Electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response

Abstract

The invention belongs to the technical field of active power distribution network demand side electric vehicle load optimization scheduling, and particularly relates to an electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response. Comprising the following steps: analyzing charging load demands of the electric vehicles to obtain data sets with different characteristics, and uniformly scheduling and managing the electric vehicles with similar characteristics; carrying out improved electric quantity and electricity price elastic matrix price type demand response modeling to obtain an electric vehicle load model based on improved electric quantity and electricity price elastic matrix demand response; electric vehicle load optimization scheduling modeling based on improved elastic electricity price demand response; and solving the electric vehicle load optimization scheduling model based on the improved elastic electricity price demand response to obtain the lowest operation cost of the power distribution network. According to the invention, charging of the electric vehicle is effectively scheduled, peak load shifting of a load curve is realized, the system stability is improved, an electric quantity unit price elastic matrix is improved, and the charging demand of the electric vehicle is correctly reflected to price change.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle load optimization scheduling on the demand side of an active distribution network, and in particular relates to an electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response. Background Art

[0002] With the development of social economy, electric vehicles are gradually replacing fuel vehicles. Compared with traditional fuel vehicles, electric vehicles have huge advantages in energy conservation, emission reduction and green travel. It is an inevitable trend for electric vehicles to be the choice of future travel mode.

[0003] At present, the number of electric vehicles and the penetration rate of distributed power sources are increasing year by year. The disorderly charging of large-scale electric vehicles connected to the distribution network will bring about many problems such as increased load peak and voltage instability.

[0004] Therefore, it is urgent to reasonably dispatch the electric vehicle load and distributed power sources in the power grid, improve the operation status of the power grid, alleviate the power load in the power grid, effectively manage the charging load, and realize load peak shaving and valley filling. To build a smart power city, electric vehicles must be charged in an orderly manner first, so the optimal dispatch of electric vehicle loads is particularly important. Summary of the invention

[0005] In view of the above-mentioned shortcomings in the prior art, such as the increasing number of electric vehicles in the market year by year, the disorderly charging of large-scale electric vehicles connected to the distribution network will lead to an increase in load peaks and voltage instability, the present invention provides an electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response. The purpose is to achieve the purpose of the invention.

[0006] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0007] The electric vehicle load optimization dispatching strategy based on improved flexible electricity price demand response includes the following steps:

[0008] Analyze the charging load demand of electric vehicles, obtain data sets with different characteristics, and unify the scheduling and management of electric vehicles with similar characteristics;

[0009] Improve the price-type demand response modeling of the electricity quantity and price elasticity matrix, and obtain the electric vehicle load model after the demand response based on the improved electricity quantity and price elasticity matrix;

[0010] Modeling of optimal dispatch of electric vehicle load based on improved flexible electricity price demand response;

[0011] The electric vehicle load optimization dispatching model based on improved flexible electricity price demand response is solved to obtain the minimum operating cost of the distribution network.

[0012] Furthermore, the analysis of the electric vehicle charging load demand obtains a data set with different characteristics; including:

[0013] Analyze the travel patterns of electric vehicles, including the probability density function of the time when electric vehicles start charging and when charging ends;

[0014] Analyze the time required to charge an electric vehicle for travel, including assuming that electric vehicle users have the same driving habits as ordinary fuel vehicle users, and the daily mileage follows a log-normal distribution, and calculate the probability density function;

[0015] Cluster analysis is performed on the charging and discharging behaviors of electric vehicles to obtain data sets with different characteristics.

[0016] Furthermore, the analysis of electric vehicle travel patterns includes:

[0017] The probability density function of the time when electric vehicles start charging and end charging is as follows:

[0018]

[0019]

[0020] In the above formula: μ s The mean of the normal distribution probability density at the start of charging, μ e The mean of the normal distribution of the probability density at the end of charging, σ s is the variance of the normal distribution of the probability density of the charging time, σ e is the variance of the normal distribution of the probability density at the end of charging, μ s =17.5,μ e =9.25;σ s =3.5;σ e =3.18, f s is the normal distribution of the charging start time, f e is the normal distribution of the charging end time, x is the time;

[0021] The analysis of the time required for charging an electric vehicle includes:

[0022] Assuming that electric vehicle users have the same driving habits as ordinary fuel vehicle users, the daily mileage follows a log-normal distribution, and its probability density function is as follows:

[0023]

[0024] In the formula, the mean μ s =3.3, variance σ s =0.88;

[0025] Combined with the user's travel characteristics, the required charging time is calculated as shown in the following formula:

[0026]

[0027] Where: S s i SOC is the initial state of charge of the electric vehicle; S n i is the desired state of charge SOC; E s i is the battery capacity; η i c is the charging efficiency, P c i is the charging power;

[0028] The cluster analysis of the charging and discharging behavior of the electric vehicle includes:

[0029] The parameters of each electric vehicle are obtained through Monte Carlo sampling: the time of access to the grid T s,i , Off-grid time T e,i , end the state of charge SOCe,i, and obtain all the electric vehicle data sets X = {x 1 ,x 2 ,...xn}, normalize the data to the extreme value, use formula (5) to calculate the Euclidean distance between data points, and then calculate the Gaussian distance between two points according to formula (6), and then form the similarity matrix K:

[0030] D ij =||x i -x j || (5)

[0031]

[0032] In the above formula: D ij is the Euclidean distance between points i and j, K ij is the Gaussian distance between points i and j, x i is the mileage of tram i, x j is the mileage of tram j;

[0033] Construct the Laplace matrix L according to the similarity matrix K, and further construct the regularized Laplace matrix Ls; calculate the first i largest eigenvalues ​​of the Laplace matrix Ls, and the eigenvector {u 1 u 2 ...u i}; Using the matrix B formed by normalizing the eigenvectors, each row of the matrix B is regarded as a point, and k-means is used to divide it into i clusters; the output limit of the aggregator, the time of disconnection from the grid, and the state of charge at the end are used as characteristic states to form cluster samples. After k-means clustering is completed, different clusters represent different charging and discharging behaviors of electric vehicles, and the cluster center of each cluster represents the typical characteristics of the charging and discharging behavior of this type of electric vehicle.

