Method and device for adjusting control strategy of charging and discharging station and storage medium

Abstract

The invention provides a method and a device for adjusting a control strategy of a charging and discharging station, and a storage medium. The method comprises the steps that an adjustment target for adjusting a control strategy of a charging and discharging station is determined, the charging and discharging station is used for charging a vehicle, the control strategy is used for representing a rule for controlling the charging and discharging station, and the adjustment target is used for representing the operation state of associated equipment of the charging and discharging station; expanding the adjustment target based on the charging demand information of the vehicle to obtain an expanded adjustment target; based on the extended adjustment target, the immigration number and the emigration number of the vehicles are determined, the immigration number is the number of the vehicles entering the charging and discharging station, and the emigration number is the number of the vehicles leaving the charging and discharging station; determining the output power of the charging and discharging station based on the immigration number and the emigration number; and the control strategy is adjusted according to the output power, so that the problem of low efficiency of adjusting the control strategy of the charging and discharging station is solved.

Description

Technical Field

[0001] The present invention relates to the field of machine learning and intelligent control, and in particular to a method, device and storage medium for adjusting a control strategy of a charging and discharging station. Background Art

[0002] At present, the basic concept of the Biogeography Based Optimization (BBO) algorithm is derived from the theory of biogeography. It is usually used to deal with complex optimization problems and has been widely used in distribution network planning, load distribution, and optimal configuration of microgrids. The standard BBO algorithm is easy to operate and has good scalability, but it also has problems such as high computational complexity, easy convergence speed, and poor optimization results. As a result, there is a problem of low efficiency in adjusting the control strategy of the charging and discharging station when it is applied.

[0003] Therefore, there is currently no effective solution to the above-mentioned problem of low efficiency in adjusting the control strategy of the charging and discharging station. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and storage medium for adjusting the control strategy of a charging and discharging station, so as to at least solve the problem of low efficiency in adjusting the control strategy of the charging and discharging station.

[0005] According to an embodiment of the present invention, a method for adjusting the control strategy of a charging and discharging station is provided. The method may include: determining an adjustment target for adjusting the control strategy of the charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize the rules for controlling the charging and discharging station, and the adjustment target is used to characterize the operating status of the associated equipment of the charging and discharging station; based on the charging demand information of the vehicle, the adjustment target is expanded to obtain the expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; based on the expanded adjustment target, the number of vehicles moving in and moving out is determined, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station; based on the number of moving in and the number of moving out, the output power of the charging and discharging station is determined; and the control strategy is adjusted according to the output power.

[0006] Optionally, the method also includes: determining a learning rate of the charging and discharging station, wherein the learning rate is used to screen adjustment targets whose matching degree is higher than a matching degree threshold; determining an adjustment target for adjusting the control strategy of the charging and discharging station, including: determining an adjustment target that matches the learning rate.

[0007] Optionally, the method further includes: determining an objective function of an adjustment model, wherein the adjustment model is used to adjust the output power of the charging and discharging station; determining an adjustment target that matches the learning rate, including: determining the objective function that matches the learning rate as the adjustment target.

[0008] Optionally, the method further includes: generating a random number using a chaotic system, wherein the random number is used to characterize the amount of expansion of the adjustment target.

[0009] Optionally, based on the charging demand information of the vehicle, the adjustment target is expanded to obtain an expanded adjustment target, including: determining the expansion probability of the charging and discharging station based on the charging demand information of the vehicle, wherein the expansion probability is used to characterize the amount of expansion of the adjustment target; and expanding the adjustment target according to the expansion probability or a random number to obtain the expanded adjustment target.

[0010] Optionally, based on the expanded adjustment target, determining the number of vehicles moving in and moving out includes: calling a cosine migration model to analyze the expanded adjustment target to determine the number of vehicles moving in and moving out.

[0011] Optionally, after determining the output power of the charging and discharging station based on the number of in-migrants and the number of out-migrants, the method further includes: adjusting the output power based on the charging demand information and the power status information of the vehicle to obtain an adjusted output power.

[0012] Optionally, the method also includes: obtaining power difference information between the charging power and discharging power of the vehicle; based on the power difference information, screening the adjusted output power to obtain the screened output power, wherein the screened output power satisfies the constraints of the adjustment model, and the adjustment model is used to adjust the output power of the charging and discharging station.

[0013] Optionally, adjusting the control strategy according to the output power includes: adjusting the control strategy according to the screened output power.

[0014] According to another embodiment of the present invention, a device for adjusting the control strategy of a charging and discharging station is provided. The device may include: a first determination unit, used to determine an adjustment target for adjusting the control strategy of the charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize the rules for controlling the charging and discharging station, and the adjustment target is used to characterize the operating status of the associated equipment of the charging and discharging station; an expansion unit, used to expand the adjustment target based on the charging demand information of the vehicle to obtain the expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; a second determination unit, used to determine the number of vehicles moving in and moving out based on the expanded adjustment target, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station; a third determination unit, used to determine the output power of the charging and discharging station based on the number of moving in and the number of moving out; and an adjustment unit, used to adjust the control strategy according to the output power.

[0015] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0016] According to another embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0017] According to another embodiment of the present invention, a computer program product is provided, which may include a computer program, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0018] According to another embodiment of the present invention, a computer program product is provided, which may include a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and the computer program is configured to execute the steps of any of the above method embodiments when executed.

[0019] According to another embodiment of the present invention, a computer program is provided, which is configured to execute the steps in any of the above method embodiments when running.

[0020] Through the embodiment of the present invention, an adjustment target for adjusting the control strategy of a charging and discharging station is determined, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize the rules for controlling the charging and discharging station, and the adjustment target is used to characterize the operating status of the associated equipment of the charging and discharging station; based on the charging demand information of the vehicle, the adjustment target is expanded to obtain the expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; based on the expanded adjustment target, the number of vehicles moving in and moving out is determined, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station; based on the number of moving in and the number of moving out, the output power of the charging and discharging station is determined; the control strategy is adjusted according to the output power, thereby solving the technical problem of low efficiency in adjusting the control strategy of the charging and discharging station and achieving the technical effect of improving the efficiency of adjusting the control strategy of the charging and discharging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0022] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for adjusting a control strategy of a charging and discharging station according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a method for adjusting a control strategy of a charging and discharging station according to an embodiment of the present invention;

[0024] Figure 3 is a flow chart of a SaDE-BBO algorithm according to an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of an S2G ​​system optimization architecture based on a SaDE-BBO algorithm according to an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of energy exchange between photovoltaic power generation and power grid according to an embodiment of the present invention;

[0027] Figure 6 It is a structural block diagram of a device for adjusting a control strategy of a charging and discharging station according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0030] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for adjusting a control strategy of a charging and discharging station according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal 10 may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0031] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for adjusting the control strategy of the charging and discharging station in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0032] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] In this embodiment, a method for adjusting the control strategy of the charging and discharging station running on the above mobile terminal is provided. Figure 2 is a flow chart of a method for adjusting a control strategy of a charging and discharging station according to an embodiment of the present invention. Figure 2 As shown, the method may include the following steps:

[0034] Step S202, determining an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of associated equipment of the charging and discharging station.

[0035] In the technical solution provided in step S202 of the present invention, an adjustment target for adjusting the control strategy of the charging and discharging station can be determined. The charging and discharging station can be used to charge vehicles. The control strategy can be used to characterize the rules for controlling the charging and discharging station. The adjustment target can be used to characterize the operating state of the associated equipment of the charging and discharging station, and can also be called an objective function.

[0036] Optionally, this embodiment constructs a two-layer optimization model, which includes an upper optimization model and a lower optimization model. The upper optimization model comprehensively considers the operating costs of the Electric Vehicle Aggregator (EVA) in each region and the load fluctuations of the power grid. The upper optimization model takes the reduction of the operating costs of the EVA in each region to the minimum and the minimization of the load fluctuation as the objective function. The lower optimization model uses the EVA charging and discharging power obtained by the upper optimization model as input, converts the active power loss of the distribution network and various voltage regulation methods into economic costs, and sets the minimization of the comprehensive operating cost of the distribution network as the objective function. The above objective function can be determined as an adjustment target for adjusting the control strategy of the charging and discharging station.

[0037] Step S204: Expand the adjustment target based on the charging demand information of the vehicle to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle.

[0038] In the technical solution provided in the above step S204 of the present invention, after determining the adjustment target for adjusting the control strategy of the charging and discharging station, the determined adjustment target can be expanded based on the charging demand information of the vehicle to obtain the expanded adjustment target. The charging demand information can be used to represent the electric energy required by the vehicle.

[0039] Optionally, based on the charging demand information of the vehicle, an expansion probability of expanding the adjustment target can be determined, and the adjustment target is further expanded based on the expansion probability to obtain an expanded adjustment target, thereby achieving the purpose of increasing the randomness of the adjustment target.

