / or,

[0085] The third objective function is:

[0086]

[0087] Where x3 represents the degree of greenness of energy, P p P represents the power output of the new energy power generation equipment. t This indicates the power of the load equipment.

[0088] Optionally, the power constraint of the transformer includes:

[0089] P T ≤S T ;

[0090] Among them, P T S represents the downstream power of the transformer. T This indicates the rated capacity value of the transformer;

[0091] And / or,

[0092] The output constraints of the new energy power generation equipment include:

[0093] 0≤P P ≤S P ;

[0094] Among them, P P S represents the output power of the new energy power generation equipment. P This indicates the configuration capacity value of the new energy power generation equipment;

[0095] And / or,

[0096] The power constraints of the flexible DC transmission equipment include:

[0097] -S VL ≤P VL ≤S VL ;

[0098] Among them, P VL S represents the power of the flexible DC transmission equipment. VL This indicates the configured capacity value of the flexible DC transmission equipment;

[0099] And / or,

[0100] The capacity constraints and power constraints of the energy storage device include:

[0101] S i =S i-1 +P in(i-1) -P out(i-1)

[0102] 0≤S i ≤S S ,0≤P in(i-1) ≤P SM ,0≤P out(i-1) ≤P SM ;

[0103] Among them, S i S represents the capacity value of the energy storage device at time i. SP represents the configured capacity value of the energy storage device. in P represents the charging power of the energy storage device. out P represents the discharge power of the energy storage device. SM This indicates the maximum charging power of the energy storage device or the maximum discharging power of the energy storage device.

[0104] And / or,

[0105] The power constraints of the charging device include:

[0106] P Ci ≤P zk ;

[0107]

[0108] Among them, P Ci Let P represent the charging power of vehicle i, I represent the maximum number of vehicles charging at a given moment, and P represent the charging power of vehicle i. z The rated power of the charging device is represented by K, the type of the charging device is represented by K, and the quantity of the k-th type of charging device is represented by J.

[0109] And / or,

[0110] The charging constraints of the load equipment include:

[0111]

[0112] Among them, P Cit ΔS represents the charging power of car i at time t. im This represents the amount of electricity required to charge vehicle i.

[0113] Thirdly, embodiments of this application also provide an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the microgrid capacity configuration method described above.

[0114] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the microgrid capacity configuration method described above.

[0115] Fifthly, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0116] The microgrid capacity configuration method of this application includes obtaining charging time characteristic parameters and charging space characteristic parameters of the load devices; determining the configuration capacity value of the charging devices based on the charging time characteristic parameters and the charging space characteristic parameters; and configuring the microgrid capacity based on the configuration capacity value of the charging devices. When load devices such as electric vehicles are connected to a microgrid, this method, by considering the charging characteristics of the load devices, can rationally plan the capacity configuration of the charging devices and flexibly adjust the capacity resources of the microgrid, thereby improving the resource utilization rate of the microgrid. Attached Figure Description

[0117] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0118] Figure 1 This is one of the flowcharts of the microgrid capacity configuration method provided in the embodiments of this application;

[0119] Figure 2 This is an architecture diagram of the microgrid provided in the embodiments of this application;

[0120] Figure 3 This is the second flowchart of the microgrid capacity configuration method provided in the embodiments of this application;

[0121] Figure 4 This is a structural diagram of a microgrid capacity configuration device provided in an embodiment of this application;

[0122] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0123] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0124] This application provides a microgrid capacity configuration method. See also... Figure 1 , Figure 1 This is a flowchart of the microgrid capacity configuration method provided in the embodiments of this application, such as... Figure 1 As shown, it includes the following steps:

[0125] Step 101: Obtain the charging time characteristic parameters and the charging space characteristic parameters of the load device;

[0126] In this step, the load equipment mainly refers to electric vehicles such as private cars, taxis, buses, and special-purpose vehicles. Charging time characteristic parameters include parking duration and parking time, while charging space characteristic parameters include parking location and daily mileage. Among these, the vehicle's daily mileage significantly affects the charging frequency and the required charging amount.

[0127] For example, taking a 24-hour working day as the evaluation period, we consider the charging time characteristic parameters and charging space characteristic parameters of vehicles with different uses:

[0128] (1) Private Cars: Compared to other vehicle types, electric passenger vehicles are more numerous and have longer parking times. Statistics were compiled on the starting time of parking, parking duration, and mileage. Based on people's daily habits, it was assumed that the moment a car begins parking is the starting time for charging. Generally, most private cars choose to charge between 9:00 PM and 3:00 AM, followed by 5:00 AM to 7:00 AM and 11:00 AM to 1:00 PM. The daily mileage of a car determines the daily charging requirement. Generally, the average daily mileage of a private car is approximately 40 km. Considering the distribution of parking areas, private cars may choose to charge in residential parking lots, industrial and commercial parking lots, and designated public service parking areas. Most vehicles charge at night in residential areas, followed by during the day in industrial and commercial areas, both using slow charging, while fast charging is used in public areas. Except for the longer permitted parking time in residential areas at night, the permitted parking time in other areas is relatively shorter.

