Data analysis method, system and device for participation of electric vehicle charging load aggregator in auxiliary service market, and storage medium

Abstract

The invention discloses a data analysis method, system and device for participation of an electric vehicle charging load aggregator in an auxiliary service market, and a storage medium. The data analysis method comprises the following steps: an aggregator selects electric vehicles capable of participating in an auxiliary service market according to an actual scheduling condition, and determines an electric vehicle charging strategy; analyzing the electric vehicles based on the electric vehicle charging strategy, aggregating the electric vehicle cluster into a virtual power plant by an aggregator, and constructing a cost model of the virtual power plant and a model for adjusting up and down schedulable capacity; based on a cost model of a virtual power plant and a model of up and down regulation of schedulable capacity, through scene data analysis and determination of a market clearing strategy, operation cost minimization is realized. According to the method, the electric vehicle cluster forms the virtual power plant in an aggregation mode, participates in the auxiliary service market, and a data analysis method for optimally participating in the auxiliary service market is provided by analyzing scene data and optimizing internal resources, so that the operation cost is minimized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a data analysis method, system, device and storage medium for a cluster to participate in an auxiliary service market as an electric vehicle charging load aggregator in an aggregation mode. Background Art

[0002] In recent years, intermittent energy sources such as wind power and solar energy have developed rapidly. The randomness and intermittent characteristics of large-scale renewable energy have led to a decline in the quality of power in the power grid. Electric vehicle systems can be used as a flexible and adjustable load to provide regulation services and reserves. Compared with traditional regulation units, they have fast response speed and no climbing power constraints; they have high response accuracy, and the on-board power electronic converter can accurately track the AGC signal; they have high efficiency, and the charging and discharging efficiency of electric vehicles can reach 80%, which is much higher than that of general energy storage stations.

[0003] CN109193721A discloses a method for optimizing electric vehicle charging and discharging strategies based on reinforcement learning, constructing a physical model of electric vehicle battery charging and discharging and a method for calculating electric vehicle frequency modulation benefits; using a reinforcement learning algorithm to train electric vehicles to participate in the frequency modulation market; establishing a frequency modulation benefit evaluation plan, and using the trained model to evaluate the frequency modulation benefits of electric vehicles.

[0004] CN116961032A discloses an electric vehicle aggregation frequency modulation control method based on edge-cloud collaboration, including dividing electric vehicle charging areas based on electric vehicle travel characteristics and building electric vehicle aggregators; counting the number of grid connections, charging demand and maximum charging power that can be carried out in each charging area within 24 hours; based on the edge-cloud collaborative control architecture, the trading center is used as the central cloud entity, and each electric vehicle aggregator is used as the edge entity to build an electric vehicle aggregation frequency modulation control framework; performing central cloud entity calculations and establishing an upper-level mixed integer linear programming model, the objective function of which is to minimize the total cost and conditional risk value of all electric vehicle aggregators purchasing electricity and obtaining frequency modulation compensation.

[0005] Both of the above methods fail to consider the possible participation of other aggregators and the constraints of internal resources, making it difficult to determine their bid coefficients and minimize their own operating costs. Summary of the invention

[0006] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a data analysis method for electric vehicle charging load aggregators participating in the ancillary service market, which takes into account the strategies of other aggregators and the constraints of internal resources, and determines the electric vehicle charging strategy of the aggregator with the goal of minimizing cost.

[0007] To this end, the present invention is implemented through the following technical solution: a data analysis method for electric vehicle charging load aggregators participating in ancillary service market, comprising the steps of:

[0008] S1, the aggregator selects electric vehicles that can participate in the ancillary service market and determines the electric vehicle charging strategy based on the actual dispatch situation;

[0009] S2, based on the electric vehicle charging strategy, the electric vehicles are analyzed, and the aggregator aggregates the electric vehicle cluster into a virtual power plant, builds the cost model of the virtual power plant and the model for adjusting the dispatchable capacity up and down;

[0010] S3, based on the cost model of virtual power plants and the model of adjusting the dispatchable capacity up and down, minimizes operating costs through scenario data analysis and determination of market clearing strategies.

[0011] Furthermore, the step S1 comprises:

[0012] S11, for electric vehicles that arrive at the grid and provide ancillary services, the aggregator calculates the minimum charging time T based on the charging status of the electric vehicle min to determine the provision of ancillary services;

[0013]

[0014] In the formula, C B is the battery capacity of the electric vehicle, SOC out is the SOC of the electric vehicle when it is off-grid, SOC in is the SOC of the electric vehicle when it reaches the grid, η is the efficiency of the charging power, P N is the maximum charging power;

[0015] S12, for electric vehicles that provide ancillary services, the aggregator calculates the charging baseline based on the charging requirements of the vehicle owner as follows:

[0016]

[0017] Where P base represents the electric vehicle charging baseline;

[0018] The electric vehicle will maintain a charging baseline without external control signals;

[0019] S13, tracking control instructions After receiving the control instructions from the dispatch center, the aggregator splits and distributes them to each spare electric vehicle (i.e., the electric vehicle providing auxiliary services).