[0040] Step S206, based on the expanded adjustment target, determine the number of vehicles moving in and moving out, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station.

[0041] In the technical solution provided in the above step S206 of the present invention, after the adjustment target is expanded based on the charging demand information of the vehicle and the expanded adjustment target is obtained, the number of vehicles moving in and moving out can be determined based on the expanded adjustment target. The number of vehicles moving in can be the number of vehicles entering the charging and discharging station, which can also be called the moving-in rate. The number of vehicles moving out can be the number of vehicles leaving the charging and discharging station, which can also be called the moving-out rate.

[0042] Optionally, after obtaining the expanded adjustment target, the cosine migration model may be called to calculate the number of vehicles moving in and moving out, that is, to calculate the vehicle moving-in rate and moving-out rate, so as to determine the output power of the charging and discharging station.

[0043] Step S208: determining the output power of the charging and discharging station based on the number of incoming and outgoing migrations.

[0044] In the technical solution provided in the above step S208 of the present invention, after determining the number of vehicles moving in and moving out, the output power of the charging and discharging station can be determined based on the determined number of vehicles moving in and moving out.

[0045] Optionally, after determining the migration rate of vehicles in and out, a differential migration operation based on a ring structure may be performed on the migration rate in and out to obtain the output power of the charging and discharging station.

[0046] Step S210: adjusting the control strategy according to the output power.

[0047] In the technical solution provided in the above step S210 of the present invention, after the output power of the charging and discharging station is determined, the control strategy can be adjusted according to the determined output power, thereby achieving the purpose of optimizing the control strategy.

[0048] In the above steps S202 to S210 of the present invention, an adjustment target for adjusting the control strategy of the charging and discharging station is determined, wherein the charging and discharging station is used to charge the vehicle, the control strategy is used to characterize the rules for controlling the charging and discharging station, and the adjustment target is used to characterize the operating status of the associated equipment of the charging and discharging station; based on the charging demand information of the vehicle, the adjustment target is expanded to obtain the expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; based on the expanded adjustment target, the number of vehicles moving in and moving out is determined, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station; based on the number of moving in and the number of moving out, the output power of the charging and discharging station is determined; the control strategy is adjusted according to the output power, thereby solving the technical problem of low efficiency in adjusting the control strategy of the charging and discharging station and achieving the technical effect of improving the efficiency of adjusting the control strategy of the charging and discharging station.

[0049] The above method of this embodiment is further introduced below.

[0050] As an optional embodiment, the method also includes: determining a learning rate of the charging and discharging station, wherein the learning rate is used to screen adjustment targets whose matching degree is higher than a matching degree threshold; determining an adjustment target for adjusting the control strategy of the charging and discharging station, including: determining an adjustment target that matches the learning rate.

[0051] In this embodiment, the learning rate of the charging and discharging station can be determined. After the learning rate of the charging and discharging station is determined, an adjustment target that matches the learning rate can be determined. Among them, the learning rate can be used to screen adjustment targets whose matching degree is higher than a matching degree threshold. The matching degree threshold can be a threshold pre-set according to actual conditions.

[0052] It should be noted that the BBO algorithm originated from the optimization thinking contained in the process of species migration between habitats. In this algorithm, the activity place of species in the ecological environment is defined as the habitat. There are many factors in the ecological environment that can affect the quality of the habitat environment. These factors are called fitness index variables (Suitability Index Variables, referred to as SIVs). The suitability of the habitat for the survival of species and the quality of the environment are described by the fitness index (HabitatSuitability Index, referred to as HSI). The standard BBO algorithm is easy to operate and has strong scalability, but it also has defects. Therefore, this embodiment optimizes the initial population, migration model, migration operator, mutation operator and selection mechanism, and proposes a self-adaptive differential evolution-biogeography optimization (Self-adaptive Differential Evolution Biogeography Based Optimization, referred to as SaDE-BBO) algorithm, which has an important optimization effect on the station-to-grid (S2G) system.

[0053] Optionally, in the early stage of the algorithm, using a larger learning rate for extensive search helps to quickly find potential optimization directions in the S2G system. As the number of iterations increases, gradually reducing the learning rate for a more refined search can better adapt to the complex characteristics of the S2G system, accurately adjust the charging and discharging power of the EVA in each area and the action value of the voltage regulating equipment, and improve the operating efficiency and stability of the system. This embodiment can quickly find the optimization direction of the S2G system and the dynamic learning rate that adapts to its complex characteristics. The learning rate can be expressed by the following formula:

[0054] lr(t+1)=lr(t)(1-t / T)

[0055] Wherein, lr(t) can be used to represent the current learning rate, lr(t+1) can be used to represent the updated learning rate, t can be used to represent the step size, and T can be used to represent the update period. That is, this embodiment determines the objective function of the two-layer optimization model (adjustment model) through the dynamic learning rate.

[0056] As an optional embodiment, the method also includes: determining an objective function of an adjustment model, wherein the adjustment model is used to adjust the output power of the charging and discharging station; determining an adjustment target that matches the learning rate, including: determining the objective function that matches the learning rate as the adjustment target.

[0057] In this embodiment, the objective function of the adjustment model can be determined, and the objective function matching the learning rate can be further determined as the adjustment target. The adjustment model can be a double-layer optimization model, which can be used to adjust the output power of the charging and discharging station.

[0058] Optionally, the upper optimization model takes the lowest EVA operating cost in each region as the objective function. During the entire scheduling cycle, the peak electricity price is high, the more discharge, the greater the profit; the valley electricity price is low, the more charging, the lower the charging cost. Therefore, the lowest EVA operating cost in each region is taken as the objective function:

[0059]

[0060] Among them, f1 can be used to represent the objective function that minimizes the EVA operating cost of each region, T can be used to represent the number of time periods in the scheduling cycle, and m can be used to represent the number of EVAs. can be used to represent the unit charging price and discharging price of the i-th EVA in period t respectively; can be used to represent the charging power and discharging power of the i-th EVA in time period t, and Δt can be used to represent the length of the time period.

[0061] Optionally, the upper optimization model takes the minimum load fluctuation of the active distribution network as the objective function, and the relative load fluctuation rate D t and load fluctuation standard deviation D v There are two indicators that reflect load fluctuations. t The smaller the value, the better the effect of each EVA charging and discharging power tracking the active output and load of the distributed power source; D v The smaller the value, the more stable the load fluctuation. Therefore, the objective function is to minimize the load fluctuation of the active distribution network:

[0062] minf2=m1D t +m2D v

[0063]

[0064] Among them, P av1 It can be used to represent the average load value in T time periods, and n can be used to represent the number of distributed power sources. It can be used to represent the output of distributed generation j during period t; P load，t Can be used to represent the base load during period t; P av2 It can be used to represent the average value of the optimized load curve within T time periods; m1 and m2 can be used to represent weight coefficients, which are generally 1.

[0065] Optionally, the data set of the upper optimization model includes: the number of time periods T in the scheduling cycle, the number of EVAs m, and the unit charging electricity price of the i-th EVA in time period t and discharge price Charging power of the i-th EVA in period t and discharge power The average load value P in T time periods, the output of distributed generation Pf, the basic load Pw in the time period, and the average value P of the optimized load curve in T time periods av2 、EVA maximum charging power and maximum discharge power Charging efficiency η of electric vehicles c and discharge efficiency η d 、Total charging demand of EVA The input of the upper optimization model is the relevant parameters of each region, such as the number of EVAs, unit charging and discharging electricity price, charging and discharging power, etc., the load of the power grid, including the load average, basic load, etc. The output of distributed power sources. The output of the upper optimization model is the optimized EVA charging and discharging power of each region, so as to achieve the goal of minimizing the regional EVA operating cost and minimizing load fluctuation.

[0066] Optionally, the lower layer optimization model objective function can be expressed by the following formula:

[0067]

[0068] Among them, K can be used to represent the number of distributed generation connected to the distribution network, and πt can be used to represent the marginal price of network loss in time period t. It can be used to represent the output cost of distributed power generation and static reactive power compensation cost respectively; It can be used to represent the reactive power output distributed at node k during period t; It can be used to represent the reactive power output of static reactive power compensation during period t, P loss,t It can be expressed by the following formula:

[0069]

[0070] Among them, m can be used to represent the number of distribution network nodes, V i，t 、V j，t can be used to represent the voltage of node i and node j during time period t respectively; G ij , B ij , δ ij It can be used to express the conductance, susceptance and voltage phase difference between node i and node j respectively.

[0071] Optionally, the data set of the lower optimization model is: the EVA charge and discharge power obtained by the upper optimization model, the upper and lower limits V of the node voltage in T time periodsi，max and V i，min , the upper and lower limits of reactive power output of distributed power sources during period t and Active power output of distributed generation at node k during period t The maximum apparent power SDG of the distributed power inverter, the active and reactive power of the load at node k during the period t and The input of the lower optimization model is the EVA charging and discharging power obtained by the upper optimization model, and the output is a solution to minimize the comprehensive operating cost of the distribution network, including the active power distribution of the distribution network, the economic costs corresponding to various voltage regulation means, etc., while satisfying the operation constraints of the distribution network and the output constraints of various voltage regulation equipment.

