Electric vehicle charging and discharging scheduling method and apparatus, device, medium, and product

Abstract

The present application relates to an electric vehicle charging and discharging scheduling method and apparatus, a device, a medium, and a product. The method comprises: by taking the lowest total operation cost of a target EVA in an energy market as an objective, constructing a target scheduling function corresponding to the target EVA; determining a constraint condition of the target scheduling function, wherein the constraint condition is used for constraining the charging and discharging power and energy output of the target EVA; on the basis of the constraint condition and the target scheduling function, determining charging and discharging powers of the target EVA at different moments; on the basis of the charging and discharging powers of the target EVA at different moments, and power state data of electric vehicles having deployed under the target EVA at corresponding moments, determining a scheduling strategy of the target EVA, wherein the power state data comprises at least one of state of charge, battery charging power, and battery discharging power; and according to the scheduling strategy of the target EVA, performing charging and discharging scheduling on the electric vehicles having deployed under the target EVA. The use of the method can reduce the total operation costs of an EVA.

Description

Electric vehicle charging and discharging scheduling method, device, equipment, medium and product

[0001] The present application claims priority to the Chinese patent application No. CN202411032285.7, filed on July 30, 2024, and entitled "Electric vehicle charging and discharging scheduling method, device, equipment, medium and product", the contents of which are hereby incorporated by reference in their entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of power systems, in particular to an electric vehicle charging and discharging scheduling method, device, equipment, medium and product. BACKGROUND

[0003] With the development of electric vehicle technology, more and more electric vehicles are put into the market. At present, the mainstream electric vehicle charging mode is night charging mode, so the plug-in time of electric vehicles is usually longer than the charging time required. As a result, the power system is occupied by excess. Therefore, electric vehicles can provide load transfer and other services for the power system.

[0004] However, since a single electric vehicle cannot improve the service effect, an electric vehicle aggregator (EVA) is used to aggregate a large number of electric vehicles, and then participate in the electricity market to improve the service effect of electric vehicles for the power system, thereby improving the charging flexibility of electric vehicles. In this scenario, how to reduce the total operating cost of the EVA is a problem that needs to be solved at present. SUMMARY

[0005] Therefore, it is necessary to provide an electric vehicle charging and discharging scheduling method, device, equipment, medium and product to reduce the total operating cost of the EVA.

[0006] In a first aspect, the present application provides an electric vehicle charging and discharging scheduling method, comprising:

[0007] Constructing a target scheduling function corresponding to a target EVA, taking the minimum total operating cost of the target EVA in the energy market as the target;

[0008] Determining the constraint conditions of the target scheduling function; wherein the constraint conditions are used to constrain the charging and discharging power and energy output of the target EVA;

[0009] According to the constraint conditions and the target scheduling function, determining the corresponding charging and discharging power of the target EVA at different times;

[0010] Based on the charging and discharging power of the target EVA at different times, and the power status data of each electric vehicle deployed with the target EVA at the corresponding times, the scheduling strategy of the target EVA is determined; wherein, the power status data includes at least one of battery power, battery charging power and battery discharging power;

[0011] Based on the target EVA's scheduling strategy, charge and discharge scheduling is performed on each electric vehicle already deployed for the target EVA.

[0012] In one embodiment, with the goal of minimizing the total operating cost of the target EVA in the energy market, a target scheduling function corresponding to the target EVA is constructed, including:

[0013] Determine the energy acquisition cost of the target EVA in the first market; and,

[0014] Determine the target EVA resource growth amount in the second market;

[0015] With the goal of minimizing the difference between energy acquisition cost and resource growth, a target scheduling function corresponding to the target EVA is constructed.

[0016] In one embodiment, determining the energy acquisition cost of the target EVA in the first market includes:

[0017] Obtain the equivalent resource value of energy in the first market at different times, and the first resource demand of the target EVA for energy in the first market at different times;

[0018] For any given moment, determine the first product of the equivalent resource value of energy in the first market at that moment and the demand for the first resource;

[0019] The sum of the first products corresponding to different times within the preset time period is used as the energy acquisition cost of the target EVA in the first market.

[0020] In one embodiment, the second market includes a frequency modulation market and a demand response market; accordingly, determining the resource growth amount of the target EVA in the second market includes:

[0021] Determine the primary resource growth target for EVA in the FM market; and,

[0022] Identify the second resource growth volume for target EVA in the demand response market;

[0023] Based on the first and second resource growth rates, determine the resource growth rate of the target EVA in the second market.

[0024] In one embodiment, determining the first resource increase of the target EVA in the FM market includes:

[0025] Obtain the first and second resource values ​​of the frequency modulation market at different times; wherein, the first resource value corresponds to the frequency modulation capacity of the frequency modulation market, and the second resource value corresponds to the frequency modulation mileage of the frequency modulation market;

[0026] Obtain the FM mileage of the target EVA in the FM market at different times, and the upper limit of resource investment of the target EVA in the FM market at different times;

[0027] Based on the first resource value, the second resource value, the frequency modulation mileage, and the upper limit of resource investment, determine the first resource growth amount of the target EVA in the frequency modulation market.