[0020] Furthermore, in S11, the aggregator obtains the duration of the electric vehicle on the grid through the online and offline time of the electric vehicle provided by the owner to the aggregator, which is defined as:

[0021] T SET =(T out -T in ) (2)

[0022] T out is the off-grid time of the electric vehicle, T in It’s the internet time for electric cars;

[0023] If T SET ≤T min , the electric vehicle must be charged at maximum power until it is disconnected from the grid, so the electric vehicle cannot provide auxiliary services; if T SET >T min , electric vehicles provide auxiliary services;

[0024] In S13, the actual charging power P of the electric vehicle actual Adjustable range is constrained: 0≤P actual ≤P N .

[0025] Furthermore, the step S2 comprises:

[0026] S21, when electric vehicle clusters are aggregated into virtual power plants to participate in the ancillary service market, the aggregator's operating cost C i is the dispatch fee paid by the aggregator to the electric vehicle owner, and the operating cost C i is set to a quadratic function of the dispatchable capacity, defined as:

[0027]

[0028] Among them, q i is the dispatchable capacity of aggregator i, a c,i , b c,i 、c c,i are the equivalent cost coefficients of aggregator i; the sum of the dispatchable capacities of all N aggregators is the dispatchable capacity of the virtual power plant, and the sum of the operating costs of all N aggregators is the operating cost of the virtual power plant;

[0029] The dispatchable capacity of the virtual power plant is obtained by judging the number of controllable electric vehicles and calculating the real-time charging power. The dispatchable capacity is divided into two categories: upward adjustment and downward adjustment. The model of the upward and downward adjustment of the dispatchable capacity is expressed as:

[0030]

[0031]

[0032] Where P k,actual represents the dispatchable capacity actually provided by the kth electric vehicle; P k,N represents the maximum dispatchable capacity that the k-th electric vehicle can provide; q R+ Indicates increasing the dispatchable capacity; q R- represents the dispatchable capacity to be reduced; N is the total number of electric vehicles that can provide auxiliary services at time t.

[0033] Furthermore, the operating constraints of the model for adjusting the dispatchable capacity up and down include: feasible time constraints, power balance constraints, controllability constraints, battery SOC constraints and battery charging power constraints.

[0034] Furthermore, in step S3, the specific process of the scene data analysis is as follows:

[0035] Assume that there are N electric vehicle aggregators EVA participating in the ancillary service market. The ancillary service market requires market members to provide linear bidding. Multiple electric vehicle aggregators EVAs provide non-decreasing quotations to the trading center in each trading period, and the dispatch center clears them uniformly. The method is as follows:

[0036] λ i,t =a i,t q i,t +b i,t (13)

[0037] Among them, a i,t and b i,t is the bidding coefficient of the ith EV aggregator EVA i; q i,t represents the dispatchable capacity of aggregator i at time t;

[0038] In addition to internal costs and constraints, aggregators also need to consider the participation of other aggregators; the bidding coefficients for EVA i and EVA j conform to the two-dimensional normal distribution, expressed as follows:

[0039] (a ij ,b ij )~(μ a,ij ,μ b,ij ,σ a,ij ,σ b,ij ,ρ ij )(14)

[0040] The subscripts i and j of the symbols represent EVA i and EVA j, respectively, and μ a,ij and μ b,ij denote the bidding coefficient a of EVA j respectively ij and b ij The estimated mean value, σ a,ij and σb,ij denote the bidding coefficient a of EVA j respectively ij and b ij The estimated standard deviation of ij is the bidding coefficient a ij The correlation coefficient of

[0041] According to the probability density function, EVA i generates the participation scenarios of EVA j through Monte Carlo simulation and obtains the optimal strategy to minimize the operating cost of the i-th EV aggregator;

[0042] The steps for generating scene data are as follows:

[0043] 1) For the autocorrelation matrix of the two-dimensional normal distribution

[0044]

[0045] Generate a lower triangular matrix

[0046]

[0047] So that M = CC Τ ;

[0048] 2) Generate an independent two-dimensional standard normal distribution random vector η = [η a ,η b ] T , and η a ~N(0,1),η b ~N(0,1); N(0,1) refers to the normal distribution in probability theory, with a mean of 0 and a variance of 1;

[0049] 3) Obtain the prediction result of EVA j: [a ij ,b ij ] T =[μ a,ij ,μ b,ij ] T +Cη.

