Quantitative evaluation method and system for carrying capacity of electric vehicle in court

Abstract

The invention relates to a quantitative evaluation method and system for the bearing capacity of an electric vehicle in a transformer area. The quantitative evaluation method comprises the specific steps that the charging start time, the charging end time and the mileage of the electric vehicle are simulated through a probability density function; determining the time required for charging based on the driving mileage; dividing the electric vehicles into controllable electric vehicles and uncontrollable electric vehicles according to charging modes of the electric vehicles, and respectively establishing boundary models of energy accumulation and charging power; fusing the boundary models of the two types of electric vehicles by using a time sequence summation method to obtain a boundary model of an electric vehicle cluster; establishing a multi-objective optimization function, and solving the multi-objective function by using a genetic algorithm; calculating the maximum load rate under the current number of electric vehicles based on the optimal solution of the multi-objective optimization function; and gradually increasing the number of the electric vehicles in the district, updating the maximum load rate, and taking the number of the electric vehicles during overload as the maximum bearing capacity of the electric vehicles in the district.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle and power grid interaction, and in particular to a quantitative evaluation method and system for the carrying capacity of electric vehicles in a substation. Background Art

[0002] With the rapid development of electric vehicles, the large-scale access of electric vehicles to the distribution network will have a significant impact on the operation of the power grid. Especially in the residential community scenario, the centralized access of electric vehicles is superimposed on the original basic load, making the load distribution in the substation more complicated, which is easy to cause problems such as heavy overload and voltage over-limit in the substation, threatening the safety and stability of the power grid. This makes accurate assessment of the carrying capacity of electric vehicles important for the optimization of power grid operation. The existing electric vehicle carrying capacity assessment method usually takes the charging behavior of a single electric vehicle as a decision variable, and determines the maximum access scale of electric vehicles in the residential community through an orderly charging regulation strategy. However, with the increase in the penetration rate of electric vehicles, a single electric vehicle as a regulation object will lead to frequent parameter transmission, exponential growth in communication burden, and cannot efficiently meet actual needs. Therefore, in order to simplify the computational complexity and improve the evaluation efficiency, an aggregate modeling method is usually adopted to treat the electric vehicle cluster as a whole for regulation.

[0003] The Australian invention patent with patent publication number AU2014262212A1 discloses a multi-objective random programming method for electric vehicle charging load based on a non-dominated sorting genetic algorithm, which discloses a mathematical model for establishing the remaining power state and return time distribution; selecting charging load optimization sub-goals, including: taking the minimum network loss in the entire optimization cycle as the EV charging load optimization sub-goal; minimizing the load peak through the power node in the entire optimization cycle is regarded as the sub-goal of EV charging load optimization; establishing a multi-objective random optimization model of the distribution network based on the electric vehicle charging load, using the improved dominant sorting genetic algorithm to solve it, taking the distribution network loss and the power node peak as the constraint load and the load fluctuation optimization as the sub-goal, so as to realize the multi-objective random programming of the EV charging load.

[0004] In the above scheme, a single electric vehicle is taken as the control object, and the corresponding boundary conditions are not changed according to the performance of the electric vehicle, which makes the analysis result inaccurate. At the same time, when considering the constraints, the energy consumption of electric vehicles in daily work, that is, the required charging time of electric vehicles, is not actually considered. When making charging plans for electric vehicle clusters, the results are not reasonable enough. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a quantitative evaluation method and system for the carrying capacity of electric vehicles in a substation, obtains the charging load characteristic parameters through the Monte Carlo method, and then generates an electric vehicle cluster aggregation model, and equates the electric vehicle cluster in the residential community to one electric vehicle. Combined with the operating constraints of the substation power grid, the access upper limit of electric vehicles is scientifically evaluated, providing important technical support and theoretical basis for the planning, access and management of electric vehicles in residential communities.

