Electric vehicle layered optimization regulation and control method considering multi-subject interests

Abstract

The invention relates to an electric vehicle layered optimization regulation and control method considering multi-subject interests. The method comprises the following steps: firstly, constructing a market clearing model for an electric power peak regulation auxiliary service based on a power grid operator, constructing a regulation and control bidding model considering a bidding success rate based on an electric vehicle aggregator, and constructing a charging response model considering an incentive price based on a user; then, based on the market clearing model, the regulation and control bidding model and the charging response model, constructing a regulation and control model of a three-layer regulation and control framework of a power grid operator, an electric vehicle aggregator and a user; and finally, predicting and solving the regulation and control model based on the three-layer regulation and control architecture to obtain reference power, jointly constructing an objective function based on the reference power and actual power, performing deviation correction, and finishing optimization regulation and control of the electric vehicle when the objective function is minimum. The power peak regulation of the power grid side is realized, the operation cost of the power grid is reduced, the benefit of an electric vehicle aggregator is maximized, and the charging requirement of a user is met.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle optimization and control, and in particular to a hierarchical optimization and control method for electric vehicles taking into account the interests of multiple subjects. Background Art

[0002] With the rapid development of electric vehicles, electric vehicles have become an adjustable and flexible load that cannot be ignored. If their charging behavior can be guided through reasonable market mechanisms and corresponding technical means, it can not only eliminate the adverse effects of load overload and reduced power quality caused by disorderly charging, but also realize load peak shaving and valley filling, improve the utilization rate of renewable energy, and grid frequency regulation, thereby improving the reliability and economy of grid operation.

[0003] In recent years, a large number of domestic and foreign scholars have conducted relevant research in the direction of large-scale electric vehicle charging load control. From the perspective of control architecture, related research is mainly divided into centralized and distributed optimization control methods. However, centralized control methods have problems such as heavy computing and communication burdens and poor scalability and flexibility, while distributed optimization control methods have problems such as increased algorithm complexity and easy to fall into local optimality. In addition, from the perspective of stakeholders, related research usually only considers the interest interaction process between multiple electric vehicle aggregators, or aggregators and electric vehicle users, or aggregators and power grid operators.

[0004] Therefore, in the relevant technology, there is an urgent need for a method that can improve the flexibility and practicality of electric vehicle charging load regulation. Summary of the invention

[0005] Based on this, it is necessary to provide a hierarchical optimization control method for electric vehicles that takes into account the interests of multiple subjects and can improve the flexibility and practicality of electric vehicle charging load control in response to the above technical problems.

[0006] In a first aspect, the present application provides a hierarchical optimization and control method for electric vehicles taking into account the interests of multiple entities. The method comprises:

[0007] A market clearing model for power peak load ancillary services is built based on grid operators, a regulation bidding model taking into account bidding success rate is built based on electric vehicle aggregators, and a charging response model taking into account incentive prices is built based on users;

[0008] A control model of a three-layer control structure of grid operators, electric vehicle aggregators and users is constructed based on the market clearing model, control bidding model and charging response model;

[0009] The control model is predicted and solved based on the three-layer control architecture to obtain the reference power, and the objective function is jointly constructed based on the reference power and the actual power, and the deviation is corrected. When the objective function is minimized, the optimization control of the electric vehicle is completed.

[0010] Optionally, in one embodiment of the present application, the optimization objective formula of the market clearing model for power peak load ancillary services is:

[0011]

[0012] Among them, c P,j,t is the subsidy unit price of the jth aggregator at time t in the peak load ancillary service, x P,j,t is the optimized charging power of the jth aggregator at time t in the peak load ancillary service, B P,j,t is the bid winning result of the j-th aggregator at time t, 1 indicates winning, 0 indicates not winning, is the charging power baseline before optimization of the jth aggregator at time t, T S is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, N A is the total number of electric vehicle aggregators.

