Pricing Method and Device for Electric Vehicle Charging Stations in Industrial and Commercial Parks

Abstract

This application relates to a pricing method and apparatus for charging and discharging at electric vehicle charging stations within industrial and commercial parks. The method includes: determining the charging and discharging energy cost and battery aging cost for each vehicle model within the target park; constructing a decision model for the owner's charging and discharging power curve for each target electric vehicle under each vehicle model based on charging and discharging price information, charging and discharging energy cost, and battery aging cost; determining the electricity market purchase cost, owner electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle model within the target park; and constructing and solving an optimization decision model for the target park based on the owner's charging and discharging power curve decision model to obtain the target charging and discharging price information for the target park. This solves the problems of existing charging station pricing methods that do not use game theory to model the conflict of interest among parties, and that are unable to effectively consider battery aging costs, thus failing to provide reasonable pricing for electric vehicle services.

Description

Technical Field

[0001] This application relates to the field of charging station pricing technology, and in particular to a method and apparatus for pricing the charging and discharging of electric vehicles at charging stations in industrial and commercial parks. Background Technology

[0002] Vehicle-to-Grid (V2G) technology can achieve various functions such as peak shaving and valley filling, frequency regulation, and promoting the consumption of renewable energy. Among these, electric vehicles (EVs) have strong application potential in industrial and commercial parks, where they can be connected to the parks as a flexible resource to promote the energy-efficient use of the park's microgrids.

[0003] Because the interests of the power grid and vehicle owners are not aligned, simply considering the issue of balancing the interests between the power grid and individual EVs as a matter of the power grid completely and arbitrarily regulating electric vehicles is inappropriate. Game theory analysis of the behavior of all parties is needed. Existing technologies can use an optimization scheduling method that considers the interests of the power grid, EV aggregators, and EVs to participate in peak shaving. This can be simply modeled as a multi-objective optimization and converted to a single-objective optimization through weighted summation. However, it does not use game theory to model the conflict of interests among the parties, and the model is overly simplistic and lacks accuracy. Furthermore, existing technologies can consider the willingness of EV users to participate in V2G, but they do not consider guiding users through price adjustments, nor do they use game theory for modeling and analysis. Existing technologies can also model the interaction between aggregators and vehicle owners through master-slave game theory, and use aggregation game theory to model the interaction among vehicle owners to propose an optimization scheduling method for EVs participating in secondary frequency regulation, but it does not consider EV discharge.

[0004] Furthermore, pricing for electric vehicle charging and discharging within industrial and commercial parks faces the challenge of pricing V2G services. Existing technologies can consider price incentives for V2G, but these incentive prices are based on rules rather than optimization, and the rationale for these rules is not explained. Some existing technologies can also analyze and describe the medium-term flexibility cost of electric vehicles through opportunity cost, or propose a flexibility cost formula based on power and accumulated charge that is applicable to flexibility loads. However, these formulas only consider the charging scenario when considering the cost of electric vehicles and do not take into account costs such as battery aging during discharging, making them difficult to directly apply to discharging scenarios.

[0005] Understandably, battery aging is a major challenge limiting V2G applications. Existing technologies have found that high-frequency V2G services significantly shorten the lifespan of electric vehicle batteries. Therefore, when considering V2G services, it is necessary to take into account the lifespan and degradation of electric vehicle batteries. Consequently, some existing technologies can use the rainflow cycle counting method to quantify battery aging and propose scheduling methods with minimizing battery aging as one of the optimization objectives. In addition, existing technologies can also use battery active material loss models to describe battery aging and propose V2G participation frequency modulation scheduling methods that can actively suppress battery aging. However, the models used are mostly aging models and are relatively complex, making it difficult to embed them into optimization models for rapid solution.

[0006] In summary, existing charging station pricing methods do not use game theory to model the conflict of interest among the parties involved, and they are unable to effectively consider the cost of battery aging, thus failing to provide reasonable pricing for electric vehicle services. These issues urgently need to be addressed. Summary of the Invention

[0007] This application provides a pricing method and device for charging and discharging prices of electric vehicle charging stations in industrial and commercial parks, in order to solve the problems that existing charging station pricing methods do not use game theory to model the conflict of interests between the parties, and are difficult to effectively consider the cost of battery aging, thus failing to make reasonable pricing for electric vehicle services.