[0006] The technical solution of the present invention is as follows: On the one hand, the present invention proposes a quantitative evaluation method for the carrying capacity of electric vehicles in a station area, and the specific steps include: The probability density function is used to simulate the charging start time, charging end time and driving mileage of the electric vehicle; and the charging time required is determined based on the driving mileage; the charging start time, charging end time and charging time required together constitute the charging load characteristic parameter; Electric vehicles are divided into controllable electric vehicles and uncontrollable electric vehicles according to their charging methods, and boundary models of energy accumulation and charging power are established respectively; then the boundary models of the two types of electric vehicles are fused using the time series summation method to obtain the boundary model of the electric vehicle cluster; The Monte Carlo sampling method is used to extract the charging load characteristic parameters of electric vehicles in the area, and the parameters are input into the boundary model of the electric vehicle cluster to obtain the boundary range. A multi-objective optimization function is established with the goal of minimizing the total load variance in the area and the total charging cost of the electric vehicle cluster. The initial number of electric vehicles and the number of basic loads are set, and the genetic algorithm is used to solve the multi-objective function. The maximum load rate under the current number of electric vehicles is calculated based on the optimal solution of the multi-objective optimization function. Gradually increase the number of electric vehicles in the substation area and update the maximum load rate, using the number of electric vehicles during overload as the maximum carrying capacity of electric vehicles in the substation area.

[0007] As a preferred implementation, the probability density function of the charging start time, charging end time and driving mileage is specifically: Probability density function of charging start time as follows:

[0008] In the formula, is the charging start time, coefficient =17.47, =3.41; Probability density function of charging end time as follows:

[0009] In the formula, is the charging end time, coefficient =8.92, =3.24; Probability density function of mileage as follows:

[0010] In the formula, is the mileage, coefficient =3.47, =0.88.

[0011] As a preferred embodiment, the time required for charging is determined based on the driving mileage. Specifically:

[0012] In the formula, is the mileage, The power consumption per 100 kilometers is is the charging power.

[0013] As a preferred implementation, the boundary model of energy accumulation and charging power of the uncontrollable electric vehicle is specifically: The uncontrollable electric vehicle is an electric vehicle that needs to be charged at rated power immediately after being connected to the charging pile. The set of all uncontrollable electric vehicles is , energy accumulation With charging power The boundaries are as follows:

[0014] In the formula, Electric vehicles belong to the collection , and Respectively The time when electric vehicles are connected to and off the grid, and Respectively The upper and lower bounds of energy accumulation of an electric vehicle at time t, and Respectively The upper and lower bounds of the power of an electric vehicle at time t; For the The rated power of an electric vehicle, is the sampling period of the uncontrollable electric vehicle; is the energy accumulation at the end of the previous sampling period, Specify energy requirements for off-grid use.

[0015] As a preferred implementation, the boundary model of energy accumulation and charging power of the controllable electric vehicle is specifically: The controllable electric vehicle is an electric vehicle that can delay charging time after connecting to the power grid and can control the start and stop of charging. The set of all controllable electric vehicles is , energy accumulation With charging power The boundaries are as follows:

[0016] In the formula, Electric vehicles belong to the collection , and Respectively The time when electric vehicles are connected to and off the grid, and Respectively The upper and lower bounds of energy accumulation of an electric vehicle at time t, and Respectively The upper and lower bounds of the power of an electric vehicle at time t; For the The rated power of an electric vehicle, is the sampling period of the controllable car; is the maximum energy demand of a controllable electric vehicle, Specify the energy requirements for controllable electric vehicles when off-grid.

[0017] As a preferred implementation, the steps of fusing the boundary models of two types of electric vehicles using the time series summation method to obtain the boundary model of the electric vehicle cluster are specifically as follows: Using vector groups To represent the boundary model of the electric vehicle cluster, it is divided into scheduling time area, energy accumulation boundary and power boundary model. Each model is as follows: The scheduling time zone model is as follows:

[0018] In the formula, , They are the earliest and latest times for orderly charging of electric vehicle clusters; The energy accumulation boundary model is as follows:

[0019] In the formula, , are the maximum and minimum energy accumulation values ​​of the electric vehicle cluster respectively; The power boundary model is as follows:

[0020] In the formula, , are the maximum and minimum power values ​​of the electric vehicle cluster respectively; Vector group Charging power for electric vehicle clusters.

[0021] As a preferred implementation, the multi-objective optimization function established with the goal of minimizing the total load variance of the substation area and minimizing the total charging cost of the electric vehicle cluster is specifically: The objective function is established with the minimum variance of the total load in the substation area as the goal. :

[0022] In the formula, is the orderly charging load of electric vehicles at time t, is the basic load; is the variance calculation; Establish an objective function with the goal of minimizing the total charging cost of electric vehicle clusters :

[0023] In the formula, is the time-of-use electricity price at time t; The objective function established with the goal of minimizing the total load variance of the substation area and the objective function established with the goal of minimizing the total charging cost of the electric vehicle cluster constitute a multi-objective optimization function, and the constraints are preset.

