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

Abstract

The application relates to an electric vehicle charging and discharging scheduling method, device, equipment, medium and product. The application comprises the following steps: constructing a target scheduling function corresponding to a target EVA with the lowest total operation cost of the target EVA in an energy market as a target; determining constraint conditions of the target scheduling function; wherein the constraint conditions are used for constraining the charging and discharging power and energy output of the target EVA; determining the corresponding charging and discharging power of the target EVA at different time according to the constraint conditions and the target scheduling function; determining a scheduling strategy of the target EVA according to the corresponding charging and discharging power of the target EVA at different time and the power state data of each electric vehicle deployed by the target EVA at the corresponding time; wherein the power state data comprises at least one of a battery capacity, a battery charging power and a battery discharging power; and performing charging and discharging scheduling on each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA. The method can reduce the total operation cost of the EVA.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, equipment, medium and product for electric vehicle charging and discharging scheduling. Background Technology

[0002] With the development of electric vehicle technology, more and more electric vehicles are entering the market. Currently, the mainstream electric vehicle charging mode is nighttime charging mode. Therefore, the plugging time of electric vehicles is usually longer than the charging time, resulting in the power system being over-occupied. Therefore, electric vehicles can provide load transfer and other services to the power system.

[0003] However, since a single electric vehicle cannot improve service efficiency, Electric Vehicle Aggregators (EVAs) have emerged to aggregate large numbers of electric vehicles to participate in the electricity market, thereby improving the service efficiency of electric vehicles to the power system and enhancing charging flexibility. In this scenario, reducing the total operating cost of EVAs is a pressing issue that needs to be addressed. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, medium, and product for electric vehicle charging and discharging scheduling to address the aforementioned technical problems, which can reduce the total operating cost of EVA.

[0005] Firstly, this application provides a method for scheduling the charging and discharging of an electric vehicle, including:

[0006] 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.

[0007] 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;

[0008] Based on the constraints and the target scheduling function, determine the charging and discharging power of the target EVA at different times;

[0009] 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;

[0010] Based on the target EVA's scheduling strategy, charge and discharge scheduling is performed on each electric vehicle already deployed for the target EVA.

[0011] In one of the embodiments, a target EVA corresponding target scheduling function is constructed with the target of minimizing the total operation cost of the target EVA in the energy market, including:

[0012] determining the energy acquisition cost of the target EVA in the first market; and

[0013] determining the resource growth amount of the target EVA in the second market;

[0014] constructing the target EVA corresponding target scheduling function with the target of minimizing the difference between the energy acquisition cost and the resource growth amount.

[0015] In one of the embodiments, the energy acquisition cost of the target EVA in the first market is determined, including:

[0016] obtaining the equivalent resource value of the energy in the first market at different time points, and the first resource demand amount of the target EVA for the energy in the first market at different time points;

[0017] for any time point, determining the first product of the equivalent resource value of the energy in the first market at the time point and the first resource demand amount;

[0018] taking the sum of the first products corresponding to different time points in a preset period as the energy acquisition cost of the target EVA in the first market.

[0019] In one of the embodiments, the second market includes the frequency modulation market and the demand response market; accordingly, the resource growth amount of the target EVA in the second market is determined, including:

[0020] determining the first resource growth amount of the target EVA in the frequency modulation market; and

[0021] determining the second resource growth amount of the target EVA in the demand response market;

[0022] determining the resource growth amount of the target EVA in the second market according to the first resource growth amount and the second resource growth amount.

[0023] In one of the embodiments, the first resource growth amount of the target EVA in the frequency modulation market is determined, including:

[0024] obtaining the first resource value and the second resource value of the frequency modulation market at different time points; 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;

[0025] obtaining the frequency modulation mileage of the target EVA for the frequency modulation market at different time points, and the resource input upper limit of the target EVA for the frequency modulation market at different time points;

[0026] According to the first resource value, the second resource value, the frequency modulation mileage, and the resource input upper limit, a first resource growth amount of the target EVA in the frequency modulation market is determined.

[0027] In one of the embodiments, determining a second resource growth amount of the target EVA in the demand response market comprises:

[0028] Obtaining demand response resources of the demand response market at different time points, and second resource demand amounts of the target EVA for the demand response market at the different time points;

[0029] For any time point, determining a second product between the demand response resources and the second resource demand amounts;

[0030] Taking a sum value of the second products corresponding to the different time points in a preset time period as the second resource growth amount of the target EVA in the demand response market.

[0031] In a second aspect, the application further provides an electric vehicle charging and discharging scheduling device, comprising:

[0032] A function construction module is configured to construct a target scheduling function corresponding to the target EVA with the lowest total operation cost of the target EVA in the energy market as a target.

[0033] A constraint condition determination module is configured to determine constraint conditions of the target scheduling function, wherein the constraint conditions are used to constrain the charging and discharging power and the energy output of the target EVA.