[0028] In one embodiment, determining the second resource growth amount of the target EVA in the demand response market includes:

[0029] Acquire demand response resources in the demand response market at different times, and the second resource demand of the target EVA for the demand response market at different times;

[0030] For any given moment, determine the second product between the demand response resource and the demand for the second resource;

[0031] The sum of the second products corresponding to different times within the preset time period is taken as the second resource growth amount of the target EVA in the demand response market.

[0032] Secondly, this application also provides an electric vehicle charging and discharging scheduling device, comprising:

[0033] The function construction module is used to construct the target scheduling function corresponding to the target EVA with the goal of minimizing the total operating cost of the target EVA in the energy market;

[0034] The constraint determination module is used to determine the constraints of the target scheduling function; wherein, the constraints are used to constrain the charging and discharging power and energy output of the target EVA;

[0035] The charge / discharge power determination module is used to determine the charge / discharge power of the target EVA at different times based on the constraints and the target scheduling function.

[0036] The scheduling strategy determination module determines the scheduling strategy of the target EVA based on the charging and discharging power of the target EVA at different times and the power status data of each electric vehicle deployed by the target EVA at the corresponding times; wherein, the power status data includes at least one of battery power, battery charging power and battery discharging power.

[0037] The scheduling module is used to schedule the charging and discharging of each electric vehicle deployed for the target EVA according to the scheduling strategy of the target EVA.

[0038] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0039] With the goal of minimizing the total operating cost of the target EVA in the energy market, a target scheduling function corresponding to the target EVA is constructed.

[0040] Determine the constraints of the target scheduling function; whereby the constraints are used to constrain the charging and discharging power and energy output of the target EVA;

[0041] Based on the constraints and the target scheduling function, determine the charging and discharging power of the target EVA at different times;

[0042] Based on the charging and discharging power of the target EVA at different times, and the power status data of each electric vehicle deployed with the target EVA at the corresponding times, the scheduling strategy of the target EVA is determined; wherein, the power status data includes at least one of battery power, battery charging power and battery discharging power;

[0043] Based on the target EVA's scheduling strategy, charge and discharge scheduling is performed on each electric vehicle already deployed for the target EVA.

[0044] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0045] With the goal of minimizing the total operating cost of the target EVA in the energy market, a target scheduling function corresponding to the target EVA is constructed.

[0046] Determine the constraints of the target scheduling function; whereby the constraints are used to constrain the charging and discharging power and energy output of the target EVA;

[0047] Based on the constraints and the target scheduling function, determine the charging and discharging power of the target EVA at different times;

[0048] Based on the charging and discharging power of the target EVA at different times, and the power status data of each electric vehicle deployed with the target EVA at the corresponding times, the scheduling strategy of the target EVA is determined; wherein, the power status data includes at least one of battery power, battery charging power and battery discharging power;

[0049] Based on the target EVA's scheduling strategy, charge and discharge scheduling is performed on each electric vehicle already deployed for the target EVA.

[0050] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0051] With the goal of minimizing the total operating cost of the target EVA in the energy market, a target scheduling function corresponding to the target EVA is constructed.

[0052] Determine the constraints of the target scheduling function; whereby the constraints are used to constrain the charging and discharging power and energy output of the target EVA;

[0053] Based on the constraints and the target scheduling function, determine the charging and discharging power of the target EVA at different times;

[0054] Based on the charging and discharging power of the target EVA at different times, and the power status data of each electric vehicle deployed with the target EVA at the corresponding times, the scheduling strategy of the target EVA is determined; wherein, the power status data includes at least one of battery power, battery charging power and battery discharging power;

[0055] Based on the target EVA's scheduling strategy, charge and discharge scheduling is performed on each electric vehicle already deployed for the target EVA.

[0056] The aforementioned electric vehicle charging and discharging scheduling method, device, equipment, medium, and product aim to minimize the total operating cost of the target EVA in the energy market. A target scheduling function corresponding to the target EVA is constructed, and its constraints are determined. During the solution process for the target scheduling function, these constraints are considered to ensure that the solution result—the charging and discharging power of the target EVA at different times—minimizes the total operating cost of the target EVA in the energy market. Furthermore, based on the calculated charging and discharging power at different times, the scheduling of each electric vehicle deployed with the target EVA is conducted by considering the power state data of each electric vehicle at the corresponding time, making the scheduling of each electric vehicle more rational. In other words, the entire process reduces the operating cost of the target EVA while ensuring scheduling rationality. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 is a flowchart illustrating an electric vehicle charging and discharging scheduling method in one embodiment;

[0059] Figure 2 is a flowchart illustrating the steps of constructing the target scheduling function in one embodiment;