[0072] As an optional embodiment, the method further includes: generating a random number using a chaotic system, wherein the random number is used to characterize the amount of expansion of the adjustment target.

[0073] In this embodiment, a chaotic system can be used to generate random numbers. The random numbers can be used to characterize the amount of expansion of the adjustment target. Optionally, a piecewise chaotic map (Logistic) is used to generate random numbers for initialization, which can increase the diversity of the initial population and better cover the solution space of the S2G system. The generated random numbers are within [0, 1], which provides a wider starting point for the algorithm to search in the S2G system.

[0074] Optionally, a piecewise logistic chaotic map is used to generate random numbers:

[0075]

[0076] Among them, σ can be used to represent the chaos attraction factor, x i Can be used to represent random numbers in [0, 1].

[0077] As an optional embodiment, based on the charging demand information of the vehicle, the adjustment target is expanded to obtain the expanded adjustment target, including: based on the charging demand information of the vehicle, determining the expansion probability of the charging and discharging station, wherein the expansion probability is used to characterize the amount of expansion of the adjustment target; according to the expansion probability or a random number, the adjustment target is expanded to obtain the expanded adjustment target.

[0078] In this embodiment, based on the charging demand information of the vehicle, the expansion probability of the charging and discharging station can be determined. Further, according to the determined expansion probability or random number, the adjustment target is expanded to obtain the expanded adjustment target. Among them, the expansion probability is used to characterize the amount of expansion of the adjustment target, which can be a mutation probability or a crossover probability.

[0079] Optionally, the fixed mutation and crossover probabilities of the traditional algorithm may not be optimal at different optimization stages. This embodiment can better adapt to changes in the charging demand of electric vehicles in the S2G system by adaptively adjusting the mutation and crossover probabilities according to the diversity of the population and the fitness value of the individual. When the charging demand of electric vehicles is more concentrated, increasing the mutation probability helps to explore more possible solutions to meet the needs of the S2G system. If the population diversity is low, increasing the mutation probability can introduce more diversity and improve the algorithm's search ability in the S2G system. When the individual fitness value is close to the optimal solution, the crossover probability is reduced to avoid destroying the better solution structure and ensure that the S2G system can stably develop towards the optimal solution. The mutation probability can be expressed by the following formula:

[0080] pm=pm0*(1+(diversity / diversity m ax))*(1+α ev )

[0081] Among them, diversityσ can be used to represent the population diversity metric, diversity m axσ can be used to represent the maximum diversity, pm0 can be used to represent the current mutation probability, pm(t+1) can be used to represent the updated mutation probability, and α ev Can be used to represent impact factors related to electric vehicles.

[0082] Optionally, when the charging demand of electric vehicles is relatively concentrated, α ev Increase, thereby increasing the probability of mutation. If the population diversity is low, the probability of mutation can be increased to introduce more diversity; if the individual fitness value is close to the optimal solution, the crossover probability can be reduced to avoid destroying the better solution structure. The crossover probability can be expressed by the following formula:

[0083] pc=pc0*(1-(fitness / average f itness))*(1+β eva )

[0084] Among them, fitness can be used to represent individual fitness value, average f itness can be used to represent the average fitness value of the population, pc0 can be used to represent the current crossover probability, pc(t+1) can be used to represent the updated crossover probability, β eva It can be used to represent the influencing factors related to the coordination degree between charging and discharging stations and electric vehicles.

[0085] As an optional embodiment, based on the expanded adjustment target, determining the number of vehicles moving in and moving out includes: calling a cosine migration model to analyze the expanded adjustment target to determine the number of vehicles moving in and moving out.

[0086] In this embodiment, after determining the expanded adjustment target, the cosine migration model can be called to analyze the expanded adjustment target to determine the number of migrations in and out. Optionally, the linear migration model in the BBO algorithm is relatively simple to describe the migration in and out of the population, while the cosine migration model has been verified to have the best performance. In the S2G system, the calculation method of the migration rate and migration rate can better simulate the changes in various parameters of the power station and the power grid:

[0087]

[0088] Among them, I and E can be used to represent the maximum probability of migration in and out, respectively, and S i Can be used to represent the population size, S max Can be used to represent the maximum population size, β region Can be used to represent regional related impact factors, f region It can be used to represent a function that reflects the travel patterns and charging and discharging requirements of electric vehicles in a specific area.

[0089] As an optional embodiment, after determining the output power of the charging and discharging station based on the number of in-migration and the number of out-migration, the method further includes: adjusting the output power based on the charging demand information and the power status information of the vehicle to obtain the adjusted output power.

[0090] In this embodiment, after determining the output power of the charging and discharging station based on the number of in-migration and out-migration, the output power is adjusted based on the charging demand information and the power status information of the vehicle to obtain the adjusted output power. Optionally, the mutation mechanism of the differential evolution algorithm has good global search capabilities in the solution space, which complements the BBO algorithm. Integrating the mutation mechanism in the differential evolution algorithm into the BBO algorithm, the obtained differential migration operator can quickly explore different combinations of power station output power and grid reception strategies in the S2G system:

[0091]

[0092] c rand <c r or d<d r

[0093] Among them, I i (d) can be used to represent the dth SIV of the ith habitat, c rand Can be used to represent random numbers, c r Can be used to represent the crossover rate.

[0094] Optionally, in order to reduce the randomness of SIV mutations in the later stages of iteration, the adaptive differential optimization algorithm is integrated into the mutation operation. When performing the mutation operation, not only the difference between the optimal fitness of the current population and the current individual is considered, but also the direction and amplitude of the mutation are adjusted according to the difference between the electric vehicle's required power and the current power state, so that the algorithm can better adapt to the dynamic characteristics of electric vehicles in the S2G system. For example, when the electric vehicle's required power is large and the current power state is low, the direction and amplitude of the mutation are adjusted to prompt the algorithm to find a better S2G operation plan to meet the charging needs of the electric vehicle and improve the stability of the power grid:

[0095]

[0096] Among them, β can be used to represent the adaptive adjustment parameter, I best Can be used to represent the optimal fitness of the current population, α ev It can be used to represent the impact factor related to electric vehicles, E demand It can be used to represent the total power demand of electric vehicles, E current It can be used to indicate the current power status of the electric vehicle.

[0097] As an optional embodiment, the method also includes: obtaining power difference information between the charging power and the discharging power of the vehicle; based on the power difference information, screening the adjusted output power to obtain the screened output power, wherein the screened output power satisfies the constraints of the adjustment model, and the adjustment model is used to adjust the output power of the charging and discharging station.

[0098] In this embodiment, the power difference information between the charging power and the discharging power of the vehicle can be obtained. Further based on the obtained power difference information, the adjusted output power can be screened to obtain the screened output power. The screened output power satisfies the constraint conditions of the adjustment model.

[0099] It should be noted that the constraints of the upper optimization model include power flow constraints, peak-valley difference constraints, electric vehicle charging and discharging power and state constraints, and power balance constraints. Among them, the power flow constraints can be expressed by the following formula:

[0100]

[0101] Among them, P G，t It can be used to represent the active power injected by the upper network into the distribution network, P loss，t The power flow constraint system is the network loss in period t. The peak-to-valley difference constraint can be expressed by the following formula:

[0102] Ploadl,max -P loadl,min ≤P load,max -P load,min

[0103] Among them, from left to right, the formula can be used to represent: the original load peak value of the distribution network, the original load valley value of the distribution network, the load peak value after optimization, and the load valley value after optimization. The charging and discharging power constraints can be expressed by the following formula:

[0104]

[0105] Among them, c i，t Can be used to represent 0-1 variables, representing the charging state; d i，t is a 0-1 variable, representing the discharge state; It can be used to represent the maximum charging power and the maximum discharging power of the i-th EVA in time period t. The charging and discharging state constraints can be expressed by the following formula:

[0106] 0≤c i,t +d i,t ≤1i=1,2,…,m; t=1,2,…,T

[0107] The power balance constraint can be expressed by the following formula:

[0108]

[0109] Among them, η c , η d It can be used to indicate the charging efficiency and discharging efficiency of electric vehicles respectively; It can be used to represent the total charging demand of the i-th EVA.

[0110] It should be noted that the constraints of the lower optimization model include: power flow constraints, node voltage constraints and static VAR compensation output constraints. Among them, the power flow constraints can be expressed by the following formula:

[0111]

[0112] Among them, P i，t , Q i，t can be used to represent the active and reactive power injected into node i during period t. The node voltage constraint can be expressed by the following formula:

[0113] V i,min ≤V i,t ≤V i,max

[0114] Among them, V i，max 、V i，minThey can be used to represent the upper and lower limits of the voltage at node i during period t. The static VAR compensation output constraint can be expressed by the following formula:

[0115] Q t SVC,min ≤Q t SVC,max

[0116]

[0117] in, It can be used to indicate the upper and lower limits of reactive power output of distributed generation in period t respectively; It can be used to represent the active power output of the distributed power source at node k during period t; SDG,max can be used to represent the maximum apparent power of the distributed power source inverter; They can be used to represent the active and reactive power of the load at node k in period t respectively.

