Charging electricity price optimization method based on master-slave game and related devices

Abstract

The present application proposes a charging electricity price optimization method and related devices based on a master-slave game. The method includes: obtaining first charging demand information and environmental impact information; determining second charging demand information based on the first charging demand information and / or the environmental impact information; determining a first objective function and a plurality of first constraint conditions based on the second charging demand information and power plant operation information; determining a second objective function and a plurality of second constraint conditions based on the second charging demand information and electric vehicle parameters; determining robust constraint conditions based on power flow constraint conditions; solving a master-slave game model based on the first objective function, the second objective function, the plurality of first constraint conditions, the plurality of second constraint conditions, and the robust constraint conditions to determine target optimization parameters. This can enable the orderly access of electric vehicles, thereby reducing the pressure on the economic operation and stability of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and in particular, to a charging electricity price optimization method and related devices based on a master-slave game. Background Art

[0002] As an important part of the demand-side load, the coordinated development of electric vehicles (EVs) and distributed renewable energy is one of the important ways to achieve urban energy development. The random access of a large number of EVs to the virtual power plant for unordered charging will further exacerbate the peak-valley difference of the system load, bringing greater pressure to the economic operation and stability of the power system.

[0003] Currently, most strategies are to guide the orderly charging of electric vehicles on the premise of a fixed electricity price, which may not be able to fully mobilize the enthusiasm of users in an open market environment. In addition, the charging behavior characteristics of EVs (such as charging power and charging time, etc.) are all controlled by the virtual power plant operator, without considering the different interest demands between the operator and the users. At the same time, the risk brought by the uncertainty of the output of a high proportion of renewable energy to the dispatching of the virtual power plant operator cannot be ignored. Currently, the methods for dealing with the uncertainty of distributed generation (DG) power generation and the uncertainty of EC charging behavior are too conservative, restrictive, and cannot fully consider the different interest demands of electric vehicle users and renewable energy output at the same time. Summary of the Invention

[0004] The embodiments of the present application provide a charging electricity price optimization method and related devices based on a master-slave game, which can make the access of EVs more orderly, fully consider the different interest demands of electric vehicle users and renewable energy output at the same time, and are beneficial to guiding the effective charging of electric vehicles and solving the instability brought by the access of uncertain renewable energy.

[0005] In a first aspect, the embodiments of the present application provide a charging electricity price optimization method based on a master-slave game, including:

[0006] Obtain the first charging demand information and environmental impact information for the next decision period, where the first charging demand information includes the charging demand reported by the owners of electric vehicles, and the environmental impact information includes the environmental information affecting the charging situation of electric vehicles;

[0007] Determine the second charging demand information based on the first charging demand information and / or environmental impact information;

[0008] Determine a first objective function and multiple first constraint conditions based on the second charging demand information and power plant operation information. The first constraint conditions include power flow constraint conditions, operation constraint conditions, and / or electricity price constraint conditions, and use the first objective function as the upper-level function. The first objective function includes an objective function for calculating the cost of the virtual power plant operator; and, determine a second objective function and multiple second constraint conditions based on the second charging demand information and electric vehicle parameters. The second constraint conditions include charging amount constraint conditions and / or charging situation constraint conditions, and use the second objective function as the lower-level function. The second objective function includes an objective function for calculating the charging cost;

[0009] Determine robust constraint conditions based on the power flow constraint conditions. The robust constraint conditions are used to constrain the voltage safety of nodes in the circuit;

[0010] Solve the master-slave game model based on the first objective function, the second objective function, the multiple first constraint conditions, the multiple second constraint conditions, and the robust constraint conditions to determine the target optimization parameters. The target optimization parameters are used to formulate the electricity price and / or power for the next decision period and the periods corresponding to different electricity prices and / or different powers.

[0011] In a possible embodiment, the determining the second charging demand information based on the first charging demand information and / or environmental impact information includes:

[0012] Obtain the quantity of demand information of the first charging demand information; determine whether the information quantity requirement is met based on the quantity of demand information and a preset demand information threshold. If the quantity of demand information is not less than the preset demand information threshold, it is considered that the information quantity requirement is met. If the quantity of demand information is less than the preset demand information threshold, it is considered that the information quantity requirement is not met;

[0013] If the information quantity requirement is met, the second charging demand information is the first charging demand information;

[0014] If the information quantity requirement is not met, conduct demand forecasting based on the environmental impact information and the first charging demand information to obtain predicted charging demand information; determine the second charging demand information based on the predicted charging demand information and the first charging demand information.

[0015] In a possible embodiment, the charging demand reported by the owner of the electric vehicle includes a first charging period demand and a first charging electricity quantity demand. The environmental impact information includes temperature factors and precipitation factors. The conducting demand forecasting based on the environmental impact information and the first charging demand information to obtain predicted charging demand information includes:

[0016] Perform data standardization based on the temperature factor, the precipitation factor, and a preset grading standard to obtain a temperature parameter and a precipitation parameter;

[0017] Determine a prediction data set based on the temperature parameter, the precipitation parameter, the first charging period demand, and the first charging power demand;

[0018] Determine predicted charging demand information based on the prediction data set and a pre-trained charging prediction model.

[0019] In a possible embodiment, the second charging demand information includes a second charging period demand and a second charging power demand, and the first objective function includes the following first formula:

[0020] ;

[0021] Wherein, is the cost function of the distributed power source DG of the i-th electric vehicle EV, is the active power output of the distributed power source DG corresponding to the i-th electric vehicle EV; is the charging electricity price of the electric vehicle EV at time t, is the charging power of the i-th electric vehicle EV; is the time-of-use electricity selling price of the virtual power plant operator in the day-ahead spot market, is the time-of-use electricity purchasing price of the virtual power plant operator in the day-ahead spot market, is the electricity selling power at time t, is the electricity purchasing power at time t;

[0022] The second objective function includes the following second formula:

[0023] ;

[0024] Wherein, is the charging cost of the electric vehicle EV, is the charging power of the electric vehicle EV at time t, is the charging electricity price of the electric vehicle EV at time t.

[0025] In a possible embodiment, the power flow constraint conditions include the following third formula, fourth formula, and fifth formula:

[0026] The third formula is: ;

[0027] The fourth formula is: ;

[0028] The fifth formula is: ;

[0029] Among them, is the active power flow from grid node n to n+1, is the reactive power flow from grid node n to n+1; is the active power of node n, is the reactive power of node n; is the voltage of node n; is the line resistance between nodes n and n+1; is the line impedance between nodes n and n+1; is the uncertainty parameter of renewable energy output, is the maximum allowable voltage deviation, is the reference voltage amplitude at node 0; is the active power of the load at node n, is the reactive power of the load at node n; is the active power of the controllable distributed generation DG at node n, is the reactive power of the controllable distributed generation DG at node n;

[0030] The operating constraint conditions include the following sixth formula and seventh formula:

[0031] The sixth formula is: ;

[0032] Among them, is the active output of the controllable distributed generation DG, is the reactive output of the controllable distributed generation DG; is the upper limit of the active output of the controllable distributed generation DG, is the lower limit of the active output of the controllable distributed generation DG, is the lower limit of the reactive output of the controllable distributed generation DG, is the upper limit of the reactive output of the controllable distributed generation DG; is the upper limit of the upward ramp rate, is the upper limit of the downward ramp rate;

[0033] The seventh formula is: ;

[0034] Among them, is the actual output of the controllable distributed generation, is the predicted output of the controllable distributed generation, is the prediction error, is the maximum prediction error;

[0035] The electricity price constraint conditions include the following eighth formula:

[0036] ;

[0037] Among them, is the charging electricity price of the electric vehicle EV in the t period, is the average electricity purchase price in the day-ahead market, is the time-of-use electricity purchase price of the virtual power plant operator in the day-ahead spot market.

[0038] In a possible embodiment, the charging amount constraint condition includes the following ninth formula:

[0039] ;

[0040] Among them, is the battery capacity of the electric vehicle EV, is the initial state of the electric vehicle EV battery during charging, is the charging efficiency;

[0041] The charging situation constraint condition includes the following tenth formula: ;

[0042] Among them, is the upper limit of the charging power of the electric vehicle EV in the t period, is the set of time periods allowing the electric vehicle EV to charge, is the upper limit of the charging power of the electric vehicle EV.

