Method and device for dispatching charging of electric vehicles in residential areas considering low-voltage distribution network

Abstract

The present invention provides a residential area electric vehicle charging scheduling method considering a low-voltage distribution network, comprising the following steps: obtaining electric vehicle behavior parameters, electric vehicle predicted demand, electric vehicle availability, battery charging predicted status and predicted electricity price; constructing a model that takes into account the uncertainty of electric vehicle user travel; constructing an electric vehicle charging scheduling optimization model and obtaining the constraints of the optimization model; linearizing the constructed electric vehicle charging scheduling optimization model and solving it to obtain the electric vehicle charging power. The present invention considers the errors caused by the uncertainty of arrival time and departure time to the simulation results, and proposes a low-voltage distribution network residential area EV charging scheduling that can reduce the errors caused by uncertainty, which can effectively achieve peak shaving and valley filling on the power grid side and reduce the total cost of residents.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage distribution network electric vehicle charging dispatching, in particular to a residential area electric vehicle charging dispatching method and device considering a low-voltage distribution network. Background Art

[0002] Electric vehicles (EVs) have been actively promoted by various countries under the circumstances of global warming, energy shortages, environmental protection needs and rapid technological development. Governments and researchers in various countries attach great importance to the development of EVs and related industries. Currently, major countries in the world have successively introduced medium- and long-term strategic plans for the development of EVs. With the increase in the number of EVs, large-scale EVs are connected to the power grid, and their charging and discharging behaviors have an unignorable impact on the planning, operation and operation of the power system and the operation of the power market. Therefore, it is necessary to plan and coordinate the charging and discharging behaviors of EVs to reduce the impact of charging and discharging on the low-voltage distribution network.

[0003] However, affected by many factors, charging load has complex characteristics. For a single vehicle, it is mainly determined by the user's travel needs, and is also affected by factors such as user usage habits and equipment characteristics. For the regional power system, it is also affected by the number of EVs and the degree of perfection of charging facilities. At the same time, there are many uncertain factors in the EV charging process, such as the uncertainty of user behavior and the intermittent access to renewable power energy. The existence of such uncertain factors will affect the modeling analysis, causing errors in the results, and then affecting the charging decision and the hard indicators of user charging. Therefore, how to reduce the impact of uncertain factors in charging scheduling is also very important.

[0004] In order to effectively reduce the negative impact of large-scale EV charging on the power grid, there have been many research results on orderly charging at home and abroad. The methods to achieve orderly EV charging can be mainly divided into two categories. The first is the direct charging load control method, in which the charging service operator directly controls the disconnection or charging power of the EV charging pile based on the EV charging demand and the load level of the distribution network; the second is the electricity price guidance method, in which the charging service provider provides time-sharing preferential electricity prices according to the power grid conditions, allowing users to respond independently according to charging needs and electricity price incentives to achieve the purpose of orderly charging. Summary of the invention

[0005] The present invention comprehensively considers the characteristics and uncertainty of EV user behavior, and proposes a charging scheduling method for residential EV robustness one day in advance in a low-voltage distribution network that can reduce the impact of uncertainty. First, the EV user behavior is randomly generated, and then an overall optimization model is established. At the same time, the uncertainty reduction method is applied, the model is solved using a linear programming algorithm, and the power flow is calculated on a low-voltage test feeder network. The proposed method can reduce the impact of uncertainty on decision-making and user hard indicators by reducing uncertainty, and realize the charging scheduling of EVs in residential areas of low-voltage distribution networks, reduce the total charging cost of residents, and achieve the effect of peak shaving and valley filling on the power grid side.

[0006] The present invention provides a residential area electric vehicle charging scheduling method considering a low-voltage distribution network, comprising the following steps:

[0007] Obtaining electric vehicle behavior parameters, electric vehicle forecast demand, electric vehicle availability and battery charging forecast status and forecast electricity price;

[0008] Construct a model that takes into account the travel uncertainty of electric vehicle users;

[0009] Construct an optimization model for electric vehicle charging scheduling and obtain the constraints of the optimization model;

[0010] The constructed electric vehicle charging scheduling optimization model is linearized and solved to obtain the electric vehicle charging power.

