Joint clearing method and device for electric vehicle aggregator and readable storage medium

Abstract

The invention belongs to the technical field of power dispatching, and relates to a joint clearing method and device for an electric vehicle aggregator and a readable storage medium. An electric energy market quotation curve and an EV cluster aggregation state model are generated based on charging and discharging response characteristics of a user and an EV; constructing an electricity purchase cost equation, a frequency modulation cost equation, an electric energy market income equation and a frequency modulation market income equation; based on the electricity purchase cost minimization objective function and related constraints, constructing an electric energy market clearing model; based on the frequency modulation cost minimization objective function and related constraints, constructing a frequency modulation market clearing model; based on the sum of the electricity purchase cost and the frequency modulation cost, minimizing the objective function and related constraints, and constructing a joint clearing model; based on the sum of the electric energy market income and the frequency modulation market income, maximizing the objective function and related constraints, and constructing an aggregator income optimization model; and jointly solving the plurality of models to obtain a clearing strategy of the electric vehicle aggregator.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a joint clearing method, apparatus, and computer-readable storage medium for electric vehicle aggregators. Background Technology

[0002] With the deepening of the "dual-carbon" strategy and the rapid growth of renewable energy installed capacity, the power system is facing increasingly severe shortages of frequency regulation resources and peak-valley regulation pressure. Against this backdrop, electric vehicles, with their large-scale battery energy storage characteristics, can be integrated by electric vehicle aggregators to form dispatchable clusters, becoming an ideal vehicle for participating in both the electricity market and the frequency regulation ancillary service market.

[0003] Existing technologies for electric vehicle aggregators participating in the electricity market mainly include: constructing a multi-market participation framework to enable aggregators to engage in charge-discharge arbitrage in the energy market while providing frequency regulation services in the frequency regulation market; designing user incentive and bidding strategies to guide users to adjust their charging and discharging behavior through price signals; adopting a sequential clearing model, i.e., clearing out the energy market first and then independently clearing out the frequency regulation market; and developing cluster aggregation and collaborative scheduling methods to aggregate dispersed electric vehicle resources into a dispatchable whole. However, in the sequential clearing model of existing technologies, aggregators find it difficult to coordinate their resource allocation in the energy and frequency regulation markets, resulting in the inability to achieve globally optimal cross-market decisions for flexible resources and causing potential revenue losses. At the same time, existing methods fail to meticulously consider the impact of different user response characteristics on cluster aggregation capabilities when constructing aggregator bidding curves, resulting in a lack of precision in bidding strategies and increased bidding risks.

[0004] In summary, existing methods for electric vehicle aggregators to participate in the electricity market suffer from several drawbacks. They cannot coordinate resource allocation across multiple markets, thus failing to formulate globally optimal clearing decisions and resulting in revenue losses. Furthermore, they neglect the impact of user response characteristics, leading to lower accuracy in pricing strategies and higher bidding risks. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problems of existing methods for electric vehicle aggregators to participate in the electricity market, which are unable to coordinate the allocation of resources in multiple markets, thus failing to formulate globally optimal clearing decisions for multiple markets, resulting in revenue loss. At the same time, the method ignores the impact of user response characteristics, resulting in low accuracy of bidding strategies and high bidding risks.

[0006] To address the aforementioned technical problems, this invention provides a joint clearing method for electric vehicle aggregators, comprising: Electric energy market price curves are generated based on the charging and discharging response characteristics of EV users; individual EV state models are constructed based on the charging and discharging response characteristics of electric vehicles, and aggregated to obtain an EV cluster aggregated state model; Based on the electricity market price curve and the EV cluster aggregation state model, we construct the electricity purchase cost equation, frequency regulation cost equation, electricity market revenue equation, and frequency regulation market revenue equation. Based on the objective function of minimizing electricity purchase cost, load balance constraints of the electricity market, power generation constraints of generator sets, ramping constraints of generator sets, and charging and discharging power constraints, an electricity market clearing model is constructed. Based on the objective function of minimizing frequency regulation costs, frequency regulation demand constraints, frequency regulation capacity and mileage constraints, and frequency regulation unit ramping constraints, a frequency regulation market clearing model is constructed. A joint clearing model is constructed based on the objective function of minimizing the sum of electricity purchase cost and frequency regulation cost, and the coordination constraint between charging and discharging power and frequency regulation capacity. Based on the objective function of maximizing the sum of revenue from the electricity market and the frequency regulation market, and constrained by charging and discharging power and frequency regulation capacity, an aggregator revenue optimization model is constructed. By jointly solving the electric energy market clearing model, the frequency regulation market clearing model, the joint clearing model, and the aggregator revenue optimization model, the clearing strategy of electric vehicle aggregators is obtained.

[0007] Preferably, an energy market price curve is generated based on the charge and discharge response characteristics of EV users, including: Based on the battery discharge depth and battery cycle number of electric vehicles, calculate the battery loss cost per unit discharge energy; based on the battery loss cost per unit discharge energy, calculate the discharge battery loss of EV users during the grid access period; based on the sum of discharge battery loss and charging incentive price, obtain the discharge compensation price for EV users. The dead zone inflection point charging price for EV users is obtained by multiplying the charging price sensitivity coefficient of EV users by the charging incentive price; the dead zone inflection point charging price for EV users is calculated by multiplying the discharging price sensitivity coefficient of EV users, the charging incentive price, and the discharge battery loss. The saturation zone inflection point charging price for EV users is obtained by calculating the difference between the dead zone inflection point charging price and the linear zone span of charging. The saturation zone response price for EV users is obtained by calculating the sum of the dead zone initial response price and the linear zone span of discharging. Based on the dead-zone initiation response price and saturation zone response price of EV users, a relationship function between discharge quantity and discharge compensation price is constructed; based on the dead-zone inflection point charging price and saturation zone inflection point charging price of EV users, a relationship function between charging quantity and charging incentive price is constructed. Based on the relationship function between discharge quantity and discharge compensation price, and the relationship function between charging quantity and charging incentive price, a phased step aggregation method is used to generate an electric energy market price curve.

[0008] Preferably, a single EV state model is constructed based on the charging and discharging response characteristics of electric vehicles, and then aggregated to obtain an EV cluster aggregated state model, including: Based on the operating characteristics of EVs during grid connection, a single EV state model is constructed, which includes constraints on power, initial state of charge, and charge / discharge ramp rate. The Monte Carlo sampling method was used to generate multiple sets of individual EV charging time and initial state of charge samples. Each set of samples was input into the individual EV state model to obtain the operating state of the individual EV corresponding to each set of samples during the grid connection period. The operating status of individual EVs corresponding to each group of samples during the grid connection period is aggregated to obtain an EV cluster aggregated state model that includes power, initial total energy and charge / discharge ramp rate constraints.

