Method and device for pricing charging and discharging prices of electric vehicle charging stations in industrial and commercial parks

Abstract

The invention relates to a charging and discharging price pricing method and device for an electric vehicle charging station in an industrial and commercial park, and the method comprises the steps: determining the charging and discharging energy cost and the battery aging cost corresponding to each vehicle type in a target park; according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost, constructing a vehicle owner charging and discharging power curve decision model of each target electric vehicle under each vehicle type; and determining the electricity market electricity purchase cost of the target park, the vehicle owner electricity interaction cost and the number of the target electric vehicles corresponding to each vehicle type, constructing a park optimization decision model of the target park in combination with the vehicle owner charging and discharging power curve decision model, and solving the park optimization decision model to obtain target charging and discharging price pricing information of the target park. Therefore, the problems that an existing charging station pricing method does not use the game theory to carry out modeling on benefit conflicts among all parties, it is difficult to effectively consider the battery aging cost, and reasonable electric vehicle service pricing cannot be carried out are solved.

Description

Technical Field

[0001] The present application relates to the technical field of charging station pricing, and in particular to a method and device for pricing charging and discharging prices of electric vehicle charging stations in industrial and commercial parks. Background Art

[0002] Vehicle to Grid (V2G) interaction can achieve multiple functions such as peak load shaving, frequency modulation, and promotion of renewable energy consumption. Among them, electric vehicles (EV) have strong application potential in industrial and commercial park scenarios. They can be connected to industrial and commercial parks as a flexible resource to promote the efficient use of energy in park microgrids.

[0003] Since the interests of the park and the car owners are inconsistent, it is not appropriate to simply consider the problem of balancing the interests between the park and the individual EVs as the park completely and arbitrarily regulating the electric vehicles. It is necessary to introduce game theory to analyze the behavior of all parties. The existing technology can use an optimization scheduling method for electric vehicles to participate in peak shaving that considers the interests of the power grid, electric vehicle aggregators, and electric vehicles. It is simply modeled as a multi-objective optimization and converted to a single-objective optimization through weighted sum, but it does not use game theory to model the conflicts of interest between the parties, and the model is too simplified and not accurate; in addition, the existing technology can also consider the willingness of EV users to participate in V2G, but does not consider guiding users by adjusting prices, nor does it use game theory to model and analyze; the existing technology can also model the interaction between aggregators and car owners through master-slave games, and use aggregation games to model the interaction between car owners to propose an optimization scheduling method for electric vehicles to participate in secondary frequency regulation, but it does not consider the discharge of electric vehicles.

[0004] Furthermore, the pricing of electric vehicle charging and discharging in industrial and commercial parks also faces the problem of how to price electric vehicle V2G services. Existing technologies can consider price incentives for V2G, but their incentive prices are based on rules rather than optimization, and they do not explain the rationality of the rules; some existing technologies can also analyze and describe the medium-term flexibility costs of electric vehicles through opportunity costs, or propose a flexibility cost formula based on the adjustable range of power and accumulated electricity for flexible loads, but when considering the cost of electric vehicles, they only consider the charging scenario, and do not consider the costs of battery aging during discharge, so it is difficult to directly apply to the discharge scenario.

[0005] It is understandable that battery aging is the main difficulty limiting V2G applications. Existing technologies have found that high-frequency V2G services will significantly shorten the life of electric vehicle batteries. Therefore, when considering V2G services, it is necessary to consider the life and degradation of electric vehicle batteries. Therefore, some existing technologies can use the rain flow cycle counting method to quantify battery aging and propose a scheduling method with minimizing battery aging as one of the optimization goals; in addition, existing technologies can also use the battery active material loss model to describe battery aging, and based on this, a V2G frequency modulation scheduling method that can actively suppress battery aging is proposed, but most of the models used are aging models and are relatively complex, making it difficult to embed them into the optimization model for rapid solution.

[0006] In summary, the existing charging station pricing method does not use game theory to model the conflicts of interest between the parties, and it is difficult to effectively consider the cost of battery aging, making it impossible to reasonably price electric vehicle services, which needs to be urgently resolved. Summary of the invention

[0007] The present application provides a charging and discharging price pricing method and device for electric vehicle charging stations in industrial and commercial parks to solve the problems that the existing charging station pricing method does not use game theory to model the conflicts of interest between the parties, and it is difficult to effectively consider the battery aging cost and cannot reasonably price electric vehicle services.

