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

Abstract

The invention provides a charging electricity price optimization method based on a master-slave game and a related device. The method comprises the following steps: acquiring first charging demand information and environmental influence information; determining second charging demand information based on the first charging demand information and / or the environmental influence information; 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; determining a second objective function and a plurality of second constraint conditions based on the second charging demand information and the electric vehicle parameters; determining a robust constraint condition based on the power flow constraint condition; and based on the first target function, the second target function, the plurality of first constraint conditions, the plurality of second constraint conditions and the robust constraint condition, performing master-slave game model solving to determine target optimization parameters. Therefore, the electric vehicles can be accessed in order, so that the pressure of economic operation and stability of an electric power system is reduced.

Description

Technical Field

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

[0002] Electric vehicles (EVs) are an important part of the demand-side load, and their coordinated development with distributed renewable energy is one of the important ways to achieve urban energy development. A large number of EVs randomly connected to virtual power plants for disorderly charging will further aggravate the peak-to-valley difference of system load, bringing greater pressure to the economic operation and stability of the power system.

[0003] At present, most strategies are to guide electric vehicles to charge in an orderly manner under the premise of a fixed electricity price, which may not fully mobilize the enthusiasm of users in an open market environment. In addition, the characteristics of EV charging behavior (such as charging power and charging time, etc.) are controlled by the virtual power plant operator, and the interests of operators and users are not taken into account. At the same time, the uncertainty of a high proportion of renewable energy output brings risks to the dispatch of virtual power plant operators that cannot be ignored. The current methods for dealing with the uncertainty of distributed generation (DG) generation and the uncertainty of EC charging behavior are too conservative, limited, and cannot fully consider the interests of electric vehicle users and renewable energy output at the same time. Summary of the invention

[0004] The embodiment of the present application provides a charging electricity price optimization method and related devices based on master-slave game, which can make EV access more orderly, while fully considering the interests of electric vehicle users and renewable energy output, which is conducive to guiding electric vehicles to charge effectively while solving the instability caused by uncertain renewable energy access.

[0005] In a first aspect, an embodiment of the present application provides a charging price optimization method based on a master-slave game, comprising: Acquire first charging demand information and environmental impact information for the next decision period, wherein the first charging demand information includes charging demand reported by an owner of the electric vehicle, and the environmental impact information includes environmental information that affects charging conditions of the electric vehicle; Determining second charging requirement information based on the first charging requirement information and / or the environmental impact information; 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.

[0006] In a possible embodiment, determining the second charging requirement information based on the first charging requirement information and / or the 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 volume requirement is met, the second charging requirement information is the first charging requirement information; If the information volume 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.

[0007] 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 power demand, the environmental impact information includes a temperature factor and a precipitation factor, and the demand forecasting based on the environmental impact information and the first charging demand information to obtain the forecast charging demand information includes: Performing data standardization based on the temperature factor, the precipitation factor and a preset classification standard to obtain temperature parameters and precipitation parameters; Determine a prediction data set based on the temperature parameter, the precipitation parameter, the first charging period requirement, and the first charging power requirement; The predicted charging demand information is determined based on the prediction data set and a pre-trained charging prediction model.

[0008] In a possible embodiment, 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.

[0009] In a possible embodiment, the power flow constraint condition includes 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.

[0010] In a possible embodiment, the charge amount 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).

[0011] In a possible embodiment, 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.

[0012] In a possible embodiment, solving a 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 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.

[0013] In a second aspect, an embodiment of the present application provides a charging price optimization device based on a master-slave game, comprising: A data acquisition module, used 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, and the environmental impact information includes environmental information that affects the charging condition of the electric vehicle; a demand determination module, configured to determine second charging demand information based on the first charging demand information and / or the environmental impact information; 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.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, comprising 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 program includes instructions for executing some or all of the steps described in the first aspect of the embodiment of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes some or all of the steps described in the first aspect.

[0016] 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 embodiment of the present application. The computer program product may be a software installation package.