[0009] Preferably, the process of constructing the electricity purchase cost equation includes: The power generation cost of each generator set in each time period is obtained by multiplying the declared price and the power generation of each generator set in the generator set set in each time period. The total power generation cost for each time period is obtained by summing the power generation costs of all generator sets in the generator set set; the target power generation cost for the generator set set is obtained by summing the total power generation costs for multiple preset time periods. The charging and discharging prices of the EV cluster aggregation state model are obtained based on the electric energy market price curve for each time period. Calculate the first product of the charging price and the charging power of the EV cluster aggregation state model in each time period, and calculate the second product of the discharging price and the discharging power of the EV cluster aggregation state model in each time period. Based on the difference between the first product and the second product, the difference between the charging cost and the discharging benefit of the EV cluster aggregation state model in each time period is obtained. Based on the sum of the differences between charging costs and discharging benefits in the EV cluster aggregation state model over multiple preset time periods, the difference between the target charging cost and discharging benefit in the EV cluster aggregation state model is obtained. The electricity purchase cost equation is constructed based on the difference between the target power generation cost of the generator set set and the difference between the target charging cost and the discharge revenue of the EV cluster aggregation state model.

[0010] Preferably, the process of constructing the frequency modulation cost equation includes: The capacity cost of each frequency modulation unit in each time period is obtained by multiplying the capacity bid price of each frequency modulation unit in the set of frequency modulation units in each time period by the frequency modulation capacity. The mileage cost of each frequency regulation unit in each time period is obtained by multiplying the mileage price of each unit by the frequency regulation mileage in each time period. The frequency regulation cost of each frequency regulation unit in each time period is obtained by summing the capacity cost and mileage cost of each unit in each time period. The first total frequency regulation cost is obtained by summing the frequency regulation costs of all frequency regulation units in the set of frequency regulation units within multiple preset time periods. The capacity cost of the EV cluster aggregation state model in each time period is obtained by multiplying the capacity quotation of the EV cluster aggregation state model in each time period with the frequency regulation capacity. The mileage cost of the EV cluster aggregation state model in each time period is obtained by multiplying the mileage price of the EV cluster aggregation state model in each time period with the frequency regulation mileage. The frequency regulation cost of the EV cluster aggregation state model in each time period is obtained by summing the capacity cost and mileage cost of the EV cluster aggregation state model in each time period. The second total frequency modulation cost is obtained by summing the frequency modulation costs of the EV cluster aggregation state model within multiple preset time periods. The frequency modulation cost equation is constructed based on the sum of the first total frequency modulation cost and the second total frequency modulation cost.

[0011] Preferably, the process of constructing the electricity market revenue equation includes: The difference between the discharge power and the charging power of the EV cluster aggregation state model in multiple preset time periods is calculated, and the product of this difference and the clearing price of the electric energy market is calculated to obtain the first revenue in each time period. The first electrical energy market revenue is obtained by summing the first revenue over multiple preset time periods. Calculate the third product of the charging power and the charging incentive price of the EV cluster aggregation state model in each time period. Based on the sum of the third products in multiple preset time periods, obtain the charging price of the EV cluster aggregation state model. Calculate the fourth product of the discharge power and discharge subsidy price of the EV cluster aggregation state model in each time period. Based on the sum of the fourth products in multiple preset time periods, obtain the discharge price of the EV cluster aggregation state model. The difference between the charging price and the discharging price is used to obtain the second electric energy market revenue based on the EV cluster aggregation state model. Based on the sum of the revenue from the first and second electric energy markets, an equation for the revenue from the electric energy market is constructed.

[0012] Preferably, the process of constructing the frequency modulation market revenue equation includes: Calculate the fifth product of the frequency modulation capacity and the frequency modulation capacity clearing price of the EV cluster aggregation state model in each time period, and the sixth product of the frequency modulation mileage and the frequency modulation mileage clearing price of the EV cluster aggregation state model in each time period. Based on the sum of the fifth and sixth products, the frequency modulation market revenue for each time period is obtained; Based on the sum of FM market revenue over multiple preset time periods, an FM market revenue equation is constructed.

[0013] Preferably, the clearing strategy of electric vehicle aggregators includes the clearing strategy of aggregators in the electric energy market and the clearing strategy of aggregators in the frequency regulation market.

[0014] The present invention also provides a joint clearing device for electric vehicle aggregators, comprising: The EV cluster aggregation state model construction module is used to generate electric energy market price curves based on the charging and discharging response characteristics of EV users; it constructs individual EV state models based on the charging and discharging response characteristics of electric vehicles and aggregates them to obtain the EV cluster aggregation state model. The cost and benefit equation construction module is used to construct the electricity purchase cost equation, frequency regulation cost equation, electricity market benefit equation, and frequency regulation market benefit equation based on the electricity market price curve and the EV cluster aggregation state model. The electricity market clearing model construction module is used to construct an electricity market clearing model based on the objective function of minimizing electricity purchase cost, load balance constraints of the electricity market, generator set power generation constraints, generator set ramping constraints, and charging and discharging power constraints. The frequency regulation market clearing model construction module is used to construct a frequency regulation market clearing model based on the objective function of minimizing frequency regulation costs, frequency regulation demand constraints, frequency regulation capacity and mileage constraints, and frequency regulation unit ramping constraints. The joint clearing model construction module is used to construct a joint clearing model based on the objective function of minimizing the sum of electricity purchase cost and frequency regulation cost, and the coordination constraints of charging and discharging power and frequency regulation capacity. The module for constructing the aggregator revenue optimization model is used to construct an aggregator revenue optimization model based on the objective function of maximizing the sum of electricity market revenue and frequency regulation market revenue, as well as charging and discharging power constraints and frequency regulation capacity constraints. The clearing strategy acquisition module is used to jointly solve the electric energy market clearing model, the frequency regulation market clearing model, the joint clearing model, and the aggregator revenue optimization model to obtain the clearing strategy of electric vehicle aggregators.

[0015] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described joint clearing method for electric vehicle aggregators.