[0034] Furthermore, the improved electricity quantity and price elasticity matrix price-based demand response modeling includes: price-based demand response and incentive-based demand response;

[0035] The price-based demand response includes: time-of-use electricity price, real-time electricity price and peak electricity price;

[0036] The incentive-based demand response includes: direct load control, interruptible load and demand-side bidding.

[0037] Furthermore, the method for constructing the price-based demand response-improved electricity quantity and price elasticity matrix is ​​as follows:

[0038] Elasticity factor The elasticity influence factor α is defined as: the weight of the impact of the change in electricity price in a certain period of time on the change in electricity consumption in a certain period of time; α ij Reflects the load response ΔP generated by the change in electricity price in period j in period i i The proportion of the total power change in time period i is expressed as:

[0039] ΔP ij =α ij ΔP i (7)

[0040] The electric vehicle load is divided according to the transferable period length T, and the load in each time period is divided into:

[0041]

[0042] Where P i,T is the power consumption of the electric vehicle load in time period i with a transferable time period of T, P i is the electric vehicle load in time period i.

[0043] In the time-of-use electricity price, the load with a transferable period of T is transferred to the diagonal side of the elasticity matrix. The total length of the transfer period is 2T-1, and the self-elasticity impact weight is:

[0044]

[0045] In the above formula, The load of electric vehicles in the transferable period T accounts for the total load P in the period i.i The ratio of n is the number of time periods, and T is the transferable time period;

[0046] The mutual elasticity coefficient influence weight is:

[0047]

[0048] After introducing the elastic influence right, the improved self-elasticity coefficient, mutual elasticity coefficient and electricity quantity and price elasticity matrix are:

[0049]

[0050]

[0051]

[0052] In the above formula, ε′ ii is the elastic coefficient, ε′ ij is the mutual elasticity coefficient, E′ is the improved electricity quantity and price elasticity matrix;

[0053] The relationship between the change rate of electricity price and the change rate of electricity consumption of load peak and valley time-of-use electricity price is expressed as:

[0054]

[0055] Where ΔP EV is the load response of electric vehicles, P 1 ,P 2 …P n is the original load before response, ΔC is the electricity price response, C 1 ,C 2 …C n The original time-of-use electricity price;

[0056] The electric vehicle load after demand response based on the improved electricity quantity and price elasticity matrix is ​​obtained as follows:

[0057]

[0058] Where: P′ EV,n is the charging amount of electric vehicles after the demand response of the electricity quantity and electricity price elasticity matrix at time n.

[0059] Furthermore, the electric vehicle load optimization dispatch modeling based on improved elastic electricity price demand response includes:

[0060] (1) The objective function is established as follows:

[0061]

[0062] Where: C PV , CWT are the operation and maintenance costs of distributed photovoltaic and wind turbines, C DR ,t is the demand response cost of electric vehicles at time t, C B,t is the main grid electricity purchase cost at time t, T is the dispatch period;

[0063] Including: distributed power generation operation and maintenance costs, electric vehicle demand response costs and main grid electricity purchase costs;

[0064] (2) The constraints of the multi-objective optimization scheduling model include: main grid power purchase constraints, demand response constraints, electricity price constraints, distributed generation output constraints, electricity quantity and price elasticity matrix constraints, and power balance constraints.

[0065] Furthermore, the operation and maintenance cost of the distributed power source is expressed as:

[0066] C=cP

[0067] C WT =C mt P WT,t (17)

[0068] In the above formula, c mt P is the distributed generation maintenance cost coefficient PV is the photovoltaic output at time t, P WT is the fan output at time t, C PV is the photovoltaic operation and maintenance cost, C WT The operation and maintenance cost of the wind turbine;

[0069] The electric vehicle demand response cost is expressed as:

[0070] C DR,t =c DR,t ΔP EV,t (18)

[0071] In the above formula: C DR,t is the EV demand response cost, c DR is the cost coefficient of demand response, ΔP EV,t is the change in charging load of electric vehicles participating in demand response at time t;

[0072] The main grid electricity purchase cost is expressed as:

[0073] C B,t =λ t P G,t (19)

[0074] In the above formula: C B,t is the cost of purchasing electricity for the main grid, λ t is the electricity purchase price at time t, P G,tis the total load of the distribution network at time t;

[0075] The main grid purchase electricity constraint is expressed as:

[0076] 0≤P B,t ≤P B max (20)

[0077] In the above formula: P B max The maximum amount of electricity purchased by the main network, P B,t The power purchased by the main grid at time t;

[0078] The demand response constraint is expressed as:

[0079]

[0080]

[0081] In the above formula, ΔP EV,t is the change in electric vehicle charging load at time t, is the minimum value of the change in electric vehicle charging load, It is the maximum value of the change in charging load of electric vehicles;

[0082] The electricity price constraint is expressed as:

[0083] Δc min ≤Δc t ≤Δc max (twenty three)

[0084] In the above formula: Δc t is the change in the charging price of electric vehicles at time t; Δc min , Δc max are the minimum and maximum changes in the price of electric vehicle charging, respectively;

[0085] The output constraint of the distributed generation is expressed as:

[0086]

[0087] In the above formula: are the maximum outputs of photovoltaic and wind turbine, respectively, PV,t is the photovoltaic output at time t, P WT,t is the output of the fan at time t;

[0088] The electricity quantity and electricity price elasticity matrix constraint is that the response of the electric vehicle load in time period t satisfies the following electricity quantity and electricity price elasticity matrix constraint:

[0089]

[0090] In the above formula: ΔP EV,t is the change in charging load of electric vehicles participating in demand response at time t, E′ is the improved elastic coefficient matrix, ΔC t is the electricity price response during period t, C t is the electricity price at time t, P EV,t is the load size of the electric vehicle at time t;

[0091] The power balance constraint is expressed as:

[0092] P B,t +P PV,t +P WT,t =P L,t +P EV,t -ΔP EV,t (26)

[0093] In the above formula: P L,t The base load does not participate in demand response, ΔP EV,t is the charging load response of electric vehicles in period t, P B,t is the power purchased by the main grid at time t.