[0008] The first aspect of this application provides a method for pricing the charging and discharging of electric vehicles at charging stations in industrial and commercial parks, comprising the following steps: determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle type in the target park; constructing a decision model for the owner's charging and discharging power curve of each target electric vehicle under each vehicle type 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 type in the target park, and constructing a park optimization decision model for the target park 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, and solving the park optimization decision model to obtain the target charging and discharging price information for the target park.

[0009] Optionally, in one embodiment of this application, determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle type within the target park includes: obtaining a set of dwell times for any vehicle type among all vehicle types, and obtaining the dwell time corresponding to each target electric vehicle through the set of dwell times; calculating the charging and discharging energy cost of each target electric vehicle based on the charging and discharging price information and the dwell time; determining the battery degradation ratio, battery capacity degradation information, and total battery purchase and replacement cost corresponding to each target electric vehicle, and calculating the battery aging cost through the dwell time, the battery degradation ratio, the battery capacity degradation information, and the total battery purchase and replacement cost.

[0010] Optionally, in one embodiment of this application, the step of constructing a decision model for the owner's charge / discharge power curve for each target electric vehicle under each vehicle type based on the charge / discharge price information, the charge / discharge energy cost, and the battery aging cost includes: obtaining the maximum charge / discharge power, upper and lower limits of battery capacity, initial battery capacity, and target battery capacity for any vehicle type among all vehicle types; determining the owner's objective function for each target electric vehicle based on the charge / discharge energy cost, the battery aging cost, preset power fluctuation penalty, and energy shortage penalty; and determining the charge / discharge complementary constraints and upper and lower limits of battery capacity corresponding to the owner's objective function based on the dwell time set, the maximum charge / discharge power, the upper and lower limits of battery capacity, the initial battery capacity, and the target battery capacity, so as to construct the decision model for the owner's charge / discharge power curve based on the charge / discharge complementary constraints, the upper and lower limits of battery capacity, and the owner's objective function.

[0011] Optionally, in one embodiment of this application, the step of constructing a park optimization decision model for the target park 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, and solving the park optimization decision model to obtain the target charging and discharging price information for the target park, includes: determining the electricity market purchase cost corresponding to the target park in the day-ahead phase; obtaining the charging service revenue and owner discharging 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 vehicles, the charging service revenue, and the owner's discharging subsidy; determining the park objective function for the target park in the day-ahead phase based on the electricity market purchase cost and the owner's electricity interaction cost, and setting price tier constraints for the target park based on a preset target price tier interval; constructing the park optimization decision model based on the park objective function and the price tier constraints; and solving the park optimization decision model using a preset heuristic algorithm to obtain the target charging and discharging price information for the target park.

[0012] A second aspect of this application provides a pricing device for charging and discharging at electric vehicle charging stations in industrial and commercial parks, comprising: a determining 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 decision model for the owner's charging and discharging power curve of each target electric vehicle under each vehicle type based on the charging and discharging price information, the charging and discharging energy cost, and the battery aging cost; and a pricing module for determining the electricity market purchase cost, the owner's electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle type in the target park, and constructing a park optimization decision model for the target park 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, and solving the park optimization decision model to obtain the target charging and discharging price information for the target park.

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

[0014] Optionally, in one embodiment of this application, the modeling module includes: a second acquisition unit, configured to acquire the maximum charging / discharging power, upper and lower limits of battery capacity, initial battery capacity, and target battery capacity corresponding to any model among all vehicle types; a first setting unit, configured to determine the owner objective function corresponding to each target electric vehicle based on the charging / discharging energy cost, the battery aging cost, a preset power fluctuation penalty, and an energy shortage penalty; and a first construction unit, configured to determine the charging / discharging complementary constraints and upper and lower limits of battery capacity corresponding to the owner objective function based on the set of dwell times, the maximum charging / discharging power, the upper and lower limits of battery capacity, the initial battery capacity, and the target battery capacity, so as to construct the owner charging / discharging power curve decision model based on the charging / discharging complementary constraints, the upper and lower limits of battery capacity, and the owner objective function.