[0016] The joint clearing method for electric vehicle aggregators provided in this application establishes an operational framework for electric vehicle aggregators to participate in the electricity and frequency regulation markets. It generates electricity market price curves based on the charging and discharging response characteristics of EV users, constructs individual EV state models based on these characteristics, and aggregates them to obtain an EV cluster aggregation state model. Then, based on the electricity market price curves and the EV cluster aggregation state model, it constructs the aggregator's electricity purchase costs, constraints, and revenues in the electricity market, as well as the aggregator's frequency regulation costs, constraints, and revenues in the frequency regulation market. Clearing models for both markets are constructed separately. Finally, with the goal of maximizing the aggregator's total revenue in both markets, it comprehensively considers the aggregator's costs, revenues, and various technical constraints in both the electricity and frequency regulation markets, achieving overall synergistic optimization of resource allocation and bidding strategies between the two markets, thereby realizing cross-market resource synergistic optimization. This application fully considers the charging and discharging response characteristics of users, enabling aggregators to accurately formulate bidding strategies. Simultaneously, the joint clearing model allows aggregators to make globally optimal clearing decisions across multiple markets, maximizing aggregator revenue. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This application provides a schematic diagram of the aggregator participation framework in the energy-frequency modulation market. Figure 2 A flowchart of the joint clearing method for electric vehicle aggregators provided in this application; Figure 3 The daily load curves and charge / discharge benchmarks for scenarios 1 and 2 provided in the embodiments of this application; Figure 4 The frequency modulation requirements and the frequency modulation capacity bid amounts for scenarios 1 and 2 provided in the embodiments of this application; Figure 5 The daily load curves and charge / discharge benchmarks for scenarios 3 and 4 provided in the embodiments of this application; Figure 6 The frequency modulation requirements and the frequency modulation capacity bid amounts for scenarios 3 and 4 provided in the embodiments of this application; Figure 7 This illustrates the changes in aggregator revenue under different incentive electricity prices provided in the embodiments of this application. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0019] Please see Figure 1 , Figure 1 The diagram illustrates the framework for aggregators participating in the energy-frequency regulation market provided in this application. Electric Vehicle Aggregators (EVAs), as market participants, compete with conventional generator sets in the energy market. By optimizing the charging and discharging strategies of their electric vehicle (EV) clusters, EVAs aim to reduce charging costs and obtain discharging revenue. Simultaneously, EVAs compete with dedicated frequency regulation resources in the frequency regulation market, obtaining corresponding frequency regulation revenue by providing frequency regulation capacity and mileage services to the power grid.

[0020] To maximize EVA's profits, this application provides a joint clearing method for electric vehicle aggregators, such as... Figure 2 As shown, the method specifically includes: S10: Generate an electric energy market price curve based on the charging and discharging response characteristics of EV users; construct a single EV state model based on the charging and discharging response characteristics of electric vehicles, and aggregate them to obtain an EV cluster aggregated state model.

[0021] S20: Based on the electricity market price curve and the EV cluster aggregation state model, construct the electricity purchase cost equation, frequency regulation cost equation, electricity market revenue equation, and frequency regulation market revenue equation.

[0022] S30: Based on the objective function of minimizing electricity purchase cost, load balance constraints of the electricity market, generator power generation constraints, generator ramping constraints, and charging and discharging power constraints, an electricity market clearing model is constructed.

[0023] S40: Based on the objective function of minimizing frequency regulation costs, frequency regulation demand constraints, frequency regulation capacity and mileage constraints, and frequency regulation unit ramping constraints, a frequency regulation market clearing model is constructed.

[0024] S50: Based on the objective function of minimizing the sum of electricity purchase cost and frequency regulation cost, and the coordination constraint between charging and discharging power and frequency regulation capacity, a joint clearing model is constructed.

[0025] S60: Based on the objective function of maximizing the sum of revenue from the electricity market and the frequency regulation market, and the constraints of charging and discharging power and frequency regulation capacity, an aggregator revenue optimization model is constructed.

[0026] S70: By jointly solving the electric energy market clearing model, the frequency regulation market clearing model, the joint clearing model, and the aggregator revenue optimization model, the clearing strategy of electric vehicle aggregators is obtained.

[0027] Specifically, the clearing strategies of electric vehicle aggregators include those in the electric energy market and those in the frequency regulation market. The clearing strategies in the electric energy market mainly include pricing for charging and discharging, winning bids for charging and discharging capacity, and winning bids for charging and discharging power. The clearing strategies in the frequency regulation market mainly include pricing for frequency regulation capacity, pricing for frequency regulation mileage, winning bids for frequency regulation capacity, and winning bids for frequency regulation mileage.

[0028] Furthermore, in some embodiments of this application, an energy market price curve is generated based on the charge and discharge response characteristics of EV users, including steps 1-1 to 1-5: Step 1-1: Calculate the battery loss cost per unit of discharge energy based on the battery discharge depth and battery cycle number of the electric vehicle; calculate the battery loss of EV users during the grid connection period based on the battery loss cost per unit of discharge energy; obtain the discharge compensation price for EV users based on the sum of the battery loss and the charging incentive price.

[0029] Specifically, the relationship between the depth of battery discharge and the number of battery cycles is as follows: , in, Indicates the depth of battery discharge; This indicates the number of battery cycles at the depth of discharge D.

[0030] When the depth of battery discharge is D, the total charge and discharge energy of the battery for: , in, This indicates the battery capacity.

[0031] Furthermore, the average total charge / discharge energy of the battery at different depths of discharge. for: , in, The number of batteries with different depths of discharge; This represents the depth of discharge of the j-th battery.

[0032] Additional battery degradation cost per unit of discharged energy The calculation formula is: , in, This indicates the cost of purchasing the battery.

[0033] Furthermore, for EV users participating in the discharge response, the aggregator needs to pay additional battery loss compensation. Therefore, the discharge compensation price in this application comprehensively considers both loss costs and charging incentive prices; the discharge compensation price for EV users... The calculation formula is: , in, Indicates the charging incentive price; This indicates the battery loss during the EV user's registration period.

[0034] Steps 1-2: Based on the product of the charging price sensitivity coefficient and the charging incentive price of EV users, obtain the dead zone inflection point charging price of EV users; based on the discharging price sensitivity coefficient, the charging incentive price, and the discharge battery loss of EV users, calculate the dead zone initial response price of EV users.

[0035] Specifically, the dead-zone inflection point electricity price is calculated based on the user's initial response price and price sensitivity coefficient. It is the critical price at which users begin to respond. The formula for calculating the dead-zone inflection point charging electricity price for EV users is as follows: , in, This represents the dead zone inflection point charging price for the i-th EV user; This represents the charging price sensitivity coefficient for the i-th EV user.

[0036] The formula for calculating the dead-zone start-up response price for EV users is as follows: , in, This represents the dead-zone start-up response price for the i-th EV user; This represents the discharge price sensitivity coefficient for the i-th EV user.

[0037] Steps 1-3: Based on the difference between the dead zone inflection point charging price and the linear range of charging for EV users, obtain the saturation zone inflection point charging price for EV users; based on the sum of the dead zone initial response price and the linear range of discharging for EV users, obtain the saturation zone response price for EV users.

[0038] Specifically, the inflection point electricity price in the saturation zone is determined by combining the user's saturation response price and the linear zone span. It represents the critical price at which the user's response reaches saturation. The formula for calculating the inflection point charging electricity price in the saturation zone for EV users is as follows:

[0039] in, This represents the charging price at the saturation inflection point for the i-th EV user; This indicates the range of the linear charging zone.