[0017] By implementing the embodiment of the present application, first charging demand information and environmental impact information for the next decision period are obtained, wherein 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 that affects the charging condition of the electric vehicle; second charging demand information is determined based on the first charging demand information and / or environmental impact information; a first objective function and a plurality of first constraints are determined based on the second charging demand information and the power plant operation information, wherein the first constraint includes a flow constraint, an operation constraint and / or an electricity price constraint, and the first objective function is used 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 the second objective function and multiple second constraints, the second constraint includes a charging amount constraint and / or a charging condition constraint, and the second objective function is used as a lower function, and the second objective function includes an objective function for calculating the charging cost; based on the flow constraint, a robust constraint is determined, and the robust constraint is used to constrain the 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 constraint, a 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 of the next decision period and the time period corresponding to different electricity prices and / or different powers. In this way, the electricity price and output power that make the difference in interests between operators and users in the next decision period and the corresponding specific time period can be obtained, which can enable electric vehicles to be connected in an orderly manner, thereby reducing the pressure on the economic operation and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0019] Figure 1a is a system architecture diagram of a power system provided in an embodiment of the present application; Figure 1b is a system architecture diagram of another power system provided in an embodiment of the present application; Figure 2 It is a flow chart of a charging electricity price optimization method based on master-slave game provided in an embodiment of the present application; Figure 3 It is a model architecture diagram of a master-slave game model provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the method architecture of a charging electricity price optimization method based on master-slave game provided in an embodiment of the present application; Figure 5It is a structural schematic diagram of a charging electricity price optimization device based on master-slave game proposed in an embodiment of the present application; Figure 6 It is a structural schematic diagram of another charging electricity price optimization device based on master-slave game provided in an embodiment of the present application; Figure 7 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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 part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 optional example also includes steps or units that are not listed, or in an optional example also includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] Electric vehicles (EVs) are an important part of the demand-side load. Their coordinated development with distributed renewable energy is one of the important ways to achieve urban energy development. A large number of EVs randomly connected to virtual power plants for disorderly charging will further aggravate the peak-to-valley difference of system load and bring greater pressure to the economic operation and stability of the power system. At present, most strategies are to guide electric vehicles to charge in an orderly manner under 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) are controlled by virtual power plant operators, and the interests of operators and users are not considered. At the same time, the uncertainty of high-proportion renewable energy output brings risks to virtual power plant operators' dispatching that cannot be ignored. The current methods for dealing with the uncertainty of distributed generation (DG) generation and the uncertainty of EC charging behavior are too conservative, limited, and cannot fully consider the interests of electric vehicle users and renewable energy output at the same time.

[0024] In response to the above problems, the embodiments of the present application provide a charging electricity price optimization method and related devices based on master-slave game, which can obtain the electricity price and output power that meet the interest differences between operators and users in the next decision period and the corresponding specific time period, so that electric vehicles can be connected in an orderly manner, thereby reducing the pressure on the economic operation and stability of the power system.

[0025] See also Figure 1a , Figure 1a is a system architecture diagram of a power system provided in an embodiment of the present application, such as Figure 1a As shown, the power system 100 includes: a decision-making side 110 , a power generation side 120 and a power consumption side 130 .

[0026] Among them, the decision side 110 conducts command, dispatch and control decision for the power system 100, which can be used to monitor the operation of the power system 100 in real time and perform power management and dispatch. Specifically, by collecting a large amount of real-time data from at least the power generation side and the power consumption side 130, including output power, load conditions, user power consumption and other information, the power balance of the entire power grid is accurately monitored. The power generation side 120 is a distributed power source (distributed generation, DG), which can specifically include a variety of renewable energy generation and a variety of non-renewable energy generation. Specifically, renewable energy generation can include solar photovoltaic power generation, wind power generation, hydropower generation, biomass power generation, etc. Non-renewable energy generation can include traditional thermal power generation; and renewable energy has certain uncertainty and volatility. For example, solar photovoltaic power generation is affected by light factors, and wind power generation relies on wind energy to drive turbines to rotate and generate electricity. However, wind speed has strong randomness and volatility. The wind speed may change dramatically in a short period of time. For example, a strong wind may suddenly strike, and then the wind speed drops rapidly. The uncertainty of wind speed leads to unstable output power of wind turbines; hydroelectric power generation may be affected by precipitation and flow, so the renewable energy connected to the power system 100 has certain uncertainty. The power consumption side 130 is mainly for electric vehicles and / or charging piles.

[0027] See also Figure 1b , Figure 1b is a system architecture diagram of another power system provided in an embodiment of the present application, such as Figure 1b As shown, the power system 100 includes: a virtual power plant operator 101, a distributed power source 102 and an electric vehicle 103.

[0028] Among them, the virtual power plant operator 101 regards many distributed power sources 102 as important controllable resources. The virtual power plant operator 101 integrates these dispersed distributed power sources through advanced communication technology and monitoring systems. The virtual power plant operator 101 can optimize the scheduling of distributed power sources 102 and reasonably arrange the output of distributed power sources 102. The virtual power plant operator 101 participates in the demand response management of electric vehicles 103. Distributed power sources 102 and electric vehicles 103 have a complementary and collaborative relationship under the framework of the virtual power plant. Distributed power sources 102 are mainly used as production units of electric energy, while electric vehicles 103 can be consumers of electric energy (when charging).