[0043] In a possible embodiment, determining the robust constraint condition based on the power flow constraint condition includes:

[0044] Determine the voltage-power relationship matrix based on the third formula and the fifth formula, and the voltage-power relationship matrix is used to characterize the relationship between the voltage change of each node and the power of each device;

[0045] Determine the robust equality based on the voltage-power relationship matrix and the fourth formula;

[0046] Determine the robust constraint condition based on the robust equality and the strong duality theory.

[0047] In a possible embodiment, solving the master-slave game model based on the first objective function, the second objective function, the multiple first constraint conditions, the multiple second constraint conditions, and the robust constraint condition to determine the target optimization parameter includes:

[0048] Determine the third objective function based on the first objective function, the second objective function, and the optimization theory. The third objective function is a single-layer function that combines the upper-layer function and the lower-layer function. The optimization theory includes at least one of the duality theory and the KKT theory. The third objective function includes the following eleventh formula:

[0049] ;

[0050] Among them, is the dual variable of the ninth formula of the equation, is the dual variable of the charging power upper limit constraint ;

[0051] Based on the third objective function, the first constraint condition, the second constraint condition, and the robust constraint condition, solve to determine the target optimization parameters. The target optimization parameters include the target electricity price, the target power, and the target time period, and there is a corresponding relationship between the target time period, the target electricity price, and the target power.

[0052] In a second aspect, an embodiment of the present application provides a charging electricity price optimization device based on a master-slave game, including:

[0053] A data acquisition module, configured to acquire first charging demand information and environmental impact information for the next decision period. The first charging demand information includes the charging demand reported by the owners of electric vehicles, and the environmental impact information includes environmental information affecting the charging situation of electric vehicles;

[0054] A demand determination module, configured to determine second charging demand information based on the first charging demand information and / or environmental impact information;

[0055] A function determination module, configured to determine a first objective function and a plurality of first constraint conditions based on the second charging demand information and power plant operation information. The first constraint conditions include a power flow constraint condition, an operation constraint condition, and / or an electricity price constraint condition, and use the first objective function as the upper-layer function. The first objective function includes an objective function for calculating the cost of the virtual power plant operator; and, determine a second objective function and a plurality of second constraint conditions based on the second charging demand information and electric vehicle parameters. The second constraint conditions include a charging amount constraint condition and / or a charging situation constraint condition, and use the second objective function as the lower-layer function. The second objective function includes an objective function for calculating the charging cost;

[0056] A robust determination module, configured to determine a robust constraint condition based on the power flow constraint condition. The robust constraint condition is used to constrain the voltage safety of nodes in the circuit;

[0057] A model solution module, configured to perform master-slave game model solution based on the first objective function, the second objective function, the plurality of first constraint conditions, the plurality of second constraint conditions, and the robust constraint condition, and determine target optimization parameters. The target optimization parameters are used to formulate the electricity price and / or power for the next decision period and the time periods corresponding to different electricity prices and / or different powers.

[0058] In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing some or all of the steps described in the first aspect of the embodiments of the present application.

[0059] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, the computer program including program instructions, and the program instructions, when executed by a processor, cause the processor to execute some or all of the steps described in the first aspect.

[0060] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0061] By implementing the embodiments of the present application, first charging demand information and environmental impact information for the next decision period are obtained, where the first charging demand information includes the charging demand reported by the owner of the electric vehicle, and the environmental impact information includes environmental information affecting the charging of the electric vehicle; second charging demand information is determined based on the first charging demand information and / or the environmental impact information; a first objective function and a plurality of first constraint conditions are determined based on the second charging demand information and power plant operation information, the first constraint conditions including a power flow constraint condition, an operation constraint condition, and / or a power price constraint condition, and the first objective function is used as an upper-layer function, and the first objective function includes an objective function for calculating the cost of the virtual power plant operator; and a second objective function and a plurality of second constraint conditions are determined based on the second charging demand information and electric vehicle parameters, the second constraint conditions including a charging amount constraint condition and / or a charging condition constraint condition, and the second objective function is used as a lower-layer function, and the second objective function includes an objective function for calculating the charging cost; a robust constraint condition is determined based on the power flow constraint condition, and the robust constraint condition is used to constrain the voltage safety of nodes in the circuit; a master-slave game model is solved based on the first objective function, the second objective function, the plurality of first constraint conditions, the plurality of second constraint conditions, and the robust constraint condition to determine target optimization parameters, and the target optimization parameters are used to formulate the power price and / or power for the next decision period and the time periods corresponding to different power prices and / or different powers. In this way, the power price, output power, and corresponding specific time periods that can meet the different interest demands between the operator and the user in the next decision period can be obtained, the orderly access of electric vehicles can be achieved, and further the pressure on the economic operation and stability of the power system can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the drawings required for use in the embodiments of the present invention or the background art will be described below.

[0063] Figure 1a is a system architecture diagram of a power system provided by an embodiment of the present application;

[0064] Figure 1b is a system architecture diagram of another power system provided by an embodiment of the present application;

[0065] Figure 2 is a schematic flowchart of a charging electricity price optimization method based on a master-slave game provided by an embodiment of the present application;

[0066] Figure 3 is a model architecture diagram of a master-slave game model provided by an embodiment of the present application;

[0067] Figure 4 is a schematic diagram of the method architecture of a charging electricity price optimization method based on a master-slave game provided by an embodiment of the present application;

[0068] Figure 5 is a schematic structural diagram of a charging electricity price optimization device based on a master-slave game proposed by an embodiment of the present application;

[0069] Figure 6 is a schematic structural diagram of another charging electricity price optimization device based on a master-slave game provided by an embodiment of the present application;

[0070] Figure 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0072] The terms "first", "second", etc. in the description, claims, and the above-mentioned drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or electronic device that includes a series of steps or units is not limited to the listed steps or units, but in an alternative example, it further includes steps or units that are not listed, or in an alternative example, it further includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0073] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0074] As an important part of the demand-side load, the coordinated development of electric vehicles (EVs) and distributed renewable energy is one of the important ways to achieve urban energy development. The random access of a large number of EVs to the virtual power plant for unordered charging will further exacerbate the peak-valley difference of the system load, bringing greater pressure to the economic operation and stability of the power system. Currently, most strategies are to guide the orderly charging of electric vehicles on the premise of a fixed electricity price, which may not fully mobilize the enthusiasm of users in an open market environment. In addition, the charging behavior characteristics of EVs (such as charging power and charging time, etc.) are controlled by the virtual power plant operator, without considering the different interest demands between the operator and the users. At the same time, the uncertainty of the output of a high proportion of renewable energy brings risks to the dispatching of the virtual power plant operator that cannot be ignored. Currently, the methods for dealing with the uncertainty of distributed generation (DG) power generation and the uncertainty of EC charging behavior are too conservative, restrictive, and cannot fully consider the different interest demands of electric vehicle users and renewable energy output at the same time.

[0075] In view of the above problems, the embodiments of this application provide a charging electricity price optimization method and related device based on the master-slave game, which can obtain the electricity price, output power, and corresponding specific time periods that meet the different interest demands between the operator and the users in the next decision-making period, enabling the orderly access of electric vehicles, and thus reducing the pressure on the economic operation and stability of the power system.

[0076] Please refer to Figure 1a , Figure 1a which is a system architecture diagram of a power system provided by the embodiments of this application, asFigure 1a As shown in the figure, the power system 100 includes: a decision-making side 110, a power generation side 120, and a power consumption side 130.

[0077] Among them, the decision-making side 110 conducts command, dispatching, and control decisions for the power system 100. Specifically, it can be used to monitor the operation of the power system 100 in real time and conduct power management and dispatching. Specifically, by collecting a large amount of real-time data from at least the power generation side and the power consumption side 130, including information such as output power, load conditions, and power consumption of users, it accurately monitors the power balance status of the entire power grid. The power generation side 120 is a distributed generation (DG), which can specifically include various renewable energy power generations and various non-renewable energy power generations. Specifically, renewable energy power generation can include solar photovoltaic power generation, wind power generation, hydropower generation, biomass energy power generation, etc., and non-renewable energy power generation can include traditional thermal power generation; and renewable energy has certain uncertainties and fluctuations. For example, solar photovoltaic power generation is affected by lighting factors, and wind power generation relies on wind energy to drive the turbine to rotate and generate electricity. However, the wind speed has strong randomness and volatility. The wind speed may change sharply in a short period of time. For example, a strong wind may suddenly come, and then the wind speed quickly drops. This uncertainty of the wind speed leads to unstable output power of the wind turbine; hydropower generation may be affected by precipitation and flow, etc. Therefore, the renewable energy connected to the power system 100 has certain uncertainties. The power consumption side 130 is mainly for electric vehicles and / or charging piles.