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

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

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks.

[0018] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks.

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

[0020] The embodiments of this application determine the charging and discharging energy cost and battery aging cost corresponding to each vehicle type within the target park; construct a decision model for the owner's charging and discharging power curve for each target electric vehicle under each vehicle type based on charging and discharging price information, charging and discharging energy cost, and battery aging cost; determine the electricity market purchase cost, owner electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle type in the target park; and construct a park optimization decision model for the target park 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, and solve the park optimization decision model to obtain the target charging and discharging price information for the target park. This application uses a master-slave game-based park optimization and pricing method to provide appropriate incentives to car owners through suitable pricing methods, thereby encouraging them to participate in V2G and improving the overall energy utilization efficiency of the park. This solves the problems of existing charging station pricing methods that do not use game theory to model the conflict of interest between parties, and that are difficult to effectively consider battery aging costs, making it impossible to conduct reasonable electric vehicle service pricing.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

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

[0023] Figure 1 This is a flowchart illustrating a pricing method for charging and discharging electric vehicle charging stations within an industrial or commercial park, according to an embodiment of this application.

[0024] Figure 2 A schematic diagram of the logical architecture of a pricing method for charging and discharging electric vehicles at charging stations in industrial and commercial parks, provided as an embodiment of this application;

[0025] Figure 3 This is an example diagram of a pricing device for charging and discharging electric vehicles at charging stations in industrial and commercial parks according to an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] Among them, 10-Pricing device for charging and discharging electric vehicles in industrial and commercial parks, 100-Determination module, 200-Modeling module, 300-Pricing module, 401-Memory, 402-Processor, and 403-Communication interface. Detailed Implementation

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

[0029] The following describes a pricing method and apparatus for charging and discharging electric vehicle charging stations in industrial and commercial parks, based on embodiments of this application, with reference to the accompanying drawings. Addressing the problems mentioned in the background section, this application provides a pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks. In this method, the charging and discharging energy cost and battery aging cost corresponding to each vehicle type within the target park are determined. Based on the charging and discharging price information, the charging and discharging energy cost, and the battery aging cost, a decision model for the owner's charging and discharging power curve for each target electric vehicle under each vehicle type is constructed. The electricity market purchase cost, the owner's electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle type in the target park are determined. 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 information for the target park. This application uses a master-slave game-based park optimization and pricing method to provide appropriate incentives to car owners through suitable pricing methods, thereby encouraging them to participate in V2G and improving the overall energy utilization efficiency of the park. This solves the problems of existing charging station pricing methods not using game theory to model the conflict of interest between parties, and failing to effectively consider battery aging costs, thus making it impossible to price electric vehicle services reasonably.

[0030] To facilitate understanding by those skilled in the art of the pricing logic of the electric vehicle charging station charging and discharging price method in industrial and commercial parks as described in this application, the following explains the subscript symbols, parameters and variables involved in this application.

[0031] The subscripts and their definitions are shown in Table 1:

[0032] Table 1

[0033]

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

[0035] Table 2

[0036]

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

[0038] Table 3

[0039]

[0040]

[0041] Specifically, Figure 1 This is a flowchart illustrating a pricing method for charging and discharging at electric vehicle charging stations within an industrial or commercial park, provided as an embodiment of this application.

[0042] like Figure 1 As shown, the pricing method for charging and discharging at electric vehicle charging stations within this industrial and commercial park includes the following steps:

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

[0044] It is understood that the embodiments of this application are mainly intended to solve the charging and discharging pricing problem in industrial and commercial parks. Therefore, the embodiments of this application can assume that the park is a price taker in the electricity spot market and needs to optimize decision-making and determine the market bidding volume in the day-ahead stage.

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

[0046] However, the goals of car owners and the park are not aligned. Therefore, embodiments of this application model the game relationship between the park and car owners using a master-slave game model, such as... Figure 2 As shown, the park makes its decision and sets the price before the car owner, thus placing the park in a leader position in the master-slave game.