[0011] Furthermore, the method for obtaining the electric vehicle behavior parameters and the electric vehicle predicted demand is:

[0012] Get electric vehicle travel data and use normal distribution to describe electric vehicle mobility behavior:

[0013]

[0014] Where: N(μ,σ) is a normal distribution with expectation μ and standard deviation σ; is the random variable of daily mileage of a single electric vehicle; is the arrival time random variable of a single electric vehicle; is the departure time random variable of a single electric vehicle, and the mathematical expectation of the random variable is used As the predicted value for the next day;

[0015] When the electric vehicle arrives, its battery state of charge is It is a linear function of , and therefore also follows a normal distribution:

[0016]

[0017] Among them: B maxis the battery capacity when fully charged; ζ is the energy consumption per kilometer of the electric vehicle; is the battery state of charge of a single electric vehicle upon arrival.

[0018] Furthermore, the method for obtaining the availability of the electric vehicle is:

[0019] Electric vehicle availability refers to whether electric vehicles can be called by aggregators for charging scheduling. The specific formula is as follows:

[0020]

[0021] in: is the availability of an electric vehicle at time t; τ init is the time period from the start of optimization to time t, in minutes; T is the set of all time periods in the scheduling cycle; To round down;

[0022] The probability that the vehicle has arrived is P 1 , the probability that the vehicle has not left 2 They are:

[0023]

[0024]

[0025] Where: τ init is the period from the start of optimization to time t, in minutes; μ arr , σ arr is the mathematical expectation and standard deviation of the normal distribution of arrival time; μ dep , σ dep is the mathematical expectation and standard deviation of the normal distribution of departure time;

[0026] Then the probability that any electric car i is available at time t is:

[0027]

[0028] Furthermore, the method of constructing an electric vehicle charging scheduling optimization model and obtaining the constraint conditions of the optimization model is as follows:

[0029] There are n terminal nodes in the low-voltage distribution network in the residential area, all of which are residential users who purchase electric vehicles. With the goal of minimizing the total charging cost of all electric vehicles during the charging cycle, taking into account the charging constraints of electric vehicle owners and the voltage amplitude of low-voltage distribution network nodes, line current amplitude and root node three-phase unbalanced safety constraints, the following charging scheduling optimization model is established:

[0030]

[0031] Where: K is the set of user nodes; L is the set of all distribution lines; is the charging power of the kth electric vehicle in period t; Indicates the maximum charging power of a single electric vehicle; represents the availability of electric vehicles; η is the charging efficiency of electric vehicles; is the apparent power passing through the transformer; is the maximum apparent power of the transformer; δ is the conservative parameter of the unbalanced three-phase current at the root node; is the three-phase active power of the root node; is the average value of the three-phase active power of the root node.

[0032] Furthermore, the method of constructing a model that takes into account the uncertainty of electric vehicle users' travel is:

[0033] Whether each vehicle is available at each moment has a probability value, namely Therefore, we set a threshold v α ,when , the vehicle is considered available at time t, and set Indicates whether the availability probability at a certain moment is greater than the threshold v α , Defined as:

[0034]

[0035] in: When , the optimized scheduling assumes that the vehicle can be used for charging;

[0036] The uncertainty of the battery charge level is determined by the random value of the initial charge Based on deviation from expected value To alleviate: When the random value Greater than expected When the electric car is fully charged, it stops charging immediately; when the battery is initially charged Less than expected When the battery power level threshold v is set B This reduces the risk of the battery not being fully charged.

[0037] The residential area electric vehicle charging dispatching device considering the low voltage distribution network includes:

[0038] A data acquisition module, used to obtain electric vehicle behavior parameters, electric vehicle predicted demand, electric vehicle availability and battery charging predicted status and predicted electricity price;

[0039] The electric vehicle user travel uncertainty model building module is used to build a model that takes into account the travel uncertainty of electric vehicle users;

[0040] An electric vehicle charging scheduling optimization model building module is used to build an electric vehicle charging scheduling optimization model and obtain the constraint conditions of the optimization model;

[0041] The electric vehicle charging scheduling optimization model solving model is used to linearize the constructed electric vehicle charging scheduling optimization model and solve it to obtain the electric vehicle charging power.

[0042] A computing device comprising:

[0043] one or more processing units;

[0044] A storage unit for storing one or more programs,

[0045] Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the above-mentioned residential area electric vehicle charging scheduling method considering the low-voltage distribution network.