[0034] A charging and discharging power determination module is configured to determine the charging and discharging power corresponding to the target EVA at different time points according to the constraint conditions and the target scheduling function.

[0035] A scheduling strategy determination module is configured to determine a scheduling strategy of the target EVA according to the charging and discharging power corresponding to the target EVA at different time points and the power state data of each electric vehicle deployed by the target EVA at the corresponding time points, wherein the power state data comprises at least one of the battery capacity, the battery charging power, and the battery discharging power.

[0036] A scheduling module is configured to perform charging and discharging scheduling on each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA.

[0037] In a third aspect, the application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0038] Constructing a target scheduling function corresponding to the target EVA with the lowest total operation cost of the target EVA in the energy market as a target.

[0039] determine a constraint condition of the target scheduling function; wherein the constraint condition is used for constraining the charging and discharging power and the energy output of the target EVA;

[0040] determine the charging and discharging power of the target EVA at different time instants according to the constraint condition and the target scheduling function;

[0041] determine a scheduling strategy of the target EVA according to the charging and discharging power of the target EVA at different time instants and the power state data of each electric vehicle deployed by the target EVA at corresponding time instants; wherein the power state data comprises at least one of a battery power, a battery charging power and a battery discharging power;

[0042] perform charging and discharging scheduling on each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA.

[0043] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0044] build a target scheduling function corresponding to the target EVA with the lowest total operation cost of the target EVA in an energy market as a target;

[0045] determine a constraint condition of the target scheduling function; wherein the constraint condition is used for constraining the charging and discharging power and the energy output of the target EVA;

[0046] determine the charging and discharging power of the target EVA at different time instants according to the constraint condition and the target scheduling function;

[0047] determine a scheduling strategy of the target EVA according to the charging and discharging power of the target EVA at different time instants and the power state data of each electric vehicle deployed by the target EVA at corresponding time instants; wherein the power state data comprises at least one of a battery power, a battery charging power and a battery discharging power;

[0048] perform charging and discharging scheduling on each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA.

[0049] In a fifth aspect, the present application further provides a computer program product comprising a computer program, the computer program being executed by a processor to implement the following steps:

[0050] build a target scheduling function corresponding to the target EVA with the lowest total operation cost of the target EVA in an energy market as a target;

[0051] determine a constraint condition of the target scheduling function; wherein the constraint condition is used for constraining the charging and discharging power and the energy output of the target EVA;

[0052] determine the corresponding charging and discharging power of the target EVA at different time according to the constraint condition and the target scheduling function;

[0053] determine the scheduling strategy of the target EVA according to the corresponding charging and discharging power of the target EVA at different time, and the power state data of each electric vehicle deployed by the target EVA at the corresponding time; wherein the power state data comprises at least one of the battery power, the battery charging power and the battery discharging power;

[0054] perform charging and discharging scheduling on each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA.

[0055] The electric vehicle charging and discharging scheduling method, device, equipment, medium and product described above take the minimum total operation cost of the target EVA in the energy market as the target, construct the target scheduling function corresponding to the target EVA, and determine the constraint condition of the target scheduling function. In the process of solving the target scheduling function, the constraint condition of the target scheduling function is considered, so that the solving result, i.e. the corresponding charging and discharging power of the target EVA at different time, can make the total operation cost of the target EVA in the energy market the lowest. Further, in the process of scheduling each electric vehicle deployed by the target EVA according to the corresponding charging and discharging power at different time obtained by solving, the power state data of each electric vehicle at the corresponding time is considered, so that the scheduling of each electric vehicle is more reasonable. That is, the whole process reduces the operation cost of the target EVA on the premise of ensuring the rationality of the scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0057] Figure 1 a flowchart of the electric vehicle charging and discharging scheduling method in one embodiment;

[0058] Figure 2 a flowchart of the target scheduling function construction step in one embodiment;

[0059] Figure 3 a flowchart of the energy acquisition cost determination step in one embodiment;

[0060] Figure 4 a flowchart of the resource growth amount determination step in one embodiment;

[0061] Figure 5 a flowchart of a scheduling strategy determination step in an embodiment;

[0062] Figure 6 a structural block diagram of an electric vehicle charging and discharging scheduling device in an embodiment;

[0063] Figure 7 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0065] Before introducing the electric vehicle charging and discharging scheduling method provided by the present application, it needs to be explained that under the background of decarbonization of the power system and electrification of the transportation system, the number of electric vehicles (EV) is rapidly increasing. With the continuous improvement of battery capacity and the continuous reduction of battery degradation cost, plus the support of vehicle-to-grid (V2G) technology, electric vehicles can become a flexible resource in the electricity market.