[0029] Based on this, the present application provides a charging electricity price optimization method and related devices based on master-slave game, and the present application is described in detail below in conjunction with the accompanying drawings.

[0030] See also Figure 2 , Figure 2is a flow chart of a charging electricity price optimization method based on master-slave game provided in an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps: S210, obtaining first charging demand information and environmental impact information in the next decision period, wherein 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 that affects the charging condition of the electric vehicle.

[0031] 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 owner of the electric vehicle. The first charging demand can specifically include the charging time period and charging power within the next decision period. The first charging demand information includes multiple demand information, and the multiple demand information 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.

[0032] S220: Determine second charging requirement information based on the first charging requirement information and / or the environmental impact information.

[0033] The second charging demand information is demand information for substituting into an objective function or boundary condition, and 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.

[0034] 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 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.

[0035] Among them, the first charging demand information includes multiple pieces, and the number of the first charging demand information is the number of demand information actively reported by the car owner. Based on the number of demand information actively reported by the car owner and the preset demand information threshold, it is judged whether the number of the actively reported demand information meets the standard. If the number reaches the preset demand information threshold, the reported first charging demand information can be used as the second charging demand information; if the number does not reach the preset demand information threshold, the demand can be predicted through environmental impact information to determine the predicted charging demand information, and the second charging demand information is determined based on the predicted charging demand information and at least one of the first charging demand information.

[0036] 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 the environmental impact information and the first charging demand information, predicting the future charging demand, and obtaining the predicted charging demand information. The prediction algorithm may be a statistical method, such as linear regression, time series analysis, etc., or a more complex machine learning algorithm, such as a neural network, etc. Then, the predicted charging demand information is fused and analyzed with the first charging demand information to determine the final second charging demand information. The fusion method may be to use the predicted charging demand information as a reference to adjust some parameters in the first charging demand information; or to combine the two according to a certain weight distribution principle to obtain a new demand information set as the second charging demand information. When performing demand prediction, the accuracy and reliability of the prediction need to be considered. The prediction model may be trained and optimized by continuously collecting actual charging data and environmental data to improve the accuracy of the prediction. At the same time, a variety of prediction methods may be used for comparison and verification to select the prediction result that best suits the current situation. When determining the second charging demand information, different weights can be assigned to different factors based on the user's personalized settings or historical behavior data when integrating 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.

[0037] Specifically, the owner's historical charging behavior can be collected, including but not limited to the previous charging time, power, charging speed, selected charging mode (such as standard charging, fast charging, etc.), environmental information at the time (such as temperature, humidity, etc.), and corresponding charging costs. These data can be obtained from the charging record of the device, charging-related applications or smart charging systems. A user preference model is established based on the collected user setting information and historical behavior data. Machine learning algorithms, such as cluster analysis, can be used to classify users with similar preferences. 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.). According to the determined weight factors, the first charging demand information and the predicted charging demand information are weighted and calculated 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 on 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 calculation, the second charging demand information that comprehensively considers the user's preferences can be obtained, which is more in line with the user's actual needs.

[0038] It can be seen that in this embodiment, by obtaining the first charging demand information actively reported by the car 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 of the user side and improve the robustness.

[0039] In a possible embodiment, the charging demand reported by the owner of the electric vehicle includes a first charging time period demand and a first charging power demand, the environmental impact information includes a temperature factor and a precipitation factor, and the demand forecasting 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 time period demand and the first charging power demand; and determining the predicted charging demand information based on the predicted data set and a pre-trained charging prediction model.

[0040] Among them, data normalization can be performed by minimum and maximum normalization, Z-score normalization and other methods, and then the temperature parameters and precipitation parameters are obtained by grading based on the preset grading standards. Then, a prediction data set is formed based on the temperature parameters and precipitation parameters as well as the first charging period demand and the first charging power demand, and the prediction data set is used to input the charging prediction model to determine the predicted charging demand information.

[0041] Specifically, determining the prediction data set based on the temperature parameter, the precipitation parameter, 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 and the temperature factor, the precipitation factor and the historical charging database. Specifically including: constructing a correlation coefficient matrix of the temperature parameters and the precipitation parameters to understand the linear relationship between them. If there is a strong correlation between the temperature and the precipitation, it may be necessary to consider the impact of this relationship on the determination of the principal components in the subsequent principal component analysis. 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 impact of temperature and precipitation on charging time and power.