[0040] The formula for calculating the saturation zone response price for EV users is:

[0041] in, This represents the saturation zone response price for the i-th EV user; This indicates the span of the linear region of discharge.

[0042] Steps 1-4: Based on the dead-zone initiation response price and saturation zone response price of EV users, construct the relationship function between discharge quantity and discharge compensation price; based on the dead-zone inflection point charging price and saturation zone inflection point charging price of EV users, construct the relationship function between charging quantity and charging incentive price.

[0043] Specifically, the relationship between discharge quantity and discharge compensation price is expressed as follows: , in, This indicates that the i-th EV user has a discharge compensation price of Discharge power at that time; This represents the maximum discharge capacity of the i-th EV user during time period t.

[0044] Furthermore, based on the charging response characteristics of EVs, the linear region between EV charging quantity and charging incentive price can be modeled as an uncertain set, and the relationship function between charging quantity and charging incentive price is: , in, This indicates that the i-th EV user has a charging incentive price of Charging power at that time; This represents the maximum charging capacity of the i-th EV user during time period t; This represents a user's sensitivity coefficient to changes in electricity prices. This represents the parameters of a quadratic function.

[0045] Steps 1-5: Based on the relationship function between discharge quantity and discharge compensation price and the relationship function between charging quantity and charging incentive price, a phased step aggregation method is used to generate the electric energy market price curve.

[0046] Specifically, the electricity market price curve is represented as follows: , in, This represents the price quote curve for the i-th EV user, indicating their planned electricity consumption at an electricity price of x. ; Indicates the number of EV users; This represents a piecewise step-type aggregate price curve; This indicates the reported electricity consumption for segment h; Indicates the total number of segments.

[0047] Furthermore, based on the charging and discharging response characteristics of electric vehicles, a single EV state model is constructed, and an aggregated EV cluster state model is obtained, including steps 2-1 to 2-3: Step 2-1: Based on the operating characteristics of EVs during grid connection, construct a single EV state model that includes constraints on power, initial state of charge, and charge / discharge ramp rate.

[0048] Specifically, when establishing an EVA model composed of multiple individual EVs, it is necessary to first model the individual EVs. When an EV is connected to the grid, it can be simplified as an energy storage unit, and the time window for interaction with the grid is […]. ], Indicates the grid connection time. This indicates the off-grid time. During grid-connected operation, EVs must simultaneously meet power and energy limits. Therefore, the power operating boundary for a single EV is: , in, This represents the energy exchange power between the i-th EV user and the power grid during time period t; Indicates the maximum charging power; This indicates the maximum discharge power.

[0049] The initial state-of-charge operating boundary of a single EV is: , in, , These represent the upper and lower limits of the initial state of charge of the i-th EV, respectively. Indicates the research time interval; This represents the initial state of charge of the i-th EV at time t; , Let represent the charging power and discharging power of EV at time t, respectively; , Let represent the charging efficiency and discharging efficiency of the i-th EV, respectively; This represents the charging time of the i-th EV within the research time interval; Indicates the battery capacity of the EV; This indicates the minimum initial state of charge required to prevent over-discharge. This represents the initial state of charge required for the i-th EV to finish charging; This indicates the time when the i-th EV ends its energy exchange with electrical energy.

[0050] Furthermore, considering the initial state of charge and charge / discharge state of a single EV, the operating boundary of a single EV can be obtained as follows: , in, This indicates that the i-th EV is in a charging state; This indicates that the i-th EV is in a discharging state.

[0051] The single-unit EV charge and discharge response constraint ensures the safe operation of electric vehicles during grid connection by limiting the adjustable time period, setting upper and lower limits for power operation and safety boundaries for the initial state of charge, and prohibiting simultaneous charging and discharging.

[0052] Specifically, considering the EV response charging incentive, the EVA can be adjusted during the EV charging period as follows: , in, This represents the minimum time required for the i-th EV to complete its charging requirement. This indicates the initial state of charge of the EV as it begins charging. This represents the charging period of the i-th EV; This represents the adjustable time period for the charging incentive of the i-th EV response; This indicates that the i-th EV responds to the charging stimulus; This indicates that the i-th EV does not respond to the charging stimulus.

[0053] Considering the EV response discharge excitation, the EVA can be regulated during the EV discharge period for the following time period: , in, This represents the adjustable time period of the discharge excitation of the i-th EV response; This indicates that the i-th VE responds to the discharge excitation; This indicates that the i-th EV does not respond to the discharge excitation.

[0054] A single EV cannot simultaneously charge and discharge during grid-connected periods, and the charging and discharging power at each moment must be within its allowable minimum and maximum ranges. When the EV is not connected to the grid, its charging and discharging power is 0. This operational constraint is expressed as: , in, The variable is in the range of 0 to 1, used to control that the i-th EV cannot be charged and discharged simultaneously. This indicates that the i-th EV is in a charging state at time t. This indicates that the i-th EV is in a discharged state at time t.

[0055] During grid-connected operation, the initial state of charge (SBC) of a single EV must always be maintained within the set minimum and maximum ranges, and must not exceed the user's required charging capacity. During off-grid periods, the adjustable SBC of a single EV is considered to be 0. This operational constraint is expressed as: , Unlike traditional energy storage systems, the rate of change of charging and discharging power of electric vehicle batteries exhibits a significant nonlinear characteristic with respect to the initial state of charge. Therefore, the piecewise ramp rate constraint based on the initial state of charge is expressed as: , , in, , These represent the maximum charging ramp rate and the maximum discharging ramp rate of the i-th EV during time period t, respectively. , , , These represent different initial charge state segment thresholds.

[0056] The constraints for changes in charging and discharging power are: , .

[0057] Step 2-2: Use Monte Carlo sampling to generate multiple sets of charging time and initial state of charge samples for individual EVs. Input each set of samples into the individual EV state model to obtain the operating state of the individual EV corresponding to each set of samples during the grid connection period.

[0058] Steps 2-3: Aggregate the operating status of individual EVs corresponding to each group of samples during the grid connection period to obtain an EV cluster aggregated state model that includes power, initial total energy and charge / discharge ramp rate constraints.

[0059] Specifically, the EV cluster aggregation state model is represented as follows: , in, , Let represent the upper and lower energy boundaries of the EV cluster at time t, respectively; This indicates the number of EVs that the EVA can control. , Let represent the upper and lower power boundaries of the EV cluster at time t, respectively.

[0060] Furthermore, the process of constructing the electricity purchase cost equation includes steps 3-1 to 3-7: Step 3-1: Based on the product of the declared price and the power generation of each generator set in the generator set set in each time period, obtain the power generation cost of each generator set in each time period.

[0061] Step 3-2: Based on the sum of the power generation costs of all generator sets in the generator set set within each time period, obtain the total power generation cost within each time period; based on the sum of the total power generation costs within multiple preset time periods, obtain the target power generation cost of the generator set set.