[0066] Due to the driving habits of electric vehicle owners, especially the current mainstream night charging habit, the plug-in time of EV is usually much longer than the charging time required. The charging flexibility generated thereby, i.e., the charging or discharging time can be adjusted, enables EV to provide load shifting, frequency regulation, demand response, etc. services for the power grid. However, this behavior of providing flexibility for the operation of the power grid may also cause certain losses to the electric vehicle owners, such as battery wear caused by discharging, state of charge unable to meet driving demand, etc. These losses can be compensated by participating in the electricity market. However, due to the limited battery capacity and power of a single electric vehicle, it is usually necessary to aggregate a large number of electric vehicles for EVA to reach the threshold for participating in the electricity market. In the scheduling process of EVA participating in the market, EVA first needs to quantify the available regulation capacity of its electric vehicles, and then make a unified bid in the electricity market.

[0067] Currently, the bidding scheduling model of EVA participating in the electricity market only carries out arbitrage in the energy market, ignoring the high subsidies of the frequency regulation market, the demand response market, etc. In addition, there is no joint scheduling strategy for EVA participating in the energy market, the frequency regulation market and the demand response market.

[0068] Secondly, the uncertainty in the EVA scheduling model is another important issue, which comes from multiple aspects: first, the parameters of the electric vehicle itself, such as rated energy and power, charging demand, plug-in time and departure time, directly affect its available regulation capacity, but these parameters are usually random; second, the frequency regulation market signal, the frequency regulation signal has high time granularity, usually sent once every few seconds, usually in the EVA participating in the frequency regulation market, it is assumed that the frequency regulation signal is neutral in the scheduling time interval (that is, the influence of the frequency regulation signal on the charging and discharging decision of the electric vehicle is ignored), however, the biased regulation signal may seriously affect the regulation performance of the electric vehicle, and may eventually affect the state of charge at departure and eventually affect the driving plan of the electric vehicle owner. The parameters of the electric vehicle and the frequency regulation signal are determined or influenced by external factors such as weather and consumer behavior, making them random and difficult to predict. In addition, the high time granularity of the frequency regulation signal makes it computationally burdensome to model it accurately.

[0069] In this background, the present application provides a new electric vehicle charging and discharging scheduling method to fully utilize the flexibility of electric vehicles. Not only can improve the operating income of EVA, but also can provide more flexibility for the power grid, and ensure the stable operation of the power system.

[0070] In one embodiment, as shown in Figure 1 A method for electric vehicle charging and discharging scheduling is provided. The embodiment illustrates the method applied to a terminal. It should be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and can be realized through the interaction of the terminal and the server. In the embodiment, the method includes the following steps:

[0071] S110, constructing a target scheduling function corresponding to a target EVA, with the target EVA having the lowest total operating cost in the energy market as the target.

[0072] The target EVA is the EVA that needs to be scheduled at present. The target scheduling function is used to determine the scheduling strategy for the target EVA. The energy market includes an energy acquisition market and an energy output market. In the field of power systems, the energy market can include various power trading markets.

[0073] For example, in the embodiment, the resource acquisition cost of the target EVA in the energy market can be determined, and the resource output cost corresponding to the resource output by the target EVA can be determined. The difference between the resource output cost and the resource acquisition cost is taken as the total operating cost of the target EVA.

[0074] S120, determining the constraint condition of the target scheduling function.

[0075] The constraint condition is used to constrain the charging and discharging power and energy output of the target EVA.

[0076] In S130, the charging and discharging power of the target EVA at different time points is determined according to the constraint condition and the target scheduling function.

[0077] In an example, the target scheduling function can be solved, and in the solving process, the constraint condition is used as a constraint to determine the solution of the target scheduling function, and the solution is used as the charging and discharging power of the target EVA at different time points.

[0078] Optionally, in the embodiment, the target scheduling function can be solved in various ways, for example, the particle swarm algorithm or the bird swarm algorithm can be used to solve the target scheduling function. Commercial optimization solvers can also be used to solve the target scheduling function, and no limitation is made in this regard.

[0079] In S140, the scheduling strategy of the target EVA is determined according to the charging and discharging power of the target EVA at different time points and the power state data of each electric vehicle deployed by the target EVA at the corresponding time point.

[0080] The power state data includes at least one of the battery power, the battery charging power and the battery discharging power. The scheduling strategy of the target EVA is the scheduling mode for different electric vehicles. In an example, the scheduling strategy includes the power output value of each electric vehicle at different time points.

[0081] In an example, for any time point, the charging and discharging power of the target EVA at the time point is obtained, and the charging and discharging power at the time point is distributed to each electric vehicle deployed by the target EVA, so that the electric vehicle can complete the charging and discharging power at the time point. In the process of distributing the charging and discharging power to each electric vehicle, the power state data of each electric vehicle at the time point needs to be considered to avoid incorrect scheduling.