[0013] Optionally, in one embodiment of the present application, the optimization objective formula of the bidding model taking into account the bidding success rate is:

[0014]

[0015] Among them, c P,j,t is the subsidy unit price of the jth aggregator at time t in the peak load ancillary service, x P,j,t is the optimized charging power of the jth aggregator at time t in the peak load ancillary service, B P,j,t is the bid winning result of the j-th aggregator at time t, 1 indicates winning, 0 indicates not winning, is the charging power baseline before optimization of the jth aggregator at time t, T S is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, N A is the total number of electric vehicle aggregators, c PA,i,t is the subsidy unit price of the ith electric vehicle at time t in the peak load auxiliary service, x P,i,t is the optimized charging power of the ith electric vehicle at time t in the peak load auxiliary service, N is the total number of electric vehicles, P(c P,i,t ) is the bidding success rate.

[0016] Optionally, in one embodiment of the present application, the bidding success rate is calculated using the following probability formula:

[0017]

[0018] Among them, c P,i,t is the subsidy unit price of the ith electric vehicle at time t, The bid price for electric vehicle aggregators when the bidding success rate is 50%.

[0019] Optionally, in one embodiment of the present application, the optimization objective formula of the charging response model taking into account the incentive price is:

[0020]

[0021] Among them, c PA,i,t is the subsidy unit price of the ith electric vehicle at time t in the peak load auxiliary service, c EV,t The unit price of charging an electric vehicle, P i,t is the charging subsidy price, x P,i,t is the optimized charging power of the ith EV at time t in the peak load auxiliary service, T S is the unit time, t P1 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, and N is the total number of electric vehicles.

[0022] Optionally, in one embodiment of the present application, the charging subsidy price is determined based on a user response probability function, and the user response probability function is:

[0023]

[0024] Among them, P i,t Subsidy price for charging, SOC i is the SOC of the i-th electric vehicle, SOC b is the expected SOC, SOC lim it is the upper limit SOC, ε i is the price sensitivity factor, e i is the power sensitivity factor, and q is the random error.

[0025] Optionally, in one embodiment of the present application, predicting and solving the control model based on the three-layer control architecture includes:

[0026] Establish the state space equation of the controlled object for prediction.

[0027] In a second aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the method described in each of the above embodiments.

[0028] In a third aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in each of the above embodiments are implemented.

[0029] The above-mentioned electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects firstly builds a market clearing model for power peak load auxiliary services based on power grid operators, builds a control bidding model taking into account the bidding success rate based on electric vehicle aggregators, and builds a charging response model taking into account the incentive price based on users; then, a control model of a three-layer control framework of power grid operators, electric vehicle aggregators and users is built based on the market clearing model, the control bidding model and the charging response model; finally, the control model is predicted and solved based on the three-layer control framework to obtain the reference power, and the objective function is jointly constructed based on the reference power and the actual power, and the deviation is corrected. When the objective function is minimized, the electric vehicle optimization control is completed. In other words, for power grid auxiliary services, a three-layer control framework of power grid operators, electric vehicle aggregators and users is constructed with electric vehicle aggregators as the link. In the day-ahead stage, according to the multi-layer control framework, a market clearing model of power grid operators for power peak load auxiliary services, a bidding model of power grid aggregators taking into account the bidding success rate, and an electric vehicle user response model taking into account the incentive price are proposed respectively. In the intraday stage, the electric vehicle optimization control strategy based on model predictive control is established with the goal of minimizing the deviation between the reference value solved by the day-ahead model and the actual value within the day. The peak load regulation of power grid side is realized, which not only reduces the cost of grid operation, but also maximizes the interests of electric vehicle aggregators, meets the charging needs of users, and further promotes the sustainable development of multiple market players such as grid and electric vehicle aggregators. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 An application environment diagram of an electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects in one embodiment;

[0031] Figure 2 A schematic flow chart of a hierarchical optimization control method for electric vehicles taking into account the interests of multiple subjects in one embodiment;

[0032] Figure 3 is a schematic diagram of the hierarchical relationship between a grid operator, an electric vehicle aggregator, and a user in one embodiment;

[0033] Figure 4A schematic diagram of an electric vehicle optimization control strategy based on model predictive control in one embodiment;

[0034] Figure 5 A schematic diagram of an IEEE-33 node distribution network of nine charging stations in one embodiment;

[0035] Figure 6 A schematic diagram of real-time control results of nine charging stations within a day in one embodiment;

[0036] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] The electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or it can be placed on the cloud or other network servers. Among them, the terminal can be but not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0039] In one embodiment, Figure 2 As shown in the figure, a hierarchical optimization control method for electric vehicles considering the interests of multiple subjects is provided. Figure 1 The server in the example is used to illustrate the following steps:

[0040] S201: Build a market clearing model for power peak load ancillary services based on grid operators, build a regulation bidding model that takes into account bidding success rate based on electric vehicle aggregators, and build a charging response model that takes into account incentive prices based on users.