[0050] Furthermore, the market clearing strategy in step S3 is an up-and-down frequency modulation market clearing strategy;

[0051] The above-mentioned up / down frequency market clearing strategy is as follows:

[0052]

[0053] In the formula, represents the upward dispatchable capacity of EVA j; represents the downward dispatchable capacity of EVA j; and are the upward and downward clearance amounts of EVA j, are the ancillary services market demands for upward and downward adjustments, a i and b i is the bidding coefficient of EVA j, n is the total number of aggregators participating in the ancillary service market; represent the upward and downward clearing prices at time t respectively.

[0054] Furthermore, in step S3, the aggregator operating cost is the dispatch fee paid by the aggregator to the electric vehicle owner, and the income comes from the ancillary service market. The minimum operating cost of electric vehicles participating in the ancillary service market is:

[0055]

[0056]

[0057] Where s is the different bidding scenarios generated by Monte Carlo simulation, -E s () represents the expected cost under different scenarios, and They are the upward adjustment of the clearing price and the downward adjustment of the clearing price at time t respectively; and are the upward and downward clearing amounts at time t, respectively; π S represents the cost of the aggregator participating in ancillary services in scenario s; C S Represents the operating cost of the aggregator in scenario s.

[0058] The present invention also provides a data analysis system for electric vehicle charging load aggregators to participate in ancillary service markets, which includes:

[0059] Electric vehicle charging strategy determination unit: The aggregator selects electric vehicles that can participate in the ancillary service market and determines the electric vehicle charging strategy based on the actual dispatch situation;

[0060] Cost-capacity model building unit: Analyze electric vehicles based on their charging strategies. Aggregators aggregate electric vehicle clusters into a virtual power plant, build a cost model for the virtual power plant, and a model for adjusting the dispatchable capacity up and down.

[0061] Operation cost minimization unit: Based on the cost model of the virtual power plant and the model for adjusting the dispatchable capacity up and down, the operation cost is minimized through scenario data analysis and determination of market clearing strategies.

[0062] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the method are implemented when the processor executes the computer program.

[0063] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described are implemented.

[0064] The present invention has the following beneficial effects:

[0065] (1) Electric vehicle clusters can form virtual power plants through aggregation and participate in the ancillary service market, making it impossible for the dispatch center to control the huge and scattered demand response resources, greatly reducing the burden on the dispatch center.

[0066] (2) By analyzing scenario data and optimizing internal resources, we propose the best data analysis method for participating in the auxiliary service market and minimize operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a data analysis method for electric vehicles participating in ancillary service markets according to the present invention;

[0068] Figure 2 A flow chart for determining an electric vehicle charging strategy for the present invention;

[0069] Figure 3 An operation constraint diagram used to construct a cost-capacity model of a virtual power plant for the present invention;

[0070] Figure 4 A flow chart of the electric vehicle aggregator participating in the ancillary service market of the present invention;

[0071] Figure 5 This is a structural diagram of the data analysis system for electric vehicles participating in the auxiliary service market of the present invention;

[0072] Figure 6 A schematic diagram of a logical structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to facilitate the understanding of the present application, the present application is described in more detail below in conjunction with the accompanying drawings and specific embodiments. The preferred embodiments of the present application are provided in the accompanying drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described in this specification. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive.

[0074] It should be noted that, unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the present application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0075] Example 1

[0076] like Figure 1 As shown, the present invention is a data analysis method for electric vehicles participating in the auxiliary service market, which includes the steps of:

[0077] S1, the aggregator selects electric vehicles that can participate in the ancillary service market and determines the electric vehicle charging strategy based on the actual dispatch situation;

[0078] S2, based on the electric vehicle charging strategy, the electric vehicles are analyzed. The aggregator aggregates the electric vehicle cluster into a virtual power plant, builds the cost model of the virtual power plant and the model for adjusting the dispatchable capacity up and down;

[0079] S3, based on the cost model of virtual power plants and the model of adjusting the dispatchable capacity up and down, minimizes operating costs through scenario data analysis and determination of market clearing strategies.

[0080] Combination Figure 2 As shown, the aggregator provided by the present invention selects electric vehicles that can participate in the auxiliary service market according to the actual scheduling situation and determines the electric vehicle charging strategy, which specifically includes the following steps:

[0081] S11: For electric vehicles that arrive at the grid and provide ancillary services, the aggregator calculates the minimum charging time T based on the state of charge (SOC) of the electric vehicle. min to determine the provision of ancillary services.

[0082]

[0083] In the formula, C B is the battery capacity of the electric vehicle, SOC out is the SOC of the electric vehicle when it is off-grid, SOC in is the SOC of the electric vehicle when it reaches the grid, η is the efficiency of the charging power, P N is the maximum charging power.