[0046] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein when the computer program is executed by the processor, the steps of the above-mentioned residential area electric vehicle charging scheduling method considering a low-voltage distribution network are implemented.

[0047] The advantages and positive effects of the present invention are:

[0048] The present invention is an EV charging scheduling for electric vehicle users in residential areas of low-voltage distribution networks. Currently, many charging scheduling methods rarely consider the errors caused by the uncertainty of arrival time and departure time in the actual simulation of the residential area. The present invention proposes EV charging scheduling for residential areas of low-voltage distribution networks that can reduce the errors caused by uncertainty, which can effectively realize peak shaving and valley filling on the grid side and reduce the total cost of residents. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, but it should be understood that these drawings are designed only for explanation purposes and are not intended to limit the scope of the present invention. In addition, unless otherwise specified, these drawings are intended only to conceptually illustrate the structural configurations described herein and are not necessarily drawn to scale.

[0050] Figure 1 A low-voltage distribution network feeder test network diagram according to an embodiment of the present invention;

[0051] Figure 2 The real-time electricity price prediction curve diagrams of three schemes of the embodiments of the present invention;

[0052] Figure 3The charging load curve diagrams of three schemes of the embodiments of the present invention;

[0053] Figure 4 It is a three-phase superimposed total load curve diagram of three schemes of an embodiment of the present invention;

[0054] Figure 5 The three-phase total load curve diagrams of the three schemes of the embodiments of the present invention are respectively;

[0055] Figure 6 It is a curve diagram of the total charging cost of residents in three schemes of the embodiment of the present invention;

[0056] Figure 7 For different v of the embodiment of the present invention B EV average final charge curve for the following situations. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limitations on the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0058] The retail package pricing method considering price-type demand response constructs a demand response model, analyzes the cost-benefit function of electricity retailers including electricity sales revenue, electricity purchase expenditure, response income, etc., and builds a retail package pricing model to provide decision support for retailers from the perspective of optimizing pricing.

[0059] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0060] The residential area electric vehicle charging scheduling method considering the low-voltage distribution network of the present invention comprises the following steps:

[0061] Obtaining electric vehicle behavior parameters, electric vehicle forecast demand, electric vehicle availability and battery charging forecast status and forecast electricity price;

[0062] Construct a model that takes into account the travel uncertainty of electric vehicle users;

[0063] Construct an optimization model for electric vehicle charging scheduling and obtain the constraints of the optimization model;

[0064] The constructed electric vehicle charging scheduling optimization model is linearized and solved to obtain the electric vehicle charging power.

[0065] Specifically, the method for obtaining the electric vehicle behavior parameters and the electric vehicle predicted demand is:

[0066] Get electric vehicle travel data and use normal distribution to describe electric vehicle mobility behavior:

[0067]

[0068] Where: N(μ,σ) is a normal distribution with expectation μ and standard deviation σ; is the random variable of daily mileage of a single electric vehicle; is the arrival time random variable of a single electric vehicle; is the departure time random variable of a single electric vehicle, and the mathematical expectation of the random variable is used As the predicted value for the next day;

[0069] When the electric vehicle arrives, its battery state of charge is It is a linear function of , and therefore also follows a normal distribution:

[0070]

[0071] Among them: B max is the battery capacity when fully charged; ζ is the energy consumption per kilometer of the electric vehicle; is the battery state of charge of a single electric vehicle upon arrival.

[0072] The method for obtaining the availability of the electric vehicle is as follows:

[0073] Electric vehicle availability refers to whether electric vehicles can be called by aggregators for charging scheduling. The specific formula is as follows:

[0074]

[0075] in: is the availability of an electric vehicle at time t; τ init is the time period from the start of optimization to time t, in minutes; T is the set of all time periods in the scheduling cycle; is rounded down; the above formula indicates that if time t falls within the EV charging period Inside, then Otherwise 0.