[0082] In S150, each electric vehicle deployed by the target EVA is charged and discharged according to the scheduling strategy of the target EVA.

[0083] In the above electric vehicle charging and discharging scheduling method, a target scheduling function corresponding to the target EVA is constructed with the lowest total operation cost of the target EVA in the energy market as the target, and the constraint condition of the target scheduling function is determined. In the process of solving the target scheduling function, the constraint condition of the target scheduling function is considered, so that the solving result, that is, the charging and discharging power corresponding to the target EVA at different times, can make the total operation cost of the target EVA in the energy market lowest. Further, in the process of scheduling each electric vehicle of the target EVA according to the charging and discharging power corresponding to the target EVA at different times obtained by solving, the power state data of each electric vehicle at the corresponding time is considered, so that the scheduling of each electric vehicle is more reasonable. That is, the whole process reduces the operation cost of the target EVA on the premise of ensuring the rationality of the scheduling.

[0084] On the basis of the technical solutions of the above embodiments, the application further provides an optional embodiment. In this embodiment, the process of constructing the target scheduling function corresponding to the target EVA with the lowest total operation cost of the target EVA in the energy market as the target is refined.

[0085] Referring to Figure 2 The target scheduling function construction step shown in the figure includes:

[0086] S210, determining the energy acquisition cost of the target EVA in the first market.

[0087] The first market is a resource provider, and in the field of electric power, the first market can be an electric energy market. The energy acquisition cost can be used to represent the total electricity purchase cost of the target EVA in the electric energy market.

[0088] For example, in this embodiment, the product of the unit price of energy purchased by the target EVA in the first market and the total amount of energy purchased can be taken as the energy acquisition cost of the target EVA in the first market.

[0089] S220, determining the resource growth of the target EVA in the second market.

[0090] The second market includes a resource acquirer and a resource regulator, and in the field of electric power, the second market can include a frequency modulation market and a demand response market. The energy growth can represent the electric energy sale income of the target EVA in the second market.

[0091] For example, in this embodiment, the energy acquisition cost and the energy sale income can be used to determine the resource growth of the target EVA in the second market.

[0092] S230, constructing the target scheduling function corresponding to the target EVA with the minimum difference between the energy acquisition cost and the resource growth as the target.

[0093] It should be noted that although Figure 2 The execution steps of S210 and S220 are not distinguished in sequence, and S210 can be executed first and then S220 can be executed, or S210 can be executed first and then S220 can be executed, or S210 and S220 can be executed simultaneously, and no limitation is made on this.

[0094] In the above embodiment, the process of constructing the target scheduling function corresponding to the target EVA is refined, so that the process of constructing the target scheduling function corresponding to the target EVA is more clear and rigorous.

[0095] On the basis of the technical solutions of the above embodiments, the present application further 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.

[0096] Referring to Figure 3 The energy acquisition cost determination step includes:

[0097] S310, obtaining the equivalent resource value of the energy in the first market at different times, and the first resource demand amount of the target EVA for the energy in the first market at different times.

[0098] The equivalent resource value can represent the energy price; the first resource demand amount is used to represent the charging and discharging power declared by the target EVA.

[0099] S320, for any time, determining the first product of the equivalent resource value and the first resource demand amount of the energy in the first market at the time.

[0100] S330, summing the first products corresponding to different times in a preset period as the energy acquisition cost of the target EVA in the first market.

[0101] The length of the preset period can be determined based on artificial experience, and no limitation is made on this.

[0102] Exemplarily, in this embodiment, the determination formula of the energy acquisition cost of the target EVA in the first market can be as follows:

[0103] ;

[0104] In the formula, The energy acquisition cost of the target EVA in the first market is represented by E; The energy price at time t is represented by P(t); EVA represents the declared charging and discharging power of the EVA at time t; T represents a dispatching period, i.e., the length of a preset 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] On the basis of the technical solutions of the above embodiments, the present application further provides an optional embodiment. In this embodiment, the second market includes a frequency modulation market and a demand response market, and the embodiment gives a specific process of determining the resource growth of the target EVA in the second market in the case where the second market includes the frequency modulation market and the demand response market.

[0107] Referring to Figure 4 The resource growth determination step includes:

[0108] S410, determining a first resource growth of the target EVA in the frequency modulation market.

[0109] The first resource growth is used to represent the corresponding market benefit of the target EVA in the frequency modulation market.

[0110] Exemplarily, the first resource value and the second resource value of the frequency modulation market at different times can be obtained; 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; the frequency modulation mileage of the target EVA for the frequency modulation market at different times is obtained, and the upper limit of resource input of the target EVA for the frequency modulation market at different times is obtained; the first resource growth of the target EVA in the frequency modulation market is determined according to the first resource value, the second resource value, the frequency modulation mileage, and the upper limit of resource input.