[0129] (2) Taxis: A typical taxi travels approximately 300-400 km per day, with two shift changes, usually in the afternoon and early morning. Considering that the current range of electric taxis generally meets the needs of each shift, and that stops are infrequent, it is assumed that electric taxis will charge twice daily in designated parking areas before shift changes. Based on the taxi's battery capacity, per-kilometer energy consumption, and mileage, taxis generally need to be fully charged before each stop. Regarding charging stations, due to the high time sensitivity, fast-charging stations in designated areas are primarily selected. Preliminary statistics indicate that the afternoon shift change time for taxis is highly random, and the charging start time follows a uniform distribution that can be fitted.

[0130] (3) Buses: The main operating characteristics of buses are daytime operation and nighttime rest, with a daily mileage of approximately 200km. Based on battery capacity and per-kilometer energy consumption, buses generally need to be charged twice a day. During daytime operation, due to the demand and driving tasks of the buses, the choice of charging time and duration is very important. At this time, charging should be as fast as possible to ensure that the vehicles can quickly return to their working routes without affecting daily services. Nighttime is usually the off-peak operating period for buses, and the vehicles are in a resting state. Therefore, during this period, charging speed does not need to be considered too much, and slow charging can be used. The first charging is usually done during the midday period, generally using the ultra-fast charging method in designated areas; the second charging is done at night using the slow charging method in designated areas.

[0131] (4) Special Purpose Vehicles: Special purpose vehicles mainly refer to vehicles used for municipal sanitation and urban logistics. Due to the large number of classifications, there is a lack of relevant statistical data for special purpose vehicles. They are mostly charged during the day and operated at night. The average daily mileage is about 100km, and depending on the battery capacity and energy consumption per kilometer, they are generally charged once every two days. The driving behavior of official vehicles is related to the nature of the work tasks, with an average daily mileage of about 50km. Although the daily energy consumption is not high, it needs to be charged once a day due to the nature of the work.

[0132] Step 102: Determine the configuration capacity value of the charging device based on the charging time characteristic parameters and the charging space characteristic parameters;

[0133] In this step, the first step is to determine the type of charging station required for each type of vehicle. This involves statistically determining the total number of electric vehicles operating in the area and the proportion of each type, the daily distribution of the starting charging time and charging duration for each type of vehicle, and the amount of electricity charged each time, thereby obtaining the average daily charging amount for each type of vehicle.

[0134] Secondly, it is necessary to determine the types and quantities of charging stations in the area. This involves determining the number of charging stations based on the planned vehicle-to-charging station ratio for fast and slow charging, as well as the number of vehicles. The average total charging time is then obtained by comparing the total daily charging amount of each type of vehicle with the charging power of the selected charging station. Finally, the number of charging stations is allocated using the ratio of the average total charging time to the average charging time per vehicle.

[0135] Step 103: Configure the capacity of the microgrid according to the configuration capacity value of the charging equipment.

[0136] In this step, the microgrid is rationally planned and configured according to the power capacity required by the charging equipment to ensure that the power demand of the charging equipment can be met.

[0137] In one implementation, the configuration capacity value of the charging equipment is determined based on the charging time characteristic parameters and the charging space characteristic parameters of the load equipment. Then, the microgrid capacity is configured according to the configuration capacity value of the charging equipment. In this implementation, when load equipment such as electric vehicles is connected to the microgrid, by considering the charging characteristics of the load equipment, the capacity configuration of the charging equipment can be rationally planned, and the capacity resources of the microgrid can be flexibly adjusted, thereby improving the resource utilization rate of the microgrid.

[0138] Optionally, determining the configuration capacity value of the charging device based on the charging time characteristic parameter and the charging space characteristic parameter includes:

[0139] The charging time characteristic parameters and the charging space characteristic parameters are input into a pre-built microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment;

[0140] The microgrid capacity configuration model includes an objective function and objective constraints. The objective function is constructed based on at least one of a first objective function aimed at minimizing peak-shaving pressure, a second objective function aimed at minimizing energy costs, and a third objective function aimed at maximizing the greenness of energy. The objective constraints include the power constraints of the charging equipment and the charging constraints of the load equipment.

[0141] In one implementation, a microgrid capacity configuration model can be pre-constructed. This model includes an objective function and objective constraints. The objective function is constructed based on at least one of a first objective function aimed at minimizing peak-shaving pressure, a second objective function aimed at minimizing energy costs, and a third objective function aimed at maximizing the greenness of energy. The first objective function primarily measures the peak-shaving pressure of the microgrid by the consistency and magnitude of the peak-valley difference rate of transformers in each distribution area. The second objective function primarily measures economic profit by considering the difference between the costs of investment and electricity purchase fees over a period of time and the electricity sales fees of the microgrid. The third objective function primarily measures the greenness of energy by considering the power of renewable energy generation equipment and the power of load equipment. The objective constraints of the microgrid capacity configuration model include power constraints for charging equipment and charging constraints for load equipment.