[0024] As a preferred implementation, the constraints are specifically: Charging power Within power boundaries: ; Accumulating energy Within the energy accumulation boundary: ; Outside the adjustment time range, the charging power is 0: ; Energy accumulation level constraint: In the scheduling time zone, accumulate energy Charging power Accumulated energy from the previous moment The constraint relationship; ; Transformer capacity constraint: During the charging process of electric vehicle clusters, the total load of orderly charging shall not exceed the maximum capacity of the transformer. ; .

[0025] As a preferred embodiment, the optimal solution based on the multi-objective optimization function calculates the maximum load rate under the current number of electric vehicles. Specifically: Genetic algorithm is used to solve the multi-objective function to obtain the optimal solution, which is the maximum value of the orderly charging load of electric vehicles in the substation. And the time t at which it is obtained, then according to the optimal solution Calculate the maximum load factor under the current number of electric vehicles , the specific calculation formula is as follows:

[0026] In the formula, is the rated load capacity of the area; It is the maximum value of the total load of the substation.

[0027] On the other hand, the present invention provides a quantitative evaluation system for the carrying capacity of electric vehicles in a station area, characterized by comprising: A charging load characteristic parameter construction module uses a probability density function to simulate the charging start time, charging end time and driving mileage of an electric vehicle; and determines the charging time required based on the driving mileage; the charging start time, charging end time and charging time required together constitute the charging load characteristic parameter; The boundary model building module divides electric vehicles into controllable electric vehicles and uncontrollable electric vehicles according to their charging methods, and establishes boundary models for energy accumulation and charging power respectively; then the boundary models of the two types of electric vehicles are integrated using the time series summation method to obtain the boundary model of the electric vehicle cluster; The initial feature extraction module uses the Monte Carlo sampling method to extract the charging load characteristic parameters of electric vehicles in the area, and inputs them into the boundary model of the electric vehicle cluster to obtain the boundary range; The maximum load calculation module establishes a multi-objective optimization function with the goal of minimizing the total load variance of the substation and the total charging cost of the electric vehicle cluster, sets the initial number of electric vehicles and the number of basic loads, and uses a genetic algorithm to solve the multi-objective optimization function; the maximum load rate under the current number of electric vehicles is calculated based on the optimal solution of the multi-objective optimization function; The maximum carrying capacity calculation module of the substation gradually increases the number of electric vehicles in the substation and updates the maximum load rate, using the number of electric vehicles when overloaded as the maximum carrying capacity of electric vehicles in the substation.

[0028] The present invention has the following beneficial effects: 1. The present invention uses the Monte Carlo sampling method to extract the charging load characteristic parameters of electric vehicles in the substation, which can fully consider the randomness and uncertainty of the parameters. Through a large number of random sampling, various possible charging scenarios can be simulated and input into the boundary model to obtain the boundary range, providing a reference for the substation to cope with load fluctuations in different situations, thereby enhancing the robustness of the system.

[0029] 2. The present invention simulates the charging start time, charging end time and driving mileage of electric vehicles through probability density functions, which can more realistically reflect the uncertainty and diversity of electric vehicle use in reality. The charging time is determined based on the driving mileage, and a reasonable connection between driving behavior and charging demand is established.

[0030] 3. The present invention divides electric vehicles into two categories, controllable and uncontrollable, according to the charging mode, and establishes boundary models of energy accumulation and charging power respectively, which can accurately characterize the impact of different types of electric vehicles on the load of the substation. The boundary models of the two types of electric vehicles are integrated using the time series summation method to obtain the boundary model of the electric vehicle cluster, which fully considers the load superposition of different types of electric vehicles in the time series. This helps to accurately grasp the load demand of the entire electric vehicle cluster on the substation at different times, and provide a reliable basis for the planning and operation of the substation.

[0031] 4. The present invention establishes a multi-objective optimization function with the goal of minimizing the total load variance of the substation and minimizing the total charging cost of the electric vehicle cluster, taking into account the operation stability of the substation and the charging cost of users. Minimizing the total load variance of the substation can reduce the damage to the power grid equipment caused by excessive load fluctuations, extend the life of the equipment, and reduce the operation and maintenance costs; minimizing the total charging cost of the electric vehicle cluster improves user satisfaction and promotes the promotion and application of electric vehicles.