[0118] Optionally, the speed update mechanism of the particle swarm optimization algorithm allows each individual to adjust its migration direction and speed according to its own historical optimal position and the optimal position of the entire population. In the S2G system, the speed update direction and amplitude are also adjusted according to the difference between the charging power and the discharging power of the electric vehicle. This can speed up the convergence of the algorithm in the S2G system, find the optimal EVA charging and discharging power combination and voltage regulating equipment action value in each region more quickly, and enable the S2G system to meet the needs of the power grid while minimizing operating costs and better improving the stability of the power grid:

[0119]

[0120] Among them, t can be used to represent the current iteration number, w can be used to represent the inertia weight, c1 and c2 can be used to represent the learning factor, r1 and r2 can be used to represent random numbers in [0, 1], α ev It can be used to represent the impact factor related to electric vehicles, E demand It can be used to represent the total power demand of electric vehicles, E current It can be used to indicate the current power status of the electric vehicle.

[0121] As an optional embodiment, adjusting the control strategy according to the output power includes: adjusting the control strategy according to the screened output power.

[0122] In this embodiment, the control strategy can be adjusted according to the output power after the above screening. The SaDE-BBO algorithm in this embodiment combines the advantages of the differential evolution algorithm and the biogeography optimization algorithm, and has a significant optimization effect on S2G. In principle, the differential evolution algorithm has a strong global search capability and fast convergence characteristics. In S2G ​​optimization, it can quickly explore different combinations of power station output power and grid receiving strategies in a broad solution space to find potential better solutions. By continuously performing mutation, crossover and selection operations, breaking through the limitations of local optimal solutions, and finding the globally optimal S2G operation plan, thereby improving energy transmission efficiency and system stability.

[0123] This embodiment determines an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize rules for controlling the charging and discharging station, and the adjustment target is used to characterize the operating status of associated equipment of the charging and discharging station; based on the charging demand information of the vehicle, the adjustment target is expanded to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; based on the expanded adjustment target, the number of vehicles moving in and moving out is determined, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station; based on the number of moving in and the number of moving out, the output power of the charging and discharging station is determined; the control strategy is adjusted according to the output power, thereby solving the technical problem of low efficiency in adjusting the control strategy of the charging and discharging station and achieving the technical effect of improving the efficiency of adjusting the control strategy of the charging and discharging station.

[0124] The technical solution of the embodiment of the present invention is illustrated below in conjunction with preferred implementation modes.

[0125] At present, a high proportion of new energy power generation and large-scale electric vehicles are connected to the power grid at the same time, which brings new challenges to the economic operation, safety and stability of the distribution network. The continuous development of S2G technology has enabled electric vehicles to have the dual characteristics of load and energy storage, and can respond to the needs of the active distribution network in real time on the demand side, reduce the peak-to-valley difference of the load of the active distribution network in an economical and efficient way, and promote the consumption of intermittent energy output. Therefore, it is of great significance to carry out research on optimizing the operation of the distribution network based on the S2G mode. With the widespread application of S2G technology and the gradual popularization of time-of-use electricity prices, electric vehicles can also be discharged as mobile energy storage elements and participate in the multi-source complementary coordinated optimization of the power grid.

[0126] The basic concept of the BBO algorithm is derived from biogeography theory and is usually used to deal with complex optimization problems. It has been widely used in distribution network planning, load distribution, and optimal configuration of microgrids. In terms of grid partitioning, the BBO algorithm is improved and an update method for information exchange of algorithm nodes is proposed, which reduces the time complexity of the algorithm and makes it more in line with the needs of modern power grids. In terms of microgrid scheduling, the migration model and migration strategy are improved on the basis of the BBO algorithm, thereby optimizing the scheduling of microgrids, reducing power generation costs, and reducing carbon emissions.

[0127] The standard BBO algorithm is easy to operate and has good scalability, but it also has the following shortcomings: the initial population is randomly generated, and the quality of individuals in the population is poor; the migration model uses a linear model, while the actual species migration process is more complicated than the linear model; the migration operator mainly uses local search, which can easily cause the algorithm to fall into local optimality; the mutation operator randomly searches for individuals in the entire solution space, which can easily affect the convergence speed of the algorithm in the later stage; the elite retention mechanism is used for the survival of the fittest selection strategy of the habitat, which has high computational complexity.

[0128] In order to solve the above problems, the purpose of this embodiment is to achieve efficient coordinated interaction of multi-source controllable resources. Specifically, for the EVA of each region, based on the strong guidance of time-of-use electricity prices, the operating cost of EVA in each region must be minimized. From the perspective of the active distribution network, it is necessary to give full play to its role and effectively smooth out load fluctuations. While striving to improve the economic efficiency of operation, it is necessary to focus on meeting the key requirement of safe and stable operation of the active distribution network.

[0129] Therefore, this embodiment constructs a hierarchical and partitioned optimization operation model of the active distribution network, which takes the lowest EVA operating cost and the smallest load fluctuation in each region as the upper optimization model, aiming to provide accurate input for the lower optimization model by optimizing the charging and discharging power of EVA in each region. The lowest comprehensive operating cost is used as the lower optimization model, and its main task is to deeply optimize and solve the action value of each voltage regulating device.

[0130] In order to better solve the optimization model, the BBO algorithm is comprehensively and necessarily improved. This embodiment proposes a SaDE-BBO algorithm, which provides more powerful technical support and solutions for the optimized operation of the active distribution network.

[0131] The biogeographic optimization algorithm is based on the principles of biogeography and performs optimization by simulating the migration and distribution of species in different habitats. In the S2G scenario, the BBO algorithm can regard the various parameters of the power station and the power grid as different habitat characteristics, and find the most suitable parameter combination for S2G operation by analyzing the adaptability and migration patterns of these characteristics. For example, the charging and discharging power of the power station can be optimized based on factors such as the capacity of the power station, the load demand of the power grid, and the time-of-use electricity price, so that the S2G system can meet the needs of the power grid while minimizing operating costs and better improving the stability of the power grid.

[0132] In this embodiment, a two-layer optimization model is first constructed, which includes an upper optimization model and a lower optimization model. The upper optimization model comprehensively considers the operating costs of EVA in each region and the load fluctuations of the power grid. The model takes the minimization of the operating costs of EVA in each region and the minimization of load fluctuations as the objective function. The constraints include flow constraints, peak-to-valley difference constraints, electric vehicle charging and discharging power and state constraints, and also include power balance constraints.

[0133] Optionally, for the objective function of minimizing the EVA operating cost of each region, during the entire scheduling cycle, the peak electricity price is high, the more discharge, the greater the profit; the valley electricity price is low, the more charging, the lower the charging cost. Therefore, the objective function is to minimize the EVA operating cost of each region:

[0134]

[0135] Among them, f1 can be used to represent the objective function that minimizes the EVA operating cost of each region, T can be used to represent the number of time periods in the scheduling cycle, and m can be used to represent the number of EVAs. can be used to represent the unit charging price and discharging price of the i-th EVA in period t respectively; can be used to represent the charging power and discharging power of the i-th EVA in time period t, and Δt can be used to represent the length of the time period.

[0136] Optionally, for the objective function of minimizing the load fluctuation of the active distribution network, the relative load fluctuation rate D t and load fluctuation standard deviation D v There are two indicators that reflect load fluctuations. t The smaller the value, the better the effect of each EVA charging and discharging power tracking the active output and load of the distributed power source; D v The smaller the value, the more stable the load fluctuation. Therefore, the objective function is to minimize the load fluctuation of the active distribution network:

[0137] minf2=m1D t +m2D v

[0138]

[0139] Among them, P av1 It can be used to represent the average load value in T time periods, and n can be used to represent the number of distributed power sources. It can be used to represent the output of distributed generation j during period t; P load，t Can be used to represent the base load during period t; P av2 It can be used to represent the average value of the optimized load curve within T time periods; m1 and m2 can be used to represent weight coefficients, which are generally 1.