[0076] The probability that the vehicle has arrived is P 1 , the probability that the vehicle has not left 2 They are:

[0077]

[0078]

[0079] Where: τ init is the period from the start of optimization to time t, in minutes; μ arr , σ arr is the mathematical expectation and standard deviation of the normal distribution of arrival time; μ dep , σ dep is the mathematical expectation and standard deviation of the normal distribution of departure time;

[0080] Then the probability that any electric car i is available at time t is:

[0081]

[0082] Specifically, the method of constructing an electric vehicle charging scheduling optimization model and obtaining the constraint conditions of the optimization model is as follows:

[0083] There are n terminal nodes in the low-voltage distribution network in the residential area, all of which are residential users who purchase electric vehicles. With the goal of minimizing the total charging cost of all electric vehicles during the charging cycle, taking into account the charging constraints of electric vehicle owners and the voltage amplitude of low-voltage distribution network nodes, line current amplitude and root node three-phase unbalanced safety constraints, the following charging scheduling optimization model is established:

[0084]

[0085] Where: K is the set of user nodes; L is the set of all distribution lines; is the charging power of the kth electric vehicle in period t; represents the maximum charging power of a single electric vehicle, which is 3.7 kW in this embodiment; represents the availability of electric vehicles; η is the charging efficiency of electric vehicles; is the apparent power passing through the transformer; is the maximum apparent power of the transformer; δ is the conservative parameter of the unbalanced three-phase current of the root node, which determines the deviation of the output power of a certain phase from the average value of the three-phase output power. In this embodiment, δ=0.2; is the three-phase active power of the root node; is the average value of the three-phase active power of the root node.

[0086] The power flow equation can be used for modeling, but the nonlinear power flow equation makes the dispatch model complicated. Therefore, based on the idea of ​​sensitivity, a linear approximation is performed at the grid operation point under the basic load. The voltage sensitivity matrix ω∈R is introduced |K|×|K|, then the node voltage amplitude in the period t can be rewritten as a linear function of the charging power of each electric vehicle in this period:

[0087]

[0088] in: The base load is when electric vehicle load is not added. The voltage amplitude of node k in time period t obtained by power flow calculation; ω j,k It represents the change in voltage amplitude at node k caused by the change in unit load at node j.

[0089] Similarly, the current sensitivity matrix λ∈R is introduced |L|×3×|K| , then the line current amplitude in the period t can be rewritten as a linear function of the charging power of each electric vehicle in this period:

[0090]

[0091] in: Based on the base load when electric vehicle load is not yet added The current amplitude of line l in time period t obtained by power flow calculation; λ l,r,j It represents the change in the amplitude of the r-phase current of line l when the unit load of node j changes.

[0092] In summary, by introducing the sensitivity of node voltage amplitude and line current amplitude to the load changes of each node, the original electric vehicle scheduling model is transformed into a linear programming problem, which can be efficiently solved by a mature solver.

[0093] In addition, the method of constructing a model that takes into account the travel uncertainty of electric vehicle users is:

[0094] Since the arrival and departure time of each EV is unknown, in practice EVs do not necessarily follow the expected values ​​of their arrival and departure times. Arrival or departure, so directly use calculate There is an error. Since each car is available at each moment, there is a probability value, namely Therefore, we set a threshold v α ,when , the vehicle is considered available at time t, and set Indicates whether the availability probability at a certain moment is greater than the threshold v α , Defined as:

[0095]

[0096] in: When , the optimal scheduling assumes that the vehicle can be used for charging. Instead of the model The availability of EV can be adjusted by adjusting the threshold vα. For example, when we set v α = 0, the car is considered to be available at all times. However, in reality, the available interval of the vehicle is likely to be narrower, and the battery power may not reach the expected power when picking up the car. When v is set α =1, it is considered that the car is only available in a very narrow range. In reality, the available range of the car basically includes this narrow range. The solution will be more conservative and the battery will definitely be fully charged.

[0097] Similarly, the actual daily mileage δ mil Not necessarily the expected value The same, because the initial charge B arr The uncertainty of the battery charging level may cause the final power of the electric vehicle in practice to be inconsistent with the final power calculated by the dispatcher. Based on deviation from expected value To alleviate: When the random value Greater than expected When the electric car is fully charged, it stops charging immediately; when the battery is initially charged Less than expected When the battery power level threshold v is set B This reduces the risk of the battery not being fully charged.