[0084] By providing the aggregator with the time the EV is online and off-grid, the aggregator can obtain the duration of its on-grid connection, defined as:

[0085] T SET =(T out -T in ) (2)

[0086] T out is the off-grid time of the electric vehicle, T in It’s time to go online for electric cars.

[0087] If T SET ≤T min, the electric vehicle must charge at maximum power until it is disconnected from the grid, so the electric vehicle cannot provide auxiliary services. SET >T min , electric vehicles can provide auxiliary services.

[0088] S12: For electric vehicles that can provide ancillary services, the aggregator calculates the charging baseline based on the charging requirements of the vehicle owner as follows:

[0089]

[0090] Where P base represents the electric vehicle charging baseline;

[0091] The electric vehicle will maintain a preset charging baseline without external control signals.

[0092] S13: Tracking Control Instructions After receiving the control instructions from the dispatch center, the aggregator splits and distributes them to each backup electric vehicle.

[0093] The adjustable range of electric vehicle charging power is constrained: 0≤P actual ≤P N .

[0094] Here P actual is the actual charging power of the electric vehicle.

[0095] The present invention constructs a cost-capacity model of a virtual power plant, comprising the following steps:

[0096] S21: When electric vehicle clusters are aggregated into virtual power plants to participate in the ancillary service market, the operating cost is the dispatch fee paid by the aggregator to the electric vehicle owner. In this invention, the operating cost is set as a quadratic function of the dispatchable capacity, defined as:

[0097]

[0098] Among them, q i is the dispatchable capacity of aggregator i, a c,i 、b c,i 、c c,i are all equivalent cost coefficients of aggregator i; the sum of the dispatchable capacities of all N aggregators is the dispatchable capacity of the virtual power plant; the sum of the operating costs of all N aggregators is the operating cost of the virtual power plant.

[0099] The dispatchable capacity of the virtual power plant can be obtained by judging the number of controllable electric vehicles and calculating the real-time charging power. Its dispatchable capacity is divided into two categories: upward adjustment and downward adjustment. The model of the upward and downward adjustment of the dispatchable capacity is expressed as:

[0100]

[0101]

[0102] Where P k,actual represents the dispatchable capacity actually provided by the kth electric vehicle; P k,N represents the maximum dispatchable capacity that the k-th electric vehicle can provide; q R+ Indicates increasing the dispatchable capacity; q R- represents the dispatchable capacity to be reduced; N is the total number of electric vehicles that can provide auxiliary services at time t.

[0103] The operating constraints of the model for adjusting the dispatchable capacity up and down mainly include: feasible time constraint, power balance constraint, controllability constraint, battery SOC constraint and battery charging power constraint, such as Figure 3 shown.

[0104] S22: Feasible time constraints

[0105] When electric vehicles are driving, they cannot provide backup, and their feasible time constraints can be expressed by the following formula:

[0106]

[0107] S23: Power balance constraints

[0108] When electric vehicles are charged in the grid, the power balance can be expressed as:

[0109]

[0110] When electric vehicles enter the grid, the power balance can be expressed as:

[0111]

[0112] Among them, SOC end is the SOC of the electric vehicle when it is off the grid, d is the mileage of the electric vehicle after leaving the grid, and M is the power consumption of the electric vehicle per kilometer.

[0113] S24: Controllable Constraints

[0114] The controllable coefficient of time t can be expressed by the following formula:

[0115]

[0116] In order to meet the travel needs of electric vehicle owners, the controllability constraint is expressed as: 0<k(t)<1.

[0117] S25: Battery SOC constraint

[0118] SOCmin ≤SOC(t)≤SOC max (11)

[0119] S26: Battery charging power constraint

[0120] η×P base Δt≤(SOC max -SOC(t-1))×C B (12)

[0121] Combination Figure 4 As shown, the present invention provides a flow chart of the electric vehicle aggregator clearing algorithm, determines the bidding strategy, market clearing strategy, and expected model, and specifically includes the following steps:

[0122] S31: Scene Data Analysis

[0123] Assume that there are N EVAs participating in the ancillary service market, which requires market members to provide linear bidding. EVAs provide non-decreasing quotations to the trading center in each trading period, and the dispatch center clears them uniformly, as follows:

[0124] λ i,t =a i,t q i,t +b i,t (13)

[0125] Among them, a i,t and b i,t is the bidding coefficient of EVA i; q i,t represents the dispatchable capacity of aggregator i at time t.