[0078] Please refer to Figure 1b , Figure 1b is the system architecture diagram of another power system provided by the embodiment of the present application. As Figure 1b shown in the figure, the power system 100 includes: a virtual power plant operator 101, a distributed power source 102, and an electric vehicle 103.

[0079] Among them, the virtual power plant operator 101 regards numerous distributed power sources 102 as important adjustable resources. The virtual power plant operator 101 integrates these scattered distributed power sources through advanced communication technologies and monitoring systems. The virtual power plant operator 101 can optimize the dispatching of the distributed power source 102 and reasonably arrange the output of the distributed power source 102. The virtual power plant operator 101 participates in the demand response management of the electric vehicle 103. There is a complementary and cooperative relationship between the distributed power source 102 and the electric vehicle 103 under the framework of the virtual power plant. The distributed power source 102 mainly serves as a power production unit, while the electric vehicle 103 can be both a consumer of electric energy (when charging).

[0080] Based on this, the present application provides a charging electricity price optimization method and related devices based on master-slave game, which will be described in detail below with reference to the accompanying drawings.

[0081] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of a charging electricity price optimization method based on master-slave game provided by an embodiment of the present application. As Figure 2 shown, the method includes the following steps:

[0082] S210, obtain the first charging demand information and environmental impact information for the next decision period. The first charging demand information includes the charging demand reported by the owners of electric vehicles, and the environmental impact information includes the environmental information that affects the charging of electric vehicles.

[0083] Among them, the next decision period is the period to be predicted, and the time length is not limited. The next decision period can be one day. The first charging demand information is the charging demand for the next decision period actively reported by the owners of electric vehicles. The first charging demand may specifically include the charging time period and the charging power within the next decision period. The first charging demand information includes multiple demand information, that is, the demand information reported by multiple owners. The environmental impact information is the environmental information that may affect charging in the next decision period, which may specifically include temperature, precipitation, etc. These factors may affect the charging demand and charging parameters, such as power and duration.

[0084] S220, determine the second charging demand information based on the first charging demand information and / or the environmental impact information.

[0085] Among them, the second charging demand information is the demand information used to substitute into the objective function or boundary conditions. The second charging demand information can be determined by at least one of the first charging demand information and the environmental impact information. Specifically, the second charging demand information can be determined based on the first charging demand information and the environmental impact information, or based on the first charging demand information, or based on the environmental impact information.

[0086] In a possible embodiment, determining the second charging demand information based on the first charging demand information and / or environmental impact information includes: obtaining the quantity of demand information of the first charging demand information; determining whether the information quantity requirement is met based on the quantity of demand information and a preset demand information threshold. If the quantity of demand information is not less than the preset demand information threshold, the information quantity requirement is met; if the quantity of demand information is less than the preset demand information threshold, the information quantity requirement is not met. If the information quantity requirement is met, the second charging demand information is the first charging demand information. If the information quantity requirement is not met, demand prediction is performed based on the environmental impact information and the first charging demand information to obtain predicted charging demand information, and the second charging demand information is determined based on the predicted charging demand information and the first charging demand information.

[0087] Among them, there are multiple pieces of first charging demand information, and the quantity of the first charging demand information is the quantity of demand information actively reported by the vehicle owner. It is judged whether the quantity of demand information actively reported meets the standard based on the quantity of demand information actively reported by the vehicle owner and the preset demand information threshold. If the quantity reaches the preset demand information threshold, the reported first charging demand information can be used as the second charging demand information. If the quantity does not reach the preset demand information threshold, demand prediction can be performed through the environmental impact information to determine the predicted charging demand information, and the second charging demand information is determined based on at least one of the predicted charging demand information and the first charging demand information.

[0088] Specifically, determining the second charging demand information based on the predicted charging demand information and the first charging demand information may include: using a suitable prediction algorithm or model, combining environmental impact information and the first charging demand information to predict the future charging demand and obtain the predicted charging demand information. The prediction algorithm can be a statistics-based method, such as linear regression, time series analysis, etc., or a more complex machine learning algorithm, such as neural network, etc. Then, fuse and analyze the predicted charging demand information with the first charging demand information to determine the final second charging demand information. The fusion method can be to use the predicted charging demand information as a reference to adjust some parameters in the first charging demand information; or according to a certain weight allocation principle, combine the two to obtain a new demand information set as the second charging demand information. When making demand predictions, the accuracy and reliability of the predictions need to be considered. By continuously collecting actual charging data and environmental data, the prediction model can be trained and optimized to improve the prediction accuracy. At the same time, multiple prediction methods can also be used for comparison and verification to select the prediction result that is most suitable for the current situation. When determining the second charging demand information, different weights can also be assigned to different factors according to the user's personalized settings or historical behavior data when fusing the predicted charging demand information and the first charging demand information, so as to obtain the second charging demand information that better meets the user's needs.

[0089] Specifically, the historical charging behaviors of vehicle owners can be collected, including but not limited to the charging time, battery level, charging speed, selected charging mode (such as standard charging, fast charging, etc.), ambient information at that time (such as temperature, humidity, etc.), and the corresponding charging cost. These data can be obtained from the charging records of the device, charging-related applications, or intelligent charging systems. Based on the collected user-set information and historical behavior data, a user preference model is established. Machine learning algorithms, such as clustering analysis, can be used to group users with similar preferences into one category. According to the analysis results of the user preference model, weight factors are determined for different demand factors (such as charging speed, charging time, etc.). Based on the determined weight factors, weighted calculations are performed on the first charging demand information and the predicted charging demand information to obtain the fused second charging demand information. The specific calculation method is as follows: The charging power in the second charging demand information = the charging power in the first charging demand information × A + the charging power in the predicted charging demand information × B, where A is the charging power weight factor and B is the charging power weight factor. For the demand information regarding charging time: The charging time in the second charging demand information = the charging time in the first charging demand information × C charging time weight factor + the charging time in the predicted charging demand information × D charging time weight factor, where C is the charging time weight factor and D is the charging time weight factor. Through the above weighted calculations, the second charging demand information that comprehensively considers user preferences can be obtained, and this information better meets the actual needs of users.

[0090] It can be seen that in this embodiment, by obtaining the first charging demand information actively reported by the vehicle owner and determining the second charging demand information based on the first charging demand information or based on the first charging demand information and the predicted charging demand information, the data can fully consider the needs on the user side and improve the robustness.

[0091] In a possible embodiment, the charging demands reported by the owner of the electric vehicle include the first charging time period demand and the first charging power demand, the environmental impact information includes temperature factors and precipitation factors, and the demand prediction based on the environmental impact information and the first charging demand information to obtain the predicted charging demand information includes: performing data standardization based on the temperature factors, the precipitation factors, and a preset grading standard to obtain temperature parameters and precipitation parameters; determining a prediction data set based on the temperature parameters, the precipitation parameters, the first charging time period demand, and the first charging power demand; and determining the predicted charging demand information based on the prediction data set and a pre-trained charging prediction model.

[0092] Among them, data normalization can be performed by methods such as min-max normalization and Z-score standardization, and then the temperature parameters and precipitation parameters are obtained through level division based on a preset grading standard. Then, a prediction data set is formed based on the temperature parameters, precipitation parameters, the first charging period demand, and the first charging power demand, and this prediction data set is used to input into a charging prediction model to determine the predicted charging demand information.

[0093] Specifically, determining the prediction data set based on the temperature parameters, the precipitation parameters, the first charging period demand, and the first charging power demand includes: determining the temperature influence degree parameter and the precipitation influence degree parameter based on the principal component analysis method, temperature factors, precipitation factors, and the historical charging database. Specifically, it includes: constructing a correlation coefficient matrix of the temperature parameters and precipitation parameters to understand their linear relationship. If there is a strong correlation between temperature and precipitation, this relationship may need to be considered in the subsequent principal component analysis for the determination of the principal components. According to the correlation coefficient matrix, the principal components are determined by methods such as eigenvalue decomposition. For example, the eigenvalues and eigenvectors are calculated, and the eigenvectors corresponding to the larger eigenvalues are selected as the principal components. These principal components will be used as new variables to describe the influence of temperature and precipitation on the charging time and power.