[0041] In the embodiment of the present application, the power grid operator is a subject that calls multiple electric vehicle aggregators to achieve a balance between supply and demand, with the goal of minimizing the total regulation cost. Under this goal, multiple electric vehicle aggregators are arranged to compete with multiple subjects, and unified clearing is carried out considering the interests of multiple aggregators. Its essence is an economic dispatch process that takes into account the constraints of safe operation of the power system. Under the constraints of power grid security and physical operation characteristics, the load resources with the lowest bid in the system are preferentially called. Electric vehicle aggregators mainly collect profits through the difference between the response subsidy of the power grid operator and the economic subsidy paid to electric vehicle users. Therefore, for the power grid operator, the total regulation cost of the electric vehicle load resources that need to participate in the grid regulation is the lowest to achieve the best economic benefits; for the electric vehicle aggregator, the goal is to maximize the benefits of participating in the auxiliary service market; for the user, the user-level optimization goal is to minimize the charging cost. A market clearing model for power peak load ancillary services is constructed based on the power grid operator, a regulation bidding model taking into account the bidding success rate is constructed based on the electric vehicle aggregator, and a charging response model taking into account the incentive price is constructed based on the user.

[0042] Specifically, in one embodiment of the present application, the optimization objective formula of the market clearing model for power peak load ancillary services is:

[0043]

[0044] Among them, c P,j,t is the subsidy unit price of the jth aggregator at time t in the peak load ancillary service, x P,j,t is the optimized charging power of the jth aggregator at time t in the peak load ancillary service, B P,j,t is the bid winning result of the j-th aggregator at time t, 1 indicates winning, 0 indicates not winning, is the charging power baseline before optimization of the jth aggregator at time t, T S is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, N A is the total number of electric vehicle aggregators.

[0045] In one embodiment of the present application, the grid demand response includes peak load regulation. In the peak load regulation auxiliary service process, there are also regulation time constraints and power balance constraints. The regulation time constraint is t p0 ≤t≤t p1 , the power balance constraint is:

[0046]

[0047] Among them, P power,t is the power supply of the main transformer at time t, P load,tis the normal load power of the main transformer at time t.

[0048] In one embodiment of the present application, the optimization objective formula of the bidding model taking into account the bidding success rate is:

[0049]

[0050] Among them, c P,j,t is the subsidy unit price of the jth aggregator at time t in the peak load ancillary service, x P,j,t is the optimized charging power of the jth aggregator at time t in the peak load ancillary service, B P,j,t is the bid winning result of the j-th aggregator at time t, 1 indicates winning, 0 indicates not winning, is the charging power baseline before optimization of the jth aggregator at time t, T S is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, N A is the total number of electric vehicle aggregators, c PA,i,t is the subsidy unit price of the ith electric vehicle at time t in the peak load auxiliary service, x P,i,t is the optimized charging power of the ith electric vehicle at time t in the peak load auxiliary service, N is the total number of electric vehicles, P(c P,i,t ) is the bidding success rate.

[0051] In one embodiment of the present application, the bidding success rate is calculated using the following probability formula:

[0052]

[0053] Among them, c P,i,t is the subsidy unit price of the ith electric vehicle at time t, The bid price for electric vehicle aggregators when the bidding success rate is 50%.

[0054] In one embodiment of the present application, there is a competitive relationship between multiple electric vehicle aggregators participating in the power peak load ancillary service market. Therefore, electric vehicle aggregators need to consider the success rate of bidding. The lower the bid that the load aggregator applies to the grid operator, the greater the possibility of successful bidding. Specifically, a probability formula can be used for calculation. In addition, when electric vehicle aggregators participate in the peak load ancillary service and make bids, they also need to consider the proportion of their own power bids. The bid range of electric vehicle aggregators can be expressed as:

[0055]

[0056] Among them, D PA,j,t is the adjustable power reported by the jth electric vehicle aggregator at time t, DPsum,j,t is the maximum adjustable power quotation of the jth electric vehicle aggregator at time t, c Pmax,t The maximum subsidy price for grid operators.