[0140] Optionally, the upper optimization model constraint conditions may at least include: power flow constraint, peak-to-valley difference constraint, charge-discharge power constraint, charge-discharge state constraint and power balance constraint. The power flow constraint may be expressed by the following formula:

[0141]

[0142] Among them, P G，t It can be used to represent the active power injected by the upper network into the distribution network, P loss，t The power flow constraint system network loss in period t. The peak-to-valley difference constraint can be expressed by the following formula:

[0143] P loadl,max -P loadl,min ≤P load,max -P load,min

[0144] Among them, from left to right, the formula can be used to represent: the original load peak value of the distribution network, the original load valley value of the distribution network, the load peak value after optimization, and the load valley value after optimization. The charging and discharging power constraints can be expressed by the following formula:

[0145]

[0146] Among them, c i，t Can be used to represent 0-1 variables, representing the charging state; d i，t is a 0-1 variable, representing the discharge state; It can be used to represent the maximum charging power and the maximum discharging power of the i-th EVA in time period t. The charging and discharging state constraints can be expressed by the following formula:

[0147] 0≤c i,t +d i,t ≤1i=1,2,…,m; t=1,2,…,T

[0148] The power balance constraint can be expressed by the following formula:

[0149]

[0150] Among them, η c , η d It can be used to indicate the charging efficiency and discharging efficiency of electric vehicles respectively; It can be used to represent the total charging demand of the i-th EVA.

[0151] The data set of the upper optimization model includes: the number of time periods T in the scheduling cycle, the number of EVAs m, and the unit charging electricity price of the i-th EVA in time period t and discharge price Charging power of the i-th EVA in period t and discharge power The average load value P in T time periods, the output of distributed generation Pf, the basic load Pw in the time period, and the average value P of the optimized load curve in T time periods av2 、EVA maximum charging power and maximum discharge power Charging efficiency η of electric vehicles c and discharge efficiency η d 、Total charging demand of EVA The input of the upper optimization model is the relevant parameters of each region, such as the number of EVAs, unit charging and discharging electricity price, charging and discharging power, etc., the load of the power grid, including the load average, basic load, etc. The output of distributed power sources. The output of the upper optimization model is the optimized EVA charging and discharging power of each region, so as to achieve the goal of minimizing the regional EVA operating cost and minimizing load fluctuation.

[0152] The lower optimization model uses the EVA charging and discharging power obtained from the upper optimization model as input, and transforms the active power loss of the distribution network and various voltage regulation methods into economic costs. The objective function is to minimize the comprehensive operation cost of the distribution network, and to build the lower optimization model under the conditions of satisfying the distribution network operation constraints and the output constraints of each voltage regulation equipment.

[0153] The objective function of the lower optimization model can be expressed by the following formula:

[0154]

[0155] Among them, K can be used to represent the number of distributed generation connected to the distribution network, and πt can be used to represent the marginal price of network loss in time period t. It can be used to represent the output cost of distributed power generation and static reactive power compensation cost respectively; It can be used to represent the reactive power output distributed at node k during period t; It can be used to represent the reactive power output of static reactive power compensation during period t, P loss,t It can be expressed by the following formula:

[0156]

[0157] Among them, m can be used to represent the number of distribution network nodes, V i，t 、V j，t can be used to represent the voltage of node i and node j during time period t respectively; G ij , B ij , δ ij It can be used to express the conductance, susceptance and voltage phase difference between node i and node j respectively.

[0158] The constraints of the lower optimization model include: power flow constraints, node voltage constraints and static VAR compensation output constraints. Among them, the power flow constraints can be expressed by the following formula:

[0159]

[0160] Among them, P i，t , Q i，t can be used to represent the active and reactive power injected into node i during period t. The node voltage constraint can be expressed by the following formula:

[0161] V i,min ≤V i,t ≤V i,max

[0162] Among them, V i，max 、V i，min They can be used to represent the upper and lower limits of the voltage at node i during period t. The static VAR compensation output constraint can be expressed by the following formula:

[0163] Q t SVC,min ≤Q t SVC,max

[0164]

[0165] in, It can be used to indicate the upper and lower limits of reactive power output of distributed generation in period t respectively; It can be used to represent the active power output of the distributed power source at node k during period t; SDG,max can be used to represent the maximum apparent power of the distributed power source inverter; They can be used to represent the active and reactive power of the load at node k in period t respectively.

[0166] The data set of the lower optimization model is: the EVA charging and discharging power obtained by the upper optimization model, the upper and lower limits V of the node voltage in T time periods i，max and V i，min, the upper and lower limits of reactive power output of distributed power sources during period t and Active power output of distributed generation at node k during period t The maximum apparent power SDG of the distributed power inverter, the active and reactive power of the load at node k during the period t and The input of the lower optimization model is the EVA charging and discharging power obtained by the upper optimization model, and the output is a solution to minimize the comprehensive operating cost of the distribution network, including the active power distribution of the distribution network, the economic costs corresponding to various voltage regulation means, etc., while satisfying the operation constraints of the distribution network and the output constraints of various voltage regulation equipment.

[0167] The BBO algorithm originated from the optimization thinking contained in the process of species migration between habitats. In this algorithm, the activity place of species in the ecological environment is defined as the habitat. There are many factors in the ecological environment that can affect the quality of the habitat environment. These factors are called fitness index variables SIVs. The suitability of the habitat for the survival of species and the quality of the environment are described by the fitness index HSI. The standard BBO algorithm is easy to operate and has strong scalability, but it also has defects. Therefore, this embodiment optimizes the initial population, migration model, migration operator, mutation operator and selection mechanism, and proposes the SaDE-BBO algorithm, which has an important optimization effect on the S2G system from charging and discharging station to power grid.

[0168] Figure 3 is a flow chart of a SaDE-BBO algorithm according to an embodiment of the present invention. Figure 3 As shown, the process of the SaDE-BBO algorithm may include at least the following steps:

[0169] Step S301, searching for the target function using the learning rate.

[0170] In the above steps, the optimization direction of the S2G system and the dynamic learning rate that adapts to its complex characteristics can be quickly found: in the early stage of the algorithm, using a larger learning rate for extensive search helps to quickly find potential optimization directions in the S2G system. As the number of iterations increases, the learning rate is gradually reduced for a more refined search, which can better adapt to the complex characteristics of the S2G system, accurately adjust the charging and discharging power of the electric vehicle aggregator (EVA) in each region and the action value of the voltage regulating equipment, and improve the operating efficiency and stability of the system. This embodiment can quickly find the optimization direction of the S2G system and the dynamic learning rate that adapts to its complex characteristics. The learning rate can be expressed by the following formula:

[0171] lr(t+1)=lr(t)(1-t / T)

[0172] Wherein, lr(t) can be used to represent the current learning rate, lr(t+1) can be used to represent the updated learning rate, t can be used to represent the step size, and T can be used to represent the update period. That is, this embodiment determines the objective function of the two-layer optimization model (adjustment model) through the dynamic learning rate.

[0173] Step S302, using piecewise chaotic mapping to generate an initial population.

[0174] In the above steps, adaptive mutation and crossover probabilities are used to adapt to changes in charging demand of EVs in the S2G system: the fixed mutation and crossover probabilities of traditional algorithms may not be optimal at different optimization stages. This embodiment can better adapt to changes in charging demand of electric vehicles in the S2G system by adaptively adjusting the mutation and crossover probabilities according to the diversity of the population and the fitness value of the individual. When the charging demand of electric vehicles is more concentrated, increasing the mutation probability helps to explore more possible solutions to meet the needs of the S2G system. If the population diversity is low, increasing the mutation probability can introduce more diversity and improve the algorithm's search ability in the S2G system. When the individual fitness value is close to the optimal solution, the crossover probability is reduced to avoid destroying the better solution structure and ensure that the S2G system can stably develop towards the optimal solution. The mutation probability can be expressed by the following formula:

[0175] pm=pm0*(1+(diversity / diversity m ax))*(1+α ev )

[0176] Among them, diversityσ can be used to represent the population diversity metric, diversity m axσ can be used to represent the maximum diversity, pm0 can be used to represent the current mutation probability, pm(t+1) can be used to represent the updated mutation probability, and α ev Can be used to represent impact factors related to electric vehicles.

[0177] When the charging demand of electric vehicles is concentrated, α ev Increase, thereby increasing the probability of mutation. If the population diversity is low, the probability of mutation can be increased to introduce more diversity; if the individual fitness value is close to the optimal solution, the crossover probability can be reduced to avoid destroying the better solution structure. The crossover probability can be expressed by the following formula:

[0178] pc=pc0*(1-(fitness / average f itness))*(1+β eva )

[0179] Among them, fitness can be used to represent individual fitness value, average f itness can be used to represent the average fitness value of the population, pc0 can be used to represent the current crossover probability, pc(t+1) can be used to represent the updated crossover probability, β eva It can be used to represent the influencing factors related to the coordination degree between charging and discharging stations and electric vehicles.

[0180] Optionally, chaos initialization is performed for a wider search of the algorithm in the S2G system: using piecewise chaotic mapping (Logistic) to generate random numbers for initialization can increase the diversity of the initial population so that it can better cover the solution space of the S2G system. The generated random numbers are within [0, 1], which provides a wider starting point for the algorithm's search in the S2G system.

[0181] Optionally, a piecewise logistic chaotic map is used to generate random numbers:

[0182]

[0183] Among them, σ can be used to represent the chaos attraction factor, x i Can be used to represent random numbers in [0, 1].