[0126] In addition to internal costs and constraints, aggregators also need to consider the participation of other aggregators. For EVA i, the bidding coefficient of EVA j conforms to the two-dimensional normal distribution and can be expressed as follows:

[0127] (a ij ,b ij )~(μ a,ij ,μ b,ij ,σ a,ij ,σ b,ij ,ρ ij )(14)

[0128] The subscripts i and j of the symbols represent EVA i and EVA j, respectively, and μ a,ij and μ b,ij denote the bidding coefficient a of EVA j respectively ij and b ij The estimated mean value, σ a,ij and σ b,ij denote the bidding coefficient a of EVA j respectively ijand b ij The estimated standard deviation of ij is the bidding coefficient a ij The correlation coefficient of

[0129] According to the probability density function, EVA i generates the participation scenario of EVA j through Monte Carlo simulation and obtains the optimal strategy. The steps for scenario data generation are as follows:

[0130] (1) For the autocorrelation matrix of the two-dimensional normal distribution

[0131]

[0132] Generate a lower triangular matrix

[0133]

[0134] So that M = CC Τ .

[0135] (2) Generate an independent two-dimensional standard normal distribution random vector η = [η a ,η b ] T , and η a ~N(0,1),η b ~N(0,1);

[0136] (3) Obtain the prediction result of EVA j: ij ,b ij ] T =[μ a,ij ,μ b,ij ] T +Cη.

[0137] S32: Market Clearing Strategy

[0138] The clearing process of the up / down frequency modulation market is as follows:

[0139]

[0140]

[0141] In the formula, represents the upward dispatchable capacity of EVA j; represents the downward dispatchable capacity of EVA j; and are the upward and downward clearance amounts of EVA j, are the ancillary services market demands for upward and downward adjustments, a i and b i is the bidding coefficient of EVA j, n is the total number of aggregators participating in the ancillary service market; represent the upward and downward clearing prices at time t respectively.

[0142] S33: Minimize operating costs

[0143] In the present invention, the aggregator operating cost is the dispatch fee to the owner, and the income comes from the ancillary service market. The minimum operating cost of electric vehicles participating in the ancillary service market is:

[0144]

[0145]

[0146] Where s is the different bidding scenarios generated by Monte Carlo simulation, -E s () represents the expected cost under different scenarios, and They are the upward adjustment of the clearing price and the downward adjustment of the clearing price at time t respectively; and are the upward and downward clearing amounts at time t, respectively; π S represents the cost of the aggregator participating in ancillary services in scenario s; C S Represents the operating cost of the aggregator in scenario s.

[0147] Example 2

[0148] This embodiment provides a data analysis system for electric vehicle charging load aggregators participating in ancillary service markets, which is used to implement the method described in Embodiment 1, such as Figure 5 As shown, it includes:

[0149] Electric vehicle charging strategy determination unit: The aggregator selects electric vehicles that can participate in the ancillary service market and determines the electric vehicle charging strategy based on the actual dispatch situation;

[0150] Cost-capacity model building unit: Analyze electric vehicles based on their charging strategies. Aggregators aggregate electric vehicle clusters into a virtual power plant, build a cost model for the virtual power plant, and a model for adjusting the dispatchable capacity up and down.

[0151] Operation cost minimization unit: Based on the cost model of the virtual power plant and the model for adjusting the dispatchable capacity up and down, the operation cost is minimized through scenario data analysis and determination of market clearing strategies.

[0152] Specifically, the electric vehicle charging strategy determination unit includes:

[0153] The minimum charging time calculation subunit, for electric vehicles that arrive at the grid and provide ancillary services, the aggregator calculates the minimum charging time T based on the charging status of the electric vehicle minto determine the provision of ancillary services;

[0154]

[0155] In the formula, C B is the battery capacity of the electric vehicle, SOC out is the SOC of the electric vehicle when it is off-grid, SOC in is the SOC of the electric vehicle when it reaches the grid, η is the efficiency of the charging power, P N is the maximum charging power;

[0156] Charging baseline calculation subunit: For electric vehicles that provide auxiliary services, the aggregator calculates the charging baseline based on the charging requirements of the car owner as follows:

[0157]

[0158] Where P base represents the electric vehicle charging baseline;

[0159] The electric vehicle will maintain a charging baseline without external control signals;

[0160] A control instruction allocation subunit tracks the control instruction. After receiving the control instruction from the dispatch center, the aggregator splits it and allocates it to each standby electric vehicle (i.e., the electric vehicle providing auxiliary services).

[0161] In the minimum charging time calculation subunit, the aggregator obtains the duration of the electric vehicle in the power grid through the online and offline time of the electric vehicle provided by the owner to the aggregator, which is defined as:

[0162] T SET =(T out -T in ) (2)

[0163] T out is the off-grid time of the electric vehicle, T in It is the Internet time of electric vehicles;

[0164] If T SET ≤T min , the electric vehicle must be charged at maximum power until it is disconnected from the grid, so the electric vehicle cannot provide auxiliary services; if T SET >T min , electric vehicles provide auxiliary services;

[0165] In the control instruction distribution subunit, the actual charging power P of the electric vehicle actual Adjustable range is constrained: 0≤P actual ≤P N .