[0008] The first aspect of the present application provides a charging and discharging price pricing method for an electric vehicle charging station in an industrial and commercial park, comprising the following steps: determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle model in the target park; constructing a charging and discharging power curve decision model for each target electric vehicle owner under each vehicle model based on the charging and discharging price information, the charging and discharging energy cost and the battery aging cost; determining the electricity market purchase cost, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each vehicle model of the target park, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, constructing a park optimization decision model for the target park, and solving the park optimization decision model to obtain the target charging and discharging price pricing information for the target park.

[0009] Optionally, in one embodiment of the present application, determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle model in the target park includes: obtaining a set of residence times corresponding to any vehicle model among all vehicle models, and obtaining the residence time corresponding to each target electric vehicle through the residence time set; calculating the charging and discharging energy cost of each target electric vehicle based on the charging and discharging price information and the residence time; determining the battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs corresponding to each target electric vehicle, and calculating the battery aging cost through the residence time, the battery attenuation ratio, the battery capacity attenuation information, and the total battery purchase and replacement costs.

[0010] Optionally, in one embodiment of the present application, the owner's charging and discharging power curve decision model for each target electric vehicle under each model is constructed according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost, including: obtaining the maximum charging and discharging power, upper and lower limits of battery power, initial battery power and target battery power corresponding to any model among all models; determining the owner's objective function corresponding to each target electric vehicle according to the charging and discharging energy cost, the battery aging cost, a preset power fluctuation penalty and an energy shortage penalty; based on the residence time set, the maximum charging and discharging power, the upper and lower limits of battery power, the initial battery power and the target battery power, determining the charging and discharging complementary constraints and the upper and lower limit constraints of battery power corresponding to the owner's objective function, so as to construct the owner's charging and discharging power curve decision model according to the charging and discharging complementary constraints, the upper and lower limit constraints of battery power and the owner's objective function.

[0011] Optionally, in one embodiment of the present application, the park optimization decision model of the target park is constructed based on the number of cars, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, and the park optimization decision model is solved to obtain the target charging and discharging price pricing information of the target park, including: determining the electricity market purchase cost corresponding to the target park in the day-ahead stage; obtaining the charging service income and the owner's discharge subsidy corresponding to each target electric vehicle, and calculating the owner's electricity interaction cost corresponding to the target park based on the number of cars, the charging service income and the owner's discharge subsidy; determining the park objective function of the target park in the day-ahead stage according to the electricity market purchase cost and the owner's electricity interaction cost, and setting the price level constraint of the target park based on a preset target price level interval; constructing the park optimization decision model based on the park objective function and the price level constraint; solving the park optimization decision model by a preset heuristic algorithm to obtain the target charging and discharging price pricing information of the target park.

[0012] The second aspect of the present application provides a charging and discharging price pricing device for an electric vehicle charging station in an industrial and commercial park, including: a determination module, used to determine the charging and discharging energy cost and battery aging cost corresponding to each vehicle model in the target park; a modeling module, used to construct a car owner's charging and discharging power curve decision model for each target electric vehicle under each vehicle model according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost; a pricing module, used to determine the target park's electricity market purchase cost, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each vehicle model, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, construct a park optimization decision model for the target park, and solve the park optimization decision model to obtain the target charging and discharging price pricing information for the target park.

[0013] Optionally, in one embodiment of the present application, the determination module includes: a first acquisition unit, used to acquire a set of residence times corresponding to any model among all model types, and obtain the residence time corresponding to each target electric vehicle through the residence time set; a first calculation unit, used to calculate the charging and discharging energy cost of each target electric vehicle based on the charging and discharging price information and the residence time; a second calculation unit, used to determine the battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs corresponding to each target electric vehicle, and calculate the battery aging cost through the residence time, the battery attenuation ratio, the battery capacity attenuation information and the total battery purchase and replacement costs.

[0014] Optionally, in one embodiment of the present application, the modeling module includes: a second acquisition unit, used to obtain the maximum charging and discharging power, upper and lower limits of battery power, initial battery power and target battery power corresponding to any model of all models; a first setting unit, used to determine the owner's objective function corresponding to each target electric vehicle according to the charging and discharging energy cost, the battery aging cost, the preset power fluctuation penalty and the energy shortage penalty; a first construction unit, used to determine the charging and discharging complementary constraints and the upper and lower limit constraints of battery power corresponding to the owner's objective function based on the residence time set, the maximum charging and discharging power, the upper and lower limits of battery power, the initial battery power and the target battery power, so as to construct the owner's charging and discharging power curve decision model according to the charging and discharging complementary constraints, the upper and lower limit constraints of battery power and the owner's objective function.