[0142] Specifically, the first objective function can be seen in the following expression (1):

[0143]

[0144] Where N represents the number of power supply areas, x1 represents the overall peak-shaving pressure of the power supply areas, g1 represents the peak-valley difference rate of the distribution transformer in the first power supply area, and g nLet g represent the peak-valley difference rate of the distribution transformer in the nth power supply area, and g represent the average peak-valley difference rate of the distribution transformer in the power supply area.

[0145] And / or,

[0146] The second objective function can be seen in the following expression (2):

[0147]

[0148] Where x2 represents the difference between the cost of purchasing electricity and the revenue from selling electricity, S P,i S represents the configuration capacity value of new energy power generation equipment. s,i S represents the configured capacity value of the energy storage device. VL,i S represents the configured capacity value of flexible DC transmission equipment. Z,i C represents the configuration capacity value of the charging device. P C represents the manufacturing value of the new energy power generation equipment. s C represents the manufacturing value of the energy storage device. SV C represents the manufacturing value of the flexible DC transmission equipment. Z P represents the manufacturing value of the charging device. 1,i P represents the power output. 2,i R1 represents the on-grid power, R2 represents the on-grid electricity price, and R1 represents the on-grid electricity price.

[0149] And / or,

[0150] The third objective function can be found in the following expression (3):

[0151]

[0152] Where x3 represents the degree of greenness of energy, P p P represents the power output of the new energy power generation equipment. t Indicates the power of the load equipment;

[0153] Assuming the car owner knows the type of charging equipment to choose before arriving at the charging station, and that there are sufficient charging stations and no queuing, the maximum charging power of each vehicle must be less than the power of the selected charging station, and the total charging power of the area must not exceed the sum of the charging power of all devices. Therefore, the power constraints of the charging equipment can be seen in the following expressions (4) and (5):

[0154] P Ci ≤P zk (4);

[0155]

[0156] Among them, P CiLet P represent the charging power of vehicle i, I represent the maximum number of vehicles charging at a given moment, and P represent the charging power of vehicle i. z The rated power of the charging device is represented by K, the type of the charging device is represented by K, and the quantity of the k-th type of charging device is represented by J.

[0157] The load device needs to be fully charged within the parking time acceptable to the vehicle owner. Such charging will not affect the vehicle's use for the owner. Therefore, the charging constraint of the load device can be seen in the following expression (6):

[0158]

[0159] Among them, P Cit ΔS represents the charging power of car i at time t. im This represents the amount of electricity required for vehicle i. When a vehicle participates in orderly charging, its charging power can be adjusted within the charging power range allowed by the charging equipment within a specified time period T. When a vehicle participates in two-way vehicle-to-grid interaction, both its charging power and its discharging power can be adjusted.

[0160] The charging time characteristic parameters and charging space characteristic parameters of the load devices are input into a pre-constructed microgrid capacity configuration model. The parameters of the microgrid capacity configuration model are continuously optimized by constructing an objective function based on at least one of the following: a first objective function aimed at minimizing peak-shaving pressure, a second objective function aimed at minimizing energy costs, and a third objective function aimed at maximizing the greenness of energy. This process finds the optimal capacity configuration value of the charging devices that meets the constraints. This implementation method determines the capacity configuration value of the charging devices by using the charging time characteristic parameters, charging space characteristic parameters, and the pre-constructed microgrid capacity configuration model. Configuring the capacity of the microgrid based on this capacity configuration value helps to reduce the peak-shaving pressure of the microgrid while improving its carrying capacity, economic efficiency, and the greenness of energy.

[0161] Optionally, the objective function is constructed based on the first objective function, the second objective function, the third objective function, the first weight parameter of the first objective function, the second weight parameter of the second objective function, and the third weight parameter of the third objective function;

[0162] The step of inputting the charging time characteristic parameters and the charging space characteristic parameters into a pre-constructed microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment includes:

[0163] The charging time characteristic parameters, the charging space characteristic parameters, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function are input into the microgrid capacity configuration model to obtain the capacity value of the charging equipment.

[0164] In one implementation, the objective function is constructed based on a first objective function, a second objective function, a third objective function, a first weight parameter of the first objective function, a second weight parameter of the second objective function, and a third weight parameter of the third objective function, as shown in the following expression (7):

[0165] m = max(c1x) 1_ +c2x 2_ +c3x 3_ (7);

[0166] Where c1 represents the first weight parameter, c2 represents the second weight parameter, c3 represents the third weight parameter, and x 1_ It has a negative linear correlation with the first objective function x1, x 2_ It has a negative linear correlation with the second objective function x2, x 3_ It has a positive linear correlation with the third objective function x3.

[0167] In this implementation, different weights can be assigned to different sub-objective functions based on their importance in the multi-objective function of the microgrid capacity configuration model. These weights allow the microgrid capacity configuration model to be flexibly adjusted according to changes in the external environment or internal strategy adjustments, thereby improving the adaptability of the microgrid capacity configuration model.