[0166] Specifically, the cost-capacity model building unit includes:

[0167] Operation cost calculation subunit: When electric vehicle clusters are aggregated into virtual power plants to participate in the ancillary service market, the aggregator's operating cost C i is the dispatch fee paid by the aggregator to the electric vehicle owner, and the operating cost C i is set to a quadratic function of the dispatchable capacity, defined as:

[0168]

[0169] Among them, q i is the dispatchable capacity of aggregator i, a c,i , b c,i 、c c,i are the equivalent cost coefficients of aggregator i; the sum of the dispatchable capacities of all N aggregators is the dispatchable capacity of the virtual power plant, and the sum of the operating costs of all N aggregators is the operating cost of the virtual power plant;

[0170] Subunit for constructing the dispatchable capacity model: The dispatchable capacity of the virtual power plant is obtained by judging the number of controllable electric vehicles and calculating the real-time charging power. The dispatchable capacity is divided into two categories: up-regulation and down-regulation. The model for up-regulating the dispatchable capacity is expressed as:

[0171]

[0172]

[0173] Where P k,actual represents the dispatchable capacity actually provided by the kth electric vehicle; P k,N represents the maximum dispatchable capacity that the k-th electric vehicle can provide; q R+ Indicates increasing the dispatchable capacity; q R- represents the dispatchable capacity to be reduced; N is the total number of electric vehicles that can provide auxiliary services at time t.

[0174] The operating constraints of the model for adjusting the dispatchable capacity up and down include: feasible time constraints, power balance constraints, controllability constraints, battery SOC constraints and battery charging power constraints.

[0175] Specifically, in the operation cost minimization unit, the specific process of the scenario data analysis is as follows:

[0176] Assume that there are N electric vehicle aggregators EVA participating in the ancillary service market. The ancillary service market requires market members to provide linear bidding. Multiple electric vehicle aggregators EVAs provide non-decreasing quotations to the trading center in each trading period, and the dispatch center clears them uniformly. The method is as follows:

[0177] λ i,t =a i,t q i,t +b i,t (13)

[0178] Among them, a i,t and b i,t is the bidding coefficient of the ith EV aggregator EVA i; q i,t represents the dispatchable capacity of aggregator i at time t;

[0179] In addition to internal costs and constraints, aggregators also need to consider the participation of other aggregators; the bidding coefficients for EVA i and EVA j conform to the two-dimensional normal distribution, expressed as follows:

[0180] (a ij ,b ij )~(μ a,ij ,μ b,ij ,σ a,ij ,σ b,ij ,ρ ij )(14)

[0181] The subscripts i and j of the symbols represent EVA i and EVA j, respectively, and μ a,ij and μ b,ij denote the bidding coefficient a of EVA j respectively ij and b ij The estimated mean value, σ a,ij and σ b,ij denote the bidding coefficient a of EVA j respectively ij and b ij The estimated standard deviation of ij is the bidding coefficient a ij The correlation coefficient of

[0182] According to the probability density function, EVA i generates the participation scenarios of EVA j through Monte Carlo simulation and obtains the optimal strategy to minimize the operating cost of the i-th EV aggregator;

[0183] The steps for generating scene data are as follows:

[0184] 1) For the autocorrelation matrix of the two-dimensional normal distribution

[0185]

[0186] Generate a lower triangular matrix

[0187]

[0188] So that M = CC Τ ;

[0189] 2) Generate an independent two-dimensional standard normal distribution random vector η = [η a ,η b ] T , and η a ~N(0,1),η b ~N(0,1); N(0,1) refers to the normal distribution in probability theory, with a mean of 0 and a variance of 1;

[0190] 3) Obtain the prediction result of EVA j: [a ij ,b ij ] T =[μ a,ij ,μ b,ij ] T +Cη.

[0191] Specifically, in the operating cost minimization unit, the market clearing strategy is an up-and-down frequency adjustment market clearing strategy;

[0192] The above-mentioned up / down frequency market clearing strategy is as follows:

[0193]

[0194]

[0195] In the formula, represents the upward dispatchable capacity of EVA j; represents the downward dispatchable capacity of EVA j; and are the upward and downward clearance amounts of EVA j, are the ancillary services market demands for upward and downward adjustments, a i and b i is the bidding coefficient of EVA j, n is the total number of aggregators participating in the ancillary service market; represent the upward and downward clearing prices at time t respectively.