[0042] For example, the standardized temperature and precipitation and the corresponding time period include the following table, and then calculate the correlation coefficient matrix R between the standardized temperature value and the standardized precipitation value, and the correlation coefficient is 0.3. Then the principal components are determined by solving the eigenvalue and eigenvector. The eigenvalue corresponding to the first principal component is 1.3, and the eigenvector is (0.8, 0.6), which means that this principal component integrates the main information of temperature and precipitation, and the temperature contributes more to it; the eigenvalue corresponding to the second principal component is 0.7, and the eigenvector is (-0.6, 0.8), which means that this principal component reflects other changes in temperature and precipitation to a certain extent. Calculate the contribution rate 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 rate and the degree of influence of each principal component on the charging time and power, the comprehensive influence can be obtained. For example, at a specific moment, assuming that the value of the first principal component is 1.5 and the value of the second principal component is -0.5, then the comprehensive impact on charging time is: 1.5×1-(-0.5)×2=1.5+1=2.5 minutes (shortened by 2.5 minutes); the comprehensive impact on charging power is: 1.5×14-(-0.5)×5=21+2.5=23.5 watts (increased by 23.5 watts). Based on the comprehensive impact, the predicted value directly based on the reported charging demand forecast is further corrected to obtain the final predicted charging demand information.

[0043]

[0044] 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 nonlinear 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 changes in charging demand in different time periods, and can predict future charging demand trends based on historical charging time series data. It can also be a decision tree model, including random forests, gradient boosting trees, etc.

[0045] It can be seen that in this embodiment, by incorporating multi-dimensional factors such as temperature parameters, precipitation parameters, first charging period requirements, and first charging power requirements, the prediction data set can more comprehensively reflect the characteristics of charging requirements, which helps to improve the accuracy of charging demand prediction. Accurate charging demand prediction can improve the rationality of charging resource allocation.

[0046] S230, determining a first objective function and multiple first constraints based on the second charging demand information and the power plant operation information, the first constraints including flow constraints, operation constraints and / or electricity price constraints, and using the first objective function as an 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 multiple second constraints based on the second charging demand information and electric vehicle parameters, the second constraints including charging amount constraints and / or charging condition constraints, and using the second objective function as a lower-level function, the second objective function including an objective function for calculating the charging cost.

[0047] Among them, the power plant operation information may include the cost function of the distributed power source of the virtual power plant operator, the charging price, the charging power, the time-sharing electricity price, the time-sharing electricity purchase price, the electricity sale and purchase power, etc. 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 sale revenue on the basis of meeting the load demand. The first objective function is subject to the first constraint. The decision-making side needs to determine the power generation plan of the distributed power source DG, the power purchase and sale plan of the day-ahead spot market, and the EV charging price in each period. The first constraint may include a flow constraint, and at least one of an operation constraint and an electricity price constraint. The operation constraint is used to constrain the upper and lower limits of the DG's power generation and the increase and decrease rate of the power. The flow constraint is used to constrain the voltage, current, power and other power of each node in the power system 100 to ensure the normal operation of the power system 100. The electricity price constraint is used to constrain the relationship between the electricity purchase price and the electricity sale price to ensure the stability of the virtual power plant operator's income and the charging cost of the car owner. Taking the first objective function as the upper function can coordinate the relationship between the virtual power plant operator cost and other factors in general. On the basis of satisfying the constraints of power flow, operation and electricity price, by optimizing the virtual power plant operator cost, a reasonable economic framework can be provided for the charging optimization problem at the lower level.

[0048] Among them, the electric vehicle parameters include charging power, which can be determined based on the reported second charging demand information, and the second objective function specifically constructs the charging cost of the electric vehicle owner. The second objective function is subject to the second constraint condition, and the second constraint condition can include at least one of the charging amount constraint condition and the charging condition constraint condition. The charging amount is used to constrain the actual charging amount required for charging the electric vehicle EV, and the charging condition constraint condition is used to constrain the upper and lower limits of the charging power and the charging period. Using the second objective function as a lower-level function can optimize specific charging behaviors.

[0049] In a possible embodiment, 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: ; The second objective function includes the following second formula: .

[0050] 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; where, 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.

[0051] It should be noted that in the master-slave game, EV owners are at the bottom and will adjust their charging behavior based on the charging electricity price issued by the virtual power plant operator with the goal of minimizing charging costs. The goal of EV owners is to adjust the charging power so that the charging cost is The lowest, that is, the ninth formula is the orderly charging expression of the owner of the i-th EV.

[0052] The first objective function is determined according to the optimization scheduling requirements of the virtual power plant operator, which is to reduce the power supply cost and increase the power sales revenue on the basis of meeting the load demand. The virtual power plant operator needs to decide the DG power generation plan, the power purchase and sales plan in the day-ahead spot market, and the EV charging electricity price in each period.