[0057] The adjustable power quotation of the electric vehicle aggregator at each moment is determined by the charging power after optimization and the charging power baseline before optimization of the electric vehicle. The specific formula is as follows:

[0058]

[0059] Among them, x P,i,t is the optimized charging power of the ith electric vehicle at time t in the peak load auxiliary service, It is the charging power baseline before optimization of the i-th electric vehicle at time t.

[0060] In one embodiment of the present application, the optimization objective formula of the charging response model taking into account the incentive price is:

[0061]

[0062] Among them, c PA,i,t is the subsidy unit price of the ith electric vehicle at time t in the peak load auxiliary service, c EV,t The unit price of charging an electric vehicle, P i,t is the charging subsidy price, x P,i,t is the optimized charging power of the ith EV at time t in the peak load auxiliary service, T s is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, and N is the total number of electric vehicles.

[0063] In one embodiment of the present application, in order to fully mobilize the flexible resource of electric vehicles, an incentive price is proposed to guide electric vehicle users to charge. The battery SOC, charging subsidies and user personality characteristics are integrated into the Sigmoid function to establish a user decision model based on the Sigmoid function. The Sigmoid function is a monotonically increasing function with a value interval of (0, 1). When the value approaches negative infinity, the value is 0, and when the value approaches positive infinity, the value is 1. It can be used as a response model.

[0064] Specifically, in one embodiment of the present application, the charging subsidy price is determined based on a user response probability function, and the user response probability function is:

[0065]

[0066]

[0067] Among them, Pi,t Subsidy price for charging, SOC i is the SOC of the i-th electric vehicle, SOC b is the expected SOC, SOC lim it is the upper limit SOC, ε i is the price sensitivity factor, e i is the power sensitivity factor, and q is the random error.

[0068] S203: Constructing a control model of a three-layer control architecture of power grid operators, electric vehicle aggregators and users based on the market clearing model, the control bidding model and the charging response model.

[0069] In the present application embodiment, Figure 3 As shown in the figure, a three-layer control framework for electric vehicle aggregators is constructed based on the market clearing model for power peak load auxiliary services of power grid operators, the control bidding model of electric vehicle aggregators taking into account the bidding success rate, and the charging response model of users taking into account the incentive price. Among them, electric vehicle aggregators (EVA) play a role as a link in the aggregation and control of flexible loads such as electric vehicles. Electric vehicle aggregators need to face the loads in the region downward, and the goal is to integrate the flexible load resources of users they represent to participate in the unified control of power grid operators (PGO). Electric vehicle aggregators use private cars, buses, heavy trucks and other types of electric vehicle load equipment according to the regulation needs of the power system. They interact with the control center of the power grid operator upward, and play a role as a link with the "power grid operator dispatch center" and "multiple types of electric vehicles".

[0070] When EVA participates in the peak-shaving auxiliary market, it first obtains the PGO baseline power, which is used as a reference for EVA to settle the income from participating in auxiliary services. During the demand period released by PGO, EVA increases the power consumption based on the baseline power to obtain service income. When declaring on the day before, PGO first releases the demand information received from the dispatch center, EVA predicts the response of electric vehicles EV, encourages users to participate in auxiliary services through price compensation, evaluates the regulation capacity of electric vehicle EV clusters, and declares the market with the goal of maximizing income. PGO clears the winning capacity based on the declared information and sends the capacity to EVA. EVA executes the power plan, and PGO assesses the completion and settles the final income. In the above process, a master-slave game is formed between EVA and electric vehicles EV. EVA formulates charging electricity prices and subsidized electricity prices, and electric vehicles EV generate corresponding charging strategies based on the electricity price information. In addition, due to the certain market peak-shaving demand, a non-cooperative game is formed between multiple EVAs. EVA formulates a market declaration strategy that is beneficial to its own income based on the peak-shaving market information.

[0071] S205: Predict and solve the control model based on the three-layer control architecture to obtain reference power, and jointly construct an objective function based on the reference power and actual power, and perform deviation correction. When the objective function is minimized, the electric vehicle optimization control is completed.