[0094] Furthermore, the electric vehicle load optimization dispatching model based on improved elastic electricity price demand response is solved to obtain the minimum operating cost of the distribution network, including:

[0095] Initialization: The charging load data of a single electric vehicle is obtained through the Monte Carlo method, and the initial charging load data of all electric vehicles is accumulated. The electric vehicles are clustered and uniformly managed and dispatched, and then the photovoltaic, wind turbine and basic load data under typical days are initialized;

[0096] Modeling: Establish an electric vehicle load optimization dispatch model based on improved elastic electricity price demand response, optimize the dispatch of electric vehicle loads, main grid purchase power, photovoltaic and wind turbine outputs that participate in electricity price demand response, and thus build an operation model;

[0097] Solution: By calling the solver to solve the electric vehicle load optimization scheduling model based on improved flexible electricity price demand response, the electric vehicle charging load, main grid purchase power, photovoltaic output and wind turbine output under typical days are obtained, and the minimum operating cost of the distribution network is calculated.

[0098] An electric vehicle load optimization dispatching device based on improved flexible electricity price demand response includes:

[0099] The analysis module is used to analyze the charging load demand of electric vehicles, obtain data sets with different characteristics, and uniformly dispatch and manage electric vehicles with similar characteristics;

[0100] The demand response modeling module is used to carry out price-type demand response modeling based on the improved electricity quantity and electricity price elasticity matrix, and obtain the electric vehicle load model after demand response based on the improved electricity quantity and electricity price elasticity matrix;

[0101] Optimal dispatch modeling module, used for modeling the optimal dispatch of electric vehicle loads based on improved elastic electricity price demand response;

[0102] The solution module is used to solve the electric vehicle load optimization scheduling model based on improved flexible electricity price demand response to obtain the minimum operating cost of the distribution network.

[0103] Furthermore, the electric vehicle load optimization scheduling device based on improved elastic electricity price demand response is used to implement any step of the electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response.

[0104] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned electric vehicle load optimization scheduling strategies based on improved elastic electricity price demand response are implemented.

[0105] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the electric vehicle load optimization scheduling strategies based on improved elastic electricity price demand response are implemented.

[0106] The present invention has the following beneficial effects and advantages:

[0107] The present invention adopts an electric vehicle load optimization scheduling strategy that improves the elastic electricity price demand response. While ensuring the economical and stable operation of the power system, effective scheduling of electric vehicle charging can achieve peak shaving and valley filling of the load curve to increase the stability of the system.

[0108] The traditional electricity quantity and electricity price elasticity matrix cannot accurately reflect the impact of other time price periods on the changes in electric vehicle load within the current time period. The present invention introduces the elasticity coefficient influencing factor into the traditional electricity quantity and electricity price elasticity matrix model, improves the electricity unit price elasticity matrix, and realizes a more reasonable and correct reflection of electric vehicle charging demand to price changes, further improves the scheduling of electric vehicles, and further realizes the peak shaving and valley filling of the load curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0110] Figure 1 It is a probability density distribution diagram of the electric vehicle grid connection and grid disconnection time of the present invention;

[0111] Figure 2 is a probability density distribution diagram of daily mileage of an electric vehicle of the present invention;

[0112] Figure 3 It is a typical daily distributed power output diagram in the present invention;

[0113] Figure 4 It is a typical daily electric vehicle load and basic load diagram in the present invention;

[0114] Figure 5 It is an optimized dispatch diagram of electric vehicle load demand response according to the present invention;

[0115] Figure 6 is a diagram of the optimized dispatching result of the distribution network in a typical day in the present invention; DETAILED DESCRIPTION

[0116] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0117] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0118] Refer to the following Figure 1-Figure 6 The technical solutions of some embodiments of the present invention are described.

[0119] Example 1

[0120] The present invention provides an embodiment, which is an electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response. Figure 1 As shown, Figure 1 This is the probability density distribution diagram of the electric vehicle grid connection and off-grid time of the present invention. The present invention first analyzes the large-scale electric vehicle load demand based on the Monte Carlo simulation sampling method; then establishes an electric vehicle demand response model based on the improved electricity quantity and price elasticity matrix, comprehensively considers the charging demand of electric vehicles and the operation constraints of the distribution network, and formulates an electric vehicle load optimization scheduling strategy based on the improved elastic electricity price demand response with the goal of minimizing the daily operating cost.

[0121] The present invention specifically comprises the following steps:

[0122] Step 1. Analyze the charging load demand of electric vehicles, obtain data sets with different characteristics, and uniformly dispatch and manage electric vehicles with similar characteristics;

[0123] Step 2. Improve the demand response modeling based on the electricity quantity and electricity price elasticity, and obtain the electric vehicle load model after the demand response based on the improved electricity quantity and electricity price elasticity matrix;

[0124] Step 3. Modeling of optimal dispatch of electric vehicles under distribution network;

[0125] Step 4. Solve the electric vehicle load optimization dispatching model based on improved elastic electricity price demand response established in the above three steps to obtain the electric vehicle load model after improved electricity quantity and price elasticity matrix demand response.

[0126] Furthermore, the step 1 analyzes the electric vehicle charging load demand to obtain a data set with different characteristics, as follows:

[0127] 1. Analyze the travel patterns of electric vehicles.

[0128] The study of electric vehicle travel patterns is the basis for the implementation of dispatch strategies. When residents in urban communities use fuel vehicles or electric vehicles, the travel characteristics are less affected. The following is the probability density function of the start and end times of electric vehicle charging based on the 2009 U.S. family car travel survey:

[0129]

[0130]

[0131] In the above formula: μ s is the mean of the normal distribution of the probability density of the start charging time, μ e is the mean of the normal distribution of the probability density at the end of charging, σ s is the variance of the normal distribution of the probability density of the charging time, σ e is the variance of the normal distribution of the probability density at the end of charging, μ s =17.5,μ e =9.25;σ s =3.5;σ e =3.18, f s is the normal distribution of the charging start time, f e is the normal distribution of the charging end time, and x is the time.

[0132] 2. Analyze the time required to charge an electric vehicle for travel.

[0133] Considering that electric vehicles are a relatively new means of transportation and their sample size is still small, the present invention assumes that electric vehicle users have the same driving habits as ordinary fuel vehicle users, and the daily mileage follows a log-normal distribution, and its probability density function is shown as follows:

[0134]

[0135] In the above formula, the mean μ s =3.3, variance σ s = is 0.88.