[0015] Optionally, in one embodiment of the present application, the pricing module includes: a cost unit, used to determine the electricity purchase cost of the target park in the power market corresponding to the day-ahead stage; a third calculation unit, used to obtain the charging service income and the owner's discharge subsidy corresponding to each target electric vehicle, so as to calculate the owner's electricity interaction cost corresponding to the target park based on the number of cars, the charging service income and the owner's discharge subsidy; a second setting unit, used to determine the park objective function of the target park in the day-ahead stage according to the electricity purchase cost of the power market and the owner's electricity interaction cost, and set the price level constraint of the target park based on a preset target price level interval; a second construction unit, used to construct the park optimization decision model based on the park objective function and the price level constraint; a solving unit, used to solve the park optimization decision model through a preset heuristic algorithm to obtain the target charging and discharging price pricing information of the target park.

[0016] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks as described in the above embodiments.

[0017] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for pricing charging and discharging prices of electric vehicle charging stations in industrial and commercial parks.

[0018] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks.

[0019] Therefore, the embodiments of the present application have the following beneficial effects:

[0020] The embodiments of the present application can determine the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the target park; construct a charging and discharging power curve decision model for each target electric vehicle owner under each vehicle type according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost; determine the target park's electricity market purchase cost, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each vehicle type, and based on the number of cars, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, construct a park optimization decision model for the target park, and solve the park optimization decision model to obtain the target charging and discharging price pricing information of the target park. The present application is based on the park optimization and pricing method of the master-slave game, so as to give the owner appropriate incentives through a suitable pricing method, so that he is willing to participate in V2G, so as to improve the overall energy utilization efficiency of the park. Thus, it solves the problems that the existing charging station pricing method does not use game theory to model the conflicts of interest between the parties, and it is difficult to effectively consider the battery aging cost, and it is impossible to reasonably price electric vehicle services.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flowchart of a method for pricing charging and discharging prices of electric vehicle charging stations in an industrial and commercial park according to an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the logical architecture of a charging and discharging price pricing method for an electric vehicle charging station in an industrial and commercial park provided for one embodiment of the present application;

[0025] Figure 3 This is an example diagram of a charging and discharging price pricing device for an electric vehicle charging station in an industrial and commercial park according to an embodiment of the present application;

[0026] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0027] Among them, 10-charging and discharging price pricing device of electric vehicle charging stations in industrial and commercial parks, 100-determination module, 200-modeling module, 300-pricing module, 401-memory, 402-processor, 403-communication interface. DETAILED DESCRIPTION

[0028] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the charging and discharging price pricing method and device of the electric vehicle charging station in the industrial and commercial park of the embodiment of the present application with reference to the accompanying drawings. In view of the problems mentioned in the above background technology, the present application provides a charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks. In the method, by determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle model in the target park; constructing the owner's charging and discharging power curve decision model for each target electric vehicle under each vehicle model according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost; determining the target park's electricity market purchase cost, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each vehicle model, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, construct the park optimization decision model of the target park, and solve the park optimization decision model to obtain the target charging and discharging price pricing information of the target park. The present application is based on the park optimization and pricing method of the master-slave game, so as to give the owner appropriate incentives through a suitable pricing method, so that he is willing to participate in V2G, so as to improve the overall energy utilization efficiency of the park. This solves the problems that the existing charging station pricing method does not use game theory to model the conflicts of interest between the parties, and it is difficult to effectively consider the battery aging costs and cannot reasonably price electric vehicle services.

[0030] In order to facilitate those skilled in the art to understand the execution logic of the charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks of the present application, the subscript symbols, parameters and variables involved in the present application are explained as follows.

[0031] Among them, the subscripts and their meanings are shown in Table 1:

[0032] Table 1

[0033]

[0034] The parameters and their meanings are shown in Table 2:

[0035] Table 2

[0036]

[0037] The variables and their meanings are shown in Table 3:

[0038] Table 3

[0039]

[0040]

[0041] Specifically, Figure 1 A flowchart of a method for pricing charging and discharging prices of electric vehicle charging stations in an industrial and commercial park provided in an embodiment of the present application.

[0042] like Figure 1 As shown, the charging and discharging price pricing method of the electric vehicle charging station in the industrial and commercial park includes the following steps:

[0043] In step S101, the charging and discharging energy cost and the battery aging cost corresponding to each vehicle type in the target park are determined.