[0094] Exemplarily, the standardized temperature and precipitation and the corresponding time periods include the following table. Then, the correlation coefficient matrix R between the standardized temperature value and the standardized precipitation value is calculated, and the correlation coefficient is 0.3. Then, the principal components are determined by solving the eigenvalues and eigenvectors. The eigenvalue corresponding to the first principal component is 1.3, and the eigenvector is (0.8, 0.6), indicating that this principal component synthesizes the main information of temperature and precipitation, and the contribution of temperature to it is greater; the eigenvalue corresponding to the second principal component is 0.7, and the eigenvector is (-0.6, 0.8), indicating that this principal component reflects other change information of temperature and precipitation to a certain extent. Calculate the contribution rates of the two principal components. The contribution rate of the first principal component is: 1.3÷(1.3 + 0.7)×100% = 65%, and the contribution rate of the second principal component is: 0.7÷(1.3 + 0.7)×100% = 35%. According to the contribution rates and the influence degrees of each principal component on the charging time and power, the comprehensive influence can be obtained. For example, at a specific moment, assuming the value of the first principal component is 1.5 and the value of the second principal component is -0.5, then the comprehensive influence on the charging time is: 1.5×1 - (-0.5)×2 = 1.5 + 1 = 2.5 minutes (shortened by 2.5 minutes); the comprehensive influence on the charging power is: 1.5×14 - (-0.5)×5 = 21 + 2.5 = 23.5 watts (increased by 23.5 watts). Based on the comprehensive influence, the predicted value directly predicted according to the reported charging demand is further corrected to obtain the final predicted charging demand information.

[0095]

[0096] Among them, the pre-trained charging prediction model can be a neural network model, such as a multi-layer perceptron (MLP), a convolutional neural network (CNN), or a recurrent neural network (RNN) and its variants (such as LSTM, GRU). These neural network models have strong non-linear fitting capabilities and can handle complex data relationships. For example, RNN and its variants are suitable for processing data with time series characteristics, such as the charging demand changes in different time periods, and can predict the future charging demand trend based on historical charging time series data. It can also be a decision tree model, including random forest, gradient boosting tree, etc.

[0097] It can be seen that in this embodiment, by incorporating multi-dimensional factors such as temperature parameters, precipitation parameters, the first charging period demand, and the first charging power demand, the prediction dataset can more comprehensively reflect the characteristics of charging demand, which helps to improve the accuracy of charging demand prediction. Accurate charging demand prediction can improve the rationality of charging resource allocation.

[0098] S230, determining a first objective function and a plurality of first constraint conditions based on the second charging demand information and the power plant operation information, the first constraint conditions including power flow constraint conditions, operation constraint conditions, and / or electricity price constraint conditions, and using the first objective function as the upper-level function, the first objective function including an objective function for calculating the cost of the virtual power plant operator; and, determining a second objective function and a plurality of second constraint conditions based on the second charging demand information and the electric vehicle parameters, the second constraint conditions including charging amount constraint conditions and / or charging situation constraint conditions, and using the second objective function as the lower-level function, the second objective function including an objective function for calculating the charging cost.

[0099] Among them, the power plant operation information may include the cost function, charging electricity price, charging power, time-of-use electricity selling price, time-of-use electricity purchasing price, electricity selling and purchasing power, etc. of the distributed power sources of the virtual power plant operator. The first objective function specifically constructs the operating cost of the virtual power plant operator. The requirements for the optimal scheduling of the virtual power plant operator are: to reduce the power supply cost and increase the electricity selling revenue on the basis of meeting the load demand. The first objective function is subject to the first constraint condition. The decision-making side needs to determine the power generation plan of the distributed power generation DG, the electricity selling and purchasing plan from the day-ahead spot market, and the EV charging electricity price for each time period. The first constraint condition may include a power flow constraint condition, and at least one of an operation constraint condition and an electricity price constraint condition. The operation constraint condition is used to constrain the upper and lower limits of the power generation power of the DG and the increase and decrease rate of the power. The power flow constraint is used to constrain the voltage, current, power and other electrical quantities of each node in the power system 100 to ensure the normal operation of the power system 100. The electricity price constraint condition is used to constrain the relationship between the electricity purchasing price and the electricity selling price to ensure the stability of the revenue of the virtual power plant operator and the charging cost of the vehicle owners. Taking the first objective function as the upper-layer function can coordinate the relationship between the cost of the virtual power plant operator and other factors as a whole. On the basis of meeting the constraints such as power flow, operation and electricity price, through the optimization of the cost of the virtual power plant operator, a reasonable economic framework can be provided for the charging optimization problem at the lower layer.

[0100] Among them, the electric vehicle parameters include the charging power, and the charging power can be determined based on the reported second charging demand information. The second objective function specifically constructs the charging cost of the electric vehicle owners. The second objective function is subject to the second constraint condition. The second constraint condition may include at least one of a charging quantity constraint condition and a charging situation constraint condition. The charging quantity is used to constrain the actual required charging quantity of the electric vehicle EV for charging. The charging situation constraint condition is used to constrain the upper and lower limits of the charging power and the charging time period. Taking the second objective function as the lower-layer function can optimize the specific charging behavior.

[0101] In a possible embodiment, the second charging demand information includes a second charging time period demand and a second charging quantity demand, and the first objective function includes the following first formula:

[0102] ;

[0103] The second objective function includes the following second formula: .

[0104] Among them, is the cost function of the distributed power generation DG of the i-th electric vehicle EV, is the active power output of the distributed power generation DG corresponding to the i-th electric vehicle EV; is the charging electricity price of the electric vehicle EV at time t, is the charging power of the \(i\)-th electric vehicle EV; is the time-of-use electricity selling price of the virtual power plant operator in the day-ahead spot market, is the time-of-use electricity purchasing price of the virtual power plant operator in the day-ahead spot market, is the electricity selling power at time \(t\), is the electricity purchasing power at time \(t\); where, is the charging cost of the electric vehicle EV, is the charging power of the electric vehicle EV at time period \(t\), is the charging electricity price of the electric vehicle EV at time \(t\).

[0105] It should be noted that in the master-slave game, the EV owners are at the lower layer and will adjust their charging behaviors with the goal of minimizing the charging cost according to the charging electricity price issued by the virtual power plant operator. The goal of the EV owners is to adjust the charging power to make the charging cost the lowest, that is, the ninth formula is the orderly charging expression of the owner of the \(i\)-th EV.

[0106] Among them, the first objective function is determined according to the optimization scheduling requirements of the virtual power plant operator. The optimization scheduling requirements of the virtual power plant operator are to reduce the power supply cost and increase the electricity selling revenue on the basis of meeting the load demand. The virtual power plant operator needs to determine the power generation plan of DG, the electricity purchasing and selling plan from the day-ahead spot market, and the EV charging electricity price for each time period.

[0107] Among them, the time period t is the unit time length included in the next decision-making period, and there is at least one time period t in the decision-making period; the cost function of the distributed power generation DG of the i-th electric vehicle EV refers to the mathematical expression of various costs generated during the operation of the distributed power generation that provides power for the i-th electric vehicle. These costs may include fuel costs (if it is fuel-powered generation), equipment depreciation costs, maintenance costs, operation costs (such as labor costs), etc.; the active power output of the distributed power generation DG corresponding to the i-th electric vehicle EV refers to the actual active power output by the distributed power generation, specifically including the actual electric power provided by the distributed power generation to drive the electric vehicle or charge its battery; the charging electricity price of the electric vehicle EV in the t time period is the electricity price required to charge the electric vehicle within the t time period; the charging power of the i-th electric vehicle EV is the charging power for charging the electric vehicle in the t time period; the day-ahead spot market is a power trading market that allows market participants to buy and sell electricity one day in advance. The time-of-use electricity selling price of the virtual power plant operator in the day-ahead spot market is that as a seller, the virtual power plant operator will determine the electricity selling prices at different time periods based on factors such as market expectations, the costs of its own power generation resources (including electric vehicle distributed power generation and other distributed generation resources), and the load demand of the power grid; the time-of-use electricity purchasing price reflects the cost for the virtual power plant operator to obtain power from the external power grid. The charging cost of the electric vehicle EV is the electricity cost to be paid when the electric vehicle is charged, and the charging power is the corresponding purchased power quantity.