[0136] Combined with the user's travel characteristics, the required charging time can be calculated as shown in the following formula:

[0137]

[0138] In the above formula: S s i SOC is the initial state of charge of the electric vehicle; S n i is the desired state of charge SOC; E s i is the battery capacity; η i c is the charging efficiency, P c i is the charging power.

[0139] 3. Cluster analysis of the charging and discharging behavior of electric vehicles.

[0140] The parameters of each electric vehicle are obtained through Monte Carlo sampling: the time of access to the grid T s,i , Off-grid time T e,i , end the state of charge SOCe,i, and obtain all the electric vehicle data sets X = {x 1 ,x 2 ,...xn}, which represent the demand information of electric vehicles. The data is normalized to the extreme value, and the Euclidean distance between data points is calculated according to formula (5). Then, the Gaussian distance between two points is calculated according to formula (6), and then the similarity matrix K is constructed.

[0141] D ij =||x i -x j || (5)

[0142]

[0143] In the above formula: D ij is the Euclidean distance between points i and j, K ij is the Gaussian distance between points i and j, x i is the mileage of tram i, x j is the mileage of tram j.

[0144] Construct the Laplacian matrix L according to the similarity matrix K, and further construct the regularized Laplacian matrix Ls. Calculate the first i largest eigenvalues ​​of the Laplacian matrix Ls and their corresponding eigenvectors {u 1 u 2 ...u i}. Using the matrix B formed by normalizing the eigenvectors, each row of the matrix B is regarded as a point, and k-means is used to divide it into i clusters. The output limit, off-grid time, and charge state at the end of the aggregator are used as characteristic states to form cluster samples. After k-means clustering is completed, different clusters represent different charging and discharging behaviors of electric vehicles, and the cluster center of each cluster represents the typical characteristics of the charging and discharging behavior of this type of electric vehicle.

[0145] Cluster analysis of the charging and discharging behaviors of electric vehicles not only takes into account the modeling of each electric vehicle to obtain a data set with different characteristics, but also takes into account the advantages of unified management of the cluster. Electric vehicles with similar characteristics are uniformly scheduled and managed, which can make the optimization result obtained in step 4 more accurate.

[0146] Furthermore, in step 2, demand response modeling is performed according to the obtained optimization results to obtain an electric vehicle load model after demand response based on the improved electricity quantity and electricity price elasticity matrix, which is as follows:

[0147] Demand response includes: price-based demand response and incentive-based demand response.

[0148] The price-based demand response includes: time-of-use electricity price, real-time electricity price and peak electricity price;

[0149] Among them, the incentive-based demand response includes: direct load control, interruptible load and demand-side bidding.

[0150] The most commonly used is price-based demand response, which guides load demand through price. Users adjust their electricity usage time according to their own needs, reducing load demand during high electricity price periods and increasing load demand during low electricity price periods, thereby achieving the effect of peak shaving and valley filling.

[0151] By guiding the changes in electric vehicle charging load through prices, the charging behavior of electric vehicles can be actively changed according to charging price signals or incentive mechanisms, guiding users to shift charging time periods and load demands, improving the temporal and spatial distribution of electric vehicle loads, and enabling users to reduce load demands during peak periods of electricity consumption and increase load demands during low periods of electricity consumption. This can not only meet users' daily charging needs, while reducing their charging costs, but also reduce the operating costs of the distribution network and improve the flexibility and economy of the grid operation.

[0152] The method for constructing the price-based demand response-improved electricity quantity and price elasticity matrix is ​​as follows:

[0153] The present invention adopts price-based demand response to construct an improved electricity price elasticity matrix to describe the electric vehicle charging load response model. In the traditional elastic electricity price matrix model, the load response ΔP of time period i is i is the change in electricity price over all periods of time, ΔC j The influence of electricity price changes in time period i on the electricity consumption in this time period is significantly greater than that in other time periods. Therefore, in order to make a more accurate prediction, the present invention introduces a flexible influence weight factor to distinguish the importance of electricity prices in different time periods, so as to reduce the effect on time periods other than time period i.

[0154] Elasticity factor The elasticity impact factor α is defined as the weight of the impact of changes in electricity prices during a certain period of time on changes in electricity consumption during a certain period of time. ij Reflects the load response ΔP generated by the change in electricity price in period j in period i i The proportion of the total power change in time period i is expressed as:

[0155] ΔP ij =α ij ΔP i (7)

[0156] Dividing the electric vehicle load according to the transferable time period length T, the load in each time period can be divided into:

[0157]

[0158] In the above formula, P i,T is the power consumption of the electric vehicle load in time period i with a transferable time period of T, P i is the electric vehicle load in time period i.

[0159] In the time-of-use electricity price, the load with a transferable period of T can be transferred to the diagonal side of the elasticity matrix, and the total length of the transfer period is 2T-1. Therefore, the self-elasticity impact weight is:

[0160]

[0161] In the above formula, The load of electric vehicles in the transferable period T accounts for the total load P in the period i. i The ratio of n is the number of time periods, and T is the transferable time period.

[0162] In this way, the mutual elasticity coefficient influence weight is:

[0163]

[0164] After introducing the elastic influence right, the improved self-elasticity coefficient, mutual elasticity coefficient and electricity quantity and price elasticity matrix are:

[0165]

[0166]

[0167]

[0168] In the above formula, ε i ' i is the elastic coefficient, ε i ' j is the mutual elasticity coefficient, and E′ is the improved electricity quantity and price elasticity matrix.

[0169] The relationship between the price change rate of load peak and valley time-of-use electricity prices and the power consumption change rate can be expressed as:

[0170]

[0171] In the above formula, ΔP EV is the load response of electric vehicles, P 1 ,P 2 …P n is the original load before response. ΔC is the electricity price response, C 1 ,C 2 …C n is the original time-of-use electricity price. Therefore, the electric vehicle load after demand response based on the improved electricity quantity and price elasticity matrix is:

[0172]

[0173] In the above formula: P E ' V,n is the charging amount of electric vehicles after the demand response of the electricity quantity and electricity price elasticity matrix at time n.