[0032] 5. The present invention uses genetic algorithms to solve multi-objective functions and can search for optimal solutions in complex solution spaces. Genetic algorithms have global search capabilities and do not rely on the gradient information of the problem. They are suitable for processing such multi-objective, nonlinear optimization problems and help find a balance between station operation and user costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the steps of the present invention; Figure 2 Schematic diagrams of energy operation areas of two types of electric vehicles, where (a) is a schematic diagram of the energy operation area of ​​an uncontrollable charging vehicle, and (b) is a schematic diagram of the energy operation area of ​​a controllable charging vehicle; Figure 3Schematic diagrams of charging power boundaries of two types of electric vehicles, where (a) is a schematic diagram of the charging power boundary of an uncontrollable charging vehicle, and (b) is a schematic diagram of the charging power boundary of a controllable charging vehicle; Figure 4 Schematic diagram of the energy operation area and charging power boundary of an electric vehicle cluster, where (a) is a schematic diagram of the energy operation area of ​​an electric vehicle cluster, and (b) is a schematic diagram of the charging power boundary of an electric vehicle cluster; Figure 5 A schematic diagram for evaluating the acceptance capacity of electric vehicles in the area under disorderly charging; Figure 6 A schematic diagram for evaluating the acceptance capacity of electric vehicles in the area under orderly charging; Figure 7 Schematic diagram of the optimal dispatching scheme for electric vehicles under orderly charging; Figure 8 Schematic diagram of quantitative evaluation of the maximum access capacity of the area under different participation levels. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0036] It should be understood that the terms used in the description of the present invention are for the purpose of describing particular embodiments only and are not intended to limit the present invention.

[0037] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0038] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0039] Embodiment 1: See also Figure 1 , a quantitative evaluation method for the carrying capacity of electric vehicles in a station area, the specific steps include: The probability density function is used to simulate the charging start time, charging end time and driving mileage of the electric vehicle; and the charging time required is determined based on the driving mileage; the charging start time, charging end time and charging time required together constitute the charging load characteristic parameter; Electric vehicles are divided into controllable electric vehicles and uncontrollable electric vehicles according to their charging methods, and boundary models of energy accumulation and charging power are established respectively; then the boundary models of the two types of electric vehicles are fused using the time series summation method to obtain the boundary model of the electric vehicle cluster; The Monte Carlo sampling method is used to extract the charging load characteristic parameters of electric vehicles in the area, and the parameters are input into the boundary model of the electric vehicle cluster to obtain the boundary range. A multi-objective optimization function is established with the goal of minimizing the total load variance in the area and the total charging cost of the electric vehicle cluster. The initial number of electric vehicles and the number of basic loads are set, and the genetic algorithm is used to solve the multi-objective function. The maximum load rate under the current number of electric vehicles is calculated based on the optimal solution of the multi-objective optimization function. Gradually increase the number of electric vehicles in the substation area and update the maximum load rate, using the number of electric vehicles during overload as the maximum carrying capacity of electric vehicles in the substation area.

[0040] As a preferred implementation of this embodiment, the probability density function of the charging start time, charging end time and driving mileage is specifically: Probability density function of charging start time as follows:

[0041] In the formula, is the charging start time, coefficient =17.47, =3.41; Probability density function of charging end time as follows:

[0042] In the formula, is the charging end time, coefficient =8.92, =3.24; Probability density function of mileage as follows:

[0043] In the formula, is the mileage, coefficient =3.47, =0.88.

[0044] In this embodiment, the charging start and end times and the driving mileage of the electric vehicle on a typical day all approximately obey a normal distribution.

[0045] As a preferred implementation of this embodiment, the time required for charging is determined based on the mileage. Specifically:

[0046] In the formula, is the mileage, The power consumption per 100 kilometers is is the charging power.

[0047] In this embodiment, usually, the user will choose to charge the electric vehicle to full capacity, so the electric vehicle charging capacity is equal to its daily power consumption. By calculating the daily driving distance and the power consumption per 100 kilometers, the time required for charging the electric vehicle can be obtained.