[0044] It can be understood that the embodiments of the present application are mainly intended to solve the charging and discharging pricing problem of industrial and commercial parks. Therefore, the embodiments of the present application may assume that the park is a price acceptor in the electricity spot market, and it is necessary to optimize decisions and determine the market declaration quantity in the day-ahead stage.

[0045] Electric vehicles can be considered as energy storage resources, providing peak load shaving and valley filling services. However, the ownership of electric vehicles lies with employees and external charging customers, and the park cannot arbitrarily dispatch them. It can only guide the charging and discharging behavior of car owners by setting the price of electric vehicle services.

[0046] However, the goals of the car owner and the park are not consistent. Therefore, the embodiment of the present application models the game relationship between the park and the car owner through a master-slave game model, such as Figure 2 As shown, the park makes decisions and gives prices before the car owner, so the park is in a leading position in the master-slave game.

[0047] It should be noted that the embodiments of the present application first need to determine the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the target park, so as to provide reliable data support for the construction of the charging and discharging power curve decision model for electric vehicle owners.

[0048] Optionally, in one embodiment of the present application, the charging and discharging energy cost and battery aging cost corresponding to each vehicle model in the target park are determined, including: obtaining a set of residence times corresponding to any vehicle model among all vehicle models, and obtaining the residence time corresponding to each target electric vehicle through the residence time set; calculating the charging and discharging energy cost of each target electric vehicle based on the charging and discharging price information and the residence time; determining the battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs corresponding to each target electric vehicle, and calculating the battery aging cost through the residence time, battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs.

[0049] In the actual implementation process, the embodiment of the present application may assume that when an electric vehicle is connected to a park for charging, the park prices the charging and discharging behaviors as follows: And record the arrival and departure time of the electric vehicle as t arr ,t dep , the residence time is The cost of charging and discharging electric vehicles is C charge for:

[0050]

[0051] In addition, charging and discharging of electric vehicles will cause battery aging, and the cost of battery aging is C deg It can be expressed as:

[0052]

[0053] Among them, Q loss Indicates battery capacity attenuation; k aban The allowable attenuation ratio relative to the maximum battery capacity is generally taken as 20%. If the cumulative attenuation ratio of the battery reaches this value, the battery needs to be replaced; C B The total cost of purchasing and replacing batteries for electric vehicles.

[0054] It should be noted that battery cycle aging is related to temperature, charge and discharge rate, and total charge throughput. The embodiment of the present application may adopt a simplified idea that is easy to embed and optimize, that is, it is considered that battery aging can be expressed as a linear function of discharge energy (or the sum of discharge and charge), then:

[0055]

[0056] Among them, the coefficient k deg It indicates the battery capacity attenuation caused by each 1kWh of discharge, in kWh. A constant coefficient can be used, or k can be updated after the optimization is completed based on the actual charge and discharge amount, charge and discharge depth, current rate and other influencing factors. deg , and then optimize, and iterate.

[0057] Therefore, the embodiment of the present application effectively ensures the reliability of the subsequently constructed charging and discharging power curve decision model for electric vehicle owners by calculating the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the park.

[0058] In step S102, a charging and discharging power curve decision model for each owner of each target electric vehicle under each vehicle type is constructed according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost.

[0059] Furthermore, the embodiments of the present application can construct a decision model for electric vehicle owners to minimize charging costs (i.e., a charging and discharging power curve decision model for owners) based on the charging and discharging energy costs and the battery aging costs, and the electric vehicle owners can decide the charging and discharging power curve P reported to the park. t c / d .

[0060] Optionally, in one embodiment of the present application, a charging and discharging power curve decision model for the owner of each target electric vehicle under each model is constructed according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost, including: obtaining the maximum charging and discharging power, the upper and lower limits of the battery power, the initial battery power and the target battery power corresponding to any model among all models; determining the owner's objective function corresponding to each target electric vehicle according to the charging and discharging energy cost, the battery aging cost, the preset power fluctuation penalty and the energy shortage penalty; based on the residence time set, the maximum charging and discharging power, the upper and lower limits of the battery power, the initial battery power and the target battery power, determining the charging and discharging complementary constraints and the upper and lower limit constraints of the battery power corresponding to the owner's objective function, so as to construct the owner's charging and discharging power curve decision model according to the charging and discharging complementary constraints, the upper and lower limit constraints of the battery power and the owner's objective function.