[0062] Step 3-3: Obtain the charging and discharging prices of the EV cluster aggregation state model for each time period based on the electric energy market price curve.

[0063] Steps 3-4: Calculate the first product of the charging price and the charging power of the EV cluster aggregation state model in each time period, and calculate the second product of the discharging price and the discharging power of the EV cluster aggregation state model in each time period.

[0064] Steps 3-5: Based on the difference between the first product and the second product, obtain the difference between the charging cost and the discharge benefit of the EV cluster aggregation state model in each time period.

[0065] Steps 3-6: Based on the sum of the differences between charging costs and discharging benefits in the EV cluster aggregation state model over multiple preset time periods, obtain the target difference between charging costs and discharging benefits in the EV cluster aggregation state model.

[0066] Steps 3-7: Based on the difference between the target power generation cost of the generator set set and the target charging cost and discharge revenue of the EV cluster aggregation state model, the electricity purchase cost equation is constructed.

[0067] The process of constructing the frequency modulation cost equation includes steps 4-1 to 4-9: Step 4-1: Based on the product of the capacity quotation and the frequency regulation capacity of each frequency regulation unit in the frequency regulation unit set within each time period, obtain the capacity cost of each frequency regulation unit within each time period.

[0068] Step 4-2: Based on the product of the mileage quotation and the mileage of each frequency modulation unit in each time period, obtain the mileage cost of each frequency modulation unit in each time period.

[0069] Step 4-3: Based on the sum of the capacity cost and mileage cost of each frequency regulation unit in each time period, obtain the frequency regulation cost of each frequency regulation unit in each time period.

[0070] Step 4-4: Based on the sum of the frequency regulation costs of all frequency regulation units in the preset set of frequency regulation units within multiple time periods, obtain the first total frequency regulation cost.

[0071] Steps 4-5: Based on the product of the capacity quotation and frequency regulation capacity of the EV cluster aggregation state model in each time period, obtain the capacity cost of the EV cluster aggregation state model in each time period.

[0072] Steps 4-6: Based on the product of the mileage quote and the frequency regulation mileage of the EV cluster aggregated state model in each time period, obtain the mileage cost of the EV cluster aggregated state model in each time period.

[0073] Steps 4-7: Based on the sum of capacity cost and mileage cost of the EV cluster aggregation state model in each time period, obtain the frequency regulation cost of the EV cluster aggregation state model in each time period.

[0074] Steps 4-8: Based on the sum of frequency modulation costs of the EV cluster aggregation state model within multiple preset time periods, obtain the second total frequency modulation cost.

[0075] Steps 4-9: Based on the sum of the first total frequency modulation cost and the second total frequency modulation cost, construct the frequency modulation cost equation.

[0076] The process of constructing the electricity market revenue equation includes steps 5-1 to 5-6: Step 5-1: Calculate the difference between the discharge power and the charging power of the EV cluster aggregation state model in multiple preset time periods, and calculate the product of the difference and the clearing price of the electric energy market to obtain the first revenue in each time period.

[0077] Step 5-2: Based on the sum of the first revenues over multiple preset time periods, obtain the first electrical energy market revenue.

[0078] Step 5-3: Calculate the third product of the charging power and the charging incentive price of the EV cluster aggregation state model in each time period. Based on the sum of the third products in multiple preset time periods, obtain the charging price of the EV cluster aggregation state model.

[0079] Step 5-4: Calculate the fourth product of the discharge power and discharge subsidy price of the EV cluster aggregation state model in each time period. Based on the sum of the fourth products in multiple preset time periods, obtain the discharge price of the EV cluster aggregation state model.

[0080] Step 5-5: Based on the difference between the charging price and the discharging price in the EV cluster aggregation state model, obtain the second electric energy market revenue.

[0081] Steps 5-6: Based on the sum of the first and second electricity market revenues, construct the electricity market revenue equation.

[0082] The process of constructing the frequency modulation market revenue equation includes steps 6-1 to 6-3: Step 6-1: Calculate the fifth product of the frequency modulation capacity and the frequency modulation capacity clearing price of the EV cluster aggregation state model in each time period, and the sixth product of the frequency modulation mileage and the frequency modulation mileage clearing price of the EV cluster aggregation state model in each time period.

[0083] Step 6-2: Based on the sum of the fifth and sixth products, obtain the frequency modulation market revenue for each time period.

[0084] Step 6-3: Based on the sum of FM market revenue over multiple preset time periods, construct the FM market revenue equation.

[0085] Specifically, the objective function for minimizing electricity purchase costs Represented as: , in, Indicates the number of time periods; Represents a set of generator sets; This represents the bid price of the m-th generator unit within time period t; This represents the power generation of the m-th generator unit within time period t; This represents the charging price of the EV cluster aggregation state model within time period t; This represents the charging power of the EV cluster aggregation state model within time period t; This represents the discharge price of the EV cluster aggregation state model within time period t; This represents the discharge power of the EV cluster aggregation state model within time period t.

[0086] The load balance constraint of the electricity market is expressed as: , in, This represents the load demand within time period t; represents the dual variable of the load balance constraint in the electricity market within time period t, and represents the nodal marginal electricity price in the electricity market within time period t.

[0087] The generator set power generation constraint is expressed as follows: , in, , These represent the lower and upper limits of the power generation declared by the m-th generator unit, respectively; , They represent dual variables and The dual variable.

[0088] The generator set ramping constraint is expressed as: , , in, , These represent the maximum upward ramp limit and the maximum downward ramp limit during the operation of the m-th generator set, respectively. This indicates the operating status of the m-th generator unit within time period t. This indicates that the m-th generator unit is operating within time period t. This indicates that the m-th generator unit stops operating within time period t; , These represent the maximum upward ramp limit and the maximum downward ramp limit of the m-th generator set when the start-stop state changes; Let represent the dual variable of the upward climbing constraint of the m-th generator unit; Let represent the dual variable of the downward ramp constraint of the m-th generator unit.

[0089] The charge / discharge power constraint is expressed as: , , in, , These represent the charging power and discharging power reported by the EV cluster aggregation state model, respectively. express The dual variable; express The dual variable; express The dual variable; express The dual variable.

[0090] Furthermore, the objective function for minimizing frequency modulation costs Represented as: ,

[0091] in, Indicates a set of frequency modulation units; , These represent the capacity and mileage quotes for u frequency regulation units within the inland region during time period t, respectively. , These represent the frequency regulation capacity and frequency regulation mileage of u frequency regulation units within the time period t; , These represent the capacity pricing and mileage pricing of the EV cluster aggregation status model within time period t, respectively. , These represent the frequency modulation capacity and frequency modulation mileage of the EV cluster aggregation state model within time period t, respectively.