[0048] As a preferred implementation of this embodiment, the uncontrollable electric vehicle is an electric vehicle that needs to be charged at rated power immediately after being connected to the charging pile. The set of all uncontrollable electric vehicles is , energy accumulation With charging power The boundaries are as follows:

[0049] In the formula, Electric vehicles belong to the collection , and Respectively The time when electric vehicles are connected to and off the grid, and Respectively The upper and lower bounds of energy accumulation of an electric vehicle at time t, and Respectively The upper and lower bounds of the power of an electric vehicle at time t; For the The rated power of an electric vehicle, is the sampling period of the uncontrollable electric vehicle; is the energy accumulation at the end of the previous sampling period, Specify energy requirements for off-grid use.

[0050] In this embodiment, since the uncontrollable electric vehicle cannot be adjusted, the upper and lower boundaries of energy accumulation and power are the same; the energy operation area and charging power boundary of the uncontrollable electric vehicle are respectively as follows: Figure 2 and Figure 3 As shown in the small figure (a) in .

[0051] As a preferred implementation of this embodiment, the boundary model of energy accumulation and charging power of the controllable electric vehicle is specifically: The controllable electric vehicle is an electric vehicle that can delay charging time after connecting to the power grid and can control the start and stop of charging. The set of all controllable electric vehicles is , energy accumulation With charging power The boundaries are as follows:

[0052] In the formula, Electric vehicles belong to the collection , and Respectively The time when electric vehicles are connected to and off the grid, and Respectively The upper and lower bounds of energy accumulation of an electric vehicle at time t, and Respectively The upper and lower bounds of the power of an electric vehicle at time t; For the The rated power of an electric vehicle, is the sampling period of the controllable car; is the maximum energy demand of a controllable electric vehicle, Specify the energy requirements for controllable electric vehicles when off-grid.

[0053] In this embodiment, the energy operation area and charging power boundary of the controllable electric vehicle are respectively as follows: Figure 2 and Figure 3 As shown in the small figure (b).

[0054] As a preferred implementation of this embodiment, using vector group To represent the boundary model of the electric vehicle cluster, it is divided into scheduling time area, energy accumulation boundary and power boundary model. Each model is as follows: The scheduling time zone model is as follows:

[0055] In the formula, , They are the earliest and latest times for orderly charging of electric vehicle clusters; The energy accumulation boundary model is as follows:

[0056] In the formula, , are the maximum and minimum energy accumulation values ​​of the electric vehicle cluster respectively; The power boundary model is as follows:

[0057] In the formula, , are the maximum and minimum power values ​​of the electric vehicle cluster respectively; Vector group Charging power for electric vehicle clusters.

[0058] In this embodiment, the energy accumulation boundary and power boundary of the controllable electric vehicle and the uncontrollable electric vehicle are respectively summed up in time series to obtain the energy accumulation boundary and power boundary of the electric vehicle cluster. The energy operation area and charging power boundary of the electric vehicle cluster are as follows: Figure 4 As shown, (a) is a schematic diagram of the energy operation area of ​​the electric vehicle cluster, and (b) is a schematic diagram of the charging power boundary of the electric vehicle cluster.

[0059] Depend on Figure 4 It can be seen that compared with a single electric vehicle, the upper and lower boundaries of energy accumulation of an electric vehicle cluster have become smooth curves, and the charging power boundary has also become a smooth curve, which brings great flexible and adjustable potential to orderly charging operation.

[0060] As a preferred implementation of this embodiment, the multi-objective optimization function established with the goal of minimizing the total load variance of the substation area and minimizing the total charging cost of the electric vehicle cluster is specifically: The objective function is established with the minimum variance of the total load in the substation area as the goal. :

[0061] In the formula, is the orderly charging load of electric vehicles at time t, is the basic load; is the variance calculation; Establish an objective function with the goal of minimizing the total charging cost of electric vehicle clusters :

[0062] In the formula, is the time-of-use electricity price at time t; The objective function established with the goal of minimizing the total load variance of the substation area and the objective function established with the goal of minimizing the total charging cost of the electric vehicle cluster constitute a multi-objective optimization function, and the constraints are preset.

[0063] In this embodiment, reducing the total load fluctuation in the substation area plays a key role in the economic and safe operation of the power grid. Therefore, in order to smooth out the load fluctuation, minimizing the total load variance in the substation area is set as the optimization goal on the power grid side.

[0064] Reducing the charging cost of EV users plays an important role in motivating users to participate in orderly scheduling. Therefore, in order to reduce the charging cost of electric vehicle users, the minimum total charging cost of the electric vehicle cluster is taken as the objective function.