[0072] In the embodiment of the present application, after the three-layer control architecture is constructed, the power of each charging station is adjusted according to the day-ahead scheduling plan, and the electric vehicle is controlled by using the model predictive control technology. The control strategy is as follows: Figure 4 As shown in the figure, the model predictive control method mainly includes three steps: model prediction, rolling optimization and deviation correction. Specifically, the model is predicted and solved based on the three-layer control architecture to obtain the reference power, and the objective function is jointly constructed based on the reference power and the actual power, and the deviation is corrected. When the objective function is minimized, the electric vehicle optimization control is completed.

[0073] Specifically, in one embodiment of the present application, predicting and solving the control model based on the three-layer control architecture includes:

[0074] Establish the state space equation of the controlled object for prediction.

[0075] In one embodiment of the present application, the model prediction is performed by establishing a state space equation of the controlled object. The specific state space equation constructed is as follows:

[0076]

[0077] Rolling optimization is the core of model predictive control. After obtaining the predicted value of the system state in a limited time domain based on model prediction, the optimized control sequence in the predicted time domain is obtained, striving to optimize the local control performance. For predictive control, the optimization process is not carried out once, but repeatedly. This is the meaning of rolling optimization, and it is also the fundamental difference between predictive control and traditional optimal control. The prediction time of the intraday rolling optimization stage is 4 hours, the time interval is 15 minutes, and the control time domain is 1 hour. The goal of rolling optimization is the intraday power reference value x solved by the day-ahead optimization model. ref,t (k) and actual power value during the day The difference is the smallest, and the specific objective function formula is as follows:

[0078]

[0079] Among them, x ref,t (k) is the daily power reference value predicted by the model built a few days ago, x P,i,t (k) is the actual power value during the day.

[0080] The deviation correction of model predictive control is to compare the output measurement value with the model's estimated value to obtain the model's prediction error, and use this error to correct the model's predicted value, thereby enhancing the model predictive control's ability to resist disturbances and overcome system uncertainties.

[0081] In specific applications, such as Figure 5 As shown in Figure 1, an IEEE-33 node distribution network with nine charging stations is established, and node 11 is used as the demand point on the execution day. The nine charging stations are distributed on different nodes. In the day-ahead stage, the total response of the grid operator at 13 to 14 and 14 to 15 is 2.00MWh respectively. After the 20th round of bidding game, the clearing results of the grid operator at 13 to 15 are shown in Tables 1 and 2.

[0082] Table 1: Results of grid operator clearing from 1:00 to 14:00

[0083]

[0084] Table 2 Results of grid operator clearing at 15:00

[0085]

[0086] Due to the error between the day-ahead prediction and the actual value during the day, the MPC control algorithm is adjusted according to the response target set during the day. The real-time control results of the nine charging stations during the day are as follows: Figure 6 As shown in Table 1, Table 2, Figure 5 and Figure 6It can be seen that the electric vehicle hierarchical optimization control method proposed in this application, which takes into account the interests of multiple subjects, provides 439.31, 683.38 and 549.81 yuan of income to aggregators No. 1, 2 and 3 respectively, and also fully utilizes the regulation capacity of electric vehicle flexible loads to achieve peak load regulation on the grid side. It can be obtained that in the small example of 9 charging stations, the aggregator can achieve a profit of 1,672.5 yuan. For aggregators with hundreds or thousands of charging stations, the expected economic benefits are huge, which has practical engineering significance.

[0087] In the above-mentioned electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects, first, a market clearing model for power peak load auxiliary services is constructed based on the power grid operator, a control bidding model taking into account the bidding success rate is constructed based on the electric vehicle aggregator, and a charging response model taking into account the incentive price is constructed based on the user; then, a control model of a three-layer control architecture of the power grid operator, the electric vehicle aggregator and the user is constructed based on the market clearing model, the control bidding model and the charging response model; finally, the control model is predicted and solved based on the three-layer control architecture to obtain the reference power, and the objective function is jointly constructed based on the reference power and the actual power, and the deviation is corrected. When the objective function is minimized, the electric vehicle optimization control is completed. In other words, for power grid auxiliary services, a three-layer control architecture of the power grid operator, the electric vehicle aggregator and the user is constructed with the electric vehicle aggregator as the link. In the day-ahead stage, according to the multi-layer control architecture, a market clearing model of the power grid operator for power peak load auxiliary services, a bidding model of the power grid aggregator taking into account the bidding success rate, and an electric vehicle user response model taking into account the incentive price are proposed respectively. In the intraday stage, the electric vehicle optimization control strategy based on model predictive control is established with the goal of minimizing the deviation between the reference value solved by the day-ahead model and the actual value within the day. The peak load regulation of power grid side is realized, which not only reduces the cost of grid operation, but also maximizes the interests of electric vehicle aggregators, meets the charging needs of users, and further promotes the sustainable development of multiple market players such as grid and electric vehicle aggregators.