[0184] Step S303, calling the cosine migration model to calculate the individual's immigration rate and emigration rate.

[0185] In the above steps, in order to better simulate the calculation method of the cosine migration model in the S2G system: the linear migration model in the BBO algorithm is relatively simple to describe the migration of populations, while the cosine migration model has been verified to have the best performance. In the S2G system, the calculation method of its migration rate and migration rate can better simulate the changes in various parameters of power stations and power grids:

[0186]

[0187] Among them, I and E can be used to represent the maximum probability of migration in and out, respectively, and S i Can be used to indicate population size; S max Can be used to represent the maximum population size, β region Can be used to represent regional related impact factors, f region It can be used to represent a function that reflects the travel patterns and charging and discharging requirements of electric vehicles in a specific area.

[0188] Step S304: performing a differential migration operation based on the ring structure on the population.

[0189] In the above steps, the differential migration operation based on the ring structure can be quickly explored in the S2G system: the mutation mechanism of the differential evolution algorithm has good global search capabilities in the solution space, which complements the BBO algorithm. The differential migration operator obtained by integrating the mutation mechanism in the differential evolution algorithm into the BBO algorithm can quickly explore different combinations of power station output power and grid reception strategies in the S2G system:

[0190]

[0191] c rand <c r or d<d r

[0192] Among them, I i (d) can be used to represent the dth SIV of the ith habitat, c rand Can be used to represent random numbers, c r Can be used to represent the crossover rate.

[0193] Step S305, performing an adaptive differential mutation operation on the population.

[0194] In the above steps, in order to find a more optimal S2G operation scheme, the adaptive differential mutation operator is used: In order to reduce the randomness of SIV mutation in the later iteration, the adaptive differential optimization algorithm is integrated into the mutation operation. When performing the mutation operation, not only the difference between the optimal fitness of the current population and the current individual is considered, but also the direction and amplitude of the mutation are adjusted according to the difference between the required power of the electric vehicle and the current power state, so that the algorithm can better adapt to the dynamic characteristics of electric vehicles in the S2G system. For example, when the electric vehicle's demand for power is large and the current power state is low, the direction and amplitude of the mutation are adjusted to prompt the algorithm to find a better S2G operation scheme to meet the charging needs of the electric vehicle and improve the stability of the power grid:

[0195]

[0196] Among them, β can be used to represent the adaptive adjustment parameter, I best Can be used to represent the optimal fitness of the current population, α ev It can be used to represent the impact factor related to electric vehicles, E demand It can be used to represent the total power demand of electric vehicles, E current It can be used to indicate the current power status of the electric vehicle.

[0197] Step S306, combining the particle swarm optimization algorithm to accelerate convergence.

[0198] In the above steps, the particle swarm optimization algorithm is used to speed up the convergence speed and update the direction and amplitude of the algorithm in the S2G system: the speed update mechanism of the particle swarm optimization algorithm allows each individual to adjust its migration direction and speed according to its own historical optimal position and the optimal position of the entire population. In the S2G system, the speed update direction and amplitude are also adjusted according to the difference between the charging power and the discharging power of the electric vehicle. This can speed up the convergence speed of the algorithm in the S2G system, find the optimal EVA charging and discharging power combination and the action value of the voltage regulating equipment in each area more quickly, and enable the S2G system to meet the needs of the power grid while minimizing operating costs and better improving the stability of the power grid:

[0199]

[0200] Among them, t can be used to represent the current iteration number, w can be used to represent the inertia weight, c1 and c2 can be used to represent the learning factor, r1 and r2 can be used to represent random numbers in [0, 1], α ev It can be used to represent the impact factor related to electric vehicles, E demand It can be used to represent the total power demand of electric vehicles, E current It can be used to indicate the current power status of the electric vehicle.

[0201] The two-layer optimization model proposed in this embodiment has many important optimization effects on the S2G station. First, the upper-layer optimization model comprehensively considers the operating costs of the EVA in each region and the grid load fluctuations, with the goal of achieving the lowest operating cost and the smallest load fluctuation. This enables the S2G station to adjust energy procurement and output according to the low-cost operation strategy, such as storing energy when electricity prices are low and outputting when they are high, thereby reducing operating costs. At the same time, the minimization of grid load fluctuations allows the S2G station to better adapt to changes in grid demand, stably output or store energy, reduce equipment losses and operating risks, and flexibly adjust the charging and discharging power according to the load to provide stable support for the grid. The constraints ensure the feasibility and effectiveness of the upper-layer optimization and provide a guarantee for the stable operation of the S2G station.

[0202] Secondly, the lower optimization model takes the EVA charging and discharging power obtained in the upper layer as input, converts the active power loss of the distribution network and the voltage regulation means into economic costs, and takes the lowest comprehensive operating cost of the active distribution network as the goal. This prompts the S2G station to pay attention to energy transmission losses, reduce the active power loss of the distribution network by optimizing equipment operating parameters and improving energy conversion efficiency, and improve energy utilization efficiency. The constraints and optimization of various voltage regulation means help the S2G station maintain a stable output voltage, improve energy quality, ensure safe and efficient operation of equipment, and reduce the impact on the power grid and other users. With the lowest comprehensive operating cost as the goal, the S2G station is prompted to reduce operating costs under the premise of meeting constraints, including reasonably arranging charging and discharging power, optimizing equipment operating time, and improving equipment utilization, so as to maximize overall benefits.

[0203] In summary, the two-layer optimization model comprehensively optimizes the operating cost, grid load stability, energy utilization efficiency, and voltage regulation of the S2G station through the synergy of the upper and lower layers, and strongly supports the efficient and stable operation of the S2G station.

[0204] The SaDE-BBO algorithm in this embodiment combines the advantages of the differential evolution algorithm and the biogeography optimization algorithm, and has a significant optimization effect on S2G. In principle, the differential evolution algorithm has a strong global search capability and fast convergence. In S2G ​​optimization, it can quickly explore different combinations of power station output power and grid receiving strategies in a broad solution space to find potential better solutions. By continuously performing mutation, crossover and selection operations, the limitations of local optimal solutions are broken through, and the globally optimal S2G operation plan is found, thereby improving energy transmission efficiency and system stability.

[0205] The biogeographic optimization algorithm is based on the principles of biogeography and performs optimization by simulating the migration and distribution of species in different habitats. In the S2G scenario, it can regard the various parameters of the power station and the power grid as different habitat characteristics, and find the most suitable parameter combination for S2G operation by analyzing the adaptability and migration patterns of these characteristics. For example, according to factors such as power station capacity, power grid load demand, and time-of-use electricity prices, the charging and discharging power of the power station is optimized, so that the S2G system can meet the needs of the power grid while maximizing the stability of the power grid.

[0206] The SaDE-BBO algorithm combines the advantages of both algorithms. It has the powerful search capability of the differential evolution algorithm and the adaptability and stability of the biogeography optimization algorithm. In S2G ​​optimization, it can dynamically adjust the search strategy according to the real-time status of the system and the objective function, and quickly and accurately find the optimal S2G operation plan. At the same time, the algorithm also has good robustness and can adapt to different operating environments and changes, providing reliable guarantee for the stable operation of the S2G system.

[0207] Figure 4 is a schematic diagram of an S2G ​​system optimization architecture based on the SaDE-BBO algorithm according to an embodiment of the present invention, such as Figure 4 As shown, the schematic diagram of the S2G system optimization architecture based on the SaDE-BBO algorithm may include at least three layers: a power grid layer 401, a vehicle aggregator layer 402, and a charging station layer 403. The power grid layer 401 includes a distribution network dispatching center 4011. The vehicle aggregator layer 402 includes n vehicle aggregators: vehicle aggregator 4021, vehicle aggregator 4022, ..., vehicle aggregator 402n. The charging station layer 403 includes a charging station 4031.

[0208] At the power grid level, the distribution network dispatch center is responsible for collecting new energy generation forecasts and load data from various EV aggregators, calculating the total charging and discharging power of EVs, and decomposing these data and passing them to EV aggregators at the EVA level. At the same time, the distribution network dispatch center adjusts and controls the operation of the power grid based on the optimization results of the SaDE-BBO algorithm.

[0209] At the EVA layer, EV aggregators formulate specific EV charging and discharging strategies based on the time period charging and discharging power instructions provided by the top layer and the day-ahead forecast error information of new energy, and use the SaDE-BBO algorithm for optimization to achieve the goal of the lowest EVA operating cost and the smallest load fluctuation in each region. In addition, EV aggregators also formulate upper-level models for vehicle information feedback in the next time period, and exchange information with the power grid layer and the charging station layer, receive data from the power grid layer, feedback vehicle information and optimization strategies to the power grid layer, and transmit EV charging and discharging strategies to the charging station layer, and receive feedback from the charging station layer on the actual charging and discharging conditions of vehicles.