[0174] Furthermore, the optimal dispatch modeling of electric vehicles under the distribution network described in step 3 is as follows:

[0175] Based on the model of step 2 above, in order to prevent the "peak on peak" phenomenon caused by electric vehicle charging, minimize the peak-to-valley difference of system load, improve the load rate of distribution network, and promote the economic operation of distribution network, the present invention selects the lowest operating cost of active distribution network with large-scale electric vehicle access as the optimization control target, takes the output power of distributed power source, the main network purchase power and the charging power of electric vehicle as the control objects, and establishes the following optimization scheduling model. First, the objective function is established as follows:

[0176]

[0177] In the above formula: C PV , C WT are the operation and maintenance costs of distributed photovoltaic and wind turbines, C DR,t is the demand response cost of electric vehicles at time t, C B,t is the main grid electricity purchase cost at time t, and T is the dispatch period.

[0178] (1) Operation and maintenance costs of distributed power generation.

[0179] The distributed power generation of the distribution network needs to be maintained on time to ensure normal operation. The specific expression of its operation and maintenance cost is:

[0180] C PV =c mt P PV,t

[0181] C WT =c mt P WT,t (17)

[0182] In the above formula, c mt P is the distributed generation maintenance cost coefficient PV is the photovoltaic output at time t, P WT is the output of the fan at time t, C PV is the photovoltaic operation and maintenance cost, C WT The operation and maintenance cost of the wind turbine.

[0183] (2) Electric vehicle demand response cost.

[0184] The electric vehicle charging price is used to guide users to shift charging time periods and their load demand, thereby effectively reducing charging costs and improving the time distribution of electric vehicle loads. Then the cost of price-based demand response is specifically expressed as:

[0185] C DR,t =c DR ΔP EV,t (18)

[0186] In the above formula: C DR,t is the EV demand response cost, c DR is the cost coefficient of demand response, ΔP EV,t is the change in charging load of electric vehicles participating in demand response at time t.

[0187] (3) Cost of purchasing electricity from the main grid.

[0188] When the output of distributed power generation does not meet the load demand, additional power needs to be purchased to meet the load demand. The specific expression is:

[0189] CB,t =λ t P G,t (19)

[0190] In the above formula: C B,t is the cost of purchasing electricity for the main grid, λ t is the electricity purchase price at time t, P G,t is the total load of the distribution network at time t.

[0191] In order to make the model converge within a reasonable range, the constraints of the multi-objective optimization scheduling model are as follows:

[0192] (1) Main grid electricity purchase restrictions.

[0193] Considering the load carrying capacity of the power transmission line, the input power of the distribution network is limited and can only be regulated within a certain range. The constraints that need to be met are expressed as follows:

[0194] 0≤P B,t ≤P B max (20)

[0195] In the above formula: P B max The maximum amount of electricity purchased by the main network, P B,t The power purchased by the main grid at time t;

[0196] (2) Demand response constraints.

[0197] The change of charging load participating in demand response is limited, and the load change is 0 during the entire scheduling period. The expression is:

[0198]

[0199]

[0200] In the above formula, ΔP EV,t is the change in electric vehicle charging load at time t, is the minimum value of the change in electric vehicle charging load, It is the maximum value of the change in charging load of electric vehicles.

[0201] (3) Electricity price constraints.

[0202] The demand response of electric vehicle charging load is guided by price. In order to prevent the electricity price from being too high or too low, the charging price changes within a certain range. The price change needs to meet the following constraints:

[0203] Δc min ≤Δc t ≤Δc max (twenty three)

[0204] In the above formula: Δc t is the change in the charging price of electric vehicles at time t; Δc min , Δc max are the minimum and maximum changes in electric vehicle charging prices, respectively.

[0205] (4) Distributed power generation output constraints.

[0206]

[0207] In the above formula: are the maximum outputs of photovoltaic and wind turbine, respectively, PV,t is the photovoltaic output at time t, P WT,t is the output of the fan at time t.

[0208] (5) Electricity quantity and price elasticity matrix constraints.

[0209] The response of electric vehicle load in period t must satisfy the following electricity quantity and price elasticity matrix constraints:

[0210]

[0211] In the above formula: ΔP EV,t is the change in charging load of electric vehicles participating in demand response at time t, E′ is the improved elastic coefficient matrix, ΔC t is the electricity price response during period t, C t is the electricity price at time t, P EV,t is the load size of the electric vehicle at time t.

[0212] (6) Power balance constraint.

[0213] The power balance constraint that needs to be met in the distribution network can be expressed as:

[0214] P B,t +P PV,t +P WT,t =P L,t +P EV,t -ΔP EV,t (26)

[0215] In the above formula: P L,t The base load does not participate in demand response, ΔP EV,t is the charging load response of electric vehicles in period t, P B,t is the power purchased by the main grid at time t.

[0216] Furthermore, step 4 solves the electric vehicle load optimization scheduling model based on improved elastic electricity price demand response to obtain the minimum operating cost of the distribution network. The method proposed in the above steps selects the electric vehicle load, photovoltaic, wind power, etc. under the actual distribution network in a certain area as the research object. In the environment of Matlab 2023a, since the model is a mixed integer linear programming problem, the solver Gurobi can be called to solve and calculate the electric vehicle load optimization scheduling model based on improved elastic electricity price demand response.

[0217] The present invention solves the electric vehicle load optimization scheduling model based on improved elastic electricity price demand response, and the specific steps are as follows:

[0218] Step (1) Initialization:

[0219] Based on the model of electric vehicle charging load demand, first, the charging load data of a single electric vehicle is obtained through the Monte Carlo method, and then the initial data of the charging load of all electric vehicles is accumulated. The electric vehicles are clustered and uniformly managed and scheduled, and then the typical solar photovoltaic, wind turbine and basic load data are initialized.

[0220] Step (2) Modeling:

[0221] An electric vehicle load optimization dispatching model based on improved flexible electricity price demand response is established to optimize the dispatching of electric vehicle loads, main grid purchased electricity, photovoltaic and wind turbine outputs that participate in electricity price demand response, thereby constructing an operation model.