[0168] Optionally, before inputting the charging time characteristic parameters, the charging space characteristic parameters, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function into the microgrid capacity configuration model, the method further includes:

[0169] The first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function are determined using the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method.

[0170] In one implementation, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function can be determined using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. Specifically:

[0171] (1) Construct the judgment matrix

[0172] The relative importance of each sub-objective function is determined by pairwise comparison analysis of the above sub-objective functions. A 1-9 scale method is used to numerically represent the judgment matrix, thereby quantifying the importance of each sub-objective function. The judgment matrix scale is shown in the table below.

[0173] Factor i compared to factor j <![CDATA[C ij Assignment Equally important 1 Slightly important 3 Obviously important 5 Strongly important 7 Extremely important 9

[0174] Based on the above judgment scaling method, construct the judgment matrix C = (C ij ) n*n :

[0175]

[0176] (2) Consistency test of the judgment matrix

[0177] ① Find the eigenvectors W = (W1, W2, ... W2) of the judgment matrix. n ), that is, the weight coefficients of each sub-objective function:

[0178]

[0179] ② Calculate λ max Value:

[0180]

[0181] ③ Calculate the consistency index CI and the random consistency ratio CR. If CR < 0.1, the constructed judgment matrix passes the consistency test. If it fails the consistency test, the judgment matrix needs to be reconstructed.

[0182]

[0183] The weighting coefficient w can be calculated using the method described above.

[0184] In this implementation, the weights of different sub-objective functions of the multi-objective function are determined by using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. This not only makes the weight allocation more scientific, reasonable and systematic, but also effectively handles the uncertainty in subjective judgment, greatly improving the accuracy and practicality of the microgrid capacity configuration model.

[0185] Optionally, the target constraints may further include at least one of the following: power constraints of the transformer, output constraints of the new energy power generation equipment, capacity constraints of the energy storage equipment, and power constraints of the energy storage equipment.

[0186] The charging time characteristic parameters and the charging space characteristic parameters are input into a pre-constructed microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment;

[0187] The charging time characteristic parameters and the charging space characteristic parameters are input into a pre-built microgrid capacity configuration model to obtain the configuration capacity values ​​of the new energy power generation equipment, the flexible DC transmission equipment, the energy storage equipment, and the charging equipment.

[0188] The process of configuring the microgrid capacity based on the configured capacity value of the charging equipment includes:

[0189] The microgrid capacity is configured based on the configuration capacity values ​​of the charging equipment, the new energy power generation equipment, the flexible DC transmission equipment, and the energy storage equipment.

[0190] In one implementation, see Figure 2 In practice, microgrids include not only charging equipment and load equipment, but also new energy power generation equipment, flexible DC transmission equipment, and energy storage equipment. When load equipment such as electric vehicles are connected to the microgrid, in order to further improve the carrying capacity of the microgrid and rationally allocate its capacity resources, in addition to considering the configuration capacity value of charging equipment, the microgrid capacity configuration model can also incorporate the power constraints of transformers, the output constraints of new energy power generation equipment, the power constraints of flexible DC transmission equipment, and the capacity constraints of energy storage equipment. Based on a multi-objective function, the configuration capacity values ​​of new energy power generation equipment, flexible DC transmission equipment, and energy storage equipment can be comprehensively optimized.

[0191] Specifically, the power constraints of the transformer include:

[0192] P T ≤S T ;

[0193] Among them, P T S represents the downstream power of the transformer. T This indicates the rated capacity value of the transformer;

[0194] And / or,

[0195] The output constraints of the new energy power generation equipment include:

[0196] 0≤P P ≤S P ;

[0197] Among them, P P S represents the output power of the new energy power generation equipment. P This indicates the configuration capacity value of the new energy power generation equipment;

[0198] And / or,

[0199] The power constraints of the flexible DC transmission equipment include:

[0200] -S VL ≤P VL ≤S VL ;

[0201] Among them, P VL S represents the power of the flexible DC transmission equipment. VL This indicates the configured capacity value of the flexible DC transmission equipment;

[0202] And / or,

[0203] The capacity constraints and power constraints of the energy storage device include:

[0204] S i =S i-1 +P in(i-1) -P out(i-1)

[0205] 0≤S i ≤S S ,0≤P in(i-1) ≤P SM ,0≤P out(i-1) ≤P SM ;

[0206] Among them, S i S represents the capacity value of the energy storage device at time i. S P represents the configured capacity value of the energy storage device. in P represents the charging power of the energy storage device. out P represents the discharge power of the energy storage device. SM This indicates the maximum charging power of the energy storage device or the maximum discharging power of the energy storage device.

[0207] In this implementation, by considering the charging time and charging space characteristics of load devices such as electric vehicles, and comprehensively allocating the capacity values ​​of new energy power generation equipment, charging equipment, flexible DC transmission equipment, and energy storage equipment, charging time can be reasonably arranged to avoid excessive load during peak hours and improve the operating efficiency of the microgrid.