[0210] At the charging station level, the charging station automatically identifies the vehicle's battery rated capacity, power level at the time of connection, and target power level set by the owner, and calculates the theoretical charging time. Depending on whether the owner is willing to participate in station-to-grid (S2G) interaction, the EV aggregation system assigns the vehicle a corresponding two-way or one-way dispatching mode. After the vehicle charging mode is determined, the substation charges the vehicle according to the power requirements of the selected mode, and feeds back the actual charging and discharging status of the vehicle to the EV aggregator at the EVA level.

[0211] Through such a three-layer structure and information interaction process, the SaDE-BBO algorithm can be applied to the S2G system to achieve optimized operation of the S2G system. Figure 5 is a schematic diagram of energy exchange between photovoltaic and power grid according to an embodiment of the present invention, such as Figure 5 As shown in the figure, based on real data, the operation mode of photovoltaic power generation in different scenarios and the energy exchange between photovoltaic power generation and the power grid are plotted. Figure 5 The power supply source and power balance status at each moment are shown in the figure.

[0212] In the preparation stage, we first determine the problem parameters: analyze the operation mode and energy exchange data between the power grid and the power grid under different photovoltaic output scenarios provided in the figure, including the power supply source at each moment, power balance status, power sales to the power grid, power purchases from the power grid, energy storage charging and discharging, charging load, photovoltaics, etc. Clarify the structure and parameters of the distribution network, such as the voltage of each node and the impedance of the line.

[0213] Determine the capacity, charging and discharging efficiency and other parameters of the energy storage system, while considering the characteristics of electric vehicles as mobile energy storage elements, such as battery capacity, charging requirements, discharge capacity, travel patterns, etc. Understand the characteristics and changing patterns of charging loads, with special attention to the charging time and location distribution of electric vehicles. Master the output characteristics and forecast information of photovoltaic systems.

[0214] Secondly, initialize the two-layer optimization model: For the upper optimization model, set the lowest EVA operating cost and the smallest load fluctuation in each region as the objective function, and determine the constraints such as power flow constraints, peak-to-valley difference constraints, electric vehicle charging and discharging power and state constraints, and power balance constraints. Take full account of the S2G characteristics and include the charging and discharging behavior of electric vehicles in the constraints to ensure their coordinated interaction with the power grid.

[0215] For the lower optimization model, the objective function is to minimize the comprehensive operating cost of the active distribution network, and the operating constraints of the active distribution network and the output constraints of each voltage regulating device are clarified. At the same time, the synergy between electric vehicles and energy storage systems in the S2G system is considered, and relevant parameters are included in the objective function and constraints.

[0216] In the implementation phase of the SaDE-BBO algorithm, first, the population is initialized: for the upper optimization model and the lower optimization model, the population is initialized respectively. The individuals in the population can be represented as the charging and discharging power combination of EVA in each region, the action value combination of the voltage regulating equipment, the charging and discharging strategy of the energy storage system, and the charging and discharging plan of the electric vehicle. According to the actual situation of the problem, the population size, individual encoding method and other parameters are determined to ensure that the population can fully reflect the diversity and complexity of the S2G system.

[0217] Secondly, for the individuals in the upper optimization model, the fitness value of the individuals is calculated according to the objective function (the lowest EVA operating cost and the smallest load fluctuation in each region) and the constraints. The impact of factors such as photovoltaic output, electricity sales to the grid, electricity purchases from the grid, energy storage charging and discharging, charging load, and electric vehicle charging and discharging on operating costs and load fluctuations is considered, with special attention paid to the contribution and impact of electric vehicles on the system when participating in S2G ​​interaction.

[0218] For individuals in the lower optimization model, the fitness value of the individuals is calculated according to the objective function (the lowest comprehensive operating cost of the active distribution network) and the constraints. The active power loss of the distribution network, the economic cost corresponding to the voltage regulation means, and the above-mentioned various energy exchange situations are comprehensively considered, and the impact of electric vehicle charging and discharging on the operation of the distribution network is also included.

[0219] Furthermore, mutation operations are performed on the upper and lower models, and mutant individuals are generated based on the individuals in the current population. The mutation strategy can be selected according to the actual situation, such as random mutation, mutation based on the best individual, etc. In the mutation process, the uncertainty of photovoltaic output, the characteristics of the energy storage system, and the flexible charging and discharging capabilities of electric vehicles are fully considered to adapt to the complex changes of the S2G system. The mutant individuals are crossed with the current individuals to generate test individuals. Then the selection operation is performed to compare the fitness values ​​of the test individuals and the current individuals, and the individuals with better fitness values ​​are selected to enter the next generation population. In the selection process, attention is paid to selecting individuals that can better coordinate the elements in the S2G system to improve the overall performance of the system.

[0220] The upper and lower models simulate the migration and distribution of species in different habitats, and treat them as different habitats according to the fitness values ​​of individuals. The immigration rate and emigration rate of each habitat are calculated, and information exchange and update between individuals are carried out based on these rates. When calculating the immigration rate and emigration rate, the adaptability and migration rules of habitat characteristics (such as EVA parameters in each area, parameters of voltage regulating equipment, parameters of energy storage system, related parameters of electric vehicles, etc.) are analyzed in combination with the photovoltaic output scenario, energy exchange situation, charging and discharging behavior of electric vehicles, travel patterns, etc., to find a better combination of parameters to achieve the optimal operation of the S2G system.

[0221] Finally, the output dynamically adjusts the search strategy: according to the real-time status of the system and the objective function, the parameters of differential evolution and biogeography optimization, such as mutation rate, crossover rate, immigration rate, and emigration rate, are continuously adjusted. The convergence of the algorithm is monitored. When the algorithm is close to convergence, the search range can be appropriately reduced to improve the search accuracy. Combined with the different photovoltaic output scenarios shown in the figure and the actual operation of electric vehicles.

[0222] This embodiment determines an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize rules for controlling the charging and discharging station, and the adjustment target is used to characterize the operating status of associated equipment of the charging and discharging station; based on the charging demand information of the vehicle, the adjustment target is expanded to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; based on the expanded adjustment target, the number of vehicles moving in and moving out is determined, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station; based on the number of moving in and the number of moving out, the output power of the charging and discharging station is determined; the control strategy is adjusted according to the output power, thereby solving the technical problem of low efficiency in adjusting the control strategy of the charging and discharging station and achieving the technical effect of improving the efficiency of adjusting the control strategy of the charging and discharging station.

[0223] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0224] In this embodiment, a device for adjusting the control strategy of a charging and discharging station is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0225] Figure 6 is a structural block diagram of a device for adjusting a control strategy of a charging and discharging station according to an embodiment of the present invention, such as Figure 6 As shown, the control strategy adjustment device 600 of the charging and discharging station may include: a first determination unit 602 , an expansion unit 604 , a second determination unit 606 , a third determination unit 608 and an adjustment unit 610 .

[0226] The first determination unit 602 is used to determine an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of associated equipment of the charging and discharging station.

[0227] The expansion unit 604 is used to expand the adjustment target based on the charging demand information of the vehicle to obtain the expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle.

[0228] The second determining unit 606 is used to determine the number of vehicles entering and the number of vehicles leaving the charging and discharging station based on the expanded adjustment target, wherein the number of vehicles entering is the number of vehicles entering the charging and discharging station, and the number of vehicles leaving is the number of vehicles leaving the charging and discharging station.

[0229] The third determining unit 608 is configured to determine the output power of the charging and discharging station based on the number of incoming migrations and the number of outgoing migrations.

[0230] The adjustment unit 610 is used to adjust the control strategy according to the output power.

[0231] As an optional embodiment, the device also includes: a fourth determination unit, used to determine the learning rate of the charging and discharging station, wherein the learning rate is used to screen adjustment targets with a matching degree higher than a matching degree threshold; the first determination unit 602 includes: a first determination module, used to determine an adjustment target that matches the learning rate.

[0232] As an optional embodiment, the device also includes: a fifth determination unit, used to determine the objective function of the adjustment model, wherein the adjustment model is used to adjust the output power of the charging and discharging station; the first determination module includes: a determination submodule, used to determine the objective function matching the learning rate as the adjustment target.

[0233] As an optional embodiment, the device further includes: a generating unit, configured to generate a random number using a chaotic system, wherein the random number is used to represent the amount of expansion of the adjustment target.

[0234] As an optional embodiment, the expansion unit 604 includes: a second determination module, used to determine the expansion probability of the charging and discharging station based on the charging demand information of the vehicle, wherein the expansion probability is used to characterize the amount of expansion of the adjustment target; an expansion module, used to expand the adjustment target according to the expansion probability or a random number to obtain the expanded adjustment target.

[0235] As an optional embodiment, the second determining unit 606 includes: a third determining module, configured to call a cosine migration model to analyze the expanded adjustment target to determine the number of migrations in and the number of migrations out.