[0053] Among them, time period t is the length of the unit time period included in the next decision period, and the decision period includes at least one time period t; the cost function of the distributed power source DG of the i-th electric vehicle EV refers to the mathematical expression of the various costs generated by the distributed power source that provides power for the i-th electric vehicle during operation. These costs may include fuel costs (if it is fuel-fired power generation), equipment depreciation costs, maintenance costs, operating costs (such as labor costs), etc.; the active output of the distributed power source DG corresponding to the i-th electric vehicle EV refers to the active power actually output by the distributed power source, specifically including the actual electric power provided by the distributed power source to drive the electric vehicle or charge its battery; the charging price of the electric vehicle EV in the t period is the electricity price required to charge the electric vehicle in the t period; the charging power of the i-th electric vehicle EV is the charging power for charging the electric vehicle in the t 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 price of virtual power plant operators in the day-ahead spot market is: As a seller, virtual power plant operators will determine the electricity selling price at different times based on market expectations, the cost of their own power generation resources (including electric vehicle distributed power sources and other distributed power generation resources), the load demand of the power grid and other factors; the time-of-use electricity purchase price reflects the cost of the virtual power plant operator to obtain electricity from the external power grid. The charging cost of electric vehicles (EVs) is the electricity fee that needs to be paid when charging electric vehicles, and the charging power is the corresponding power purchased.

[0054] It can be seen that in this embodiment, the first objective function of the upper layer takes into account the optimization of distributed power sources (DG), which can reasonably allocate the output power of each distributed power source, reduce operating costs, and increase profit margins. The output power can be adjusted in real time according to the needs of the power grid to make up for the power shortage or surplus in the power grid. The second objective function of the lower layer focuses on the charging cost of electric vehicles EV, guiding users to choose to charge during periods with lower electricity prices, making the load distribution of the power grid more uniform and improving the overall operating efficiency of the power system.

[0055] In a possible embodiment, the power flow constraint condition includes 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.

[0056] It should be noted that the robust optimization scheduling of the virtual power plant operator requires that the system can meet the node voltage security in the worst case of uncertainty in the output of intermittent renewable energy. It mainly affects the node voltage safety constraint. The fourth formula can be used as an abbreviation of the third formula. yes , as well as function. and Can together constitute the node equivalent load , .

[0057] It can be seen that in this embodiment, by constraining the first objective function through multiple constraints, it is possible to ensure the safe and stable operation of the power system, improve resource utilization efficiency, and promote the consumption of renewable energy.

[0058] In a possible embodiment, the charge amount constraint condition includes the following ninth formula: ; The charging condition constraint condition includes the following tenth formula: .

[0059] in, is the battery capacity of the electric vehicle EV, The initial state when charging the electric vehicle EV battery, is the charging efficiency; where, 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).

[0060] Among them, the charging amount constraint is used to constrain the EV to be fully charged within the charging period, and the charging condition constraint means that the EV charging power is not allowed to exceed the limit and can only be charged during the allowed period.

[0061] It can be seen that in this embodiment, the electric vehicle can be fully charged while constraining the charging power and charging time, which can stabilize the operation of the power grid, balance the power grid load, balance the access of distributed energy DG and the power fluctuation caused by electric vehicle charging, and improve user satisfaction.

[0062] S240, determining a robust constraint condition based on the power flow constraint condition, where the robust constraint condition is used to constrain voltage safety of nodes in a circuit.

[0063] It should be noted that there are various uncertain factors in the actual operation of the power system, such as load fluctuations, generator failures, line parameter changes, etc. These uncertain factors may affect the power flow distribution, which in turn causes the node voltage to exceed the limit, threatening the safe and stable operation of the power system. Therefore, it is necessary to introduce robust constraints to deal with these uncertainties and ensure that the node voltage can be kept within the safe range under various possible circumstances.

[0064] In a possible embodiment, a robust constraint condition is determined based on the power flow constraint condition, including: determining 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; and determining the robust constraint condition based on the robust equation and strong duality theory.