[0098] The charging dispatching device for electric vehicles in residential areas considering low-voltage distribution network includes:

[0099] A data acquisition module, used to obtain electric vehicle behavior parameters, electric vehicle predicted demand, electric vehicle availability and battery charging predicted status and predicted electricity price;

[0100] The electric vehicle user travel uncertainty model building module is used to build a model that takes into account the travel uncertainty of electric vehicle users;

[0101] An electric vehicle charging scheduling optimization model building module is used to build an electric vehicle charging scheduling optimization model and obtain the constraint conditions of the optimization model;

[0102] The electric vehicle charging scheduling optimization model solving model is used to linearize the constructed electric vehicle charging scheduling optimization model and solve it to obtain the electric vehicle charging power.

[0103] A computing device comprising:

[0104] one or more processing units;

[0105] A storage unit for storing one or more programs,

[0106] Among them, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the residential electric vehicle charging scheduling method considering the low-voltage distribution network in this embodiment; it should be noted that the computing device may include but is not limited to a processing unit and a storage unit; those skilled in the art can understand that the computing device including a processing unit and a storage unit does not constitute a limitation on the computing device, and may include more components, or a combination of certain components, or different components. For example, the computing device may also include input and output devices, network access devices, buses, etc.

[0107] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein when the computer program is executed by the processor, the steps of the residential electric vehicle charging scheduling method considering a low-voltage distribution network in this embodiment are implemented; it should be noted that the readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above; the program contained on the readable medium can be transmitted using any appropriate medium, including, but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. For example, the program code for performing the operation of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as C language or similar programming languages. The program code can be executed entirely on a user computing device, partially on a user device, as an independent software package, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0108] As an example, in this embodiment, the IEEE low voltage test feeder network test is used, and the single line diagram is as shown in the attached Figure 1 As shown in the figure, it includes 55 single-phase user nodes, which are evenly distributed on each phase. It is assumed that each family has an EV, which is only charged at home overnight. The EV charging efficiency is η = 0.93, the maximum charging power is 3.7kW, the battery capacity is Bmax = 30kWh, the consumption rate is ζ = 0.17kWh / km, the charging time range is 24 hours starting at 1:00 p.m. every day, and the charging scheduling cycle is 15min.

[0109] Three schemes were designed for comparative testing: Scheme 1 is disordered charging, that is, EVs are not subject to any form of coordinated control and are charged at maximum power upon arrival; Scheme 2 uses a linear programming ordered charging algorithm to optimize the entire model without considering uncertainty; Scheme 3 uses a linear programming ordered charging algorithm to optimize the entire model, but also uses the uncertainty consideration method proposed in this method, in which the availability probability threshold v α =0.6, and the battery power level threshold v B =0.7.

[0110] The calculation results of the three schemes are shown in the attached figure. Figure 2 For real-time electricity price forecast; Figure 3 , 4 For the above three schemes, the charging load of EVs on the entire line changes with time and the total load of the entire line changes with time. As can be seen from the figure, during disordered charging, the charging load is basically concentrated in the 12-24 period, which is from 4 pm to 7 pm, which is the peak period for users to charge after get off work. After the implementation of optimized scheduling, the charging load is transferred to the 48-72 period, which is from 1 am to 7 am, which is the night period. From the perspective of total load, the load peaks and valleys are reasonably transferred. The curves of Scheme 2 and Scheme 3 basically overlap, but the charging load distribution of Scheme 3 is more reasonable than that of Scheme 2, which verifies the effectiveness of considering uncertainty in this method.