[0108] It can be seen that in this embodiment, the upper-layer first objective function considers the optimization of the distributed power generation (DG), can reasonably allocate the output power of each distributed power generation, reduce the operation cost, increase the profit margin, and can adjust the output power in real time according to the demand of the power grid to make up for the power shortage or surplus in the power grid. The lower-layer second objective function focuses on the charging cost of the electric vehicle EV, guides users to choose to charge during periods with lower electricity prices, makes the load distribution of the power grid more uniform, and improves the overall operation efficiency of the power system.

[0109] In a possible embodiment, the power flow constraint conditions include the following third formula, fourth formula, and fifth formula:

[0110] The third formula is: ;

[0111] The fourth formula is: ;

[0112] The fifth formula is: ;

[0113] Among them, is the active power flow from grid node n to n + 1, is the reactive power flow from grid node n to n + 1; is the active power of node n, is the reactive power of node n; is the voltage of node n; is the line resistance between nodes n and n+1; is the line impedance between nodes n and n+1; is the uncertain parameter of the renewable energy output, is the maximum allowable voltage deviation, is the reference voltage amplitude at node 0; is the active power of the load at node n, is the reactive power of the load at node n; is the active power of the controllable distributed generation DG at node n, is the reactive power of the controllable distributed generation DG at node n;

[0114] The operating constraint conditions include the following sixth formula and seventh formula:

[0115] The sixth formula is: ;

[0116] where, is the active output of the controllable distributed generation DG, is the reactive output of the controllable distributed generation DG; is the upper limit of the active output of the controllable distributed generation DG, is the lower limit of the active output of the controllable distributed generation DG, is the lower limit of the reactive output of the controllable distributed generation DG, is the upper limit of the reactive output of the controllable distributed generation DG; is the upper limit of the upward ramp rate, is the upper limit of the downward ramp rate;

[0117] The seventh formula is: ;

[0118] where, is the actual output of the controllable distributed generation, is the predicted output of the controllable distributed generation, is the prediction error, is the maximum prediction error;

[0119] The electricity price constraint conditions include the following eighth formula:

[0120] ;

[0121] where, is the charging electricity price of the electric vehicle EV at time t, is the average value of the electricity purchase price in the day-ahead market, It is the time-of-use electricity purchase price for the virtual power plant operator in the day-ahead spot market.

[0122] It should be noted that the robust optimization scheduling of the virtual power plant operator requires that the system can meet the node voltage safety under the worst-case scenario of the uncertainty of the intermittent renewable energy output. Therefore, the uncertain parameters of the renewable energy output mainly affect the node voltage safety constraint. The fourth formula can be used as a shorthand for the third formula. The in the fourth formula is , and function. and can jointly constitute the node equivalent load , .

[0123] It can be seen that in this embodiment, by constraining the first objective function through multiple constraint conditions, the safe and stable operation of the power system can be guaranteed, the resource utilization efficiency can be improved, and the consumption of renewable energy can be promoted.

[0124] In a possible embodiment, the charging amount constraint condition includes the following ninth formula: ;

[0125] The charging situation constraint condition includes the following tenth formula: .

[0126] Among them, is the battery capacity of the electric vehicle EV, is the initial state of the electric vehicle EV battery during charging, is the charging efficiency; among them, is the upper limit of the charging power of the electric vehicle EV at time t, is the set of time periods allowing the electric vehicle EV to charge, is the upper limit of the charging power of the electric vehicle EV.

[0127] Among them, the charging amount constraint condition is used to ensure that the EV must be fully charged within the charging time period. The charging situation constraint condition means that the charging power of the EV is not allowed to exceed the limit and can only be charged during the allowed time periods.

[0128] It can be seen that in this embodiment, while the electric vehicle charging can be fully charged, constraining the charging power and charging time can stabilize the grid operation, balance the grid load, balance the power fluctuations caused by the access of distributed energy DG and electric vehicle charging, and improve user satisfaction.

[0129] S240. Determine the robust constraint condition based on the power flow constraint condition. The robust constraint condition is used to constrain the voltage safety of the nodes in the circuit.

[0130] It should be noted that there are various uncertain factors in the actual operation of the power system, such as load fluctuations, generator failures, and line parameter changes. These uncertain factors may affect the power flow distribution, which may further lead to node voltage violations and threaten the safe and stable operation of the power system. Therefore, it is necessary to introduce robust constraint conditions to cope with these uncertainties and ensure that the node voltages can be maintained within a safe range under various possible conditions.

[0131] In one possible embodiment, determining the robust constraint condition based on the power flow constraint condition includes: determining a voltage-power relationship matrix based on the third formula and the fifth formula, where the voltage-power relationship matrix is used to characterize the relationship between the voltage changes of each node and the power of each device; determining a robust equality based on the voltage-power relationship matrix and the fourth formula; and determining the robust constraint condition based on the robust equality and the strong duality theory.

[0132] Where, if n is the last node of the system, then and , substituting them into the first two equations of the third formula, that is, and , and further expanding to obtain the twelfth formula: ;

[0133] According to the twelfth formula, the relationship between the line transmission power and the equivalent load of each node can be obtained. And further expanding the relationship between the voltage amplitude and the branch transmission power in the third formula, the thirteenth formula can be obtained:

[0134] ;

[0135] Substituting the fifth formula and the twelfth formula into the thirteenth formula, a matrix expression of the voltage change of each node and the power of each device can be obtained, that is, the voltage-power relationship matrix, which is represented by the fourteenth formula and the fifteenth formula:

[0136] Fourteenth formula:

[0137] ;

[0138] Fifteenth formula: ;

[0139] Where, is a column vector composed of the voltage amplitudes of each node, represents an n-dimensional column vector with all elements being , and n is the number of nodes; and are the corresponding coefficient matrices in the thirteenth formula; is the branch-node incidence matrix, is the number of branches, representing the correlation between the branch transmission power and the node load. is the active load matrix of the nodes. is the reactive load matrix of the nodes. Substitute the fourteenth formula into the fourth formula to separate the control variables and the uncertain variables. Then, the voltage security constraint of the system can be transformed into the first robust equality, which is represented by the sixteenth formula:

[0140] Sixteenth formula: ;

[0141] where is n a diagonal matrix of order, representing the maximum DG fluctuations of each node. represents the robust control coefficient, which is n a column vector of dimension, and (i, :) represents all elements in the i-th row. represents the first constant term coefficient obtained by substituting the fourteenth formula into the fourth formula, which is used as the upper limit of the voltage security constraint. represents the second constant term coefficient obtained by substituting the fourteenth formula into the fourth formula, which is used as the lower limit of the voltage security constraint. When the robust control coefficient takes values between its maximum and minimum values, different levels of robust optimization schemes can be obtained. At this time, the virtual power plant operator can achieve a balance between robustness and economy by setting the robust control parameters. Then, using the strong duality theory, the uncertain variables in the formula are eliminated, and the first robust equality is transformed into the second robust equality that only contains deterministic variables, that is, the final robust equality, which is represented by the seventeenth formula:

[0142] Seventeenth formula: ;

[0143] where , , , are n a matrix of dual variables of order.

[0144] It can be seen that in this embodiment, the robust constraint conditions obtained through the above determination process of the robust constraint conditions can ensure that the node voltage of the power system is always within the safe range under the influence of various uncertainty factors, which helps to maintain the voltage stability of the power system, ensure the normal operation of the power system, and use the strong duality theory to eliminate the uncertain variables and transform them into a robust equality that only contains deterministic variables, greatly reducing the complexity of the problem. The transformation of the robust equality can provide corresponding optimization schemes according to the needs of different users to meet diverse requirements.

[0145] S250. Solve the master-slave game model based on the first objective function, the second objective function, the multiple first constraint conditions, the multiple second constraint conditions, and the robust constraint condition to determine the target optimization parameters, where the target optimization parameters are used to formulate the electricity price and / or power for the next decision period and the periods corresponding to different electricity prices and / or different powers.

[0146] Among them, please refer to Figure 3 , Figure 3 is the model architecture diagram of a master-slave game model provided by an embodiment of the present application. The master-slave game is a hierarchical decision-making model, where the upper layer (leader) and the lower layer (follower) have different decision-making goals and powers. The leader makes a decision first, and the follower adjusts its strategy according to the leader's decision to maximize its own interests. The solution methods can include iterative algorithms, gradient descent methods, interior point methods, etc. The basic idea of these methods is to continuously adjust the values of the decision variables under the premise of satisfying the constraint conditions, so that the objective function gradually approaches the optimal value. The finally obtained target optimization parameters can include electricity price, power, and the corresponding periods, and can be used for formulating strategies for the next decision period.