[0208] In one implementation, see Figure 3First, the parking duration, parking time, parking location, and daily mileage of load equipment such as electric vehicles can be analyzed to obtain the charging time and charging space characteristic parameters of the load equipment. Based on these parameters, the quantity and location of charging equipment within the area can be initially configured. Next, a microgrid capacity configuration model is constructed, setting constraints related to transformers, renewable energy generation equipment, flexible DC transmission equipment, energy storage equipment, and charging equipment. A multi-objective function is constructed based on three objectives: minimizing peak-shaving pressure, minimizing energy costs, and maximizing energy greenness. The first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function are determined using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The configured capacity values ​​of renewable energy generation equipment, flexible DC transmission equipment, energy storage equipment, and charging equipment are continuously optimized to output the optimal solution.

[0209] See Figure 4 , Figure 4 This is a structural diagram of a microgrid capacity configuration device provided in another embodiment of this application. (See diagram below.) Figure 4 As shown, the microgrid capacity configuration device 400 includes:

[0210] The first acquisition module is used to acquire the charging time characteristic parameters of the load device and the charging space characteristic parameters of the load device;

[0211] The first determining module is used to determine the configuration capacity value of the charging device based on the charging time characteristic parameters and the charging space characteristic parameters;

[0212] The first configuration module is used to configure the capacity of the microgrid according to the configuration capacity value of the charging device.

[0213] Optionally, the first determining module includes:

[0214] The first input unit is used to input the charging time characteristic parameters and the charging space characteristic parameters into a pre-built microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment;

[0215] The microgrid capacity configuration model includes an objective function and objective constraints. The objective function is constructed based on at least one of a first objective function aimed at minimizing peak-shaving pressure, a second objective function aimed at minimizing energy costs, and a third objective function aimed at maximizing the greenness of energy. The objective constraints include the power constraints of the charging equipment and the charging constraints of the load equipment.

[0216] Optionally, the objective function is constructed based on the first objective function, the second objective function, the third objective function, the first weight parameter of the first objective function, the second weight parameter of the second objective function, and the third weight parameter of the third objective function;

[0217] The first power transmission unit includes:

[0218] The first input subunit is used to input the charging time characteristic parameters, the charging space characteristic parameters, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function into the microgrid capacity configuration model to obtain the capacity value of the charging equipment.

[0219] Optionally, the device further includes:

[0220] The second determining module is used to determine the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function using the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method.

[0221] Optionally, the target constraints may further include at least one of the following: power constraints of the transformer, output constraints of the new energy power generation equipment, power constraints of the flexible DC transmission equipment, capacity constraints of the energy storage equipment, and power constraints of the energy storage equipment.

[0222] The first input unit includes:

[0223] The second input subunit is used to input the charging time characteristic parameters and the charging space characteristic parameters into a pre-built microgrid capacity configuration model to obtain the configuration capacity values ​​of the new energy power generation equipment, the flexible DC transmission equipment, the energy storage equipment, and the charging equipment.

[0224] The first configuration module includes:

[0225] The first configuration unit is used to configure the capacity of the microgrid according to the configuration capacity values ​​of the charging equipment, the new energy power generation equipment, the flexible DC transmission equipment, and the energy storage equipment.

[0226] Optionally, the first objective function is:

[0227]

[0228] Where N represents the number of power supply areas, x1 represents the overall peak-shaving pressure of the power supply areas, g1 represents the peak-valley difference rate of the distribution transformer in the first power supply area, and g nLet g represent the peak-valley difference rate of the distribution transformer in the nth power supply area, and g represent the average peak-valley difference rate of the distribution transformer in the power supply area.

[0229] And / or,

[0230] The second objective function is:

[0231]

[0232] Where x2 represents the difference between the cost of purchasing electricity and the revenue from selling electricity, S P,i S represents the configuration capacity value of new energy power generation equipment. s,i S represents the configured capacity value of the energy storage device. VL,i S represents the configured capacity value of flexible DC transmission equipment. Z,i C represents the configuration capacity value of the charging device. P C represents the manufacturing value of the new energy power generation equipment. s C represents the manufacturing value of the energy storage device. SV C represents the manufacturing value of the flexible DC transmission equipment. Z P represents the manufacturing value of the charging device. 1,i P represents the power output. 2,i R1 represents the on-grid power, R2 represents the on-grid electricity price, and R1 represents the on-grid electricity price.

[0233] And / or,

[0234] The third objective function is:

[0235]

[0236] Where x3 represents the degree of greenness of energy, P p P represents the power output of the new energy power generation equipment. t This indicates the power of the load equipment.