[0222] Solution: By calling the solver to solve the electric vehicle load optimization scheduling model based on improved flexible electricity price demand response, the electric vehicle charging load, main grid purchase power, photovoltaic output and wind turbine output under typical days are obtained, and the minimum operating cost of the distribution network is calculated.

[0223] Example 2

[0224] The present invention further provides an embodiment, which is an electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response. The present invention takes the actual distribution network of a certain place as an example, and verifies the rationality and innovation of the proposed strategy through MATLAB2023 and the Gurobi solver. A small distribution network in a certain area is selected as the research carrier. The distribution network is equipped with a photovoltaic power generation system with an installed capacity of 800KW and a 1MW wind power generation system. The disorderly charged private electric vehicles under the distribution network are optimized and scheduled. It is assumed that there are 300 private electric vehicles in the area. The specific parameters of the electric vehicles are as shown in Table 1 below:

[0225] Table 1 Specific parameters of electric vehicles

[0226] Charge and discharge power (kw) Battery capacity (kw.h) Charge and discharge efficiency Discharge Depth Power consumption (kw.h / km) 3.6 35 0.9 0.8 0.25

[0227] On a typical day, the wind turbines and photovoltaic power generation in the distribution network are as follows: Figure 3 shown.

[0228] The specific implementation method described in step 1 in Example 1 is:

[0229] Step 1: Conduct demand analysis modeling on the charging load of private electric vehicles. It can be seen from the analysis that the charging behavior characteristics of a large number of electric vehicles approximately conform to a certain probability model. Therefore, a random statistical method can be used to describe the charging load characteristics of large-scale electric vehicles. The present invention uses the Monte Carlo random sampling method to sample and simulate the total charging demand of large-scale electric vehicles connected to the power grid.

[0230] Assuming that the charging time, charging power, and charging method of each electric vehicle are not affected by other electric vehicles, that is, they are independent of each other, based on the Monte Carlo sampling analysis method, according to the random probability distribution of each electric vehicle's driving conditions, battery characteristics, charging time, and charging method, the corresponding random numbers are generated to obtain the daily charging load curve of a single electric vehicle. By accumulating the charging load curve of each electric vehicle, the total charging load curve of 300 electric vehicles on a typical day under the distribution network can be obtained. The charging load of electric vehicles on a typical day and the basic load are as follows: Figure 4 shown.

[0231] The demand response modeling based on the improved electricity quantity and price elasticity matrix described in step 2 of Example 1 is based on the time-of-use electricity price. The industrial time-of-use electricity price in a certain place is shown in Table 2.

[0232] Table 2 Industrial time-of-use electricity prices in a certain place

[0233] Time Time period Electricity Prices Peak hours 9:00-11:00,17:00-22:00 1.08 Normal period 8:00,12:00-16:00,23:00 0.6 Valley period 24:00-7:00 0.288

[0234] According to the experience of research on the elastic demand response of electric vehicle charging electricity and electricity prices, a day is divided into 24 time periods. The electricity and electricity price coefficient matrix is ​​actually a 24*24 square matrix. The electricity and electricity price elasticity coefficients of users in the area can be obtained as shown in Table 3.

[0235] Table 3 Electricity Price Elasticity Coefficient

[0236] ε Valley bottom Valley Time usually peak Valley bottom -0.15 0.03 0.06 0.04 Valley Time 0.03 -0.15 0.02 0.06 usually 0.06 0.02 -0.15 0.03 peak 0.04 0.06 0.03 -0.15

[0237] The specific implementation method described in step 3 of Example 1 is:

[0238] Based on the model of the above steps, in order to prevent the phenomenon of "peak on peak" caused by electric vehicle charging, minimize the peak-to-valley difference of system load, improve the load rate of distribution network, and promote the economic operation of distribution network, the present invention selects the lowest comprehensive operating cost of active distribution network with large-scale electric vehicle access as the optimization control target, and establishes an optimization scheduling mathematical model with the constraints of electricity quantity and price elastic matrix, power grid power balance constraints, distributed power supply processing constraints, and the output power of distributed power supply as constraints, as well as the main network purchase electricity and electric vehicle charging power as control objects, which is specifically given in step 3 of the invention content.

[0239] The specific implementation method of step 4 in Example 1 is:

[0240] Through the method proposed in the above steps, the actual distribution network in a certain area is selected as the research object. In the environment of Matlab 2023a, since the model is a mixed integer linear programming problem, the solver Gurobi can be called to solve the electric vehicle load optimization scheduling model based on the improved elastic electricity price demand response to obtain the original charging power of electric vehicles in a certain area and the charging power of electric vehicles after response, such as Figure 5 shown.

[0241] Depend on Figure 5 It can be seen that the flexible electricity price obtained on the basis of the time-of-use electricity price will guide the transfer of electric vehicle charging load. The peak value of electric vehicle charging load after the response is lower than the original load, and the charging load during the valley is higher than the original load, thereby realizing peak shaving and valley filling and optimizing the scheduling of electric vehicle charging load to avoid peaks on peaks. The characteristic indicators of electric vehicle load curves before and after demand response are shown in Table 4:

[0242] Table 4 Electric vehicle load curve characteristic index table

[0243] Scenario Maximum load / KW Minimum load / KW Average load / KW Peak-to-valley difference rate (%) Before demand response 246.52 3.25 112.23 98.67 After demand response 189.46 6.13 113.45 96.56

[0244] According to Table 4, the peak value and peak-to-valley difference of the typical day electric vehicle charging load after the improved elastic electricity price demand response are significantly reduced. The typical day load peak value is reduced by 57.06KW compared with before the demand response, and the peak-to-valley difference rate is reduced by 2.11% compared with before the demand response. The valley value of the typical day load is also increased from 3.25KW before the response to 6.13KW after the demand response. The average load has not changed much compared with the original. This is because the daily electric vehicle charging demand will not change. To a certain extent, peak shaving and valley filling are achieved, which is also beneficial for electric vehicle owners to save charging expenses. A win-win situation is achieved for the power grid and power users.

[0245] In addition, the optimal dispatch results of the entire distribution network on a typical day are as follows: Figure 6 shown.