[0147] In a possible embodiment, the solving the master-slave game model based on the first objective function, the second objective function, the multiple first constraint conditions, the multiple second constraint conditions, and the robust constraint condition to determine the target optimization parameters includes: determining a third objective function based on the first objective function, the second objective function, and optimization theory, where the third objective function is a single-layer function that combines the upper-layer function and the lower-layer function, and the optimization theory includes at least one of duality theory and KKT theory; solving based on the third objective function, the first constraint condition, the second constraint condition, and the robust constraint condition to determine the target optimization parameters, where the target optimization parameters include the target electricity price, the target power, and the target period, and the target period has a corresponding relationship with the target electricity price and the target power; the third objective function includes the following eleventh formula:

[0148] ;

[0149] Among them, is the dual variable of the ninth formula, is the charging power upper limit constraint 's dual variable.

[0150] Among them, determining the third objective function based on the first objective function, the second objective function, and optimization theory includes: determining the objective function of the virtual power plant operator based on the first objective function, the second objective function, the KKT (Karush-Kuhn-Tucker) conditions, and duality theory, which is expressed by the eighteenth formula:

[0151] ;

[0152] Among them, the eighteenth formula above contains a min non-linear term with respect to , which cannot be directly solved. The dual problem of EV user optimization can be expressed as the nineteenth formula, the twentieth formula, and the twenty-first formula:

[0153] Nineteenth formula: ,

[0154] Twentieth formula: ,

[0155] Twenty-first formula: ;

[0156] Among them, the twentieth formula and the twenty-first formula are used to constrain the nineteenth formula. At this time, the optimization objective of the virtual power plant operator can be equivalent to the eleventh formula above.

[0157] Since the constraint conditions in the optimization problem of the virtual power plant operator still involve the original variables of the EV user optimization problem, it is also necessary to use the complementary slackness theorem to obtain the relationship between the original variables and the dual variables. can be regarded as the shadow price of the charging power upper limit constraint. When represents increasing the EV charging upper limit, the EV owner can obtain more benefits, and at this time the EV charging power is the maximum value. Similarly, it can be known that when , the EV charging power is 0. Assuming that the EV requires time periods for charging, in order to minimize the charging cost, the EV will maintain the maximum charging power in the time periods with the lowest electricity price, keep the charging power at 0 in the with the highest electricity price, and use the remaining one time period to fill the insufficient charging demand. Therefore, the constraint relationship between the dual variables and the original variables can be obtained, which is expressed by the twenty-second formula; based on the ninth formula and the eleventh formula above, the power in the target optimization parameters can be calculated, and then the cost and the corresponding time periods can also be obtained.

[0158] Twenty-second formula: .

[0159] It can be seen that in this embodiment, by converting the double-layer function into a single layer and the non-linear into a linear, the unreasonable decision-making caused by only considering the interests of one party is avoided, the structure of the model is simplified, less time and resources are required in the calculation process, and the virtual power plant operator can use the optimized model with fast solution to quickly adjust the charging strategy to obtain lower charging costs and higher profits.

[0160] Among them, please refer to Figure 4 , Figure 4 which is a schematic diagram of the method architecture of a charging electricity price optimization method provided by an embodiment of the present application; as Figure 4 shown, first, input information is obtained. The input information includes first charging demand information and environmental impact information. The input information is substituted into the first objective function of the upper layer, the second objective function of the lower layer, and the corresponding first constraint condition, second constraint condition, and robust constraint condition, and the first objective function of the upper layer and the second objective function of the lower layer are fused to obtain a third objective function. Based on the third objective function, the target optimization parameters can be obtained.

[0161] It can be seen that through Figure 2In an embodiment, the first charging demand information and environmental impact information for the next decision period are obtained. The first charging demand information includes the charging demand reported by the owner of the electric vehicle, and the environmental impact information includes the environmental information affecting the charging situation of the electric vehicle. The second charging demand information is determined based on the first charging demand information and / or the environmental impact information. The first objective function and multiple first constraint conditions are determined based on the second charging demand information and the power plant operation information. The first constraint conditions include power flow constraint conditions, operation constraint conditions, and / or electricity price constraint conditions. The first objective function is used as the upper-level function, and the first objective function includes an objective function for calculating the cost of the virtual power plant operator. Further, the second objective function and multiple second constraint conditions are determined based on the second charging demand information and the electric vehicle parameters. The second constraint conditions include charging amount constraint conditions and / or charging situation constraint conditions. The second objective function is used as the lower-level function, and the second objective function includes an objective function for calculating the charging cost. The robust constraint conditions are determined based on the power flow constraint conditions, and the robust constraint conditions are used to constrain the voltage safety of the nodes in the circuit. The master-slave game model is solved based on the first objective function, the second objective function, the multiple first constraint conditions, the multiple second constraint conditions, and the robust constraint conditions to determine the target optimization parameters. The target optimization parameters are used to formulate the electricity price and / or power for the next decision period and the time periods corresponding to different electricity prices and / or different powers. In this way, the electricity price, output power, and corresponding specific time periods that can meet the different interest demands between the operator and the user in the next decision period can be obtained, which can enable the orderly access of electric vehicles, thereby reducing the pressure on the economic operation and stability of the power system.

[0162] Please refer to Figure 5 , Figure 5 FIG. is a schematic structural diagram of a charging electricity price optimization device based on master-slave game proposed in an embodiment of the present application. The charging electricity price optimization device 500 based on master-slave game includes: a data acquisition module 510, a demand determination module 520, a function determination module 530, a robust determination module 540, and a model solving module 550. Among them,

[0163] The data acquisition module 510 is configured to obtain the first charging demand information and environmental impact information for the next decision period. The first charging demand information includes the charging demand reported by the owner of the electric vehicle, and the environmental impact information includes the environmental information affecting the charging situation of the electric vehicle.

[0164] The demand determination module 520 is configured to determine the second charging demand information based on the first charging demand information and / or the environmental impact information.

[0165] A function determination module 530 is configured to determine a first objective function and a plurality of first constraint conditions based on the second charging demand information and power plant operation information. The first constraint conditions include power flow constraint conditions, operation constraint conditions, and / or electricity price constraint conditions, and the first objective function is used as an upper-level function. The first objective function includes an objective function for calculating the cost of a virtual power plant operator. In addition, a second objective function and a plurality of second constraint conditions are determined based on the second charging demand information and electric vehicle parameters. The second constraint conditions include charging amount constraint conditions and / or charging situation constraint conditions, and the second objective function is used as a lower-level function. The second objective function includes an objective function for calculating the charging cost.

[0166] A robust determination module 540 is configured to determine a robust constraint condition based on the power flow constraint condition. The robust constraint condition is used to constrain the voltage safety of nodes in the circuit.

[0167] A model solving module 550 is configured to solve the master-slave game model based on the first objective function, the second objective function, the plurality of first constraint conditions, the plurality of second constraint conditions, and the robust constraint condition, and determine target optimization parameters. The target optimization parameters are used to formulate the electricity price and / or power for the next decision period and the periods corresponding to different electricity prices and / or different powers.

[0168] In a possible embodiment, the demand determination module 520 is specifically configured to determine the second charging demand information based on the first charging demand information and / or environmental impact information as follows:

[0169] Obtain the quantity of demand information of the first charging demand information; determine whether the information quantity requirement is met based on the quantity of demand information and a preset demand information threshold. If the quantity of demand information is not less than the preset demand information threshold, it is considered that the information quantity requirement is met. If the quantity of demand information is less than the preset demand information threshold, it is considered that the information quantity requirement is not met.

[0170] If the information quantity requirement is met, the second charging demand information is the first charging demand information.

[0171] If the information quantity requirement is not met, demand prediction is performed based on the environmental impact information and the first charging demand information to obtain predicted charging demand information; the second charging demand information is determined based on the predicted charging demand information and the first charging demand information.