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

[0048] Optionally, in one embodiment of this application, determining the charging and discharging energy cost and battery aging cost corresponding to each vehicle type within the target park includes: obtaining a set of dwell times for any vehicle type among all vehicle types, and obtaining the dwell time for each target electric vehicle through the dwell time set; calculating the charging and discharging energy cost for each target electric vehicle based on charging and discharging price information and dwell time; determining the battery degradation ratio, battery capacity degradation information, and total cost of battery purchase and replacement for each target electric vehicle, and calculating the battery aging cost through the dwell time, battery degradation ratio, battery capacity degradation information, and total cost of battery purchase and replacement.

[0049] In actual implementation, this application embodiment can assume that when electric vehicles access the park for charging, the park prices the charging and discharging activities separately as follows: And record the arrival and departure times of the electric vehicle as t. arr ,t dep The length of stay is The cost of charging and discharging an electric vehicle, i.e., the energy cost of charging and discharging, is C. charge for:

[0050]

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

[0052]

[0053] Among them, Q loss Indicates battery capacity degradation; k aban The allowable degradation rate relative to the maximum battery capacity is generally set at 20%. When the cumulative degradation rate reaches this value, the battery needs to be replaced. 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 / discharge rate, and total charge throughput. The embodiments of this application can adopt a simplified approach that is easy to embed optimization into, that is, assuming that battery aging can be expressed as a linear function of discharge energy (or the sum of discharge and charge), then:

[0055]

[0056] Wherein, coefficient k deg This represents the battery capacity reduction caused by every 1 kWh of discharge, expressed in kWh. A constant coefficient can be used, or k can be updated after optimization based on actual charge / discharge amounts, depth of charge / discharge, current rate, and other influencing factors. deg Then optimize, and so on iteratively.

[0057] Therefore, the embodiments of this application effectively ensure the reliability of the subsequent electric vehicle owner charge / discharge power curve decision model by calculating the charging / discharge energy cost and battery aging cost corresponding to each type of vehicle in the park.

[0058] In step S102, a decision model for the owner's charge and discharge power curve of each target electric vehicle under each vehicle model is constructed based on the charge and discharge price information, charge and discharge energy cost, and battery aging cost.

[0059] Furthermore, embodiments of this application can construct a decision model (i.e., a charge / discharge power curve decision model) for electric vehicle owners to minimize charging costs based on charge / discharge energy costs and battery aging costs. The electric vehicle owner will then decide on the charge / discharge power curve P they report to the park. t c / d .

[0060] Optionally, in one embodiment of this application, a decision model for the owner's charge / discharge power curve for each target electric vehicle under each vehicle type is constructed based on charge / discharge price information, charge / discharge energy cost, and battery aging cost. This includes: obtaining the maximum charge / discharge power, upper and lower limits of battery capacity, initial battery capacity, and target battery capacity for any vehicle type; determining the owner's objective function for each target electric vehicle based on charge / discharge energy cost, battery aging cost, preset power fluctuation penalty, and energy shortage penalty; and determining the charge / discharge complementarity constraint and upper / lower limit constraint of battery capacity corresponding to the owner's objective function based on the set of dwell time, maximum charge / discharge power, upper and lower limits of battery capacity, initial battery capacity, and target battery capacity, so as to construct the owner's charge / discharge power curve decision model based on the charge / discharge complementarity constraint, upper and lower limit constraint of battery capacity, and owner's objective function.

[0061] Specifically, embodiments of this application may assume that for any type of electric vehicle i, its set of dwell time periods is... Maximum charging and discharging power P i c / d,max Battery power limits And the initial and target battery levels (i.e., the final battery level). Let be a known, given constant. For ease of representation, the subscript 'i' can be omitted in the following embodiments of this application. The optimization decision model for electric vehicle type i is as follows:

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

[0063]

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

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

[0066] In actual interaction, the park sends charging and discharging price information to electric vehicles, which then optimizes the charging and discharging power accordingly. This optimization process effectively constitutes... right The mapping, and the optimization can be simply summarized as:

[0067]

[0068] It is understandable that battery aging is a key factor affecting vehicle owners' V2G decisions. Therefore, the embodiments of this application adopt a simplified calculation method in which battery aging is proportional to the amount of charging and discharging, so that it can be well embedded in the optimization calculation.