[0111] The first resource value represents the frequency modulation capacity price of the frequency modulation market; and the second resource value represents the frequency modulation mileage price of the frequency modulation market.

[0112] Exemplarily, the determination formula of the first resource growth of the target EVA in the frequency modulation market can be as follows:

[0113] ;

[0114] In the formula, the first resource growth of the target EVA in the frequency modulation market; is the predicted frequency modulation capacity price of the frequency modulation market at time t; is the frequency modulation mileage price of the frequency modulation market at time t; is the upper limit of resource input of the target EVA in the frequency modulation market at time t; is the frequency modulation bid amount reserved by the target EVA at time t, i.e., the upper limit of resource input; In this scenario, the frequency-adjusted mileage of the target EVA at time t; T represents the scheduling period, i.e., the duration of the preset time period; This indicates the energy trading scenario in which the target EVA is located; This represents a set of energy trading scenarios.

[0115] S420 identifies the second resource growth volume of target EVA in the demand response market.

[0116] The second resource growth is used to characterize the market return of the target EVA in the demand response market.

[0117] 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.

[0118] Among them, the demand response resource represents the predicted demand response price; the second resource demand represents the target EVA at... The number of bids submitted in response to demand in a given scenario.

[0119] For example, the formula for determining the second resource growth amount of the target EVA in the demand response market can be as follows:

[0120] ;

[0121] In the formula, This indicates the second resource growth of the target EVA in the demand response market; This represents the demand response price of the target EVA at time t; Indicates in In this scenario, the target EVA's bid volume for demand response at time t; T represents the scheduling period, i.e., the duration of the preset time period; This indicates the energy trading scenario in which the target EVA is located; This represents a set of energy trading scenarios.

[0122] S430, based on the first resource growth and the second resource growth, determine the resource growth of the target EVA in the second market.

[0123] 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.

[0124] 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. This makes the process of determining the resource growth of the target EVA in the second market clearer and more rigorous.

[0125] Based on the above, the target scheduling function corresponding to the target EVA can be as follows:

[0126] ;

[0127] In the formula, obj represents the total operating cost of the target EVA in the energy market; Represents the energy price at time t; This represents the charge / discharge power declared by EVA at time t; The predicted FM capacity price at time t; The price of FM mileage in the FM market at time t; For the target EVA in In this scenario, the reserved frequency modulation bid amount at time t is the upper limit of resource investment; for In this scenario, the frequency modulation mileage of the target EVA at time t; This represents the demand response price of the target EVA at time t; Indicates in In this scenario, the target EVA's bid volume for demand response at time t; T represents the scheduling period, i.e., the duration of the preset time period; This indicates the energy trading scenario in which the target EVA is located; This represents a set of energy trading scenarios.

[0128] In this embodiment, the constraints of the target scheduling function can be as follows:

[0129] (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.:

[0130] ;

[0131] in, The planned charge / discharge power of the target EVA at time t; The average up / down frequency modulated signal over time t; The frequency modulation power plan for the target EVA at time 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.

[0132] (2) Actual charge and discharge power constraints:

[0133] ;

[0134] ;

[0135] 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.

[0136] (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.:

[0137] ;

[0138] 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.

[0139] (4) The bid volume of the target EVA in the FM market and demand response market should be non-negative:

[0140] ;

[0141] in, and These represent the bid volumes for the target EVA in the frequency modulation market and the demand response market at time t, respectively.

[0142] (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.:

[0143] ;

[0144] In the formula, Indicates the lower limit of power; Indicates the upper limit of power; P (i, t) is the actual power of the ith vehicle under the up / down frequency signal at time t.

[0145] (6) Energy constraint of the target EVA, including energy update constraint and energy upper / lower limit constraint:

[0146] The energy update constraint can be as follows:

[0147]

[0148] In the formula, E (t) represents the energy of the target EVA at time t; E (t-1) represents the energy of the target EVA at time t-1; is the average up frequency signal corresponding duration; is the average down frequency signal corresponding duration; is the charging efficiency; is the discharging efficiency; P (i, t) represents the actual charging power of the target EVA under the up frequency signal at time t; P (i, t) represents the actual discharging power of the target EVA under the up frequency signal at time t; P (i, t) represents the actual charging power of the target EVA under the down frequency signal at time t; P (i, t) represents the actual discharging power of the target EVA under the down frequency signal at time t.

[0149] The energy upper / lower limit constraint can be as follows:

[0150]

[0151] In the formula, E (i, t) represents the energy lower limit of the ith vehicle deployed by the target EVA at time t; E (t) represents the energy lower limit of the target EVA; E (t) represents the actual energy of the target EVA; E (t) represents the energy upper limit of the target EVA; E (i, t) represents the energy upper limit of the ith vehicle deployed by the target EVA at time t.