[0060] Figure 3 is a flowchart illustrating the steps for determining energy acquisition costs in one embodiment;

[0061] Figure 4 is a flowchart illustrating the steps for determining resource growth in one embodiment;

[0062] Figure 5 is a flowchart illustrating the scheduling strategy determination steps in one embodiment;

[0063] Figure 6 is a structural block diagram of an electric vehicle charging and discharging scheduling device in one embodiment;

[0064] Figure 7 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0066] Before introducing the electric vehicle charging and discharging scheduling method provided in this application, it should be noted that, against the backdrop of power system decarbonization and transportation system electrification, the number of electric vehicles (EVs) is growing rapidly. With the continuous improvement of battery capacity and the continuous reduction of battery degradation costs, coupled with the support of vehicle-to-grid (V2G) technology, electric vehicles can become a flexible resource in the electricity market.

[0067] Due to the driving habits of electric vehicle (EV) owners, especially the current mainstream practice of charging overnight, the plug-in time for EVs is typically much longer than the charging time. This resulting charging flexibility—the ability to adjust charging or discharging times—allows EVs to provide services such as load shifting, frequency regulation, and demand response to the power grid. However, this flexibility in grid operation can also cause some losses for EV owners, such as battery degradation during discharge and insufficient state of charge to meet driving needs. These losses can be compensated for by participating in the electricity market. However, because the battery capacity and power of a single EV are limited, an EVA (Electric Vehicle Alliance) typically needs to aggregate a large number of EVs to reach the threshold for participating in the electricity market. In the process of EVA participating in market dispatch, the EVA first needs to quantify the available regulation capacity of its EVs and then submit a unified bid in the electricity market.

[0068] Currently, EVA's competitive dispatch model in the electricity market only engages in arbitrage in the energy market, ignoring the high subsidies in the frequency regulation market, demand response market, and other electricity markets; furthermore, there is no joint dispatch strategy for EVA to participate in the energy market, frequency regulation market, and demand response market.

[0069] Secondly, uncertainty in the EVA scheduling model is another significant issue, stemming from several sources: First, the parameters of the electric vehicle itself, such as rated energy and power, charging demand, plug-in time, and disconnection time, directly affect its available regulation capacity, but these parameters are typically random. Second, the frequency modulation (FM) market signal. FM signals have high temporal granularity, usually transmitted every few seconds. In EVA participation in the FM market, it is generally assumed that the FM signal is neutral within the scheduling interval (i.e., ignoring the influence of the FM signal on the electric vehicle's charging and discharging decisions). However, a biased regulation signal can severely impact the regulation performance of the electric vehicle and may ultimately affect the state of charge upon disconnection, ultimately influencing the electric vehicle owner's driving plan. Both the electric vehicle parameters and the FM signal are determined or influenced by external factors, such as weather and consumer behavior, making them random and difficult to predict. Furthermore, the high temporal granularity of the FM signal imposes a significant computational burden on accurate modeling.

[0070] Against this backdrop, this application provides a novel electric vehicle charging and discharging scheduling method to fully utilize the flexibility of electric vehicles. This not only improves the operating returns of electric vehicles (EVs) but also provides greater flexibility to the power grid, ensuring the stable operation of the power system.

[0071] In one embodiment, as shown in Figure 1, a method for scheduling the charging and discharging of an electric vehicle is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0072] S110, with the goal of minimizing the total operating cost of the target EVA in the energy market, construct the target scheduling function corresponding to the target EVA.

[0073] Here, the target EVA is the EVA that currently requires electric vehicle scheduling. The target scheduling function is used to determine the scheduling strategy for the target EVA. The energy market includes the energy acquisition market and the energy output market; in the power system field, the energy market can include various electricity trading markets.

[0074] For example, in this embodiment, the resource acquisition cost of the target EVA in the energy market can be determined, and then the resource output cost corresponding to the resource output of the target EVA can be determined. The difference between the resource output cost and the resource acquisition cost is used as the total operating cost of the target EVA.

[0075] S120, Determine the constraints of the target scheduling function.

[0076] Among them, the constraints are used to constrain the charging and discharging power and energy output of the target EVA.

[0077] S130, based on the constraints and the target scheduling function, determine the charging and discharging power of the target EVA at different times.

[0078] For example, in this embodiment, the target scheduling function can be solved. During the solution process, the constraint conditions are used to determine the solution result corresponding to the target scheduling function, and the solution result is used as the charging and discharging power of the target EVA at different times.

[0079] Optionally, in this embodiment, the target scheduling function can be solved in various ways. For example, it can be solved using particle swarm optimization or bird flocking optimization. Alternatively, a commercial optimization solver can be used to solve the target scheduling function; there is no limitation on this approach.

[0080] S140, based on the charging and discharging power of the target EVA at different times, and the power status data of each electric vehicle deployed with the target EVA at the corresponding times, determine the scheduling strategy of the target EVA.