[0111] Attached Figure 5 The three-phase total load comparison diagram of the above three schemes. In this patent, the algorithm adopts constraints to address the three-phase current imbalance that may be caused by AC charging. It can be seen from the figure that the three-phase total load curves of disordered charging are quite different, while the three-phase total load curves after considering the three-phase imbalance constraint are closer, and the three-phase imbalance problem has been improved. Figure 6 is the total cost of the above three solutions. It can be seen from the figure that the total charging cost of users in each period is reduced after using charging optimization scheduling. Figure 7 is the average final EV charging capacity of the community in all time periods. Without considering uncertainty for optimal scheduling, its actual final battery charging capacity is about 50%. When considering uncertainty, the battery power level threshold is set to v B = 0.7, and then optimize the scheduling, the actual final battery charging capacity is about 70%; at the same time, set v B When is other values, the battery power can reach the set final battery power threshold. It can be obtained from the figure that when the error caused by uncertainty to the user’s hard indicators is considered, the battery power state level threshold v B As a threshold, it can reduce the errors caused by this uncertainty and improve user satisfaction.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. Method for obtaining charging power of electric vehicles in residential areas of low-voltage distribution networks, Characterized in that, It includes the following steps: Obtain electric vehicle behavior parameters, electric vehicle predicted demand, electric vehicle availability, battery charging prediction status, and predicted electricity price; Construct a model considering the travel uncertainty of electric vehicle users based on the obtained electric vehicle behavior parameters, electric vehicle predicted demand, and electric vehicle availability data; Construct an electric vehicle charging scheduling optimization model based on the obtained electric vehicle availability, battery charging prediction status, predicted electricity price data, and the constructed travel uncertainty model, and obtain the constraint conditions of this optimization model; Linearize the constructed electric vehicle charging scheduling optimization model based on the constraint conditions of the optimization model and solve it to obtain the electric vehicle charging power; The method for obtaining the electric vehicle behavior parameters and electric vehicle predicted demand is: Obtain electric vehicle travel data and use normal distribution to describe the movement behavior of electric vehicles: Where: N(μ,σ) is a normal distribution with expectation μ and standard deviation σ; is the random variable of daily mileage of a single electric vehicle; is the arrival time random variable of a single electric vehicle; is the departure time random variable of a single electric vehicle, and the mathematical expectation of the random variable is used As the predicted value for the next day; σ arr is the standard deviation of the normal distribution of arrival times; σ dep is the standard deviation of the departure time; When the electric vehicle arrives, its battery state of charge is It is a linear function of , and therefore also follows a normal distribution: Among them: B max is the battery capacity when fully charged; ζ is the energy consumption per kilometer of the electric vehicle; The battery state of charge of a single electric vehicle upon arrival; The method for obtaining the electric vehicle availability is: The electric vehicle availability is whether the electric vehicle can be called by the aggregator for charging scheduling. The specific formula is as follows: in: is the availability of an electric vehicle at time t; τ init is the time period from the start of optimization to time t, in minutes; T is the set of all time periods in the scheduling cycle; To round down; The probability that the vehicle has arrived is P 1 , the probability that the vehicle has not left 2 They are: Where: μ arr , σ arr is the mathematical expectation and standard deviation of the normal distribution of arrival time; μ dep , σ dep is the mathematical expectation and standard deviation of the normal distribution of departure time; Then the probability that any electric vehicle i is available at time t is: The method for constructing an electric vehicle charging scheduling optimization model and obtaining the constraint conditions of this optimization model is: There are n terminal nodes in the low-voltage distribution network in the residential area, all of which are residential users who purchase electric vehicles. With the goal of minimizing the total charging cost of all electric vehicles during the charging cycle, taking into account the charging constraints of electric vehicle owners and the voltage amplitude of low-voltage distribution network nodes, line current amplitude and root node three-phase unbalanced safety constraints, the following charging scheduling optimization model is established: Where: K is the set of user nodes; L is the set of all distribution lines; is the charging power of the kth electric vehicle in period t; Indicates the maximum charging power of a single electric vehicle; represents the availability of electric vehicles; η is the charging efficiency of electric vehicles; is the apparent power passing through the transformer; is the maximum apparent power of the transformer; δ is the conservative parameter of the unbalanced three-phase current at the root node; and is the three-phase active power of the root node; is the average value of the three-phase active power of the root node; The method for constructing a model considering the travel uncertainty of electric vehicle users is: Whether each vehicle is available at each moment has a probability value, namely Therefore, we set a threshold v α ,when , the vehicle is considered available at time t, and set Indicates whether the availability probability at a certain moment is greater than the threshold v α , Defined as: in: When , the optimized scheduling assumes that the vehicle can be used for charging; The uncertainty of the battery charging level is through a random value of the initial charge Based on the deviation from the expected value To mitigate: when the random value of the initial charge Is greater than the deviation from the expected value The electric vehicle stops charging immediately once the battery is full; when the random value of the initial charge Is less than the deviation from the expected value A battery state of charge level threshold v B Is set to reduce the risk of the battery not being fully charged.