[0061] Specifically, the embodiment of the present application may assume that for any type of electric vehicle i, its stay period set is Maximum charge and discharge power P i c / d,max , Battery power upper and lower limits And the initial and target battery levels (i.e. the final battery levels) is a known, given constant. For convenience of representation, the subscript i may be omitted in the following embodiments of the present application, and the optimization decision model of type i electric vehicle is as follows:

[0062] minC charge +C deg +C penalty (4)

[0063]

[0064] Among them, formula (4) indicates that the goal of electric vehicle owners is to minimize the energy cost C including charging and discharging charge 、Battery aging cost C deg and penalty term C penalty, where the penalty term includes power fluctuation penalty and energy shortage penalty; as shown in formula (5), it is intended to encourage electric vehicles to reduce power changes and reach the target power as quickly as possible. w1 and w2 in formula (5) are two smaller weighting coefficients; formula (8) is the charge and discharge complementary constraint; formula (9) represents the calculated battery power; formula (10) represents the initial battery power; formula (11) represents the terminal battery power, and the terminal battery power is related to the charge and discharge price. If the charging price is high, the electric vehicle tends to charge less: formula (12) is the upper and lower limit constraints of the battery power.

[0065] It should be noted that the above formula (8) can be linearized by introducing integer variables and using the big M method, so that the final electric vehicle decision optimization model is mixed integer linear programming (MILP).

[0066] In actual interaction, the park sends charging and discharging price information to electric vehicles, and electric vehicles optimize the charging and discharging power accordingly. It can be seen that this optimization process actually constitutes right The mapping of , and the optimization can be simplified as:

[0067]

[0068] It is understandable that battery aging is a key factor affecting the car owner's V2G decision. Therefore, the embodiment of the present application adopts a simplified calculation method in which battery aging is proportional to the charge and discharge amount, so that it can be well embedded in the optimization for calculation.

[0069] In step S103, the electricity market purchase cost of the target park, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each model are determined, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, the park optimization decision model of the target park is constructed, and the park optimization decision model is solved to obtain the target charging and discharging price pricing information of the target park.

[0070] Furthermore, the embodiment of the present application also needs to construct a decision-making model for minimizing the electricity cost of the park (i.e., a park optimization decision-making model), where the electricity cost includes the electricity purchase cost from the electricity market and the electricity interaction cost of the car owner. Through the constructed decision-making model, the park will decide on the electricity purchase curve it has recently reported to the electricity market, the electricity price it has set for the car owner, and the operation plan of the equipment in the park.

[0071] Therefore, the embodiments of the present application consider separate pricing for charging and discharging interactions of electric vehicles, thereby encouraging electric vehicles to meet the peak-shaving and valley-filling needs of the park through V2G.

[0072] Optionally, in one embodiment of the present application, based on the number of cars, the owner's charging and discharging power curve decision model, the electricity purchase cost in the electricity market and the owner's electricity interaction cost, a park optimization decision model of the target park is constructed, and the park optimization decision model is solved to obtain the target charging and discharging price pricing information of the target park, including: determining the electricity purchase cost in the electricity market corresponding to the target park in the day-ahead stage; obtaining the charging service income and the owner's discharge subsidy corresponding to each target electric vehicle, and calculating the owner's electricity interaction cost corresponding to the target park based on the number of cars, the charging service income and the owner's discharge subsidy; determining the park objective function of the target park in the day-ahead stage according to the electricity purchase cost in the electricity market and the owner's electricity interaction cost, and setting the price level constraint of the target park based on a preset target price level interval; constructing the park optimization decision model based on the park objective function and the price level constraint; solving the park optimization decision model through a preset heuristic algorithm to obtain the target charging and discharging price pricing information of the target park.

[0073] In the specific implementation process, the embodiment of the present application can record the number of type i electric vehicles as m. i , electric vehicles of the same type have exactly the same parameters, optimization models and optimization results, so the day-ahead optimization decision model of the park is as follows:

[0074] minC buy -R carElec (14)

[0075]

[0076] Among them, formula (15) represents the electricity purchase cost of the park in the power market in the day-ahead stage; formula (16) represents the net income of the park due to the owner's charging (i.e., the owner's electricity interaction cost), which includes the charging service income and the subsidy for the owner's discharging; formulas (17) and (18) indicate that the charging and discharging prices cannot be arbitrarily set, but are based on δ t c / d From Δ c / d,max Price range In the selection, formula (18) can be easily transformed into a mixed integer linear constraint through binary expansion and big M method; formula (19) indicates that there should be a minimum interval between different price levels; formula (20) gives the upper and lower limits of the charging and discharging prices set by the park; formula (21) indicates the prohibition of reverse power transmission constraint; formula (22) indicates that the total power is calculated based on the charging power of all electric vehicles; formula (23) indicates that the number of electric vehicles going to the park for charging is affected by the actual average charging cost. The higher the cost per kilowatt-hour, the fewer vehicles come to charge; formula (24) represents the above-mentioned owner optimization decision model.