[0081] The power status data includes at least one of battery charge, battery charging power, and battery discharging power. The scheduling strategy for the target EVA is the scheduling method for different electric vehicles. For example, the scheduling strategy includes the power output value of each electric vehicle at different times.

[0082] For example, at any given moment, the charging and discharging power of the target EVA at that moment is obtained, and this power is then allocated to each electric vehicle already deployed with the target EVA. The charging and discharging power at that moment is then allocated to each electric vehicle. During the allocation of the charging and discharging power to each electric vehicle, the power state data of each electric vehicle at that moment needs to be considered to avoid incorrect scheduling.

[0083] S150, according to the scheduling strategy of the target EVA, performs charging and discharging scheduling for each electric vehicle already deployed for the target EVA.

[0084] In the aforementioned electric vehicle charging and discharging scheduling method, the objective is to minimize the total operating cost of the target EVA in the energy market. A target scheduling function is constructed for the target EVA, and its constraints are determined. During the solution process for the target scheduling function, these constraints are considered to ensure that the solution, i.e., the charging and discharging power of the target EVA at different times, minimizes the total operating cost of the target EVA in the energy market. Furthermore, based on the calculated charging and discharging power at different times, the scheduling of each electric vehicle deployed with the target EVA is conducted by considering the power state data of each electric vehicle at the corresponding time, making the scheduling of each electric vehicle more rational. In other words, the entire process reduces the operating cost of the target EVA while ensuring scheduling rationality.

[0085] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this embodiment, the process of constructing the target scheduling function corresponding to the target EVA with the goal of minimizing the total operating cost of the target EVA in the energy market is refined.

[0086] Referring to the target scheduling function construction steps shown in Figure 2, the steps include:

[0087] S210, determine the energy acquisition cost of the target EVA in the first market.

[0088] In this context, the primary market refers to the resource provider; in the power sector, the primary market can be the electricity market. Energy acquisition cost can be used to characterize the total electricity purchase cost of the target EVA in the electricity market.

[0089] For example, in this embodiment, the energy purchase price per unit of the target EVA in the first market and the total amount of energy purchased can be used as the energy acquisition cost of the target EVA in the first market.

[0090] S220, determine the resource growth amount of target EVA in the second market.

[0091] The secondary market comprises resource acquirers and resource regulators. In the power sector, the secondary market can include the frequency regulation market and the demand response market. Energy growth can be used to characterize the revenue from the sale of electricity in the secondary market based on the target EVA.

[0092] For example, in this embodiment, the resource growth of the target EVA in the second market can be determined by energy acquisition costs and energy sales revenue.

[0093] S230 constructs a target scheduling function corresponding to the target EVA with the goal of minimizing the difference between energy acquisition cost and resource growth.

[0094] It should be noted that although S210 and S220 shown in Figure 2 have a sequential relationship, in actual application, the execution steps of S210 and S220 are not in any particular order. S210 can be executed first and then S220, or S210 can be executed first and then S220, or S210 and S220 can be executed simultaneously. There is no limitation on this.

[0095] In the above embodiments, the process of constructing the target scheduling function corresponding to the target EVA with the goal of minimizing the total operating cost of the target EVA in the energy market has been refined, making the process of constructing the target scheduling function corresponding to the target EVA clearer and more rigorous.

[0096] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this embodiment, the process of determining the energy acquisition cost of the target EVA in the first market is described in detail.

[0097] Referring to the steps for determining energy acquisition costs shown in Figure 3, the steps include:

[0098] S310, obtain the equivalent resource value of energy in the first market at different times, and the first resource demand of the target EVA for energy in the first market at different times.

[0099] Among them, the equivalent resource value can represent the energy price; the first resource demand is used to represent the charging and discharging power declared by the target EVA.

[0100] S320, for any given moment, determine the first product of the equivalent resource value of energy in the first market at that moment and the first resource demand.

[0101] S330 uses the sum of the first products corresponding to different times within a preset time period as the energy acquisition cost of the target EVA in the first market.

[0102] The duration of the preset time period can be determined based on human experience and is not limited thereto.

[0103] For example, in this embodiment, the formula for determining the energy acquisition cost of the target EVA in the first market can be as follows:

[0104] In the formula, Income1 represents the energy acquisition cost of the target EVA in the first market; P represents the energy price at time t; t This represents the charging and discharging power reported by EVA at time t; T represents the scheduling period, i.e. the duration of the preset time period.

[0105] The above embodiments detail the process of determining the energy acquisition cost of the target EVA in the first market, making the process of determining the energy acquisition cost clearer.

[0106] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this embodiment, the second market includes a frequency modulation market and a demand response market. This embodiment provides a specific process for determining the resource growth amount of the target EVA in the second market when the second market includes both a frequency modulation market and a demand response market.

[0107] Referring to the steps for determining resource growth shown in Figure 4, the steps include:

[0108] S410 identifies the primary resource growth target for EVA in the FM market.