[0069] In step S103, the electricity market purchase cost, vehicle owner electricity interaction cost, and the number of target electric vehicles corresponding to each type of vehicle in the target park are determined. Based on the number of vehicles, the vehicle owner charging and discharging power curve decision model, the electricity market purchase cost, and the vehicle owner 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 information for the target park.

[0070] Furthermore, this application embodiment also needs to construct a decision model for minimizing the electricity cost of the park (i.e., the park optimization decision model), where the electricity cost includes the electricity market purchase cost and the electricity interaction cost for vehicle owners. Through the constructed decision model, the park will decide on the electricity purchase curve it submits to the electricity market, the electricity price it sets for vehicle owners, and the operation plan of the equipment in the park.

[0071] Therefore, embodiments of this application incentivize electric vehicles to meet the peak shaving and valley filling needs of the park by considering separate pricing for electric vehicle charging and discharging interactions.

[0072] Optionally, in one embodiment of this application, a park optimization decision model for the target park is constructed 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 is then solved to obtain the target charging and discharging price information for the target park. This includes: determining the electricity market purchase cost for the target park during the day-ahead phase; obtaining the charging service revenue and owner discharging subsidy for each target electric vehicle, and calculating the owner's electricity interaction cost for the target park based on the number of vehicles, charging service revenue, and owner discharging subsidy; determining the park objective function for the target park during the day-ahead phase based on the electricity market purchase cost and owner's electricity interaction cost, and setting price tier constraints for the target park based on a preset target price tier interval; constructing a park optimization decision model based on the park objective function and price tier constraints; and solving the park optimization decision model using a preset heuristic algorithm to obtain the target charging and discharging price information for the target park.

[0073] In the specific implementation process, the number of electric vehicles of type i in the embodiments of this application can be recorded as m. i If electric vehicles of the same type have exactly the same parameters, optimization models, and optimization results, then the day-ahead optimization decision model for the park is as follows:

[0074] minC buy -R carElec (14)

[0075]

[0076] Equation (15) represents the electricity purchase cost of the park in the electricity market during the day-ahead phase; Equation (16) represents the net revenue generated by the park due to vehicle owners charging (i.e., the electricity interaction cost of vehicle owners), which includes charging service revenue and subsidies for vehicle owners discharging; Equations (17) and (18) indicate that the charging and discharging price cannot be arbitrarily set, but is based on δ t c / d From Δ c / d,max Price tiers In the selection, Equation (18) can be easily transformed into a mixed integer linear constraint through binary extension and the Big M method; Equation (19) indicates that there should be a minimum interval between different price levels; Equation (20) gives the upper and lower limits of the charging and discharging prices set by the park; Equation (21) indicates the constraint of prohibiting reverse power transmission; Equation (22) indicates the calculation of the total power based on the charging power of all electric vehicles; Equation (23) indicates that the number of electric vehicles going to the park for charging is affected by the average actual charging cost. The higher the cost per kilowatt-hour, the fewer vehicles will come to charge; Equation (24) indicates the above-mentioned owner optimization decision model.

[0077] Understandably, the park's day-ahead optimization decision-making model references the time-of-use pricing model, setting several tiers of constraints on charging and discharging prices rather than setting prices arbitrarily. This makes it easier for electric vehicle owners to remember and make quick decisions, and facilitates the implementation and application of charging and discharging pricing schemes.

[0078] It should be noted that the optimization decision model of the park is embedded in the optimization model, so the park optimization model is a two-layer optimization. In this embodiment, only the optimization decision of a single park is considered. The established game model is a master-slave game model with one master and many slaves. In the actual implementation process, there may be competition between multiple parks. They attract more electric vehicle owners to charge by offering preferential prices. In this embodiment, Equation (11) can be used to represent the competitive effect between multiple parks. That is, excessively high prices will lead to customers reducing consumption.