[0077] It is understandable that the park's day-ahead optimization decision model refers to the form of time-of-use electricity price setting, and sets several levels of constraints on charging and discharging prices rather than arbitrarily setting prices, which makes it easier for electric car owners to remember and make quick decisions, and facilitates the implementation of charging and discharging price plans.

[0078] It should be noted that the optimization model is embedded in the day-ahead optimization decision model of the park, so the park optimization model is a two-layer optimization. In the embodiment of the present application, only the optimization decision of a single park is considered, and the established game model is a master-slave game model with one master and multiple slaves. In the actual implementation process, there may be multiple parks competing with each other, attracting more electric car owners to charge by offering preferential prices. The embodiment of the present application can use formula (11) to characterize the competition effect between multiple parks, that is, too high a price will cause customers to reduce consumption.

[0079] In addition, considering that electric vehicles are usually charged on-demand and are not reported to the park in advance, the embodiments of the present application assume that when the park sets prices in the day-ahead, the relevant parameters of each type of electric vehicle can be predicted based on historical data and future weather and date characteristics, and used as the lower layer of the model to solve, thereby eliminating the need for electric vehicle owners to participate in the day-ahead pricing process.

[0080] In the embodiments of the present application, the established park decision model is a two-layer model and has a certain complexity: the lower layer is a MILP problem, and it is difficult to use KKT conditions or duality to transform the two-layer optimization into a single-layer optimization.

[0081] Therefore, as a feasible method, the embodiment of the present application can use a heuristic algorithm (such as a genetic algorithm, a particle swarm algorithm, etc.) to solve the problem, thereby converting the price of the park into It is set as the variable that the heuristic algorithm needs to decide. When the fitness is calculated inside the heuristic algorithm, the upper and lower optimization problems are decoupled, and there is no nonlinear problem anymore. It can be solved efficiently using a commercial solver.

[0082] Therefore, the embodiments of the present application describe the competitive relationship between the park and electric car owners by establishing a master-slave game model framework, and can use a heuristic algorithm to solve the park decision model, which is essentially a two-layer optimization problem, thereby realizing park optimization and pricing based on the master-slave game.

[0083] According to the charging and discharging price pricing method of electric vehicle charging stations in industrial and commercial parks proposed in the embodiment of the present application, by determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle model in the target park; constructing a charging and discharging power curve decision model for each target electric vehicle owner under each vehicle model based on the charging and discharging price information, charging and discharging energy cost and battery aging cost; determining the target park's electricity market purchase cost, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each vehicle model, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, construct a park optimization decision model for the target park, and solve the park optimization decision model to obtain the target charging and discharging price pricing information for the target park. This application is based on the park optimization and pricing method of the master-slave game, so as to give appropriate incentives to car owners through appropriate pricing methods, so that they are willing to participate in V2G, so as to improve the overall energy utilization efficiency of the park.

[0084] Secondly, the charging and discharging price pricing device for electric vehicle charging stations in industrial and commercial parks proposed in accordance with the embodiments of the present application is described with reference to the accompanying drawings.

[0085] Figure 3 It is a block diagram of a charging and discharging price pricing device for an electric vehicle charging station in an industrial and commercial park according to an embodiment of the present application.

[0086] like Figure 3 As shown, the charging and discharging price pricing device 10 of the electric vehicle charging station in the industrial and commercial park includes: a determination module 100, a modeling module 200 and a pricing module 300.

[0087] The determination module 100 is used to determine the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the target park.

[0088] The modeling module 200 is used to construct a charging and discharging power curve decision model for each owner of each target electric vehicle under each vehicle type according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost.

[0089] The pricing module 300 is used to determine the electricity purchase cost of the target park in the electricity market, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each model, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity purchase cost of the electricity market and the owner's electricity interaction cost, construct a park optimization decision model for the target park, and solve the park optimization decision model to obtain the target charging and discharging price pricing information of the target park.

[0090] Optionally, in one embodiment of the present application, the determination module 100 includes: a first acquisition unit, a first calculation unit and a second calculation unit.

[0091] The first acquisition unit is used to acquire a set of stay times corresponding to any vehicle type among all vehicle types, and obtain the stay time corresponding to each target electric vehicle through the stay time set.