[0109] Among them, the first resource growth amount is used to characterize the market return of the target EVA in the frequency modulation market.

[0110] For example, the first resource value and the second resource value of the FM market at different times can be obtained; wherein the first resource value corresponds to the FM capacity of the FM market and the second resource value corresponds to the FM mileage of the FM market; the FM mileage of the target EVA for the FM market at different times and the upper limit of resource investment of the target EVA for the FM market at different times can be obtained; based on the first resource value, the second resource value, the FM mileage and the upper limit of resource investment, the first resource growth amount of the target EVA in the FM market can be determined.

[0111] Among them, the first resource value represents the price of frequency modulation capacity in the frequency modulation market; the second resource value represents the price of frequency modulation mileage in the frequency modulation market.

[0112] For example, the formula for determining the first resource growth of the target EVA in the FM market can be as follows:

[0113] In the formula, Income2 represents the first resource growth of the target EVA in the FM market; The predicted FM capacity price at time t; R represents the price per mileage in the FM market at time t. ω,t For the target EVA in the ω scenario, the reserved frequency modulation bid amount at time t, i.e., the upper limit of resource investment; m ω,t Let ω represent the frequency-adjusted mileage of the target EVA at time t in the ω scenario; T represents the scheduling period, i.e. the duration of the preset time period; ω represents the energy trading scenario in which the target EVA is located; and Ω represents the set of energy trading scenarios.

[0114] S420 identifies the second resource growth volume of target EVA in the demand response market.

[0115] The second resource growth is used to characterize the market return of the target EVA in the demand response market.

[0116] For example, the demand response resources in the demand response market at different times and the second resource demand of the target EVA in the demand response market at different times can be obtained; for any given time, the second product between the demand response resources and the second resource demand is determined; and the sum of the second products corresponding to different times within a preset time period is taken as the second resource growth of the target EVA in the demand response market.

[0117] Among them, demand response resources represent the predicted demand response price; the second resource demand represents the amount of target EVA participating in the demand response bidding under scenario ω.

[0118] For example, the formula for determining the second resource growth amount of the target EVA in the demand response market can be as follows:

[0119] In the formula, Income3 represents the second resource growth of the target EVA in the demand response market; D represents the demand response price of the target EVA at time t; ω,t ω represents the bid amount for the target EVA's demand response at time t in scenario ω; T represents the scheduling period, i.e. the duration of the preset time period; ω represents the energy trading scenario in which the target EVA is located; Ω represents the set of energy trading scenarios.

[0120] S430, based on the first resource growth and the second resource growth, determine the resource growth of the target EVA in the second market.

[0121] In this embodiment, the sum of the first resource growth and the second resource growth can be used as the resource growth of the target EVA in the second market.

[0122] The above embodiments provide a specific process for determining the resource growth of the target EVA in the second market, which includes both the frequency modulation market and the demand response market, making the process of determining the resource growth of the target EVA in the second market clearer and more rigorous.

[0123] Based on the above, the target scheduling function corresponding to the target EVA can be as follows:

[0124] In the formula, obj represents the total operating cost of the target EVA in the energy market; P represents the energy price at time t; t This represents the charge / discharge power declared by EVA at time t; The predicted FM capacity price at time t; R represents the price per mileage in the FM market at time t. ω,t For the target EVA in the ω scenario, the reserved frequency modulation bid amount at time t, i.e., the upper limit of resource investment; m ω,t For the ω scenario, the frequency modulation mileage of the target EVA at time t; D represents the demand response price of the target EVA at time t; ω,t ω represents the bid amount for the target EVA's demand response at time t in scenario ω; T represents the scheduling period, i.e. the duration of the preset time period; ω represents the energy trading scenario in which the target EVA is located; Ω represents the set of energy trading scenarios.

[0125] In this embodiment, the constraints of the target scheduling function can be as follows:

[0126] (1) Energy balance constraint: Calculate at each time step, the planned charging and discharging power of the target EVA minus the actual charging and discharging power of the electric vehicle participating in frequency regulation and demand response markets should equal the actual charging and discharging power of the electric vehicle, i.e.:

[0127] Among them, P t The planned charge / discharge power of the target EVA at time t; R is the average up / down frequency modulated signal over time t; ω,t D is the frequency modulation power plan for the target EVA at time t; ω,t Plan the demand response power of the target EVA at time t; The actual power of the i-th vehicle under the up / down frequency modulation signal at time t; Let be the actual power of the target EVA under the up / down frequency modulation signal at time t.

[0128] (2) Actual charge and discharge power constraints:

[0129] in, and The actual charging and discharging power of the i-th vehicle under up / down frequency modulation signals at time t; Let be the actual power of the i-th vehicle under the up / down frequency modulation signal at time t; it should be noted that the charging and discharging power are both non-negative and the charging and discharging behaviors cannot be performed simultaneously.