[0079] Furthermore, considering that electric vehicles are usually charged on demand and not reported to the park before the date, the embodiments of this application assume that when pricing before the date in the park, the relevant parameters of various types of electric vehicles can be predicted based on historical data and future weather, date characteristics, etc., and used as the lower layer of the model for solution, so that electric vehicle owners do not need to participate in the day-ahead pricing process.

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

[0081] Therefore, as one possible approach, embodiments of this application can employ heuristic algorithms (such as genetic algorithms, particle swarm optimization, etc.) to solve the problem, thereby reducing the price of the park. Let this be the variable that the heuristic algorithm needs to decide. When calculating fitness within the heuristic algorithm, the optimization problems at the upper and lower levels are decoupled, and there are no longer nonlinear problems. Commercial solvers can be used to solve it efficiently.

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

[0083] The pricing method for electric vehicle charging stations in industrial and commercial parks proposed in this application involves determining the charging and discharging energy cost and battery aging cost for each vehicle type within the target park; constructing a decision model for the owner's charging and discharging power curve for each target electric vehicle under each vehicle type based on charging and discharging price information, charging and discharging energy cost, and battery aging cost; determining the electricity market purchase cost, owner electricity interaction cost, and the number of target electric vehicles for each vehicle type in the target park; and constructing a park optimization decision model for the target park 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, and solving the park optimization decision model to obtain the target charging and discharging price information for the target park. This application uses a master-slave game-based park optimization and pricing method to provide appropriate incentives to car owners through suitable pricing methods, thereby encouraging them to participate in V2G and improving the overall energy utilization efficiency of the park.

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

[0085] Figure 3 This is a block diagram of a pricing device for charging and discharging electric vehicles at a commercial and industrial park, according to an embodiment of this application.

[0086] like Figure 3 As shown, the pricing device 10 for charging and discharging of electric vehicles 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 for each type of vehicle in the target park.

[0088] Modeling module 200 is used to construct a decision model for the owner's charge / discharge power curve of each target electric vehicle under each vehicle model, based on charge / discharge price information, charge / discharge energy cost, and battery aging cost.

[0089] The pricing module 300 is used to determine the electricity market purchase cost, vehicle owner electricity interaction cost, and the number of target electric vehicles corresponding to each type of vehicle in the target park. Based on the number of vehicles, the vehicle owner charging and discharging power curve decision model, the electricity market purchase cost, and the vehicle owner electricity interaction cost, it constructs the park optimization decision model for the target park and solves the park optimization decision model to obtain the target charging and discharging price information for the target park.

[0090] Optionally, in one embodiment of this application, the determining 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 the set of dwell times for any model among all models, and to obtain the dwell time for each target electric vehicle through the set of dwell times.

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

[0093] The second calculation unit is used to determine the battery degradation ratio, battery capacity degradation information, and total cost of battery purchase and replacement for each target electric vehicle, and to calculate the battery aging cost by using the dwell time, battery degradation ratio, battery capacity degradation information, and total cost of battery purchase and replacement.

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

[0095] The second acquisition unit is used to acquire the maximum charging and discharging power, upper and lower limits of battery capacity, initial battery capacity, and target battery capacity for any model among all models.

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

[0097] The first building unit is used to determine the charging and discharging complementary constraints and the upper and lower limits of battery capacity corresponding to the owner's objective function based on the set of dwell time, maximum charging and discharging power, upper and lower limits of battery capacity, initial battery capacity, and target battery capacity, so as to build a decision model for the owner's charging and discharging power curve based on the charging and discharging complementary constraints, upper and lower limits of battery capacity, and the owner's objective function.

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

[0099] The cost unit is used to determine the electricity market purchase cost for the target industrial park during the day-ahead phase.

[0100] The third calculation unit is used to obtain the charging service revenue and owner discharge subsidy for each target electric vehicle, and to calculate the owner electricity interaction cost for the target park based on the number of vehicles, charging service revenue and owner discharge subsidy.

[0101] The second setting unit is used to determine the target park's objective function in the day-ahead phase based on the electricity market purchase cost and the vehicle owner's electricity interaction cost, and to set the price tier constraints for the target park based on the preset target price tier interval.

[0102] The second building unit is used to construct an optimal decision-making model for the park based on the park's objective function and price level constraints.