[0152] It should be noted that, in general, the independent system operator will issue a frequency signal every 2 seconds is the ratio of the EVA power offset (increase or decrease) to the planned charging / discharging power at t hours and d intervals. The power system will generate a control error (Area Control Error, ACE) in real-time operation. When ACE is less than 0, the independent system operator issues an up frequency signal ​​, guiding the electric vehicle to reduce the charging power or increase the discharging power on the basis of the planned charging and discharging power, to fill the power shortage; when the ACE is greater than 0, the independent system operator sends a frequency reduction signal , guiding the electric vehicle to increase the charging power or reduce the discharging power on the basis of the planned charging and discharging power, to consume power. The trajectory of the frequency regulation signal within one hour is represented as a vector . Considering the load characteristics of the electric vehicle, we define the charging power as a positive value and the discharging power as a negative value. Considering the aggregated charging and discharging power plan of the distributed energy resources (DER) , the frequency regulation capacity , and the regulation signal , the actual power of the EVA at the tth hour d interval (at the grid side) is However, 2-second time accuracy will bring huge computational burden when building the optimization problem.

[0153] Therefore, by calculating the average up / down frequency regulation signal and its corresponding duration , the power change is accurately described while ensuring computational efficiency, and the corresponding constraint conditions are as follows:

[0154] ; ; ;

[0155] ;

[0156] wherein is the average up frequency regulation signal within the tth hour; is the average down frequency regulation signal within the tth hour, is the number of within the tth hour.

[0157] On the basis of the technical solutions of the above embodiments, the present application further provides an optional embodiment. In this embodiment, the process of determining the scheduling strategy of the target EVA according to the corresponding charging and discharging power of the target EVA at different times, and the power state data of each electric vehicle deployed by the target EVA at the corresponding time is refined.

[0158] Referring to the scheduling strategy determination step shown in Figure 5 , comprising:

[0159] S510, modeling the behavior of each electric vehicle user.

[0160] For example, based on the probability distribution, the Monte Carlo algorithm is used to determine the relevant behavior parameters of the electric vehicle ​ ). Wherein is the battery capacity of the i-th electric vehicle (kWh); is the time when the i-th electric vehicle starts charging (arrival time); is the time when the i-th electric vehicle completes charging (departure time); is the remaining state-of-charge (SOC) of the i-th electric vehicle when it connects to the charging pile; is the target charging capacity of the i-th electric vehicle; is the maximum charging power of the electric vehicle i; is the maximum discharging power of the electric vehicle i; and are the charging and discharging efficiencies of the i-th electric vehicle, respectively. When the electric vehicle user submits his data, the maximum feasible is calculated and compared with the user-submitted to determine whether the charging needs of the electric vehicle can be met under the arrival-just-charging condition. The vehicle owner is given and is required to input the target charging capacity within .

[0161] S520, according to the energy balance constraint condition, determine the charging and discharging strategy of different electric vehicles, and based on the charging and discharging strategy of each electric vehicle, charge and discharge scheduling is carried out on the corresponding electric vehicle.

[0162] Wherein, the energy balance constraint condition is as follows:

[0163] ;

[0164] Wherein, is the planned charging and discharging power of the target EVA at time t; is the average up / down frequency signal within time t; is the frequency regulation power plan of the target EVA at time t; is the demand response power plan of the target EVA at time t; is the actual power of the i-th vehicle under the up / down frequency signal at time t; is the actual power of the target EVA under the up / down frequency signal at time t.

[0165] The charging and discharging strategy of different electric vehicles is determined based on the above formula, i.e. In the process of

[0166] (1) Upper and lower bounds of charging and discharging power: represent the boundary constraints of the charging and discharging power of the electric vehicle, i.e. the power regulation domain of the electric vehicle:

[0167] ;

[0168] ;

[0169] wherein, and are the lower and upper power limits of the electric vehicle i at time t, is the maximum charging power of the electric vehicle i, is the maximum discharging power of the electric vehicle i.

[0170] (2) Energy upper and lower bounds:

[0171] ;

[0172] wherein, the upper bound of energy corresponds to the case that the electric vehicle is charged at the maximum charging power after being connected to the grid until reaching the maximum capacity; the lower bound of energy corresponds to the case that the electric vehicle is discharged at the maximum discharging power after being connected to the grid, and starts charging as late as possible at the maximum power at the leaving time just reaching the required energy amount; is the remaining energy of the i-th electric vehicle; is the charging efficiency of the i-th electric vehicle; is the upper limit of the charging power of the i-th electric vehicle; denotes the time at which the i-th electric vehicle starts charging; denotes the upper bound of energy of the i-th electric vehicle; denotes the lower bound of energy of the i-th electric vehicle; denotes the discharging efficiency of the electric vehicle; denotes the charging efficiency of the electric vehicle.