[0246] Example 3

[0247] The present invention further provides an embodiment, which is an electric vehicle load optimization dispatching device based on improved elastic electricity price demand response, comprising:

[0248] The analysis module is used to analyze the charging load demand of electric vehicles and obtain data sets with different characteristics;

[0249] The demand response modeling module is used to carry out price-type demand response modeling based on the improved electricity quantity and electricity price elasticity matrix, and obtain the electric vehicle load model after demand response based on the improved electricity quantity and electricity price elasticity matrix;

[0250] Optimal dispatch modeling module, used for modeling the optimal dispatch of electric vehicle loads based on improved elastic electricity price demand response;

[0251] The solution module is used to solve the electric vehicle load optimization scheduling model based on improved flexible electricity price demand response to obtain the minimum operating cost of the distribution network.

[0252] The electric vehicle load optimization scheduling device based on improved elastic electricity price demand response described in this embodiment is used to implement the steps of the electric vehicle load optimization scheduling method based on improved elastic electricity price demand response described in any one of Examples 1-2.

[0253] Example 4

[0254] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of any one of the electric vehicle load optimization scheduling strategies based on improved elastic electricity price demand response described in Embodiment 1 or 2 are implemented.

[0255] Example 5

[0256] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the electric vehicle load optimization scheduling strategies based on improved flexible electricity price demand response described in Example 1 or 2 are implemented.

[0257] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0258] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0259] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0260] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0261] 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response is characterized by: include: Analyze the charging load demand of electric vehicles, obtain data sets with different characteristics, and unify the scheduling and management of electric vehicles with similar characteristics; Improve the price-type demand response modeling of the electricity quantity and price elasticity matrix, and obtain the electric vehicle load model after the demand response based on the improved electricity quantity and price elasticity matrix; Modeling of optimal dispatch of electric vehicle load based on improved flexible electricity price demand response; The electric vehicle load optimization dispatching model based on improved flexible electricity price demand response is solved to obtain the minimum operating cost of the distribution network.

2. The electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response according to claim 1 is characterized by: The analysis of the electric vehicle charging load demand obtains a data set with different characteristics; including: Analyze the travel patterns of electric vehicles, including the probability density function of the time when electric vehicles start charging and when charging ends; Analyze the time required to charge an electric vehicle for travel, including assuming that electric vehicle users have the same driving habits as ordinary fuel vehicle users, and the daily mileage follows a log-normal distribution, and calculate the probability density function; Cluster analysis is performed on the charging and discharging behaviors of electric vehicles to obtain data sets with different characteristics.

3. The electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response according to claim 2 is characterized by: The analysis of the travel rules of electric vehicles includes: the probability density function of the time when the electric vehicle starts charging and ends charging is as follows: In the above formula: μ s The mean of the normal distribution probability density at the start of charging, μ e The mean of the normal distribution of the probability density at the end of charging, σ s is the variance of the normal distribution of the probability density of the charging time, σ e is the variance of the normal distribution of the probability density at the end of charging, μ s =17.5,μ e =9.25;σ s =3.5;σ e =3.18, f s is the normal distribution of the charging start time, f e is the normal distribution of the charging end time, x is the time; The analysis of the time required for charging an electric vehicle includes: assuming that electric vehicle users and ordinary fuel vehicle users have the same driving habits, the daily mileage obeys a log-normal distribution, and the probability density function is as follows: In the above formula, the mean μ s =3.3, variance σ s =0.88; Combined with the user's travel characteristics, the required charging time is calculated as shown in the following formula: In the above formula: S s i SOC is the initial state of charge of the electric vehicle; S n i is the desired state of charge SOC; E s i is the battery capacity; η i c is the charging efficiency, P c i is the charging power; The cluster analysis of the charging and discharging behaviors of electric vehicles is performed to obtain data sets with different characteristics, including: The parameters of each electric vehicle are obtained through Monte Carlo sampling: the time of access to the grid T s,i , Off-grid time T e,i , end the state of charge SOCe,i, and obtain all the tram data sets X = {x1, x2, ... xn}. Normalize the data to the extreme value, use formula (5) to calculate the Euclidean distance between data points, and then calculate the Gaussian distance between two points according to formula (6) to form a similarity matrix K: D ij =||x i -x j || (5) In the above formula: D ij is the Euclidean distance between points i and j, K ij is the Gaussian distance between points i and j, x i is the mileage of tram i, x j is the mileage of tram j; Construct the Laplace matrix L according to the similarity matrix K, and further construct the regularized Laplace matrix Ls; calculate the first i largest eigenvalues ​​of the Laplace matrix Ls, and the eigenvector {u1 u2 ... u i }; Using the matrix B formed by normalizing the eigenvectors, each row of the matrix B is regarded as a point, and k-means is used to divide it into i clusters; the output limit of the aggregator, the time of disconnection from the grid, and the state of charge at the end are used as characteristic states to form cluster samples. After k-means clustering is completed, different clusters represent different charging and discharging behaviors of electric vehicles, and the cluster center of each cluster represents the typical characteristics of the charging and discharging behavior of this type of electric vehicle.

4. The electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response according to claim 1 is characterized by: The improved electricity quantity and price elasticity matrix price-based demand response modeling includes: price-based demand response and incentive-based demand response; The price-based demand response includes: time-of-use electricity price, real-time electricity price and peak electricity price; The incentive-based demand response includes: direct load control, interruptible load and demand-side bidding.

5. The electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response according to claim 4 is characterized by: The method for constructing the price-based demand response-improved electricity quantity and price elasticity matrix is ​​as follows: Elasticity factor The elasticity influence factor α is defined as: the weight of the impact of the change in electricity price in a certain period of time on the change in electricity consumption in a certain period of time; α ij Reflects the load response ΔP generated by the change in electricity price in period j in period i i The proportion of the total power change in time period i is expressed as: ΔP ij =a ij ·ΔP i (7) The electric vehicle load is divided according to the transferable period length T, and the load in each time period is divided into: Where P i,T is the power consumption of the electric vehicle load in time period i with a transferable time period of T, P i is the electric vehicle load in time period i; In the time-of-use electricity price, the load with a transferable period of T is transferred to the diagonal side of the elasticity matrix. The total length of the transfer period is 2T-1, and the self-elasticity impact weight is: In the above formula, The load of electric vehicles in the transferable period T accounts for the total load P in the period i. i The ratio of n is the number of time periods, and T is the transferable time period; The mutual elasticity coefficient influence weight is: After introducing the elastic influence right, the improved self-elasticity coefficient, mutual elasticity coefficient and electricity quantity and price elasticity matrix are: In the above formula, ε′ii is the self-elasticity coefficient, ε′ij is the mutual elasticity coefficient, and E′ is the improved electricity quantity and price elasticity matrix; The relationship between the change rate of electricity price and the change rate of electricity consumption of load peak and valley time-of-use electricity price is expressed as: Where ΔP EV is the load response of electric vehicles, P1, P2…P n is the original load before response, ΔC is the electricity price response, C1, C2…C n The original time-of-use electricity price; The electric vehicle load after demand response based on the improved electricity quantity and price elasticity matrix is ​​obtained as follows: Where: P E ' V,n is the charging amount of electric vehicles after the demand response of the electricity quantity and electricity price elasticity matrix at time n.