[0065] After finding the optimal solution of the multi-objective optimization function, the minimum relaxation priority algorithm is used to allocate the charging power of the electric vehicle cluster under the optimal solution. The charging relaxation is defined as the difference between the remaining parking time of the electric vehicle and the minimum required charging time, as shown in the following formula:

[0066] The greater the charging slack, the lower the charging priority, and the smaller the charging slack, the higher the charging priority. The electric vehicles in the electric vehicle cluster are divided into groups 1-R in descending order. The charging slack in group R is the smallest and needs to be charged immediately. The specific allocation steps are as follows: Step 1: Assume that the maximum charging power of the R group of electric vehicles is , the remaining charging power is allocated The power is allocated to the second category of electric vehicles according to the charging priority order, and the remaining charging power is allocated The specific calculation formula is as follows:

[0067] In the formula, is the total charging power.

[0068] Step 2: Calculate the Maximum charging power of electric vehicles ,if , then Group electric vehicles to charge at rated power; calculate remaining charging distribution power , and then assign it to the 2 groups; correspondingly, if , then select the electric car in group R-1 The electric vehicle is charged at the rated power and the charging power allocation process of this period is ended.

[0069] As a preferred implementation of this embodiment, the constraint conditions are specifically: Charging power Within power boundaries: ; Accumulating energy Within the energy accumulation boundary: ; Outside the adjustment time range, the charging power is 0: ; Energy accumulation level constraint: In the scheduling time zone, accumulate energy Charging power Accumulated energy from the previous moment The constraint relationship; ; Transformer capacity constraint: During the charging process of electric vehicle clusters, the total load of orderly charging shall not exceed the maximum capacity of the transformer. ; .

[0070] Embodiment 2: Based on the method of Example 1, a simulation experiment was conducted, assuming that the transformer capacity of a residential community is 400KV·A, and the load rate is greater than 0.8, which is considered an overload situation. The charging piles in the community are all 7kW slow charging, and the fixed time-of-use electricity price division is shown in Table 1. Assume that the on-board battery capacity of the EV of the present invention is 63kW·h, the charging efficiency is 0.95, and the user's orderly charging participation is 0.8.

[0071] Table 1 Time-of-use electricity price settings

[0072] In order to verify the effectiveness of the proposed quantitative evaluation method, the acceptance capacity of electric vehicles in the substation is evaluated under two scenarios: disordered charging and orderly charging. The quantitative results are as follows: Figure 5 , Figure 6 The access capacity is defined as the ratio of the maximum total access load of electric vehicles to the capacity of the transformer in the substation.

[0073] from Figure 5 , Figure 6 It can be seen that when the double capacity is connected under disordered charging, the maximum load rate of the area exceeds 80%, which is in a heavy load operation state, and the power utilization rate of the area is less than 30%; when orderly charging control is used, the maximum load rate of the area does not exceed 79.5%, the power utilization rate of the area is higher than 55%, and the maximum access capacity is more than twice that of disordered charging. At the same time, the optimal scheduling scheme for electric vehicles under orderly charging is given as follows Figure 7 shown.

[0074] In order to verify the impact of orderly charging participation on the electric vehicle carrying capacity of residential communities, the maximum access capacity of the substation area is quantitatively evaluated under different participation levels, such as Figure 8 shown.

[0075] from Figure 8 It can be seen that with the increase of orderly charging participation, the accessible capacity of the substation area gradually increases. When the orderly charging participation reaches 0.6 or above, the accessible capacity exceeds 2 times the rated capacity, and reaches 2.80 times the rated capacity at 0.8 participation, significantly improving the carrying capacity of electric vehicles in residential communities.