[0172] In a possible embodiment, the charging requirements reported by the owner of the electric vehicle include the first charging time period requirement and the first charging power requirement, and the environmental impact information includes temperature factors and precipitation factors. The demand determination module 520 is specifically configured to perform demand prediction based on the environmental impact information and the first charging demand information to obtain predicted charging demand information as follows:

[0173] Perform data standardization based on the temperature factor, the precipitation factor, and a preset classification standard to obtain a temperature parameter and a precipitation parameter;

[0174] Determine a prediction data set based on the temperature parameter, the precipitation parameter, the first charging time period requirement, and the first charging power requirement;

[0175] Determine the predicted charging demand information based on the prediction data set and a pre-trained charging prediction model.

[0176] In a possible embodiment, the robust determination module 540 is specifically configured to determine a robust constraint condition under the power flow constraint condition as follows:

[0177] Determine a voltage-power relationship matrix based on the third formula and the fifth formula, and the voltage-power relationship matrix is used to characterize the relationship between the voltage change of each node and the power of each device;

[0178] Determine a robust equality based on the voltage-power relationship matrix and the fourth formula;

[0179] Determine the robust constraint condition based on the robust equality and the strong duality theory.

[0180] In a possible embodiment, the robust determination module 540 is specifically configured to solve the master-slave game model based on the first objective function, the second objective function, the multiple first constraint conditions, the multiple second constraint conditions, and the robust constraint condition to determine the target optimization parameter as follows:

[0181] Determine a third objective function based on the first objective function, the second objective function, and optimization theory. The third objective function is a single-layer function that combines the upper-layer function and the lower-layer function. The optimization theory includes at least one of duality theory and KKT theory. The third objective function includes the following eleventh formula:

[0182] ;

[0183] Wherein, is the dual variable of the ninth formula of the equation, is the dual variable of the charging power upper limit constraint ;

[0184] Solve based on the third objective function, the first constraint condition, the second constraint condition, and the robust constraint condition to determine the target optimization parameters, where the target optimization parameters include the target electricity price, the target power, and the target time period, and the target time period has a corresponding relationship with the target electricity price and the target power.

[0185] It should be noted that, among them, for the specific functional implementation method of the charging electricity price optimization device based on the master-slave game, refer to the description of the above Figure 2 shown charging electricity price optimization method based on the master-slave game. For example, the data acquisition module is used to implement the relevant content of executing S210. Each unit or module in the charging electricity price optimization device 500 can be separately or all combined into one or several other units or modules to form, or some of the units or modules can be further split into multiple smaller units or modules in terms of function to form, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above units or modules are divided based on logical functions. In practical applications, the function of one unit (or module) is implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0186] It can be seen that the charging price optimization device based on the master-slave game described in the embodiments of the present application obtains the first charging demand information and environmental impact information for the next decision period. The first charging demand information includes the charging demand reported by the owners of electric vehicles, and the environmental impact information includes the environmental information affecting the charging of electric vehicles. The second charging demand information is determined based on the first charging demand information and / or environmental impact information. The first objective function and multiple first constraint conditions are determined based on the second charging demand information and power plant operation information. The first constraint conditions include power flow constraint conditions, operation constraint conditions, and / or electricity price constraint conditions, and the first objective function is used as the upper-layer function. The first objective function includes an objective function for calculating the cost of the virtual power plant operator. Further, the second objective function and multiple second constraint conditions are determined based on the second charging demand information and electric vehicle parameters. The second constraint conditions include charging amount constraint conditions and / or charging condition constraint conditions, and the second objective function is used as the lower-layer function. The second objective function includes an objective function for calculating the charging cost. The robust constraint condition is determined based on the power flow constraint condition, and the robust constraint condition is used to constrain the voltage safety of the nodes in the circuit. The master-slave game model is solved based on the first objective function, the second objective function, the multiple first constraint conditions, the multiple second constraint conditions, and the robust constraint condition to determine the target optimization parameters. The target optimization parameters are used to formulate the electricity price and / or power for the next decision period and the corresponding periods for different electricity prices and / or different powers. In this way, the electricity price, output power, and corresponding specific periods that meet the interest difference requirements between the operator and the user in the next decision period can be obtained, enabling the orderly access of electric vehicles, and thus reducing the pressure on the economic operation and stability of the power system.

[0187] In the case of adopting an integrated unit, please refer to Figure 6 , Figure 6 FIG. is a schematic structural diagram of another charging price optimization device based on the master-slave game provided by the embodiments of the present application. As shown in Figure 6 FIG., the charging price optimization device 500 based on the master-slave game includes a processing module 502 and a communication module 501. The processing module 502 is used to control and manage the actions of the charging price optimization device 500 based on the master-slave game. For example, it executes the steps of the data acquisition module 510, the demand determination module 520, the function determination module 530, the robust determination module 540, and the model solution module 550, and / or is used to execute other processes of the technologies described herein. The communication module 501 is used for the interaction between the charging price optimization device 500 based on the master-slave game and other devices. As shown in Figure 6As shown, the charging electricity price optimization device 500 based on the master-slave game may further include a storage module 503, which is used to store the program code and data of the charging electricity price optimization device 500 based on the master-slave game.

[0188] Among them, the processing module 502 may be a processor or a controller. For example, it may be a Central Processing Unit (CPU), a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The communication module 501 may be a transceiver, an RF circuit, or a communication interface, etc. The storage module 503 may be a memory.

[0189] Among them, all relevant contents of each scenario involved in the above method embodiment can be cited in the function description of the corresponding functional module, and will not be elaborated here. The above charging electricity price optimization device 500 based on the master-slave game can execute the above Figure 2 shown charging electricity price optimization method based on the master-slave game.

[0190] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of an electronic device proposed in an embodiment of this application. As shown in the figure, the electronic device 700 includes a processor 710, a memory 720, a communication interface 730, and one or more programs 721. The above one or more programs 721 are stored in the above memory 720 and are configured to be executed by the above processor 710.

[0191] Among them, the processor 710, the memory 720, and the communication interface 730 are interconnected and complete the communication work among them;

[0192] The memory 720 may be a volatile memory such as a Dynamic Random Access Memory (DRAM), or a non-volatile memory such as a mechanical hard disk. The above memory 720 is used to store a set of executable program codes, and the above processor 710 is used to call one or more programs 721 stored in the memory 720 and can execute as the above Figure 2Some or all of the steps of any charging electricity price optimization method based on master-slave game described in the embodiments.

[0193] Among them, the electronic device 700 may include a smart phone (such as an Android phone, an iOS phone, a Windows Phone, etc.), a tablet computer, a handheld computer, a driving recorder, an in-vehicle electronic device, a server, a laptop computer, a mobile Internet electronic device (MID, Mobile Internet Devices), or a wearable electronic device (such as a smart watch, a Bluetooth headset), etc. The above are only examples and not an exhaustive list, including but not limited to the above electronic devices.

[0194] The embodiment of the present application also provides a computer storage medium. Among them, the computer storage medium stores a computer program for electronic data exchange, and the computer program enables the computer to execute some or all of the steps of any method described in the above method embodiments. The above computer includes an electronic device.

[0195] The embodiment of the present application also provides a computer program product. The above computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the above computer program is operable to enable the computer to execute some or all of the steps of any method described in the above method embodiments. The computer program product may be a software installation package, and the above computer includes an electronic device.

[0196] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps may be adopted in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0197] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0198] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above unit division is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0199] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] In addition, each functional unit in various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0201] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to enable a computer electronic device (which can be a personal computer, an electronic device, or a network electronic device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present application. The aforementioned memory includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs and other media that can store program codes.

[0202] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory. The memory can include: flash drives, read-only memories (abbreviation: ROM), random access memories (abbreviation: RAM), magnetic disks, or optical discs, etc.