[0130] (3) Average discharge power constraint, calculated based on the actual charging and discharging power under the up / down frequency modulation signal at time t and its corresponding duration, i.e.:

[0131] In the formula, This represents the average discharge power. Average down-modulated signal Corresponding duration; Average up-modulated signal The corresponding duration; Let be the actual discharge power of the i-th vehicle under the up-modulated signal at time t; Let be the actual discharge power of the i-th vehicle under the frequency modulation signal at time t.

[0132] (4) The bid volume for the target EVA in the FM and demand response markets should be non-negative:

[0133] Among them, R ω,t and D ω,t These represent the bid volumes for the target EVA in the frequency modulation market and the demand response market at time t, respectively.

[0134] (5) Power upper and lower bound constraints: The actual charging and discharging power of an electric vehicle cannot exceed its upper and lower bounds, i.e.:

[0135] In the formula, Indicates the lower limit of power; Indicates the upper limit of power; Let be the actual power of the i-th vehicle under the up / down frequency modulation signal at time t.

[0136] (6) Energy constraints on the target EVA, including energy update constraints and energy upper and lower bound constraints:

[0137] The energy update constraint can be as follows:

[0138] In the formula, E ω,t E represents the energy of the target EVA at time t; ω,t-1 This represents the energy of the target EVA at time t-1; Average up-modulated signal The corresponding duration; Average down-modulated signal Corresponding duration; η c For charging efficiency; η d Indicates discharge efficiency; This represents the actual charging power of the target EVA under the up-modulation signal at time t; This represents the actual discharge power of the target EVA under the up-modulation signal at time t; This represents the actual charging power of the target EVA under the frequency modulation signal at time t; This represents the actual discharge power of the target EVA under a frequency modulation signal at time t.

[0139] The upper and lower limits of energy can be constrained as follows:

[0140] In the formula, This represents the lower limit of the energy of the i-th vehicle with the target EVA already deployed at time t; E represents the lower limit of the target EVA's energy; ω,t This represents the actual energy of the target EVA; This indicates the upper limit of the target EVA's energy; This represents the energy limit of the i-th vehicle with the target EVA deployed at time t.

[0141] It should be noted that, typically, an independent system operator will broadcast an FM signal every 2 seconds. t,d ∈[-1,1] refers to the ratio of the offset (increase or decrease) of EVA power over time interval d in hours t to its planned charging and discharging power. Power systems generate Area Control Error (ACE) during real-time operation. When ACE is less than 0, the independent system operator issues an up-frequency modulation signal s. t,d When ACE > 0, it guides electric vehicles to reduce charging power or increase discharging power based on the planned charging and discharging power to make up for the power deficit; when ACE is greater than 0, the independent system operator sends a down-modulation signal s. t,d <0, instructing electric vehicles to increase charging power or decrease discharging power based on planned charging and discharging power, thus consuming power. The frequency modulation signal trajectory within one hour is represented as a vector {s}. t,1 ,s t,2 ,...,s t,1800 Considering the load characteristics of electric vehicles, we define charging power as positive and discharging power as negative. We also consider the aggregated charging and discharging power plan P of Distributed Energy Resources (DER). t Frequency modulation capacity R t and adjustment signal s t,d The actual power of EVA at interval t hours (on the grid side) is P. t -s t,d R t However, a time precision of 2 seconds can impose a huge computational burden when constructing optimization problems.

[0142] Therefore, by calculating the average up / down frequency modulation signal and their corresponding duration To accurately characterize power changes while ensuring computational efficiency, the following constraints apply:

[0143] in, Let be the average up-modulated signal in the t-th hour; Let be the average down-modulated signal in hour t. It is in the hour t, s t,d The number of items ≥ 0.

[0144] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this embodiment, the process of determining the scheduling strategy of the target EVA according to the charging and discharging power of the target EVA at different times, and the power state data of each electric vehicle deployed with the target EVA at the corresponding times, is refined.

[0145] Referring to the scheduling strategy determination steps shown in Figure 5, the steps include:

[0146] S510 models the user behavior of each electric vehicle.

[0147] For example, the Monte Carlo algorithm is used based on probability distributions to determine the relevant behavioral parameters of electric vehicles i∈N. Among them B i Let be the battery capacity (kWh) of the i-th electric vehicle; The time when the i-th electric vehicle begins charging (arrival time); The time it takes to complete charging the i-th electric vehicle (departure time); The remaining state-of-charge (SOC) of the i-th electric vehicle when it is connected to the charging station; The target charging amount for the i-th electric vehicle; The maximum charging power for electric vehicle i; This represents the maximum discharge power of electric vehicle i. and These are the charging efficiency and discharging efficiency of the i-th electric vehicle, respectively. When an electric vehicle user submits their data, the maximum feasible efficiency is calculated. and with user submissions By comparing and determining whether the charging needs of electric vehicles can be met under the condition of immediate charging, information will be provided to the car owner. And it requires that its target charging capacity input value be within Within.