[0092] Frequency modulation requirement constraints are expressed as follows: , , in, , These represent the frequency modulation capacity demand and frequency modulation mileage demand within time period t, respectively. , These represent the dual variables of the frequency regulation capacity demand constraint and the frequency regulation mileage demand constraint, respectively, and are used to represent the nodal marginal electricity price in the frequency regulation market within time period t.

[0093] Frequency modulation capacity and mileage constraints are expressed as follows: , , , , , , in, , These represent the upper limit of frequency regulation capacity and the upper limit of frequency regulation mileage for the u-th frequency regulation unit within time period t, respectively; , These represent the frequency regulation capacity and frequency regulation mileage declared by the EV cluster aggregation status model within time period t, respectively. express The dual variable; express The dual variable; express The dual variable; express The dual variable; express The dual variable; express The dual variable; express The dual variable; express The dual variable; Represents the frequency regulation mileage multiplier for the u-th frequency regulation unit; The frequency-modulated mileage multiplier represents the aggregated state model of the EV cluster; express The dual variable; express The dual variable.

[0094] The ramp-up constraint for frequency regulation units is expressed as: , , , in, , These represent the upward and downward ramps of the u-th frequency-regulating generator unit within time period t, respectively. , These represent the maximum upward ramp limit and the maximum downward ramp limit during the operation of the u-th frequency regulation unit, respectively; This indicates the operating status of the u-th frequency regulating unit within time period t. This indicates that the u-th frequency regulating unit is operating within time period t. This indicates that the u-th frequency regulation unit is shut down within time period t; , These represent the maximum upward ramp limit and the maximum downward ramp limit of the u-th frequency regulation unit when its startup state changes; express The dual variable; express The dual variable.

[0095] Furthermore, the objective function is to minimize the sum of electricity purchase cost and frequency regulation cost. Represented as: , The coordination constraint between charging / discharging power and frequency modulation capacity is expressed as follows: , , in, express The dual variable; express The dual variable.

[0096] Furthermore, the objective function is to maximize the sum of revenues from the electricity market and the frequency regulation market. Represented as: , in, This indicates the market-clearing price for electrical energy. , These represent the frequency regulation capacity clearing price and the frequency regulation mileage clearing price within time period t, respectively.

[0097] The charge / discharge power constraint is expressed as: , Frequency modulation capacity constraints are expressed as follows: , , in, This represents the upper limit of frequency regulation capacity application for the EV cluster aggregation state model within time period t.

[0098] Optionally, the aggregator revenue optimization model also includes the declaration information constraint of the EV cluster aggregation state model, which is specifically expressed as follows: , in, , These represent the upper and lower limits of the charging price declared in the EV cluster aggregation state model, respectively. , These represent the upper and lower limits of the discharge price declared in the EV cluster aggregation state model, respectively. , These represent the upper and lower limits of the frequency regulation capacity price declared under the EV cluster aggregation state model, respectively. , These represent the upper and lower limits of the frequency modulation mileage price declared in the EV cluster aggregation state model, respectively.

[0099] Specifically, in the joint clearing scheme provided in this application, the aggregator, based on the analysis of the charging and discharging response characteristics of EV users, divides their response behavior into dead zones, linear zones, and saturation zones, and accordingly constructs a state model of a single EV and an interactive response model under the incentive electricity price; considering battery loss costs, a dynamic incentive electricity price strategy is formulated, and Monte Carlo sampling and aggregation methods are used to form an EV aggregation model characterizing the cluster response capability, thereby generating segmented price curves applicable to the electric energy market and the frequency regulation market; clearing models for the electric energy market and the frequency regulation market are constructed separately: the electric energy market clearing model is based on... The goal is to minimize the system's electricity purchase cost, while the frequency regulation market clearing model aims to minimize the system's frequency regulation resource procurement cost. Each model includes a corresponding objective function, system balance constraints, traditional unit operation constraints, and constraints on the charging / discharging capacity and frequency regulation service capacity of EVA (electric vehicle aggregators). Finally, a joint energy-frequency regulation market clearing optimization model is established, aiming to maximize the total revenue of electric vehicle aggregators in both markets. This model comprehensively considers their costs, revenues, and various technical constraints in both the energy market and the frequency regulation market, achieving overall synergistic optimization of resource allocation and bidding strategies for electric vehicle aggregators in both markets. Through this framework, electric vehicle aggregators can support the grid's frequency regulation needs while effectively guiding electric vehicle users to participate in grid interaction through charging / discharging incentive mechanisms, ultimately maximizing their own operating revenue.

[0100] Furthermore, this application also compares the above-mentioned technical solution (Scenario 4) with clearing solutions in different scenarios in the prior art (Scenarios 1 to Scenario 3): Scenario 1: The clearing mode is a sequential clearing mode, and the EV discharge demand is not considered.

[0101] Scenario 2: The clearing mode is a sequential clearing mode, taking into account the charging and discharging demand of EVs.

[0102] Scenario 3: The clearing mode is a joint clearing mode, which does not consider the EV discharge demand.

[0103] Scenario 4: The method provided in this application adopts a joint clearing mode, taking into account the EV charging and discharging demand response.

[0104] Assuming a typical 24-hour scheduling cycle, EVA manages 1000 EVs, each with a battery energy storage capacity of 100 kWh, a rated charging power of 30 kW, and a charge / discharge efficiency of 90%. The Monte Carlo method is used to simulate 1000 EVs for this study. The system's daily load curve is shown below. Figure 3 and Figure 5 As shown, the frequency modulation requirement is as follows Figure 4 and Figure 6 As shown, the duration of a single time period is 1 hour.

[0105] Table 1 shows the price information for generator sets, and Table 2 shows the price information for frequency regulation generator sets. Table 1

[0106] Table 2

[0107] To maximize operational revenue, this application constructs an optimization model for EVA's participation in the energy and frequency modulation market based on the EV charge-discharge response model that considers user volume and price curves. The example is solved by calling Gurobi through the PuLP toolbox in a Python environment.

[0108] Simulation results show that the incentive electricity price has a significant impact on the aggregator's revenue. Figure 7 The graph shows the changes in aggregator revenue under different incentive electricity prices. As can be seen from the graph, aggregator revenue first increases and then decreases with the increase of incentive electricity price: when the incentive electricity price is 220 yuan / MWh, the aggregator revenue reaches the highest value. This is because under a higher incentive electricity price, EV users are more willing to participate in the discharge response, thereby increasing the frequency regulation capacity and mileage of aggregators, and thus increasing the revenue of aggregators.

[0109] Specifically, when the incentive price is low, EV users are less motivated to participate in the discharge response, resulting in fewer frequency regulation capacity and mileage bids for aggregators and lower revenue. As the incentive price increases, EV user participation improves, and aggregators' frequency regulation capacity and mileage bids increase significantly, leading to higher revenue. However, when the incentive price exceeds a certain threshold, the increase in revenue tends to level off, or even fluctuates. This is because under excessively high incentive prices, aggregators need to pay higher discharge subsidies, increasing operating costs and negatively impacting revenue.