6. The electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response according to claim 1 is characterized by: The electric vehicle load optimization dispatch modeling based on improved elastic electricity price demand response includes: (1) The objective function is established as follows: Where: C PV , C WT are the operation and maintenance costs of distributed photovoltaic and wind turbines, C DR,t is the demand response cost of electric vehicles at time t, C B,t is the main grid electricity purchase cost at time t, T is the dispatch period; Including: distributed power generation operation and maintenance costs, electric vehicle demand response costs and main grid electricity purchase costs; (2) The constraints of the multi-objective optimization scheduling model include: main grid power purchase constraints, demand response constraints, electricity price constraints, distributed generation output constraints, electricity quantity and price elasticity matrix constraints, and power balance constraints.

7. The electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response according to claim 6 is characterized by: The distributed power generation operation and maintenance cost is expressed as: C PV =c mt P PV，t C WT =c mt P WT，t (17) In the above formula, c mt P is the distributed generation maintenance cost coefficient PV is the photovoltaic output at time t, P WT is the output of the fan at time t, C PV is the photovoltaic operation and maintenance cost, C WT The operation and maintenance cost of the wind turbine; The electric vehicle demand response cost is expressed as: C DR,t =c DR,t ΔP EV,t (18) Where: C DR,t is the EV demand response cost, c DR is the cost coefficient of demand response, ΔP EV,t is the change in charging load of electric vehicles participating in demand response at time t; The main grid electricity purchase cost is expressed as: C B,t =λ t P G,t (19) In the above formula: C B,t is the cost of purchasing electricity for the main grid, λ t is the electricity purchase price at time t, P G,t is the total load of the distribution network at time t; The main grid purchase electricity constraint is expressed as: In the above formula: The maximum amount of electricity purchased by the main network, P B,t The power purchased by the main grid at time t; The demand response constraint is expressed as: In the above formula, ΔP EV,t is the change in electric vehicle charging load at time t, is the minimum value of the change in electric vehicle charging load, It is the maximum value of the change in charging load of electric vehicles; The electricity price constraint is expressed as: Δc min ≤Δc t ≤Δc max (23) In the above formula: Δc t is the change in the charging price of electric vehicles at time t; Δc min , Δc max are the minimum and maximum changes in the price of electric vehicle charging, respectively; The output constraint of the distributed generation is expressed as: In the above formula: are the maximum outputs of photovoltaic and wind turbine, respectively, PV,t is the photovoltaic output at time t, P WT,t is the output of the fan at time t; The electricity quantity and electricity price elasticity matrix constraint is that the response of the electric vehicle load in time period t satisfies the following electricity quantity and electricity price elasticity matrix constraint: In the above formula: ΔP EV,t is the change in charging load of electric vehicles participating in demand response at time t, E′ is the improved elastic coefficient matrix, ΔC t is the electricity price response during period t, C t is the electricity price at time t, P EV,t is the load size of the electric vehicle at time t; The power balance constraint is expressed as: P B,t +P PV,t +P WT,t =P L,t +P EV,t -ΔP EV,t (26) In the above formula: P L,t The base load does not participate in demand response, ΔP EV,t is the charging load response of electric vehicles in period t, P B,t is the power purchased by the main grid at time t.

8. The electric vehicle load optimization dispatching strategy based on improved elastic electricity price demand response according to claim 1 is characterized by: The electric vehicle load optimization dispatching model based on improved elastic electricity price demand response is solved to obtain the minimum operating cost of the distribution network, including: Initialization: The charging load data of a single electric vehicle is obtained through the Monte Carlo method, and the initial charging load data of all electric vehicles is accumulated. The electric vehicles are clustered and uniformly managed and dispatched, and then the photovoltaic, wind turbine and basic load data under typical days are initialized; Modeling: Establish an electric vehicle load optimization dispatch model based on improved elastic electricity price demand response, optimize the dispatch of electric vehicle loads, main grid purchase power, photovoltaic and wind turbine outputs that participate in electricity price demand response, and thus build an operation model; Solution: By calling the solver to solve the electric vehicle load optimization scheduling model based on improved flexible electricity price demand response, the electric vehicle charging load, main grid purchase power, photovoltaic output and wind turbine output under typical days are obtained, and the minimum operating cost of the distribution network is calculated.

9. An electric vehicle load optimization dispatching device based on improved elastic electricity price demand response, characterized by: include: The analysis module is used to analyze the charging load demand of electric vehicles, obtain data sets with different characteristics, and uniformly dispatch and manage electric vehicles with similar characteristics; The demand response modeling module is used to carry out price-type demand response modeling based on the improved electricity quantity and electricity price elasticity matrix, and obtain the electric vehicle load model after demand response based on the improved electricity quantity and electricity price elasticity matrix; Optimal dispatch modeling module, used for modeling the optimal dispatch of electric vehicle loads based on improved elastic electricity price demand response; The solution module is used to solve the electric vehicle load optimization scheduling model based on improved flexible electricity price demand response to obtain the minimum operating cost of the distribution network.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the electric vehicle load optimization scheduling strategy based on improved flexible electricity price demand response as described in any one of claims 1 to 8 are implemented.

11. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of an electric vehicle load optimization scheduling strategy based on improved elastic electricity price demand response as described in any one of claims 1 to 8.

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