[0148] S520 determines the charging and discharging strategies for different electric vehicles based on energy balance constraints, and schedules the charging and discharging of the corresponding electric vehicles based on their respective strategies.

[0149] The energy balance constraints are as follows:

[0150] Among them, P t The planned charge / discharge power of the target EVA at time t; R is the average up / down frequency modulated signal over time t; ω,t D is the frequency modulation power plan for the target EVA at time t; ω,t Plan the demand response power of the target EVA at time t; The actual power of the i-th vehicle under the up / down frequency modulation signal at time t; Let be the actual power of the target EVA under the up / down frequency modulation signal at time t.

[0151] Based on the above formula, the charging and discharging strategies for different electric vehicles are determined, namely... The following constraints need to be considered during the process:

[0152] (1) Upper and lower limits of charging and discharging power: These represent the boundary constraints of the charging and discharging power of the electric vehicle, i.e., the power regulation domain of the electric vehicle.

[0153] In the formula, and Let be the lower and upper limits of the power of electric vehicle i at time t, respectively. The maximum charging power for electric vehicle i This represents the maximum discharge power of electric vehicle i.

[0154] (2) Energy upper and lower bounds:

[0155] In the formula, the upper bound of energy This corresponds to the scenario where an electric vehicle is charged at its maximum charging power after being connected to the grid until it reaches its maximum capacity; the lower bound of energy. This corresponds to an electric vehicle first discharging at maximum power after being connected to the grid, and then starting to charge at maximum power as late as possible so that it reaches the required amount of electricity when it leaves the grid. Let be the remaining energy of the i-th electric vehicle; The charging efficiency of the i-th electric vehicle; This represents the upper limit of the charging power for the i-th electric vehicle; This indicates the time when the i-th electric vehicle begins charging. This represents the maximum energy capacity of the i-th electric vehicle; η represents the lower limit of energy for the i-th electric vehicle; d Indicates the discharge efficiency of an electric vehicle; η c This indicates the charging efficiency of electric vehicles.

[0156] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0157] Based on the same inventive concept, this application also provides an electric vehicle charging and discharging scheduling device for implementing the electric vehicle charging and discharging scheduling method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the electric vehicle charging and discharging scheduling device provided below can be found in the limitations of the electric vehicle charging and discharging scheduling method described above, and will not be repeated here.

[0158] In an exemplary embodiment, as shown in FIG6, an electric vehicle charging and discharging scheduling device is provided, including: a function construction module 610, a constraint condition determination module 620, a charging and discharging power determination module 630, a scheduling strategy determination module 640, and a scheduling module 650, wherein:

[0159] Function construction module 610 is used to construct the target scheduling function corresponding to the target EVA with the goal of minimizing the total operating cost of the target EVA in the energy market.

[0160] The constraint determination module 620 is used to determine the constraints of the target scheduling function.

[0161] Among them, the constraints are used to constrain the charging and discharging power and energy output of the target EVA.

[0162] The charge / discharge power determination module 630 is used to determine the charge / discharge power of the target EVA at different times based on the constraints and the target scheduling function.

[0163] The scheduling strategy determination module 640 determines the scheduling strategy of the target EVA based on the charging and discharging power of the target EVA at different times and the power status data of each electric vehicle deployed by the target EVA at the corresponding times.

[0164] The power status data includes at least one of battery power, battery charging power, and battery discharging power.

[0165] The scheduling module 650 is used to schedule the charging and discharging of each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA.

[0166] In one embodiment, the function construction module 610 includes a cost determination unit for determining the energy acquisition cost of the target EVA in the first market; a growth determination unit for determining the resource growth of the target EVA in the second market; and a function construction unit for constructing a target scheduling function corresponding to the target EVA with the objective of minimizing the difference between the energy acquisition cost and the resource growth.

[0167] In one embodiment, the cost determination unit includes a first determination subunit, configured to obtain the equivalent resource value of energy in the first market at different times, and the first resource demand of the target EVA for energy in the first market at different times; a second determination subunit, configured to determine, for any given time, the first product of the equivalent resource value of energy in the first market and the first resource demand; and a cost determination subunit, configured to use the sum of the first products corresponding to different times within a preset time period as the energy acquisition cost of the target EVA in the first market.

[0168] In one embodiment, the second market includes a frequency modulation market and a demand response market; correspondingly, the growth determination unit includes a first growth determination subunit for determining a first resource growth amount of the target EVA in the frequency modulation market; a second growth determination subunit for determining a second resource growth amount of the target EVA in the demand response market; and a third growth determination subunit for determining the resource growth amount of the target EVA in the second market based on the first and second resource growth amounts.

[0169] In one embodiment, the first growth amount determination subunit is specifically used to obtain the first resource value and the second resource value of the frequency modulation market at different times; wherein, the first resource value corresponds to the frequency modulation capacity of the frequency modulation market, and the second resource value corresponds to the frequency modulation mileage of the frequency modulation market; obtain the frequency modulation mileage of the target EVA for the frequency modulation market at different times, and the upper limit of resource investment of the target EVA for the frequency modulation market at different times; and determine the first resource growth amount of the target EVA in the frequency modulation market based on the first resource value, the second resource value, the frequency modulation mileage, and the upper limit of resource investment.