[0203] The above has introduced the embodiments of the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A charging electricity price optimization method based on master-slave game, characterized in that: include: Acquire first charging demand information and environmental impact information for the next decision period, wherein the first charging demand information includes the charging demand reported by the owner of the electric vehicle, the charging demand reported by the owner of the electric vehicle includes a first charging period demand and a first charging power demand, and the environmental impact information includes environmental information that affects the charging condition of the electric vehicle, and the environmental information that affects the charging condition of the electric vehicle includes temperature factors and precipitation factors; Determining the second charging demand information based on the first charging demand information and / or the environmental impact information includes: obtaining the amount of demand information of the first charging demand information; determining whether the information volume requirement is met based on the amount of demand information and a preset demand information threshold, if the amount of demand information is not less than the preset demand information threshold, the information volume requirement is met; if the amount of demand information is less than the preset demand information threshold, the information volume requirement is not met; if the information volume requirement is met, the second charging demand information is the first charging demand information; if the information volume requirement is not met, performing demand prediction based on the environmental impact information and the first charging demand information to obtain predicted charging demand information; determining the second charging demand information based on the predicted charging demand information and the first charging demand information; wherein, performing demand prediction based on the environmental impact information and the first charging demand information to obtain predicted charging demand information includes: performing data normalization based on the temperature factor, the precipitation factor and a preset grading standard to obtain a temperature parameter and a precipitation parameter; determining a predicted data set based on the temperature parameter and the precipitation parameter for the first charging period demand and the first charging power demand; determining the predicted charging demand information based on the predicted data set and a pre-trained charging prediction model; Determine a first objective function and a plurality of first constraints based on the second charging demand information and the power plant operation information, the first constraints including power flow constraints, operation constraints and / or electricity price constraints, and use the first objective function as an upper function, the first objective function including an objective function for calculating the cost of the virtual power plant operator; and determine a second objective function and a plurality of second constraints based on the second charging demand information and the electric vehicle parameters, the second constraints including a charging amount constraint and / or a charging condition constraint, and use the second objective function as a lower function, the second objective function including an objective function for calculating the charging cost; Determining a robust constraint condition based on the power flow constraint condition, wherein the robust constraint condition is used to constrain voltage safety of nodes in the circuit; Based on the first objective function, the second objective function, the multiple first constraints, the multiple second constraints and the robust constraints, the master-slave game model is solved to determine the target optimization parameters, and the target optimization parameters are used to formulate the electricity price and / or power for the next decision period and the time periods corresponding to different electricity prices and / or different powers.

2. The method according to claim 1, characterized in that The second charging requirement information includes a second charging period requirement and a second charging power requirement, and the first objective function includes the following first formula: ; in, is the cost function of the distributed power generation DG of the i-th electric vehicle EV, is the active power output of the distributed power source DG corresponding to the i-th electric vehicle EV; is the charging electricity price of the electric vehicle EV in period t, is the charging power of the i-th electric vehicle EV; The time-of-use electricity price sold by the virtual power plant operator in the day-ahead spot market. The time-of-use electricity purchase price for virtual power plant operators in the day-ahead spot market. is the electricity sales power in period t, is the purchased power in period t; The second objective function includes the following second formula: in, The cost of charging an electric vehicle EV, is the charging power of the electric vehicle EV during period t, is the charging electricity price of the electric vehicle EV during period t.

3. The method according to claim 2, characterized in that The power flow constraint conditions include the following third formula, fourth formula and fifth formula: The third formula is: ; The fourth formula is: ; The fifth formula is: ; in, is the active power flow from grid node n to n+1, is the reactive power flow from grid node n to n+1; is the active power of node n, is the reactive power of node n; is the voltage of node n; is the line resistance between nodes n and n+1; is the line impedance of nodes n and n+1; For the uncertain parameters of renewable energy output, is the maximum allowable voltage deviation, is the reference voltage amplitude at node 0; is the active power of the load at node n, is the reactive power of the load at node n; is the active power of the controllable distributed generation DG at node n, is the reactive power of the controllable distributed generation DG at node n; The operation constraint conditions include the following sixth and seventh formulas: The sixth formula is: ; in, is the active power output of the controllable distributed power source DG, It is the reactive power output of controllable distributed power source DG; is the upper limit of the active output of the controllable distributed power source DG, is the lower limit of the active output of the controllable distributed power source DG, is the lower limit of reactive power output of controllable distributed generation DG, It is the upper limit of reactive power output of controllable distributed generation DG; Upper limit of uphill climbing rate, The upper limit of the downward climbing rate; The seventh formula is: ; in, is the actual output of the controllable distributed power source, For the predicted output of controllable distributed power sources, is the prediction error, is the maximum prediction error; The electricity price constraint condition includes the following eighth formula: ; in, is the charging electricity price of the electric vehicle EV in period t, is the average price of electricity purchased in the market on the previous day, It is the time-of-use electricity purchase price of the virtual power plant operator in the day-ahead spot market.

4. The method according to claim 2, characterized in that: The charging capacity constraint condition includes the following ninth formula: ; in, is the battery capacity of the electric vehicle EV, The initial state when charging the electric vehicle EV battery, For charging efficiency; The charging condition constraint condition includes the following tenth formula: ; in, is the upper limit of the charging power of the electric vehicle EV during period t, A collection of time periods during which electric vehicles (EV) charging is allowed. An upper limit on the charging power of electric vehicles (EVs).

5. The method according to claim 3, characterized in that: Determining a robust constraint condition based on the power flow constraint condition includes: Determine a voltage-power relationship matrix based on the third formula and the fifth formula, wherein the voltage-power relationship matrix is ​​used to characterize the relationship between the voltage change of each node and the power of each device; Determining a robust equation based on the voltage-power relationship matrix and the fourth formula; The robust constraint condition is determined based on the robust equality and strong duality theory.

6. The method according to claim 4, characterized in that The solving of the master-slave game model based on the first objective function, the second objective function, the plurality of first constraints, the plurality of second constraints and the robust constraints to determine the target optimization parameters includes: A third objective function is determined based on the first objective function, the second objective function and the optimization theory, wherein the third objective function is a single-layer function that integrates the upper-layer function and the lower-layer function, the optimization theory includes at least one of the duality theory and the KKT theory, and the third objective function includes the following eleventh formula: ; in, is the dual variable of the ninth formula of equation, The upper limit of charging power The dual variable of Based on the third objective function, the first constraint condition, the second constraint condition and the robust constraint condition, a solution is performed to determine target optimization parameters, wherein the target optimization parameters include a target electricity price, a target power and a target time period, and the target time period has a corresponding relationship with the target electricity price and the target power.

7. A charging electricity price optimization device based on master-slave game, characterized in that: include: a data acquisition module, configured to acquire first charging demand information and environmental impact information for a next decision period, wherein the first charging demand information includes charging demand reported by an owner of the electric vehicle, wherein the charging demand reported by the owner of the electric vehicle includes a first charging period demand and a first charging power demand, and wherein the environmental impact information includes environmental information that affects the charging condition of the electric vehicle, wherein the environmental information that affects the charging condition of the electric vehicle includes temperature factors and precipitation factors; A demand determination module, for determining the second charging demand information based on the first charging demand information and / or the environmental impact information, including: obtaining the quantity of demand information of the first charging demand information; determining whether the information volume requirement is met based on the quantity of demand information and a preset demand information threshold, if the quantity of demand information is not less than the preset demand information threshold, the information volume requirement is met, if the quantity of demand information is less than the preset demand information threshold, the information volume requirement is not met; if the information volume requirement is met, the second charging demand information is the first charging demand information; if the information volume requirement is not met, performing demand prediction based on the environmental impact information and the first charging demand information to obtain predicted charging demand information; determining the second charging demand information based on the predicted charging demand information and the first charging demand information; wherein, performing demand prediction based on the environmental impact information and the first charging demand information to obtain predicted charging demand information includes: performing data normalization based on the temperature factor, the precipitation factor and a preset grading standard to obtain a temperature parameter and a precipitation parameter; determining a predicted data set based on the temperature parameter and the precipitation parameter, the first charging period requirement and the first charging power requirement; determining the predicted charging demand information based on the predicted data set and a pre-trained charging prediction model; a function determination module, configured to determine a first objective function and a plurality of first constraints based on the second charging demand information and the power plant operation information, wherein the first constraints include power flow constraints, operation constraints and / or electricity price constraints, and use the first objective function as an upper function, wherein the first objective function includes an objective function for calculating the cost of the virtual power plant operator; and, based on the second charging demand information and the electric vehicle parameters, determine a second objective function and a plurality of second constraints, wherein the second constraints include a charging amount constraint and / or a charging condition constraint, and use the second objective function as a lower function, wherein the second objective function includes an objective function for calculating the charging cost; A robust determination module, used to determine a robust constraint condition based on the power flow constraint condition, wherein the robust constraint condition is used to constrain the voltage safety of nodes in the circuit; A model solving module is used to solve the master-slave game model based on the first objective function, the second objective function, the multiple first constraints, the multiple second constraints and the robust constraints, and determine the target optimization parameters. The target optimization parameters are used to formulate the electricity price and / or power of the next decision period and the time periods corresponding to different electricity prices and / or different powers.

8. An electronic device, characterized in that: The method comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 6.

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