[0173] It should be understood that, although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless explicitly stated herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0174] Based on the same inventive concept, the embodiment of the present application further provides an electric vehicle charging and discharging scheduling device for implementing the above-mentioned electric vehicle charging and discharging scheduling method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above-mentioned method, and therefore the specific limitations in one or more electric vehicle charging and discharging scheduling device embodiments provided below can refer to the limitations of the electric vehicle charging and discharging scheduling method in the foregoing, which will not be described here again.

[0175] In one exemplary embodiment, as shown in Figure 6 An electric vehicle charging and discharging scheduling device is provided, comprising: 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:

[0176] The function construction module 610 is configured to construct a target scheduling function corresponding to the target EVA, with the target of minimizing the total operation cost of the target EVA in the energy market.

[0177] The constraint condition determination module 620 is configured to determine the constraint condition of the target scheduling function.

[0178] The constraint condition is used to constrain the charging and discharging power and energy output of the target EVA.

[0179] The charging and discharging power determination module 630 is configured to determine the corresponding charging and discharging power of the target EVA at different times according to the constraint condition and the target scheduling function.

[0180] The scheduling strategy determination module 640 is configured to determine the scheduling strategy of the target EVA according to the corresponding charging and discharging power of the target EVA at different times, and the power state data of each electric vehicle deployed by the target EVA at the corresponding time.

[0181] The power state data includes at least one of the battery power, the battery charging power, and the battery discharging power.

[0182] The scheduling module 650 is configured to perform charging and discharging scheduling on each electric vehicle deployed by the target EVA according to the scheduling strategy of the target EVA.

[0183] In one embodiment, the function construction module 610 comprises a cost determination unit configured to determine the energy acquisition cost of the target EVA in the first market; a growth amount determination unit configured to determine the resource growth amount of the target EVA in the second market; and a function construction unit configured to construct the target scheduling function corresponding to the target EVA, with the target of minimizing the difference between the energy acquisition cost and the resource growth amount.

[0184] In one embodiment, the cost determining unit comprises a first determining sub-unit configured to acquire equivalent resource values of the energy in the first market at different time points, and a first resource demand amount of the target EVA for the energy in the first market at different time points; a second determining sub-unit configured to determine, for any time point, a first product of the equivalent resource value of the energy in the first market at the time point and the first resource demand amount; and a cost determining sub-unit configured to take a sum of the first products corresponding to different time points within a preset time period as the energy acquisition cost of the target EVA in the first market.

[0185] In one embodiment, the second market comprises a frequency modulation market and a demand response market; correspondingly, the growth amount determining unit comprises a first growth amount determining sub-unit configured to determine a first resource growth amount of the target EVA in the frequency modulation market, a second growth amount determining sub-unit configured to determine a second resource growth amount of the target EVA in the demand response market, and a third growth amount determining sub-unit configured to determine a resource growth amount of the target EVA in the second market according to the first resource growth amount and the second resource growth amount.

[0186] In one embodiment, the first growth amount determining sub-unit is specifically configured to acquire a first resource value and a second resource value of the frequency modulation market at different time points; the first resource value corresponds to a frequency modulation capacity of the frequency modulation market, and the second resource value corresponds to a frequency modulation mileage of the frequency modulation market; acquire a frequency modulation mileage of the target EVA for the frequency modulation market at different time points, and an upper limit of resource input of the target EVA for the frequency modulation market at different time points; and determine the first resource growth amount of the target EVA in the frequency modulation market according to the first resource value, the second resource value, the frequency modulation mileage and the upper limit of resource input.

[0187] In one embodiment, the second growth amount determining sub-unit is specifically configured to acquire a demand response resource of the demand response market at different time points, and a second resource demand amount of the target EVA for the demand response market at different time points; determine, for any time point, a second product between the demand response resource and the second resource demand amount; and take a sum of the second products corresponding to different time points within a preset time period as the second resource growth amount of the target EVA in the demand response market.

[0188] The above-mentioned various modules in the electric vehicle charging and discharging scheduling device can be realized by software, hardware and combinations thereof in whole or in part. The above-mentioned various modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules.

[0189] In one exemplary embodiment, a computer device is provided, which can be a terminal, and the internal structure diagram thereof can be as shown in Figure 7As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control ability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the external terminal in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. The computer program is executed by the processor to realize an electric vehicle charging and discharging scheduling method.

[0190] Those skilled in the art can understand that, Figure 7 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0191] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each of the above method embodiments.

[0192] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by the processor to implement the steps in each of the above method embodiments.

[0193] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by the processor to implement the steps in each of the above method embodiments.