[0170] In one embodiment, the second growth determination subunit is specifically used to acquire the demand response resources of the demand response market at different times, and the second resource demand of the target EVA for the demand response market at different times; for any given time, determine the second product between the demand response resources and the second resource demand; and take the sum of the second products corresponding to different times within a preset time period as the second resource growth of the target EVA in the demand response market.

[0171] Each module in the aforementioned electric vehicle charging and discharging scheduling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0172] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram is shown in Figure 7. The computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a charging and discharging scheduling method for electric vehicles.

[0173] Those skilled in the art will understand that the structure shown in Figure 7 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0174] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0175] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0176] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0177] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0178] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0179] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for scheduling the charging and discharging of electric vehicles, characterized in that, The method includes: With the goal of minimizing the total operating cost of the target electric vehicle aggregator EVA in the energy market, a target scheduling function corresponding to the target EVA is constructed. Determine the constraints of the target scheduling function; wherein the constraints are used to constrain the charging and discharging power and energy output of the target EVA; Based on the constraints and the target scheduling function, determine the charging and discharging power of the target EVA at different times; Based on the charging and discharging power of the target EVA at different times, and the power status data of each electric vehicle deployed with the target EVA at the corresponding times, a scheduling strategy for the target EVA is determined; wherein, the power status data includes at least one of battery power, battery charging power, and battery discharging power; According to the scheduling strategy of the target EVA, the charging and discharging of each electric vehicle that has been deployed with the target EVA is scheduled.

2. The method according to claim 1, characterized in that, The objective of constructing the target scheduling function corresponding to the target EVA, with the goal of minimizing the total operating cost of the target EVA in the energy market, includes: Determine the energy acquisition cost of the target EVA in the first market; and, Determine the resource growth amount of the target EVA in the second market; With the goal of minimizing the difference between the energy acquisition cost and the resource growth, a target scheduling function corresponding to the target EVA is constructed.

3. The method according to claim 2, characterized in that, Determining the energy acquisition cost of the target EVA in the first market includes: Obtain the equivalent resource value of energy in the first market at different times, and the first resource demand of the target EVA for energy in the first market at different times; For any given moment, determine the first product of the equivalent resource value of energy in the first market at that moment and the first resource demand; The sum of the first products corresponding to different times within a preset time period is taken as the energy acquisition cost of the target EVA in the first market.

4. The method according to claim 2, characterized in that, The second market includes the frequency modulation market and the demand response market; correspondingly, determining the resource growth of the target EVA in the second market includes: Determine the first resource growth amount for the target EVA in the FM market; and, Determine the second resource growth amount for the target EVA in the demand response market; Based on the first resource growth and the second resource growth, the resource growth of the target EVA in the second market is determined.

5. The method according to claim 4, characterized in that, Determining the first resource growth amount of the target EVA in the FM market includes: Obtain the first resource value and the second resource value of the frequency modulation market at different times; wherein, the first resource value corresponds to the frequency modulation capacity of the frequency modulation market, and the second resource value corresponds to the frequency modulation mileage of the frequency modulation market; Obtain the frequency modulation mileage of the target EVA in the frequency modulation market at different times, and the upper limit of resource investment of the target EVA in the frequency modulation market at different times; Based on the first resource value, the second resource value, the frequency modulation mileage, and the upper limit of resource investment, the first resource growth amount of the target EVA in the frequency modulation market is determined.

6. The method according to claim 4, characterized in that, Determining the second resource growth amount of the target EVA in the demand response market includes: Obtain the demand response resources of the demand response market at different times, and the second resource demand of the target EVA for the demand response market at different times; For any given moment, determine the second product between the demand response resource and the demand for the second resource; The sum of the second products corresponding to different times within the preset time period is taken as the second resource growth amount of the target EVA in the demand response market.

7. A charging and discharging scheduling device for electric vehicles, characterized in that, The device includes: The function construction module is used to construct the target scheduling function corresponding to the target EVA with the goal of minimizing the total operating cost of the target EVA in the energy market; A constraint determination module is used to determine the constraint conditions of the target scheduling function; wherein the constraint conditions are used to constrain the charging and discharging power and energy output of the target EVA; The charge / discharge power determination module is used to determine the charge / discharge power of the target EVA at different times based on the constraints and the target scheduling function. The scheduling strategy determination module determines the scheduling strategy of the target EVA based on the charging and discharging power of the target EVA at different times and the power status data of each electric vehicle deployed with the target EVA at the corresponding times; wherein, the power status data includes at least one of battery power, battery charging power and battery discharging power; The scheduling module is used to schedule the charging and discharging of each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images