2. Device for obtaining charging power of electric vehicles in residential areas of low-voltage distribution networks, Characterized in that, It includes: A data acquisition module for obtaining electric vehicle behavior parameters, electric vehicle predicted demand, electric vehicle availability, battery charging prediction status, and predicted electricity price; An electric vehicle user travel uncertainty model establishment module for constructing a model considering the travel uncertainty of electric vehicle users based on the obtained electric vehicle behavior parameters, electric vehicle predicted demand, and electric vehicle availability data; An electric vehicle charging scheduling optimization model construction module for constructing an electric vehicle charging scheduling optimization model based on the obtained electric vehicle availability, battery charging prediction status, predicted electricity price data, and the constructed travel uncertainty model, and obtaining the constraint conditions of this optimization model; An electric vehicle charging scheduling optimization model solving model for linearizing the constructed electric vehicle charging scheduling optimization model based on the constraint conditions of the optimization model and solving it to obtain the electric vehicle charging power; The method for obtaining the electric vehicle behavior parameters and electric vehicle predicted demand is: Obtain electric vehicle travel data and use normal distribution to describe the movement behavior of electric vehicles: Where: N(μ,σ) is a normal distribution with expectation μ and standard deviation σ; is the random variable of daily mileage of a single electric vehicle; is the arrival time random variable of a single electric vehicle; is the departure time random variable of a single electric vehicle, and the mathematical expectation of the random variable is used As the predicted value for the next day; σ arr is the standard deviation of the normal distribution of arrival times; σ dep is the standard deviation of the departure time; When the electric vehicle arrives, its battery state of charge is It is a linear function of , and therefore also follows a normal distribution: Among them: B max is the battery capacity when fully charged; ζ is the energy consumption per kilometer of the electric vehicle; The battery state of charge of a single electric vehicle upon arrival; The method for obtaining the electric vehicle availability is: The electric vehicle availability is whether the electric vehicle can be called by the aggregator for charging scheduling. The specific formula is as follows: in: is the availability of an electric vehicle at time t; τ init is the time period from the start of optimization to time t, in minutes; T is the set of all time periods in the scheduling cycle; To round down; The probability that the vehicle has arrived is P 1 , the probability that the vehicle has not left 2 They are: Where: μ arr , σ arr is the mathematical expectation and standard deviation of the normal distribution of arrival time; μ dep , σ dep is the mathematical expectation and standard deviation of the normal distribution of departure time; Then the probability that any electric vehicle i is available at time t is: The method for constructing an electric vehicle charging scheduling optimization model and obtaining the constraint conditions of this optimization model is: There are n terminal nodes in the low-voltage distribution network in the residential area, all of which are residential users who purchase electric vehicles. With the goal of minimizing the total charging cost of all electric vehicles during the charging cycle, taking into account the charging constraints of electric vehicle owners and the voltage amplitude of low-voltage distribution network nodes, line current amplitude and root node three-phase unbalanced safety constraints, the following charging scheduling optimization model is established: Where: K is the set of user nodes; L is the set of all distribution lines; is the charging power of the kth electric vehicle in period t; Indicates the maximum charging power of a single electric vehicle; represents the availability of electric vehicles; η is the charging efficiency of electric vehicles; is the apparent power passing through the transformer; is the maximum apparent power of the transformer; δ is the conservative parameter of the unbalanced three-phase current at the root node; and is the three-phase active power of the root node; is the average value of the three-phase active power of the root node; The method for constructing a model considering the travel uncertainty of electric vehicle users is: Whether each vehicle is available at each moment has a probability value, namely Therefore, we set a threshold v α ,when , the vehicle is considered available at time t, and set Indicates whether the availability probability at a certain moment is greater than the threshold v α , Defined as: in: When , the optimized scheduling assumes that the vehicle can be used for charging; The uncertainty of the battery charge level is determined by the random value of the initial charge Based on deviation from expected value To alleviate: When the random value of the initial power Greater than the expected deviation When the electric car is fully charged, it stops charging immediately; when the random value of the initial power Less than the expected deviation When the battery power level threshold v is set B This reduces the risk of the battery not being fully charged.

3. A computing device, Characterized in that: It includes: One or more processing units; A storage unit for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the method as claimed in claim 1.

4. A computer-readable storage medium having a non-volatile program code executable by a processor, It is characterized in that When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

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