[0196] In the operating cost minimization unit, the aggregator operating cost is the dispatch fee paid by the aggregator to the electric vehicle owner, and the income comes from the ancillary service market. The minimized operating cost of electric vehicles participating in the ancillary service market is:

[0197]

[0198]

[0199] Where s is the different bidding scenarios generated by Monte Carlo simulation, -E s () represents the expected cost under different scenarios, and They are the upward adjustment of the clearing price and the downward adjustment of the clearing price at time t respectively; and are the upward and downward clearing amounts at time t, respectively; π S represents the cost of the aggregator participating in ancillary services in scenario s; C S Represents the operating cost of the aggregator in scenario s.

[0200] Example 3

[0201] This embodiment provides a computer device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program executable by the at least one processor, and when the at least one processor executes the computer device, the computer program is used to cause the computer device to execute the method according to Embodiment 1 of the present invention.

[0202] Example 4

[0203] This embodiment provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method according to Embodiment 1 of the present invention.

[0204] refer to Figure 6 , a block diagram of an electronic device 400 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0205] like Figure 6As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0206] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 may be any type of device capable of inputting information to the electronic device 400, and the input unit 406 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 407 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 may include, but is not limited to, a disk, an optical disk. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a Wi Max device, a cellular communication device, and / or the like.

[0207] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the aforementioned metal corrosion prediction method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. In some embodiments, the computing unit 401 may be configured to perform the aforementioned metal corrosion prediction method in any other appropriate manner (e.g., by means of firmware).

[0208] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0209] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0210] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0211] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0212] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0213] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0214] It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art to the present invention based on the disclosure of the present invention should be within the protection scope of the present invention.

[0215] The above are only embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent structural transformations made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the protection scope of the present invention.

Claims

1. A data analysis method for electric vehicle charging load aggregators participating in the ancillary service market, characterized in that: include: Aggregators select electric vehicles that can participate in the ancillary service market and determine the electric vehicle charging strategy based on actual dispatch conditions; Analyze electric vehicles based on their charging strategies. Aggregators aggregate electric vehicle clusters into virtual power plants and build cost models for virtual power plants and models for adjusting dispatchable capacity up and down. Based on the cost model of the virtual power plant and the model of adjusting the dispatchable capacity up and down, the operating costs can be minimized through scenario data analysis and determination of market clearing strategies.

2. The data analysis method for electric vehicles participating in the auxiliary service market according to claim 1 is characterized in that: The aggregator selects electric vehicles that can participate in the ancillary service market based on actual dispatch conditions and determines the electric vehicle charging strategy, including: S11, for electric vehicles that arrive at the grid and provide ancillary services, the aggregator calculates the minimum charging time T based on the charging status of the electric vehicle min to determine the provision of ancillary services; In the formula, C B is the battery capacity of the electric vehicle, SOC out is the SOC of the electric vehicle when it is off-grid, SOC in is the SOC of the electric vehicle when it reaches the grid, η is the efficiency of the charging power, P N is the maximum charging power; S12, for electric vehicles that provide ancillary services, the aggregator calculates the charging baseline based on the charging requirements of the vehicle owner as follows: Where P base represents the electric vehicle charging baseline; The electric vehicle will maintain a charging baseline without external control signals; S13, tracking control instructions After receiving the control instructions from the dispatch center, the aggregator splits and distributes them to each backup electric vehicle.

3. The data analysis method for electric vehicles participating in the auxiliary service market according to claim 2 is characterized in that: In S11, the aggregator obtains the duration of the electric vehicle in the grid through the online and offline time of the electric vehicle provided by the owner to the aggregator, which is defined as: T SET =(T out -T in ) (2) T out is the off-grid time of the electric vehicle, T in It is the Internet time of electric vehicles; If T SET ≤T min , the electric vehicle must be charged at maximum power until it is disconnected from the grid, so the electric vehicle cannot provide auxiliary services; if T SET >T min , electric vehicles provide auxiliary services; In S13, the actual charging power adjustable range of the electric vehicle is constrained: 0≤P actual ≤P N , P actual Indicates the actual charging power of the electric vehicle.

4. The data analysis method for electric vehicles participating in the auxiliary service market according to claim 1 is characterized in that: The electric vehicles are analyzed based on the electric vehicle charging strategy, and the aggregator aggregates the electric vehicle cluster into a virtual power plant, constructs a cost model of the virtual power plant and a model for adjusting the dispatchable capacity up and down, including: When electric vehicle clusters are aggregated into virtual power plants to participate in the ancillary service market, the aggregator's operating cost C i is the dispatch fee paid by the aggregator to the electric vehicle owner, and the operating cost C i is set to a quadratic function of the dispatchable capacity, defined as: Among them, q i is the dispatchable capacity of aggregator i, a c,i 、b c,i 、c c,i are the equivalent cost coefficients of aggregator i, the sum of the dispatchable capacities of all N aggregators is the dispatchable capacity of the virtual power plant, and the sum of the operating costs of all N aggregators is the operating cost of the virtual power plant; The dispatchable capacity of the virtual power plant is obtained by judging the number of controllable electric vehicles and calculating the real-time charging power. The dispatchable capacity is divided into two categories: upward adjustment and downward adjustment. The model of the upward and downward adjustment of the dispatchable capacity is expressed as: Where P k,actual represents the dispatchable capacity actually provided by the kth electric vehicle; P k,N represents the maximum dispatchable capacity that the k-th electric vehicle can provide; q R+ Indicates increasing the dispatchable capacity; q R- represents the dispatchable capacity to be reduced; N is the total number of electric vehicles that can provide auxiliary services at time t.