[0237] Optionally, the power constraint of the transformer includes:

[0238] P T ≤S T ;

[0239] Among them, P T S represents the downstream power of the transformer. T This indicates the rated capacity value of the transformer;

[0240] And / or,

[0241] The output constraints of the new energy power generation equipment include:

[0242] 0≤P P ≤S P ;

[0243] Among them, P P S represents the output power of the new energy power generation equipment. P This indicates the configuration capacity value of the new energy power generation equipment;

[0244] And / or,

[0245] The power constraints of the flexible DC transmission equipment include:

[0246] -S VL ≤P VL ≤S VL ;

[0247] Among them, P VL S represents the power of the flexible DC transmission equipment. VL This indicates the configured capacity value of the flexible DC transmission equipment;

[0248] And / or,

[0249] The capacity constraints and power constraints of the energy storage device include:

[0250] S i =S i-1 +P in(i-1) -P out(i-1)

[0251] 0≤S i ≤S S ,0≤P in(i-1) ≤P SM ,0≤P out(i-1) ≤P SM ;

[0252] Among them, S i S represents the capacity value of the energy storage device at time i. S P represents the configured capacity value of the energy storage device. in P represents the charging power of the energy storage device. out P represents the discharge power of the energy storage device. SM This indicates the maximum charging power of the energy storage device or the maximum discharging power of the energy storage device.

[0253] And / or,

[0254] The power constraints of the charging device include:

[0255] P Ci ≤P zk ;

[0256]

[0257] Among them, P Ci Let P represent the charging power of vehicle i, I represent the maximum number of vehicles charging at a given moment, and P represent the charging power of vehicle i. z The rated power of the charging device is represented by K, the type of the charging device is represented by K, and the quantity of the k-th type of charging device is represented by J.

[0258] And / or,

[0259] The charging constraints of the load equipment include:

[0260]

[0261] Among them, P Cit ΔS represents the charging power of car i at time t. im This represents the amount of electricity required to charge vehicle i.

[0262] See Figure 5 , Figure 5 This is a structural diagram of the electronic device provided in one embodiment of this application, such as... Figure 5 As shown, the electronic device includes: a processor 501, a communication interface 502, a communication bus 504, and a memory 503, wherein the processor 501, the communication interface 502, and the memory 503 interact with each other through the communication bus 504.

[0263] The memory 503 is used to store computer programs; the processor 501 is used to acquire the charging time characteristic parameters and the charging space characteristic parameters of the load equipment; determine the configuration capacity value of the charging equipment according to the charging time characteristic parameters and the charging space characteristic parameters; and configure the capacity of the microgrid according to the configuration capacity value of the charging equipment.

[0264] Optionally, the processor 501 is specifically used for:

[0265] The charging time characteristic parameters and the charging space characteristic parameters are input into a pre-built microgrid capacity configuration model to obtain the configuration capacity value of the charging equipment;

[0266] The microgrid capacity configuration model includes an objective function and objective constraints. The objective function is constructed based on at least one of a first objective function aimed at minimizing peak-shaving pressure, a second objective function aimed at minimizing energy costs, and a third objective function aimed at maximizing the greenness of energy. The objective constraints include the power constraints of the charging equipment and the charging constraints of the load equipment.

[0267] Optionally, the objective function is constructed based on the first objective function, the second objective function, the third objective function, the first weight parameter of the first objective function, the second weight parameter of the second objective function, and the third weight parameter of the third objective function;

[0268] The processor 501 is specifically used for:

[0269] The charging time characteristic parameters, the charging space characteristic parameters, the first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function are input into the microgrid capacity configuration model to obtain the capacity value of the charging equipment.

[0270] Optionally, the processor 501 is further configured to:

[0271] The first weight value of the first objective function, the second weight value of the second objective function, and the third weight value of the third objective function are determined using the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method.

[0272] Optionally, the target constraints may also include at least one of the following: power constraints of transformers, output constraints of new energy power generation equipment, power constraints of flexible DC transmission equipment, and capacity constraints of energy storage equipment.

[0273] The processor 501 is specifically used for;

[0274] The charging time characteristic parameters and the charging space characteristic parameters are input into a pre-built microgrid capacity configuration model to obtain the configuration capacity values ​​of the new energy power generation equipment, the flexible DC transmission equipment, the energy storage equipment, and the charging equipment.

[0275] The processor 501 is specifically used for:

[0276] The microgrid capacity is configured based on the configuration capacity values ​​of the charging equipment, the new energy power generation equipment, the flexible DC transmission equipment, and the energy storage equipment.

[0277] Optionally, the first objective function is:

[0278]

[0279] Where N represents the number of power supply areas, x1 represents the overall peak-shaving pressure of the power supply areas, g1 represents the peak-valley difference rate of the distribution transformer in the first power supply area, and g n Let be the peak-valley difference rate of the distribution transformer in the nth power supply area. This represents the average peak-to-valley difference rate of the distribution transformers in the power supply area.

[0280] And / or,

[0281] The second objective function is:

[0282]

[0283] Where x2 represents the difference between the cost of purchasing electricity and the revenue from selling electricity, S P,i S represents the configuration capacity value of new energy power generation equipment. s,i S represents the configured capacity value of the energy storage device. VL,i S represents the configured capacity value of flexible DC transmission equipment. Z,i C represents the configuration capacity value of the charging device. P C represents the manufacturing value of the new energy power generation equipment. s C represents the manufacturing value of the energy storage device. SV C represents the manufacturing value of the flexible DC transmission equipment. Z P represents the manufacturing value of the charging device. 1,i P represents the power output. 2,i R1 represents the on-grid power, R2 represents the on-grid electricity price, and R1 represents the on-grid electricity price.