[0110] like Figure 3 and Figure 5 As shown, scenarios 2 and 4 consider EV discharge demand response, thus significantly increasing the number of successful discharge bids across multiple time periods. This increased discharge activity not only improves the revenue of the electric energy market but also increases the number of successful charging bids because EVs need to be charged after discharging. Figure 4 and Figure 6 As shown, the charging and discharging activities of electric vehicles enable them to participate in the market for frequency regulation services, directly increasing the available frequency regulation capacity. They can participate in the market's upward frequency regulation demand, increasing the aggregator's activity and revenue potential in the frequency regulation market.

[0111] By comparing the winning bids in different scenarios, it can be seen that Scenario 4, with its joint clearing model, allows aggregators to optimize simultaneously in both markets, taking into account the dynamics of both the electric energy market and the frequency regulation market, thus achieving an optimal clearing strategy across markets. Scenario 2, with its sequential clearing model, prioritizes the electric energy market. However, EV battery capacity is limited, and the remaining capacity decreases after charging and discharging, affecting the supply of frequency regulation capacity. Therefore, the joint clearing model can achieve significantly higher returns compared to the sequential clearing model.

[0112] Table 3 shows the aggregator revenue data under different scenarios: Table 3

[0113] Data on winning bids and revenue under different scenarios reveals the mutual influence between winning bids for charging / discharging and winning bids for frequency regulation capacity, and how EV discharge activities directly affect the provision of frequency regulation capacity. Since EVs require charging after discharging, this limits their short-term frequency regulation capacity provision capability. However, in the long run, through more effective charging / discharging management, aggregators can obtain more stable revenue in the frequency regulation market. Furthermore, joint clearing models and clearing schemes that consider EV charging / discharging needs can significantly improve aggregator revenue.

[0114] Based on the joint clearing method for electric vehicle aggregators provided in the above embodiments, this application also provides a joint clearing device for aggregators, which specifically includes: The EV cluster aggregation state model construction module is used to generate electric energy market price curves based on the charging and discharging response characteristics of EV users; it constructs individual EV state models based on the charging and discharging response characteristics of electric vehicles and aggregates them to obtain the EV cluster aggregation state model. The cost and benefit equation construction module is used to construct the electricity purchase cost equation, frequency regulation cost equation, electricity market benefit equation, and frequency regulation market benefit equation based on the electricity market price curve and the EV cluster aggregation state model. The electricity market clearing model construction module is used to construct an electricity market clearing model based on the objective function of minimizing electricity purchase cost, load balance constraints of the electricity market, generator set power generation constraints, generator set ramping constraints, and charging and discharging power constraints. The frequency regulation market clearing model construction module is used to construct a frequency regulation market clearing model based on the objective function of minimizing frequency regulation costs, frequency regulation demand constraints, frequency regulation capacity and mileage constraints, and frequency regulation unit ramping constraints. The joint clearing model construction module is used to construct a joint clearing model based on the objective function of minimizing the sum of electricity purchase cost and frequency regulation cost, and the coordination constraints of charging and discharging power and frequency regulation capacity. The module for constructing the aggregator revenue optimization model is used to construct an aggregator revenue optimization model based on the objective function of maximizing the sum of electricity market revenue and frequency regulation market revenue, as well as charging and discharging power constraints and frequency regulation capacity constraints. The clearing strategy acquisition module is used to jointly solve the electric energy market clearing model, the frequency regulation market clearing model, the joint clearing model, and the aggregator revenue optimization model to obtain the clearing strategy of electric vehicle aggregators.

[0115] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described joint clearing method for electric vehicle aggregators.

[0116] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0117] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] Computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An aggregation method of an electric vehicle aggregator, characterized by, The method comprises the following steps: Based on the charging and discharging response characteristics of EV users, an electricity market bidding curve is generated; based on the charging and discharging response characteristics of electric vehicles, a single EV state model is constructed, and an EV cluster aggregation state model is obtained by aggregation; Based on the electricity market bidding curve and the EV cluster aggregation state model, a power purchase cost equation, a frequency modulation cost equation, an electricity market revenue equation and a frequency modulation market revenue equation are constructed; Based on the power purchase cost minimization objective function, the load balance constraint of the electricity market, the power generation capacity constraint of the generator unit, the power generation unit climbing constraint and the charging and discharging power constraint, an electricity market clearing model is constructed; Based on the frequency modulation cost minimization objective function, the frequency modulation demand constraint, the frequency modulation capacity and mileage constraint and the frequency modulation unit climbing constraint, a frequency modulation market clearing model is constructed; Based on the sum minimization objective function of the power purchase cost and the frequency modulation cost, the charging and discharging power and the frequency modulation capacity coordination constraint, a joint clearing model is constructed; Based on the sum maximization objective function of the electricity market revenue and the frequency modulation market revenue, the charging and discharging power constraint and the frequency modulation capacity constraint, an aggregator revenue optimization model is constructed; The electricity market clearing model, the frequency modulation market clearing model, the joint clearing model and the aggregator revenue optimization model are jointly solved to obtain the clearing strategy of the electric vehicle aggregator.

2. The method of claim 1, wherein the method further comprises: Based on the charging and discharging response characteristics of EV users, an electricity market bidding curve is generated, comprising: Based on the battery discharge depth and the battery cycle number of the electric vehicle, the battery loss cost of unit discharge energy is calculated; based on the battery loss cost of unit discharge energy, the discharge battery loss of the EV user during the network access period is calculated; based on the sum of the discharge battery loss and the charging incentive price, the discharge compensation price of the EV user is obtained; Based on the product of the charging price sensitivity coefficient of the EV user and the charging incentive price, the dead zone inflection point charging price of the EV user is obtained; based on the discharge price sensitivity coefficient of the EV user, the charging incentive price and the discharge battery loss, the dead zone starting response price of the EV user is calculated; Based on the difference between the dead zone inflection point charging price of the EV user and the linear zone span of charging, the saturation zone inflection point charging price of the EV user is obtained; based on the sum of the dead zone starting response price of the EV user and the linear zone span of discharging, the saturation zone response price of the EV user is obtained; Based on the dead zone starting response price and the saturation zone response price of the EV user, a relationship function of the discharge amount and the discharge compensation price is constructed; based on the dead zone inflection point charging price and the saturation zone inflection point charging price of the EV user, a relationship function of the charging amount and the charging incentive price is constructed; Based on the relationship function of the discharge amount and the discharge compensation price and the relationship function of the charging amount and the charging incentive price, an electricity market bidding curve is generated by using a staged step aggregation method.