[0076] Embodiment three: A quantitative evaluation system for the carrying capacity of electric vehicles in a substation area, comprising: A charging load characteristic parameter construction module uses a probability density function to simulate the charging start time, charging end time and driving mileage of an electric vehicle; and determines the charging time required based on the driving mileage; the charging start time, charging end time and charging time required together constitute the charging load characteristic parameter; The boundary model building module divides electric vehicles into controllable electric vehicles and uncontrollable electric vehicles according to their charging methods, and establishes boundary models for energy accumulation and charging power respectively; then the boundary models of the two types of electric vehicles are integrated using the time series summation method to obtain the boundary model of the electric vehicle cluster; The initial feature extraction module uses the Monte Carlo sampling method to extract the charging load characteristic parameters of electric vehicles in the area, and inputs them into the boundary model of the electric vehicle cluster to obtain the boundary range; The maximum load calculation module establishes a multi-objective optimization function with the goal of minimizing the total load variance of the substation and the total charging cost of the electric vehicle cluster, sets the initial number of electric vehicles and the number of basic loads, and uses a genetic algorithm to solve the multi-objective optimization function; the maximum load rate under the current number of electric vehicles is calculated based on the optimal solution of the multi-objective optimization function; The maximum carrying capacity calculation module of the substation gradually increases the number of electric vehicles in the substation and updates the maximum load rate, using the number of electric vehicles when overloaded as the maximum carrying capacity of electric vehicles in the substation.

[0077] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A quantitative evaluation method for the carrying capacity of electric vehicles in a station area, characterized in that: The specific steps include: The probability density function is used to simulate the charging start time, charging end time and driving mileage of the electric vehicle; and the charging time required is determined based on the driving mileage; the charging start time, charging end time and charging time required together constitute the charging load characteristic parameter; Electric vehicles are divided into controllable electric vehicles and uncontrollable electric vehicles according to their charging methods, and boundary models of energy accumulation and charging power are established respectively; then the boundary models of the two types of electric vehicles are fused using the time series summation method to obtain the boundary model of the electric vehicle cluster; The Monte Carlo sampling method is used to extract the charging load characteristic parameters of electric vehicles in the area, and the parameters are input into the boundary model of the electric vehicle cluster to obtain the boundary range. A multi-objective optimization function is established with the goal of minimizing the total load variance in the area and the total charging cost of the electric vehicle cluster. The initial number of electric vehicles and the number of basic loads are set, and the genetic algorithm is used to solve the multi-objective function. The maximum load rate under the current number of electric vehicles is calculated based on the optimal solution of the multi-objective optimization function. Gradually increase the number of electric vehicles in the substation area and update the maximum load rate, using the number of electric vehicles during overload as the maximum carrying capacity of electric vehicles in the substation area.

2. The quantitative evaluation method of electric vehicle carrying capacity in a station area according to claim 1 is characterized in that: The probability density function of the charging start time, charging end time and driving mileage is specifically: Probability density function of charging start time as follows: In the formula, is the charging start time, coefficient =17.47, =3.41; Probability density function of charging end time as follows: In the formula, is the charging end time, coefficient =8.92, =3.24; Probability density function of mileage as follows: In the formula, is the mileage, coefficient =3.47, =0.

88.

3. The quantitative evaluation method of electric vehicle carrying capacity in a platform area according to claim 1 is characterized in that: Determining the time required for charging based on the driving mileage Specifically: In the formula, is the mileage, The power consumption per 100 kilometers is is the charging power.

4. The quantitative evaluation method of electric vehicle carrying capacity in a station area according to claim 1 is characterized in that: The boundary model of energy accumulation and charging power of the uncontrollable electric vehicle is specifically: The uncontrollable electric vehicle is an electric vehicle that needs to be charged at rated power immediately after being connected to the charging pile. The set of all uncontrollable electric vehicles is , energy accumulation With charging power The boundaries are as follows: In the formula, Electric vehicles belong to the collection , and Respectively The time when electric vehicles are connected to and off the grid, and Respectively The upper and lower bounds of energy accumulation of an electric vehicle at time t, and Respectively The upper and lower bounds of the power of an electric vehicle at time t; For the The rated power of an electric vehicle, is the sampling period of the uncontrollable electric vehicle; is the energy accumulation at the end of the previous sampling period, Specify energy requirements for off-grid use.

5. The method for quantitatively evaluating the carrying capacity of electric vehicles in a station area according to claim 1, characterized in that: The boundary model of energy accumulation and charging power of the controllable electric vehicle is specifically: The controllable electric vehicle is an electric vehicle that can delay charging time after connecting to the power grid and can control the start and stop of charging. The set of all controllable electric vehicles is , energy accumulation With charging power The boundaries are as follows: In the formula, Electric vehicles belong to the collection , and Respectively The time when electric vehicles are connected to and off the grid, and Respectively The upper and lower bounds of energy accumulation of an electric vehicle at time t, and Respectively The upper and lower bounds of the power of an electric vehicle at time t; For the The rated power of an electric vehicle, is the sampling period of the controllable car; is the maximum energy demand of a controllable electric vehicle, Specify the energy requirements for controllable electric vehicles when off-grid.