[0065] If n is the end node of the system, then and , put it into the first two equations of the third formula, that is, and , and further expand it to get the twelfth formula: ; According to the twelfth formula, the relationship between the line transmission power and the equivalent load of each node can be obtained. And the relationship between the voltage amplitude and the branch transmission power in the third formula is further expanded to obtain the thirteenth formula: ; Substituting the fifth and twelfth formulas into the thirteenth formula, we can obtain the matrix expression of the voltage change of each node and the power of each device, that is, the voltage-power relationship matrix, which is expressed by the fourteenth and fifteenth formulas: Fourteenth formula: ; The fifteenth formula: ; in, is a column vector composed of the voltage amplitudes of each node, Indicates that all elements are An n-dimensional column vector, where n is the number of nodes; and is the coefficient matrix corresponding to the thirteenth formula; for Branch-node association matrix, is the number of branches, indicating the correlation between branch transmission power and node load, is the active load matrix of the node, is the reactive load matrix of the node. Substituting the fourteenth formula into the fourth formula, separating the control variables and the uncertain variables, the voltage security constraint of the system can be transformed into the first robust equation, which is expressed by the sixteenth formula: Formula 16: ; in, for n The diagonal matrix of order represents the maximum DG fluctuation of each node. represents the robust control coefficient, which is n dimensional column vector, (i,:) represents all elements in the i-th row, represents the coefficient of the first constant term obtained by substituting the fourteenth formula into the fourth formula, as the upper limit of the voltage safety constraint, It represents the coefficient of the second constant term obtained by substituting the fourteenth formula into the fourth formula as the lower limit of the voltage safety constraint. When the robust control coefficient takes values ​​between its maximum and minimum values, different levels of robustness optimization schemes can be obtained. At this time, the virtual power plant operator can achieve a balance between robustness and economy by setting robust control parameters. Then, the strong duality theory is used to eliminate the uncertain variables in the formula, and the first robust equation is transformed into the second robust equation containing only deterministic variables, that is, the final robust equation, which is expressed by the seventeenth formula: Formula 17: ; in, , , , for n The dual variable matrix of order.

[0066] It can be seen that in this embodiment, the robust constraints obtained through the above-mentioned robust constraint determination process can ensure that the power system ensures that the node voltage is always within a safe range under the influence of various uncertain factors, which helps to maintain the voltage stability of the power system and ensure the normal operation of the power system. In addition, the strong duality theory is used to eliminate uncertain variables and transform them into robust equations containing only deterministic variables, which greatly reduces the complexity of the problem. The robust equation transformation can provide corresponding optimization solutions according to the needs of different users to meet diverse needs.

[0067] S250, 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 of the next decision period and the time periods corresponding to different electricity prices and / or different powers.

[0068] Among them, see Figure 3, Figure 3 It is a model architecture diagram of a master-slave game model provided in an embodiment of the present application. The master-slave game is a hierarchical decision-making model in which 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 method may include iterative algorithm, gradient descent method, interior point method, etc. The basic idea of ​​these methods is to make the objective function gradually approach the optimal value by continuously adjusting the value of the decision variable under the premise of satisfying the constraints. The target optimization parameters finally obtained may include electricity price, power, and corresponding time period, which can be used to formulate the strategy for the next decision period.

[0069] In a possible embodiment, the master-slave game model is solved based on the first objective function, the second objective function, the multiple first constraints, the multiple second constraints and the robust constraints to determine the target optimization parameters, including: determining a 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 integrates the upper function and the lower function, and the optimization theory includes at least one of the duality theory and the KKT theory; solving based on the third objective function, the first constraint, the second constraint and the robust constraint to determine the target optimization parameters, 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; 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 .

[0070] Wherein, determining the third objective function based on the first objective function, the second objective function and the optimization theory includes: determining the objective function of the virtual power plant operator based on the first objective function and the second objective function, the KKT (Karush-Kuhn-Tucker) condition and the duality theory, which is expressed by the eighteenth formula: ; Among them, the above formula 18 contains the The min nonlinear term of cannot be solved directly. The dual problem of EV user optimization can be expressed as the 19th, 20th and 21st formulas: Formula 19: , The 20th formula: , Formula 21: ; The 20th and 21st formulas are used to constrain the 19th formula. At this time, the optimization goal of the virtual power plant operator can be equivalent to the above 11th formula.

[0071] Since the constraints 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 slack theorem to obtain the relationship between the original variables and the dual variables. is the shadow price that can be considered as the upper limit constraint of charging power. It means that by increasing the upper limit of EV charging, EV owners can get more benefits, and the EV charging power is at the maximum value at this time. When , the EV charging power is 0. Assume that EV charging requires In order to minimize the charging cost, EV will charge when the electricity price is the lowest. Maintain maximum charging power during the period of time, when the electricity price is the highest Keep the charging power at 0, and the remaining period is used to fill the insufficient charging demand. Therefore, the constraint relationship between the dual variable and the original variable can be obtained, which is expressed by the twenty-second formula; based on the above ninth and eleventh formulas, the power in the target optimization parameter can be obtained, and then the cost and the corresponding period can also be obtained.