[0284] And / or,

[0285] The third objective function is:

[0286]

[0287] Where x3 represents the degree of greenness of energy, P p P represents the power output of the new energy power generation equipment. t This indicates the power of the load equipment.

[0288] Optionally, the power constraint of the transformer includes:

[0289] P T ≤S T ;

[0290] Among them, P T S represents the downstream power of the transformer. T This indicates the rated capacity value of the transformer;

[0291] And / or,

[0292] The output constraints of the new energy power generation equipment include:

[0293] 0≤P P ≤S P ;

[0294] Among them, P PS represents the output power of the new energy power generation equipment. P This indicates the configuration capacity value of the new energy power generation equipment;

[0295] And / or,

[0296] The power constraints of the flexible DC transmission equipment include:

[0297] -S VL ≤P VL ≤S VL ;

[0298] Among them, P VL S represents the power of the flexible DC transmission equipment. VL This indicates the configured capacity value of the flexible DC transmission equipment;

[0299] And / or,

[0300] The capacity constraints and power constraints of the energy storage device include:

[0301] S i =S i-1 +P in(i-1) -P out(i-1)

[0302] 0≤S i ≤S S ,0≤P in(i-1) ≤P SM ,0≤P out(i-1) ≤P SM ;

[0303] Among them, S i S represents the capacity value of the energy storage device at time i. S P represents the configured capacity value of the energy storage device. in P represents the charging power of the energy storage device. out P represents the discharge power of the energy storage device. SM This indicates the maximum charging power of the energy storage device or the maximum discharging power of the energy storage device.

[0304] And / or,

[0305] The power constraints of the charging device include:

[0306] P Ci ≤P zk ;

[0307]

[0308] Among them, P CiLet P represent the charging power of vehicle i, I represent the maximum number of vehicles charging at a given moment, and P represent the charging power of vehicle i. z The rated power of the charging device is represented by K, the type of the charging device is represented by K, and the quantity of the k-th type of charging device is represented by J.

[0309] And / or,

[0310] The charging constraints of the load equipment include:

[0311]

[0312] Among them, P Cit ΔS represents the charging power of car i at time t. im This represents the amount of electricity required to charge vehicle i.

[0313] The communication bus 504 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCT) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 504 can be divided into an address bus, a data bus, a control bus, etc. For ease of identification, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of data.

[0314] Communication interface 502 is used for communication between the aforementioned terminal and other devices.

[0315] The memory 503 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 503 may also be at least one storage device located remotely from the aforementioned processor 501. The aforementioned processor 501 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0316] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described microgrid capacity configuration method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0317] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above-described... Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0318] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0319] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0320] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for microgrid capacity configuration, the method comprising: The method comprises: obtaining a charging time characteristic parameter of a load device and a charging space characteristic parameter of the load device; determining a configuration capacity value of a charging device according to the charging time characteristic parameter and the charging space characteristic parameter; performing capacity configuration on a micro-grid according to the configuration capacity value of the charging device.

2. The microgrid capacity configuration method of claim 1, wherein, The determination of the configuration capacity value of the charging device according to the charging time characteristic parameter and the charging space characteristic parameter comprises: inputting the charging time characteristic parameter and the charging space characteristic parameter into a pre-constructed micro-grid capacity configuration model to obtain the configuration capacity value of the charging device; wherein the micro-grid capacity configuration model comprises a target function and a target constraint condition, the target function is constructed according to at least one of a first target function with the target of minimizing peak regulation pressure, a second target function with the target of minimizing energy cost, and a third target function with the target of maximizing energy green degree, and the target constraint condition comprises a power constraint of the charging device and a charging constraint of the load device.

3. The microgrid capacity configuration method of claim 2, wherein, The target function is constructed according to the first target function, the second target function, the third target function, a first weight parameter of the first target function, a second weight parameter of the second target function, and a third weight parameter of the third target function. The inputting of the charging time characteristic parameter and the charging space characteristic parameter into the pre-constructed micro-grid capacity configuration model to obtain the configuration capacity value of the charging device comprises: inputting the charging time characteristic parameter, the charging space characteristic parameter, a first weight value of the first target function, a second weight value of the second target function, and a third weight value of the third target function into the micro-grid capacity configuration model to obtain the capacity value of the charging device.

4. The microgrid capacity configuration method of claim 3, wherein, Before the inputting of the charging time characteristic parameter, the charging space characteristic parameter, the first weight value of the first target function, the second weight value of the second target function, and the third weight value of the third target function into the micro-grid capacity configuration model, the method further comprises: determining the first weight value of the first target function, the second weight value of the second target function, and the third weight value of the third target function by using the analytic hierarchy process and the fuzzy comprehensive evaluation method.