[0194] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0195] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0196] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for charging and discharging scheduling of an electric vehicle, characterized in that, The method comprises: A target scheduling function corresponding to a target EVA is constructed, with the target EVA being an electric vehicle aggregator, and the target scheduling function being constructed with the target EVA having the lowest total operating cost in an energy market as the target; Constraint conditions of the target scheduling function are determined, wherein the constraint conditions are used to constrain the charging and discharging power and energy output of the target EVA; According to the constraint conditions and the target scheduling function, the charging and discharging power corresponding to the target EVA at different times is determined; According to the charging and discharging power corresponding to the target EVA at different times and the power state data of each electric vehicle deployed by the target EVA at the corresponding time, the behavior of each electric vehicle user is modeled; the charging and discharging strategies of different electric vehicles are determined according to an energy balance constraint condition; wherein the power state data includes at least one of the battery power, the battery charging power and the battery discharging power; Based on the charging and discharging strategies of each electric vehicle, the corresponding electric vehicle is charged and discharged; The energy balance constraint condition is as follows: ； wherein, is the planned charging / discharging power of the target EVA at time t; is the average up / down frequency adjustment signal within time t; is the frequency adjustment power plan of the target EVA at time t; is the demand response power plan of the target EVA at time t; is the actual power of the ith vehicle under the up / down frequency adjustment signal at time t; is the actual power of the target EVA under the up / down frequency adjustment signal at time t; T represents the dispatching period, i.e., the length of the preset time period; represents the energy trading scenario in which the target EVA is located.

2. The method of claim 1, wherein, The target scheduling function corresponding to the target EVA is constructed with the target EVA having the lowest total operating cost in the energy market as the target, comprising: The energy acquisition cost of the target EVA in a first market is determined; and The resource growth amount of the target EVA in a second market is determined; The target scheduling function corresponding to the target EVA is constructed with the difference between the energy acquisition cost and the resource growth amount being the minimum as the target.

3. The method of claim 2, wherein, The energy acquisition cost of the target EVA in the first market is determined, comprising: The equivalent resource value of the energy in the first market at different times and the first resource demand amount of the target EVA for the energy in the first market at different times are obtained; For any time, the first product of the equivalent resource value of the energy in the first market at the time and the first resource demand amount is determined; The sum of the first products corresponding to different times within a preset period is taken as the energy acquisition cost of the target EVA in the first market.

4. The method of claim 2, wherein, The second market includes a frequency modulation market and a demand response market; accordingly, the resource growth amount of the target EVA in the second market is determined, comprising: The first resource growth amount of the target EVA in the frequency modulation market is determined; and The second resource growth amount of the target EVA in the demand response market is determined; The resource growth amount of the target EVA in the second market is determined according to the first resource growth amount and the second resource growth amount.

5. The method of claim 4, wherein, The first resource growth amount of the target EVA in the frequency modulation market is determined, comprising: The first resource value and the second resource value of the frequency modulation market at different times are obtained; 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; The frequency modulation mileage of the target EVA for the frequency modulation market at different times and the upper limit of resource input of the target EVA for the frequency modulation market at different times are obtained; According to the first resource value, the second resource value, the frequency modulation mileage and the resource input upper limit, a first resource growth amount of the target EVA in a frequency modulation market is determined.

6. The method of claim 4, wherein, The determining the second resource growth amount of the target EVA in the demand response market comprises: obtaining demand response resources of the demand response market at different time points and second resource demand amounts of the target EVA for the demand response market at different time points; for any time point, determining a second product between the demand response resources and the second resource demand amounts; summing the second products corresponding to different time points in a preset period as the second resource growth amount of the target EVA in the demand response market.

7. An electric vehicle charging and discharging scheduling device, characterized by, The device comprises: a function construction module configured to construct a target scheduling function corresponding to the target EVA with the lowest total operation cost of the target EVA in the energy market as a target; a constraint condition determination module configured to determine 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; a charging and discharging power determination module configured to determine the corresponding charging and discharging power of the target EVA at different time points according to the constraint conditions and the target scheduling function; a scheduling strategy determination module configured to model the user behavior of each electric vehicle according to the corresponding charging and discharging power of the target EVA at different time points and the power state data of each electric vehicle deployed by the target EVA at the corresponding time point; and determine the charging and discharging strategy of different electric vehicles according to the energy balance constraint condition; wherein the power state data comprises at least one of the battery capacity, the battery charging power and the battery discharging power; a scheduling module configured to perform charging and discharging scheduling on the corresponding electric vehicle based on the charging and discharging strategy of each electric vehicle. Wherein, the energy balance constraint condition is as follows: ; wherein, is the planned charging and discharging power of the target EVA at time t; is the average up / down frequency signal within t time; is the frequency adjustment power plan of the target EVA at time t; is the demand response power plan of the target EVA at time t; is the actual power of the ith vehicle under the up / down frequency signal at time t; is the actual power of the target EVA under the up / down frequency signal at time t; T represents the scheduling period, i.e. the length of the preset period; indicates the energy trading scenario in which the target EVA is located.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1 to 6.

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

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

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