[0072] Formula 22: .

[0073] It can be seen that in this embodiment, by converting the double-layer function into a single-layer and converting the nonlinearity into linearity, unreasonable decisions caused by considering only the interests of one party are avoided, the structure of the model is simplified, and less time and resources are required in the calculation process. Virtual power plant operators can use the fast-solving optimization model to quickly adjust the charging strategy to obtain lower charging costs and higher profits.

[0074] Among them, see Figure 4 , Figure 4 is a schematic diagram of the method architecture of a charging electricity price optimization method based on master-slave game provided in an embodiment of the present application; Figure 4 As shown, first, input information is obtained, and 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 and the second objective function of the lower layer, as well as the corresponding first constraint condition, second constraint condition, and robust constraint condition. The first objective function of the upper layer and the second objective function of the lower layer are fused to obtain the third objective function, and the target optimization parameters can be obtained based on the third objective function.

[0075] It can be seen that through Figure 2In an embodiment, first charging demand information and environmental impact information are obtained for the next decision period, wherein 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 that affects the charging condition 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 constraints are determined based on the second charging demand information and the power plant operation information, wherein the first constraint includes a flow constraint, an operation constraint and / or an electricity price constraint, and the first objective function is used 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 operation information, a first objective function and a plurality of first constraints are determined. The second objective function and multiple second constraints are determined based on the vehicle parameters, the second constraint includes a charging amount constraint and / or a charging condition constraint, and the second objective function is used as a lower function, and the second objective function includes an objective function for calculating the charging cost; based on the flow constraint, a robust constraint is determined, and the robust constraint is used to constrain the 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 constraint, a 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 of the next decision period and the time period corresponding to different electricity prices and / or different powers. In this way, the electricity price and output power that make the difference in interests between operators and users in the next decision period and the corresponding specific time period can be obtained, which can make electric vehicles connected in an orderly manner, thereby reducing the pressure on the economic operation and stability of the power system.

[0076] See also Figure 5 , Figure 5 is a 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 solution module 550, wherein: The data acquisition module 510 is used to 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, and the environmental impact information includes environmental information that affects the charging condition of the electric vehicle; A demand determination module 520, configured to determine second charging demand information based on the first charging demand information and / or the environmental impact information; A function determination module 530 is used 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 the first objective function is used 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 the second objective function is used as a lower function, wherein the second objective function includes an objective function for calculating the charging cost; A robust determination module 540, configured 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 a node in a circuit; The model solving module 550 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.

[0077] In a possible embodiment, the demand determination module 520, in determining the second charging demand information based on the first charging demand information and / or the environmental impact information, is specifically configured to: 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 volume requirement is met, the second charging requirement information is the first charging requirement information; If the information volume 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.

[0078] 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 power demand, the environmental impact information includes a temperature factor and a precipitation factor, and the demand determination module 520 performs demand forecasting based on the environmental impact information and the first charging demand information to obtain the forecast charging demand information, specifically for: Performing data standardization based on the temperature factor, the precipitation factor and a preset classification standard to obtain temperature parameters and precipitation parameters; Determine a prediction data set based on the temperature parameter, the precipitation parameter, the first charging period requirement, and the first charging power requirement; The predicted charging demand information is determined based on the prediction data set and a pre-trained charging prediction model.

[0079] In a possible embodiment, the robust determination module 540 is specifically configured to: 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.

[0080] In a possible embodiment, the robust determination module 540 is specifically 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 to determine the target optimization parameters: 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.

[0081] It is worth pointing out that the specific functional implementation of the charging electricity price optimization device based on the master-slave game can be found in the above Figure 2The description of the charging electricity price optimization method based on master-slave game shown in the figure, for example, the data acquisition module is used to implement the relevant content of executing S210. The various units or modules in the charging electricity price optimization device 500 based on master-slave game can be respectively or completely merged into one or several other units or modules to constitute, or one (some) of the units or modules can be further divided into multiple functionally smaller units or modules to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0082] It can be seen that the charging electricity price optimization device based on master-slave game described in the embodiment of the present application obtains the first charging demand information and environmental impact information in 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 environmental information that affects the charging situation of the electric vehicle; determines the second charging demand information based on the first charging demand information and / or the environmental impact information; determines the first objective function and multiple first constraints based on the second charging demand information and the power plant operation information, the first constraint condition includes a flow constraint condition, an operation constraint condition and / or an electricity price constraint condition, and uses the first objective function as an upper-level function, the first objective function includes an objective function for calculating the cost of the virtual power plant operator; and, based The second objective function and multiple second constraints are determined based on the second charging demand information and the electric vehicle parameters, wherein the second constraint includes a charging amount constraint and / or a charging condition constraint, and the second objective function is used as a lower function, wherein the second objective function includes an objective function for calculating the charging cost; a robust constraint is determined based on the power flow constraint, wherein the robust constraint 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 multiple first constraints, the multiple second constraints, and the robust constraint, and the target optimization parameters are determined, wherein the target optimization parameters are used to formulate the electricity price and / or power of the next decision period and the time period corresponding to different electricity prices and / or different powers. In this way, the electricity price and output power that make the interest difference between the operator and the user in the next decision period and the corresponding specific time period can be obtained, so that electric vehicles can be connected in an orderly manner, thereby reducing the pressure on the economic operation and stability of the power system.