[0088] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0089] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a hierarchical optimization and control method of electric vehicles taking into account the interests of multiple subjects is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0090] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0091] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0092] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0093] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0094] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0095] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A hierarchical optimization control method for electric vehicles taking into account the interests of multiple subjects, characterized in that: The method comprises: A market clearing model for power peak load ancillary services is built based on grid operators, a regulation bidding model taking into account bidding success rate is built based on electric vehicle aggregators, and a charging response model taking into account incentive prices is built based on users; A control model of a three-layer control structure of grid operators, electric vehicle aggregators and users is constructed based on the market clearing model, control bidding model and charging response model; The control model is predicted and solved based on the three-layer control architecture to obtain the reference power, and the objective function is jointly constructed based on the reference power and the actual power, and the deviation is corrected. When the objective function is minimized, the optimization control of the electric vehicle is completed.

2. The electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects according to claim 1 is characterized in that: The optimization objective formula of the market clearing model for power peak load ancillary services is: Among them, c P,j,t is the subsidy unit price of the jth aggregator at time t in the peak load ancillary service, x P,j,t is the optimized charging power of the jth aggregator at time t in the peak load ancillary service, B P,j,t is the bid winning result of the j-th aggregator at time t, 1 indicates winning, 0 indicates not winning, is the charging power baseline before optimization of the jth aggregator at time t, T S is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, N A is the total number of electric vehicle aggregators.

3. The electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects according to claim 1 is characterized in that: The optimization objective formula of the bidding model taking into account the bidding success rate is: Among them, c P,j,t is the subsidy unit price of the jth aggregator at time t in the peak load ancillary service, x P,j,t is the optimized charging power of the jth aggregator at time t in the peak load ancillary service, B P,j,t is the bid winning result of the j-th aggregator at time t, 1 indicates winning, 0 indicates not winning, is the charging power baseline before optimization of the jth aggregator at time t, T S is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, N A is the total number of electric vehicle aggregators, c PA,i,t is the subsidy unit price of the ith electric vehicle at time t in the peak load auxiliary service, x P,i,t is the optimized charging power of the ith electric vehicle at time t in the peak load auxiliary service, N is the total number of electric vehicles, P(c P,i,t ) is the bidding success rate.

4. The electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects according to claim 3 is characterized in that: The bidding success rate is calculated using the following probability formula: Among them, c P,i,t is the subsidy unit price of the i-th electric vehicle at time t, The bid price for electric vehicle aggregators when the bidding success rate is 50%.

5. The electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects according to claim 1 is characterized in that: The optimization objective formula of the charging response model taking into account the incentive price is: Among them, c PA,i,t is the subsidy unit price of the ith electric vehicle at time t in the peak load auxiliary service, c EV,t The unit price of charging an electric vehicle, P i,t is the charging subsidy price, x P,i,t is the optimized charging power of the ith EV at time t in the peak load auxiliary service, T S is the unit time, t P0 is the start time of the peak load auxiliary service, t P1 is the end time of the peak load auxiliary service, and N is the total number of electric vehicles.

6. The electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects according to claim 5 is characterized in that: The charging subsidy price is determined based on a user response probability function, which is: Among them, P i,t Subsidy price for charging, SOC i is the SOC of the i-th electric vehicle, SOC b is the expected SOC, SOC limit is the upper limit SOC, ε i is the price sensitivity factor, e i is the electrical sensitivity factor, and q is the random error.

7. The electric vehicle hierarchical optimization control method taking into account the interests of multiple subjects according to claim 1 is characterized in that: The predicting and solving of the control model based on the three-layer control architecture includes: Establish the state space equation of the controlled object for prediction.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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