5. The microgrid capacity configuration method of claim 2, wherein, The target constraint condition further comprises at least one of a power constraint of a transformer, an output constraint of a new energy power generation device, a power constraint of a flexible direct current power transmission device, a capacity constraint of an energy storage device, and a power constraint of the energy storage device. The inputting of the charging time characteristic parameter and the charging space characteristic parameter into the pre-constructed micro-grid capacity configuration model to obtain the configuration capacity value of the charging device comprises: inputting the charging time characteristic parameter and the charging space characteristic parameter into the pre-constructed micro-grid capacity configuration model to obtain the configuration capacity value of the new energy power generation device, the configuration capacity value of the flexible direct current power transmission device, the configuration capacity value of the energy storage device, and the configuration capacity value of the charging device. The capacity configuration on the micro-grid according to the configuration capacity value of the charging device comprises: According to the configuration capacity value of the charging device, the configuration capacity value of the new energy power generation device, the configuration capacity value of the flexible DC power transmission device and the configuration capacity value of the energy storage device, the micro-grid is configured in capacity.

6. The microgrid capacity configuration method of claim 2, wherein, The first target function is: Wherein, N represents the number of power supply areas, x1 represents the overall peak regulation pressure of the power supply area, g1 represents the peak-valley difference rate of the distribution transformer of the first power supply area, g n is the peak-valley difference rate of the distribution transformer of the nth power supply area, represents the average peak-valley difference rate of the distribution transformer of the power supply area; and / or, The second target function is: wherein x2 represents a difference between a value of a purchase electricity cost and a value of a selling electricity income, S P,i represents a configuration capacity value of the new energy power generation device, S s,i represents a configuration capacity value of the energy storage device, S VL,i represents a configuration capacity value of the flexible direct current power transmission device, S Z,i represents a configuration capacity value of the charging device, C P represents a manufacturing cost value of the new energy power generation device, C s represents a manufacturing cost value of the energy storage device, C SV represents a manufacturing cost value of the flexible direct current power transmission device, C Z represents a manufacturing cost value of the charging device, P 1,i represents a power of electricity purchase, P 2,i represents a power of electricity selling, r1 represents a price of electricity purchase, and r2 represents a price of electricity selling; and / or, The third target function is: Wherein, x3 represents the green degree of energy, P p represents the power of the new energy power generation equipment, P t represents the power of the load equipment.

7. The microgrid capacity configuration method of claim 5, wherein, The power constraint of the transformer includes: P T ≤S T ; where P T represents the off power of the transformer, S T represents the rated capacity value of the transformer; and / or, The output constraint of the new energy power generation device includes: 0 < P P ≤ S P ; wherein P P represents the output power of the new energy power generation device, S P represents the configuration capacity value of the new energy power generation device; and / or, The power constraint of the flexible DC power transmission device includes: - S VL ≤ P VL ≤ S VL ; wherein P VL represents the power of the flexible HVDC power transmission device, S VL represents the configured capacity value of the flexible HVDC power transmission device; and / or, The capacity constraint of the energy storage device and the power constraint of the energy storage device include: S i = S i-1 + P in(i-1) - P out(i-1) 0 ≤ S i ≤ S S , 0 ≤ P in(i-1) ≤ P SM , 0 ≤ P out(i-1) ≤ P SM ; wherein S i represents a capacity value of the energy storage device at the i-th moment, S S represents a configuration capacity value of the energy storage device, P in represents a charging power of the energy storage device, P out represents a discharging power of the energy storage device, P SM represents a maximum charging power of the energy storage device or a maximum discharging power of the energy storage device; and / or, The power constraint of the charging device includes: P Ci ≤P zk ; wherein P Ci represents the charging power of the i-th vehicle, I represents the maximum number of vehicles being charged at a certain time, P z represents the rated power of the charging device, K represents the type of the charging device, and J represents the number of the k-th charging device. and / or, The charging constraint of the load device includes: where P Cit represents the charging power of the i-th vehicle at time t, ΔS im represents the charging power of the i-th vehicle at time t, ΔS 8. A microgrid capacity configuration apparatus, characterized by comprising: The device includes: A first acquisition module is configured to acquire a charging time characteristic parameter of a load device and a charging space characteristic parameter of the load device; A first determination module is configured to determine a configuration capacity value of a charging device according to the charging time characteristic parameter and the charging space characteristic parameter; A first configuration module is configured to configure a micro-grid in capacity according to the configuration capacity value of the charging device.

9. An electronic device, comprising: The computer program is stored in the memory and can be run on the processor, and when the computer program is executed by the processor, the steps of the micro-grid capacity configuration method in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, The computer program is stored in the memory and can be run on the processor, and when the computer program is executed by the processor, the steps of the micro-grid capacity configuration method in any one of claims 1 to 7 are implemented.

11. A computer program product, characterised in that, The computer program is stored in the memory and can be run on the processor, and when the computer program is executed by the processor, the steps of the micro-grid capacity configuration method in any one of claims 1 to 7 are implemented.

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