[0083] In the case of integrated units, see Figure 6 , Figure 6 is a structural diagram of another charging electricity price optimization device based on master-slave game provided in an embodiment of the present application, such as Figure 6 As shown, the charging electricity price optimization device 500 based on 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 electricity price optimization device 500 based on the master-slave game, for example, executing the steps of the data acquisition module 510, the demand determination module 520, the function determination module 530, the robust determination module 540, the model solution module 550, and / or other processes for executing the technology described in this article. The communication module 501 is used for the interaction between the charging electricity price optimization device 500 based on the master-slave game and other devices. As shown in FIG. Figure 6 As shown, the charging electricity price optimization device 500 based on master-slave game may further include a storage module 503, and the storage module 503 is used to store program codes and data of the charging electricity price optimization device 500 based on master-slave game.

[0084] Among them, the processing module 502 can be a processor or a controller, for example, a central processing unit (CPU), a general 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 logic boxes, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 501 can be a transceiver, an RF circuit or a communication interface, etc. The storage module 503 can be a memory.

[0085] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here. The above charging price optimization device 500 based on master-slave game can execute the above Figure 2 The charging electricity price optimization method based on master-slave game is shown.

[0086] See also Figure 7 , Figure 7 It is a structural diagram of an electronic device proposed in an embodiment of the present 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 one or more programs 721 are stored in the memory 720 and are configured to be executed by the processor 710.

[0087] The processor 710, the memory 720, and the communication interface 730 are interconnected and perform communication work with each other; 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 memory 720 is used to store a set of executable program codes, and the processor 710 is used to call one or more programs 721 stored in the memory 720 to execute the above-mentioned Figure 2 Part or all of the steps of any charging electricity price optimization method based on master-slave game recorded in the embodiments.

[0088] Among them, the electronic device 700 may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, driving recorders, vehicle-mounted electronic devices, servers, laptops, mobile Internet electronic devices (MID, Mobile Internet Devices) or wearable electronic devices (such as smart watches, Bluetooth headsets), etc. The above are only examples and not exhaustive, including but not limited to the above electronic devices.

[0089] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0090] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0091] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0092] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in 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 schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0094] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the above-mentioned 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 is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer electronic device (which can be a personal computer, electronic device or network electronic device, etc.) to perform all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0097] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0098] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on 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 charging demand reported by an owner of the electric vehicle, and the environmental impact information includes environmental information that affects charging conditions of the electric vehicle; Determining second charging requirement information based on the first charging requirement information and / or the environmental impact information; 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 determining the second charging requirement information based on the first charging requirement information and / or the 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 volume requirement is met, the second charging requirement information is the first charging requirement information; If the information volume 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.

3. The method according to claim 2, characterized in that The charging demand reported by the owner of the electric vehicle includes a first charging period demand and a first charging power demand, the environmental impact information includes a temperature factor and a precipitation factor, and the demand forecasting based on the environmental impact information and the first charging demand information to obtain the forecast charging demand information includes: Performing data standardization based on the temperature factor, the precipitation factor and a preset classification standard to obtain temperature parameters and precipitation parameters; Determine a prediction data set based on the temperature parameter, the precipitation parameter, the first charging period requirement, and the first charging power requirement; The predicted charging demand information is determined based on the prediction data set and a pre-trained charging prediction model.

4. 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.

5. The method according to claim 4, 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.

6. The method according to claim 4, characterized in that The charging amount 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. The upper limit of charging power for electric vehicles (EV).

7. The method according to claim 5, 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.

8. The method according to claim 1, 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.

9. A charging electricity price optimization device based on master-slave game, characterized in that: include: A data acquisition module, used 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, and the environmental impact information includes environmental information that affects the charging condition of the electric vehicle; a demand determination module, configured to determine second charging demand information based on the first charging demand information and / or the environmental impact information; 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.

10. 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 8.

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