[0236] As an optional embodiment, after determining the output power of the charging and discharging station based on the number of migrations in and out, the device also includes: a first adjustment unit, which is used to adjust the output power based on the charging demand information and the vehicle's power status information to obtain the adjusted output power.

[0237] As an optional embodiment, the device also includes: an acquisition unit, used to obtain power difference information between the charging power and the discharging power of the vehicle; a screening unit, used to screen the adjusted output power based on the power difference information to obtain the screened output power, wherein the screened output power satisfies the constraints of the adjustment model, and the adjustment model is used to adjust the output power of the charging and discharging station.

[0238] As an optional embodiment, the adjustment unit 610 includes: an adjustment module, configured to adjust the control strategy according to the screened output power.

[0239] In this embodiment, the first determination unit 602 determines the adjustment target for adjusting the control strategy of the charging and discharging station, wherein the charging and discharging station is used to charge the vehicle, the control strategy is used to characterize the rules for controlling the charging and discharging station, and the adjustment target is used to characterize the operating status of the associated equipment of the charging and discharging station. The expansion unit 604 expands the adjustment target based on the charging demand information of the vehicle to obtain the expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle. The second determination unit 606 determines the number of vehicles moving in and moving out based on the expanded adjustment target, wherein the number of moving in is the number of vehicles entering the charging and discharging station, and the number of moving out is the number of vehicles leaving the charging and discharging station. The third determination unit 608 determines the output power of the charging and discharging station based on the number of moving in and the number of moving out. The adjustment unit 610 adjusts the control strategy according to the output power, thereby solving the technical problem of low efficiency in adjusting the control strategy of the charging and discharging station, and achieving the technical effect of improving the efficiency of adjusting the control strategy of the charging and discharging station.

[0240] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0241] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0242] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0243] S1, determining an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of associated equipment of the charging and discharging station;

[0244] S2, based on the charging demand information of the vehicle, expanding the adjustment target to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle;

[0245] S3, based on the expanded adjustment target, determining the number of vehicles moving in and moving out, wherein the number of vehicles moving in is the number of vehicles driving into the charging and discharging station, and the number of vehicles moving out is the number of vehicles driving out of the charging and discharging station;

[0246] S4, determining the output power of the charging and discharging station based on the number of in-migration and the number of out-migration;

[0247] S5, adjusting the control strategy according to the output power.

[0248] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0249] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0250] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0251] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0252] S1, determining an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of associated equipment of the charging and discharging station;

[0253] S2, based on the charging demand information of the vehicle, expanding the adjustment target to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle;

[0254] S3, based on the expanded adjustment target, determining the number of vehicles moving in and moving out, wherein the number of vehicles moving in is the number of vehicles driving into the charging and discharging station, and the number of vehicles moving out is the number of vehicles driving out of the charging and discharging station;

[0255] S4, determining the output power of the charging and discharging station based on the number of in-migration and the number of out-migration;

[0256] S5, adjusting the control strategy according to the output power.

[0257] An embodiment of the present disclosure further provides a computer program product, which may include a computer program, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0258] Optionally, in this embodiment, the computer program product may be configured to include a computer program for performing the following steps:

[0259] S1, determining an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of associated equipment of the charging and discharging station;

[0260] S2, based on the charging demand information of the vehicle, expanding the adjustment target to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle;

[0261] S3, based on the expanded adjustment target, determining the number of vehicles moving in and moving out, wherein the number of vehicles moving in is the number of vehicles driving into the charging and discharging station, and the number of vehicles moving out is the number of vehicles driving out of the charging and discharging station;

[0262] S4, determining the output power of the charging and discharging station based on the number of in-migration and the number of out-migration;

[0263] S5, adjusting the control strategy according to the output power.

[0264] An embodiment of the present disclosure further provides a computer program product, which may include a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and the computer program is configured to execute the steps of any of the above method embodiments when running.

[0265] Optionally, in this embodiment, the computer program product may be configured to include a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0266] S1, determining an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of associated equipment of the charging and discharging station;

[0267] S2, based on the charging demand information of the vehicle, expanding the adjustment target to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle;

[0268] S3, based on the expanded adjustment target, determining the number of vehicles moving in and moving out, wherein the number of vehicles moving in is the number of vehicles driving into the charging and discharging station, and the number of vehicles moving out is the number of vehicles driving out of the charging and discharging station;

[0269] S4, determining the output power of the charging and discharging station based on the number of in-migration and the number of out-migration;

[0270] S5, adjusting the control strategy according to the output power.

[0271] An embodiment of the present disclosure further provides a computer program, which is configured to execute the steps of any of the above method embodiments when running.

[0272] Optionally, in this embodiment, the computer program is configured to perform the following steps:

[0273] S1, determining an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of associated equipment of the charging and discharging station;

[0274] S2, based on the charging demand information of the vehicle, expanding the adjustment target to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle;

[0275] S3, based on the expanded adjustment target, determining the number of vehicles moving in and moving out, wherein the number of vehicles moving in is the number of vehicles driving into the charging and discharging station, and the number of vehicles moving out is the number of vehicles driving out of the charging and discharging station;

[0276] S4, determining the output power of the charging and discharging station based on the number of in-migration and the number of out-migration;

[0277] S5, adjusting the control strategy according to the output power.

[0278] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0279] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0280] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for adjusting the control strategy of a charging and discharging station, characterized in that: include: Determining an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of an associated device of the charging and discharging station; Based on the charging demand information of the vehicle, the adjustment target is expanded to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; Based on the expanded adjustment target, determine the number of vehicles moving in and moving out, wherein the number of vehicles moving in is the number of vehicles entering the charging and discharging station, and the number of vehicles moving out is the number of vehicles leaving the charging and discharging station; Determining the output power of the charging and discharging station based on the in-migration quantity and the out-migration quantity; The control strategy is adjusted according to the output power.

2. The method according to claim 1, characterized in that The method further comprises: Determining a learning rate of the charging and discharging station, wherein the learning rate is used to screen adjustment targets with a matching degree higher than a matching degree threshold; Determining an adjustment target for adjusting a control strategy of a charging and discharging station includes: determining the adjustment target that matches the learning rate.

3. The method according to claim 2, characterized in that The method further comprises: Determining an objective function of an adjustment model, wherein the adjustment model is used to adjust the output power of the charging and discharging station; Determining the adjustment target that matches the learning rate includes: determining the objective function that matches the learning rate as the adjustment target.

4. The method according to claim 1, characterized in that: The method further comprises: A chaotic system is used to generate a random number, wherein the random number is used to represent the amount of expansion of the adjustment target.

5. The method according to claim 4, characterized in that Based on the charging demand information of the vehicle, the adjustment target is expanded to obtain an expanded adjustment target, including: Determining, based on the charging demand information of the vehicle, an expansion probability of the charging and discharging station, wherein the expansion probability is used to characterize the amount of expansion of the adjustment target; The adjustment target is expanded according to the expansion probability or the random number to obtain an expanded adjustment target.

6. The method according to claim 1, characterized in that Based on the expanded adjustment target, determining the number of vehicles to be moved in and the number of vehicles to be moved out includes: The cosine migration model is called to analyze the expanded adjustment target to determine the in-migration quantity and the out-migration quantity.

7. The method according to claim 1, characterized in that After determining the output power of the charging and discharging station based on the number of incoming migrations and the number of outgoing migrations, the method further includes: Based on the charging demand information and the power status information of the vehicle, the output power is adjusted to obtain an adjusted output power.

8. The method according to claim 7, characterized in that The method further comprises: Acquiring power difference information between charging power and discharging power of the vehicle; Based on the power difference information, the adjusted output power is screened to obtain the screened output power, wherein the screened output power satisfies the constraint conditions of the adjustment model, and the adjustment model is used to adjust the output power of the charging and discharging station.

9. The method according to claim 8, characterized in that Adjusting the control strategy according to the output power includes: The control strategy is adjusted according to the screened output power.

10. A device for adjusting the control strategy of a charging and discharging station, characterized in that: include: a first determining unit, configured to determine an adjustment target for adjusting a control strategy of a charging and discharging station, wherein the charging and discharging station is used to charge vehicles, the control strategy is used to characterize a rule for controlling the charging and discharging station, and the adjustment target is used to characterize an operating state of an associated device of the charging and discharging station; an expansion unit, configured to expand the adjustment target based on the charging demand information of the vehicle to obtain an expanded adjustment target, wherein the charging demand information is used to represent the electric energy required by the vehicle; A second determining unit is used to determine the number of vehicles entering and the number of vehicles leaving the charging and discharging station based on the expanded adjustment target, wherein the number of vehicles entering is the number of vehicles entering the charging and discharging station, and the number of vehicles leaving is the number of vehicles leaving the charging and discharging station; A third determining unit, configured to determine the output power of the charging and discharging station based on the number of incoming migrations and the number of outgoing migrations; An adjustment unit is used to adjust the control strategy according to the output power.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 9 when executed.

12. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 9.

13. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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