3. The method of claim 1, wherein the method further comprises: Based on the charging and discharging response characteristics of electric vehicles, a single EV state model is constructed, and an EV cluster aggregation state model is obtained by aggregation, comprising: Based on the running characteristics of the EV in the network access period, a single EV state model containing power, initial state of charge and charging and discharging climbing rate constraints is constructed; The Monte Carlo sampling method is used to generate multiple groups of samples of charging duration and initial state of charge of the single EV, each group of samples is input into the single EV state model to obtain the running state of the single EV corresponding to each group of samples in the grid-connected period; The running state of the single EV corresponding to each group of samples in the grid-connected period is aggregated to obtain an EV cluster aggregation state model containing power, initial total energy and charging and discharging ramp rate constraints.

4. The method of claim 1, wherein the method further comprises: The construction process of the electricity purchase cost equation includes: Based on the product of the declared price and the power generation of each generator set in the generator set collection in each period, the power generation cost of each generator set in each period is obtained; Based on the sum of the power generation costs of all generator sets in the generator set collection in each period, the total power generation cost in each period is obtained; based on the sum of the total power generation costs in the preset multiple periods, the target power generation cost of the generator set collection is obtained; Based on the electricity energy market quotation curve, the charging quotation and the discharging quotation of the EV cluster aggregation state model in each period are obtained; The first product of the charging quotation and the charging power of the EV cluster aggregation state model in each period is calculated, and the second product of the discharging quotation and the discharging power of the EV cluster aggregation state model in each period is calculated; Based on the difference between the first product and the second product, the difference between the charging cost and the discharging benefit of the EV cluster aggregation state model in each period is obtained; Based on the sum of the difference between the charging cost and the discharging benefit of the EV cluster aggregation state model in the preset multiple periods, the target difference between the charging cost and the discharging benefit of the EV cluster aggregation state model is obtained; Based on the difference between the target power generation cost of the generator set collection and the target difference between the charging cost and the discharging benefit of the EV cluster aggregation state model, the electricity purchase cost equation is constructed.

5. The method of claim 1, wherein the method further comprises: The construction process of the frequency modulation cost equation includes: Based on the product of the capacity quotation and the frequency modulation capacity of each frequency modulation unit in the frequency modulation unit collection in each period, the capacity cost of each frequency modulation unit in each period is obtained; Based on the product of the mileage quotation and the frequency modulation mileage of each frequency modulation unit in each period, the mileage cost of each frequency modulation unit in each period is obtained; Based on the sum of the capacity cost and the mileage cost of each frequency modulation unit in each period, the frequency modulation cost of each frequency modulation unit in each period is obtained; Based on the sum of the frequency modulation costs of all frequency modulation units in the frequency modulation unit collection in the preset multiple periods, a first total frequency modulation cost is obtained; Based on the product of the capacity quotation and the frequency modulation capacity of the EV cluster aggregation state model in each period, the capacity cost of the EV cluster aggregation state model in each period is obtained; Based on the product of the mileage quotation and the frequency modulation mileage of the EV cluster aggregation state model in each period, the mileage cost of the EV cluster aggregation state model in each period is obtained; Based on the sum of the capacity cost and the mileage cost of the EV cluster aggregation state model in each period, the frequency modulation cost of the EV cluster aggregation state model in each period is obtained; Based on the sum of the frequency modulation costs of the EV cluster aggregation state model in the preset multiple periods, a second total frequency modulation cost is obtained; Based on the sum of the first total frequency modulation cost and the second total frequency modulation cost, the frequency modulation cost equation is constructed.

6. The method of claim 1, wherein the method further comprises: The construction process of the electricity energy market benefit equation includes: The difference between the discharge power and the charge power of the EV cluster aggregation state model in a plurality of preset time periods is calculated, and the product of the difference and the clearing price of the electricity market is calculated to obtain a first income in each time period; A first electricity market income is obtained based on the sum of the first incomes in the plurality of preset time periods; A third product of the charge power of the EV cluster aggregation state model and the charge incentive price in each time period is calculated, and a charge price of the EV cluster aggregation state model is obtained based on the sum of the third products in the plurality of preset time periods; A fourth product of the discharge power of the EV cluster aggregation state model and the discharge subsidy price in each time period is calculated, and a discharge price of the EV cluster aggregation state model is obtained based on the sum of the fourth products in the plurality of preset time periods; A second electricity market income is obtained based on the difference between the charge price and the discharge price of the EV cluster aggregation state model; An electricity market income equation is constructed based on the sum of the first electricity market income and the second electricity market income.

7. The method of claim 1, wherein the method further comprises: The construction process of the frequency market income equation includes: A fifth product of the frequency regulation capacity of the EV cluster aggregation state model and the frequency regulation capacity clearing price in each time period, and a sixth product of the frequency regulation mileage of the EV cluster aggregation state model and the frequency regulation mileage clearing price in each time period are calculated; A frequency market income in each time period is obtained based on the sum of the fifth product and the sixth product; A frequency market income equation is constructed based on the sum of the frequency market incomes in the plurality of preset time periods.

8. The method of claim 1, wherein the method further comprises: The clearing strategy of the electric vehicle aggregator includes the clearing strategy of the aggregator in the electricity market and the clearing strategy of the aggregator in the frequency market.

9. An aggregator's joint clearing device for electric vehicles, characterized by, It includes: An EV cluster aggregation state model construction module for generating an electricity market bidding curve based on the charge-discharge response characteristics of EV users; an individual EV state model is constructed based on the electric vehicle charge-discharge response characteristics, and an EV cluster aggregation state model is aggregated; A cost and income equation construction module for constructing a power purchase cost equation, a frequency regulation cost equation, an electricity market income equation, and a frequency market income equation based on the electricity market bidding curve and the EV cluster aggregation state model; An electricity market clearing model construction module for constructing an electricity market clearing model based on a power purchase cost minimization objective function, a load balance constraint of the electricity market, a generator capacity constraint, a generator climbing constraint, and a charge-discharge power constraint; A frequency market clearing model construction module for constructing a frequency market clearing model based on a frequency regulation cost minimization objective function, a frequency regulation demand constraint, a frequency regulation capacity and mileage constraint, and a frequency regulation unit climbing constraint; A joint clearing model construction module for constructing a joint clearing model based on a power purchase cost and frequency regulation cost sum minimization objective function, charge-discharge power and frequency regulation capacity coordination constraint; An aggregator income optimization model construction module for constructing an aggregator income optimization model based on a sum of the electricity market income and the frequency market income maximization objective function, the charge-discharge power constraint, and the frequency regulation capacity constraint; The clearing strategy obtaining module is configured to jointly solve an electricity market clearing model, a frequency regulation market clearing model, a joint clearing model and an aggregator benefit optimization model to obtain a clearing strategy of the electric vehicle aggregator.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the joint clearing method of the electric vehicle aggregator according to any one of claims 1 to 8.

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