[0092] The first calculation unit is used to calculate the charging and discharging energy cost of each target electric vehicle based on the charging and discharging price information and the residence time.

[0093] The second calculation unit is used to determine the battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs corresponding to each target electric vehicle, and calculate the battery aging cost through the residence time, battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs.

[0094] Optionally, in one embodiment of the present application, the modeling module 200 includes: a second acquisition unit, a first setting unit and a first construction unit.

[0095] Among them, the second acquisition unit is used to obtain the maximum charging and discharging power, upper and lower limits of battery power, initial battery power and target battery power corresponding to any model of all models.

[0096] The first setting unit is used to determine the owner's objective function corresponding to each target electric vehicle according to the charging and discharging energy cost, the battery aging cost, the preset power fluctuation penalty and the energy shortage penalty.

[0097] The first construction unit is used to determine the charge and discharge complementary constraints and the upper and lower limit constraints of the battery power corresponding to the owner's objective function based on the residence time set, the maximum charge and discharge power, the upper and lower limits of the battery power, the initial battery power and the target battery power, so as to construct the owner's charge and discharge power curve decision model according to the charge and discharge complementary constraints, the upper and lower limit constraints of the battery power and the owner's objective function.

[0098] Optionally, in one embodiment of the present application, the pricing module 300 includes: a cost unit, a third calculation unit, a second setting unit, a second construction unit and a solving unit.

[0099] Among them, the cost unit is used to determine the electricity market purchase cost corresponding to the target park in the day-ahead stage.

[0100] The third calculation unit is used to obtain the charging service income and the owner's discharge subsidy corresponding to each target electric vehicle, so as to calculate the owner's power interaction cost corresponding to the target park based on the number of vehicles, the charging service income and the owner's discharge subsidy.

[0101] The second setting unit is used to determine the park objective function of the target park in the day-ahead stage according to the electricity purchase cost in the electricity market and the car owner's electricity interaction cost, and to set the price level constraint of the target park based on a preset target price level interval.

[0102] The second construction unit is used to construct a park optimization decision model based on the park objective function and price level constraints.

[0103] The solving unit is used to solve the park optimization decision model through a preset heuristic algorithm to obtain the target charging and discharging price pricing information of the target park.

[0104] It should be noted that the aforementioned explanation of the embodiment of the method for pricing the charging and discharging prices of electric vehicle charging stations in industrial and commercial parks is also applicable to the charging and discharging price pricing device for electric vehicle charging stations in industrial and commercial parks in this embodiment, and will not be repeated here.

[0105] According to the charging and discharging price pricing device of electric vehicle charging stations in industrial and commercial parks proposed in the embodiment of the present application, it includes a determination module for determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the target park; a modeling module for constructing a charging and discharging power curve decision model for each target electric vehicle owner under each vehicle type according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost; a pricing module for determining the target park's electricity market purchase cost, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each vehicle type, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, construct a park optimization decision model for the target park, and solve the park optimization decision model to obtain the target charging and discharging price pricing information for the target park. This application is based on the park optimization and pricing method of the master-slave game, so as to give appropriate incentives to car owners through appropriate pricing methods, so that they are willing to participate in V2G, so as to improve the overall energy utilization efficiency of the park.

[0106] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0107] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0108] When the processor 402 executes the program, the charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks provided in the above-mentioned embodiment is implemented.

[0109] Furthermore, the electronic device further comprises:

[0110] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0111] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0112] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0113] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0114] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0115] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0116] The embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for pricing charging and discharging prices of electric vehicle charging stations in industrial and commercial parks.

[0117] The embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks.

[0118] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0119] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0120] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0122] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0123] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0124] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0125] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks, characterized in that: The following steps are involved: Determine the charging and discharging energy cost and battery aging cost for each vehicle type in the target park; Constructing a charging and discharging power curve decision model for the owner of each target electric vehicle under each vehicle type according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost; The electricity market purchase cost of the target park, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each model are determined, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity market purchase cost and the owner's electricity interaction cost, a park optimization decision model for the target park is constructed, and the park optimization decision model is solved to obtain the target charging and discharging price pricing information of the target park.

2. The method according to claim 1, characterized in that Determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the target park includes: Obtain a set of stay times corresponding to any vehicle type among all vehicle types, and obtain the stay time corresponding to each target electric vehicle through the set of stay times; Calculating the charging and discharging energy cost of each target electric vehicle based on the charging and discharging price information and the residence time; Determine the battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs corresponding to each target electric vehicle, and calculate the battery aging cost through the residence time, the battery attenuation ratio, the battery capacity attenuation information, and the total battery purchase and replacement costs.