5. The data analysis method for electric vehicles participating in the auxiliary service market according to claim 4 is characterized in that: The operating constraints of the model for adjusting the dispatchable capacity up and down include: feasible time constraints, power balance constraints, controllability constraints, battery SOC constraints and battery charging power constraints.

6. The data analysis method for electric vehicles participating in the auxiliary service market according to claim 4 is characterized in that: The specific process of the scene data analysis is as follows: Assume that there are N electric vehicle aggregators EVA participating in the ancillary service market. The ancillary service market requires market members to provide linear bidding. Multiple electric vehicle aggregators EVAs provide non-decreasing quotations to the trading center in each trading period, and the dispatch center clears them uniformly. The method is as follows: l i,t =a i,t q i,t +b i,t (13) Among them, a i,t and b i,t is the bidding coefficient of the ith EV aggregator EVA i; q i,t represents the dispatchable capacity of aggregator i at time t; In addition to internal costs and constraints, aggregators also need to consider the participation of other aggregators; the bidding coefficients for EVA i and EVA j conform to the two-dimensional normal distribution, expressed as follows: (a ij ,b ij )~(μ a,ij ,μ b,ij ,σ a,ij ,σ b,ij ,ρ ij )(14) where the subscripts i and j represent EVA i and EVA j, respectively, μ a,ij and μ b,ij denotes the bidding coefficient a of EVA j respectively ij and b ij The estimated mean value, σ a,ij and σ b,ij denote the bidding coefficient a of EVA j respectively ij and b ij The estimated standard deviation of ij is the bidding coefficient a ij The correlation coefficient of According to the probability density function, EVA i generates the participation scenarios of EVA j through Monte Carlo simulation and obtains the optimal strategy to minimize the operating cost of the i-th EV aggregator; The steps for generating scene data are as follows: 1) For the autocorrelation matrix of the two-dimensional normal distribution Generate a lower triangular matrix So that M = CC Τ ; 2) Generate an independent two-dimensional standard normal distribution random vector η = [η a ,η b ] T , and η a ~N(0,1),η b ~N(0,1); 3) Obtain the prediction result of EVA j: [a ij ,b ij ] T =[μ a,ij ,μ b,ij ] T +Cη; The market clearing strategy described is an up-and-down frequency market clearing strategy; The above-mentioned up / down frequency market clearing strategy is as follows: In the formula, represents the upward dispatchable capacity of EVA j; represents the downward dispatchable capacity of EVA j; and are the upward and downward clearance amounts of EVA j, are the ancillary services market demands for upward and downward adjustments, a i and b i is the bidding coefficient of EVA j, n is the total number of aggregators participating in the ancillary service market; represent the upward and downward clearing prices at time t respectively.

7. The data analysis method for electric vehicles participating in the auxiliary service market according to claim 6 is characterized in that: The aggregator operating cost is the dispatch fee paid by the aggregator to the electric vehicle owner. The income comes from the ancillary service market. The minimum operating cost of electric vehicles participating in the ancillary service market is: Where s is the different bidding scenarios generated by Monte Carlo simulation, -E s () represents the expected cost under different scenarios, and They are the upward adjustment of the clearing price and the downward adjustment of the clearing price at time t respectively; and are the upward and downward clearing amounts at time t, respectively; π S represents the cost of the aggregator participating in ancillary services in scenario s; C S Represents the operating cost of the aggregator in scenario s.

8. A data analysis system for electric vehicle charging load aggregators participating in the ancillary service market, characterized in that: include: Electric vehicle charging strategy determination unit: The aggregator selects electric vehicles that can participate in the ancillary service market and determines the electric vehicle charging strategy based on the actual dispatch situation; Cost-capacity model building unit: Analyze electric vehicles based on their charging strategies. Aggregators aggregate electric vehicle clusters into virtual power plants, build cost models for virtual power plants, and models for adjusting dispatchable capacity up and down. Operation cost minimization unit: Based on the cost model of the virtual power plant and the model for adjusting the dispatchable capacity up and down, the operation cost is minimized through scenario data analysis and determination of market clearing strategies.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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