6. The method for quantitatively evaluating the carrying capacity of electric vehicles in a station area according to claim 1, characterized in that: The specific steps of fusing the boundary models of two types of electric vehicles using the time series summation method to obtain the boundary model of the electric vehicle cluster are: Using vector groups To represent the boundary model of the electric vehicle cluster, it is divided into scheduling time area, energy accumulation boundary and power boundary model. Each model is as follows: The scheduling time zone model is as follows: In the formula, , They are the earliest and latest times for orderly charging of electric vehicle clusters; The energy accumulation boundary model is as follows: In the formula, , are the maximum and minimum energy accumulation values ​​of the electric vehicle cluster respectively; The power boundary model is as follows: In the formula, , are the maximum and minimum power values ​​of the electric vehicle cluster respectively; Vector group Charging power for electric vehicle clusters.

7. A quantitative evaluation method for the carrying capacity of electric vehicles in a platform area according to claim 6, characterized in that: The multi-objective optimization function established with the goal of minimizing the total load variance in the substation area and minimizing the total charging cost of the electric vehicle cluster is specifically: The objective function is established with the minimum variance of the total load in the substation area as the goal. : In the formula, is the orderly charging load of electric vehicles at time t, is the base load; is the variance calculation; Establish an objective function with the goal of minimizing the total charging cost of electric vehicle clusters : In the formula, is the time-of-use electricity price at time t; The objective function established with the goal of minimizing the total load variance of the substation area and the objective function established with the goal of minimizing the total charging cost of the electric vehicle cluster constitute a multi-objective optimization function, and the constraints are preset.

8. The method for quantitatively evaluating the carrying capacity of electric vehicles in a platform area according to claim 7, characterized in that: The constraints are specifically: Charging power Within power boundaries: ； Accumulating energy Within the energy accumulation boundary: ； Outside the adjustment time range, the charging power is 0: ； Energy accumulation level constraint: In the scheduling time zone, accumulate energy Charging power and the energy accumulated at the previous moment The constraint relationship; ； Transformer capacity constraint: During the charging process of electric vehicle clusters, the total load of orderly charging shall not exceed the maximum capacity of the transformer. ; 。 9. The method for quantitatively evaluating the carrying capacity of electric vehicles in a platform area according to claim 1, characterized in that: The optimal solution based on the multi-objective optimization function calculates the maximum load rate under the current number of electric vehicles Specifically: Genetic algorithm is used to solve the multi-objective function to obtain the optimal solution, which is the maximum value of the orderly charging load of electric vehicles in the substation. And the time t at which it is obtained, then according to the optimal solution Calculate the maximum load factor under the current number of electric vehicles , the specific calculation formula is as follows: In the formula, is the rated load capacity of the area; It is the maximum value of the total load of the substation.

10. A quantitative evaluation system for the carrying capacity of electric vehicles in a substation, characterized in that: include: The charging load characteristic parameter construction module uses the probability density function to simulate the charging start time, charging end time and driving mileage of electric vehicles; The time required for charging is determined based on the mileage; the charging start time, charging end time and the time required for charging together constitute a charging load characteristic parameter; The boundary model building module divides electric vehicles into controllable electric vehicles and uncontrollable electric vehicles according to their charging methods, and establishes boundary models for energy accumulation and charging power respectively; then the boundary models of the two types of electric vehicles are integrated using the time series summation method to obtain the boundary model of the electric vehicle cluster; The initial feature extraction module uses the Monte Carlo sampling method to extract the charging load characteristic parameters of electric vehicles in the area, and inputs them into the boundary model of the electric vehicle cluster to obtain the boundary range; The maximum load calculation module establishes a multi-objective optimization function with the goal of minimizing the total load variance of the substation and the total charging cost of the electric vehicle cluster, sets the initial number of electric vehicles and the number of basic loads, and uses a genetic algorithm to solve the multi-objective optimization function; the maximum load rate under the current number of electric vehicles is calculated based on the optimal solution of the multi-objective optimization function; The maximum carrying capacity calculation module of the substation gradually increases the number of electric vehicles in the substation and updates the maximum load rate, using the number of electric vehicles when overloaded as the maximum carrying capacity of electric vehicles in the substation.

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