3. The method according to claim 2, characterized in that The method of constructing a charging and discharging power curve decision model for each owner of each target electric vehicle under each vehicle type according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost includes: Get the maximum charge and discharge power, upper and lower limits of battery power, initial battery power and target battery power corresponding to any model among all models; Determining the owner's objective function corresponding to each target electric vehicle according to the charge and discharge energy cost, the battery aging cost, the preset power fluctuation penalty and the energy shortage penalty; Based on the residence time set, the maximum charge and discharge power, the upper and lower limits of the battery power, the initial battery power and the target battery power, the charge and discharge complementary constraints and the upper and lower limit constraints of the battery power corresponding to the vehicle owner's objective function are determined, so as to construct the vehicle owner's charge and discharge power curve decision model according to the charge and discharge complementary constraints, the upper and lower limit constraints of the battery power and the vehicle owner's objective function.

4. The method according to claim 1, characterized in that: The method of constructing a park optimization decision model for the target park based on the number of cars, the owner's charging and discharging power curve decision model, the electricity purchase cost in the power market, and the owner's electricity interaction cost, and solving the park optimization decision model to obtain the target charging and discharging price pricing information of the target park includes: Determine the electricity purchase cost of the target park in the electricity market corresponding to the day-ahead stage; Obtaining the charging service income and the owner's discharge subsidy corresponding to each target electric vehicle, so as to calculate the owner's power interaction cost corresponding to the target park based on the number of vehicles, the charging service income and the owner's discharge subsidy; Determine the park objective function of the target park in the day-ahead phase according to the electricity purchase cost in the electricity market and the vehicle owner electricity interaction cost, and set the price level constraint of the target park based on a preset target price level interval; Based on the park objective function and the price level constraint, construct the park optimization decision model; The park optimization decision model is solved by a preset heuristic algorithm to obtain the target charging and discharging price pricing information of the target park.

5. A charging and discharging price pricing device for electric vehicle charging stations in industrial and commercial parks, characterized in that: include: A determination module is used to determine the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the target park; A modeling module, for constructing a charging and discharging power curve decision model for the owner of each target electric vehicle under each vehicle type according to the charging and discharging price information, the charging and discharging energy cost and the battery aging cost; The pricing module is used to determine the electricity purchase cost of the target park in the electricity market, the owner's electricity interaction cost and the number of target electric vehicles corresponding to each model, and based on the number of vehicles, the owner's charging and discharging power curve decision model, the electricity purchase cost of the electricity market and the owner's electricity interaction cost, construct a park optimization decision model for the target park, and solve the park optimization decision model to obtain the target charging and discharging price pricing information of the target park.

6. The device according to claim 5, characterized in that The determination module comprises: A first acquisition unit is used to acquire a set of stay times corresponding to any vehicle type among all vehicle types, and obtain the stay time corresponding to each target electric vehicle through the set of stay times; A first calculation unit, configured to calculate the charge and discharge energy cost of each target electric vehicle based on the charge and discharge price information and the residence time; The second calculation unit is used to determine the battery attenuation ratio, battery capacity attenuation information, and total battery purchase and replacement costs corresponding to each target electric vehicle, and calculate the battery aging cost through the residence time, the battery attenuation ratio, the battery capacity attenuation information, and the total battery purchase and replacement costs.

7. The device according to claim 6, characterized in that The modeling module includes: The second acquisition unit is used to obtain the maximum charge and discharge power, upper and lower limits of battery power, initial battery power and target battery power corresponding to any vehicle model among all vehicle models; A first setting unit is used to determine the owner's objective function corresponding to each target electric vehicle according to the charging and discharging energy cost, the battery aging cost, the preset power fluctuation penalty and the energy shortage penalty; The first construction unit is used to determine the charge and discharge complementary constraints and the battery power upper and lower limit constraints corresponding to the vehicle owner's objective function based on the residence time set, the maximum charge and discharge power, the upper and lower limits of the battery power, the initial battery power and the target battery power, so as to construct the vehicle owner's charge and discharge power curve decision model according to the charge and discharge complementary constraints, the battery power upper and lower limit constraints and the vehicle owner's objective function.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks as described in any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the charging and discharging price pricing method for an electric vehicle charging station in an industrial and commercial park as described in any one of claims 1-4.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the charging and discharging price pricing method for electric vehicle charging stations in industrial and commercial parks as described in any one of claims 1-4.

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