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

[0104] It should be noted that the explanation of the above-mentioned embodiment of the pricing method for charging and discharging of electric vehicle charging stations in industrial and commercial parks also applies to the pricing device for charging and discharging of electric vehicle charging stations in industrial and commercial parks in this embodiment, and will not be repeated here.

[0105] The charging and discharging price pricing device for electric vehicle charging stations in industrial and commercial parks proposed in this application 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 decision model for the owner's charging and discharging power curve for each target electric vehicle under each vehicle type based on charging and discharging price information, charging and discharging energy cost, and battery aging cost; and a pricing module for determining the electricity market purchase cost, owner electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle type in the target park, and constructing a park optimization decision model for the target park 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, and solving the park optimization decision model to obtain the target charging and discharging price pricing information for the target park. This application is based on a master-slave game-theoretic park optimization and pricing method to provide appropriate incentives to car owners through suitable pricing methods, thereby encouraging them to participate in V2G and improving 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 this application. The electronic device may include:

[0107] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0108] When processor 402 executes the program, it implements the pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks provided in the above embodiments.

[0109] Furthermore, electronic devices also include:

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

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

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

[0113] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

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

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

[0116] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks.

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

[0118] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

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

[0120] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0122] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0123] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0125] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for pricing of charging and discharging prices of an electric vehicle charging station in a business park, characterized by, Includes the following steps: Determine the charging and discharging energy cost and battery aging cost for each vehicle type within the target park; Based on the charging and discharging price information, the charging and discharging energy cost, and the battery aging cost, a decision model for the owner's charging and discharging power curve of each target electric vehicle under each vehicle model is constructed. The electricity market purchase cost, vehicle owner electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle type are determined for the target park. Based on the number of vehicles, the vehicle owner charging and discharging power curve decision model, the electricity market purchase cost, and the vehicle owner 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 information for the target park. The determination of the charging and discharging energy cost and battery aging cost for each vehicle type within the target park includes: Obtain the set of dwell times for any model among all models, and use the set of dwell times to obtain the dwell time for each target electric vehicle; Based on the charging and discharging price information and the dwell time, calculate the charging and discharging energy cost of each target electric vehicle; The battery degradation ratio, battery capacity degradation information, and total cost of battery purchase and replacement are determined for each target electric vehicle. The battery aging cost is then calculated using the dwell time, the battery degradation ratio, the battery capacity degradation information, and the total cost of battery purchase and replacement. The step of constructing a decision model for the owner's charge / discharge power curve for each target electric vehicle under each vehicle type, based on the charge / discharge price information, the charge / discharge energy cost, and the battery aging cost, includes: Obtain the maximum charging and discharging power, upper and lower limits of battery capacity, initial battery capacity, and target battery capacity for any given vehicle model; The owner objective function for each target electric vehicle is determined based on the charging and discharging energy cost, the battery aging cost, the preset power fluctuation penalty, and the energy shortage penalty. Based on the set of dwell times, the maximum charging and discharging power, the upper and lower limits of battery capacity, the initial battery capacity, and the target battery capacity, the charging and discharging complementary constraints and the upper and lower limits of battery capacity corresponding to the vehicle owner's objective function are determined, so as to construct the vehicle owner's charging and discharging power curve decision model according to the charging and discharging complementary constraints, the upper and lower limits of battery capacity, and the vehicle owner's objective function; The process involves constructing an optimal decision-making model for the target park based on the number of vehicles, the vehicle owner's charging / discharging power curve decision model, the electricity market purchase cost, and the vehicle owner's electricity interaction cost. Solving this optimal decision-making model yields the target charging / discharging price information for the target park. This includes: Determine the electricity market purchase cost of the target industrial park in the day-ahead phase; Obtain the charging service revenue and owner discharge subsidy for each target electric vehicle, and calculate the owner electricity interaction cost for the target park based on the number of vehicles, the charging service revenue, and the owner discharge subsidy; The target function of the target park in the day-ahead phase is determined based on the electricity market purchase cost and the vehicle owner electricity interaction cost, and the price tier constraints of the target park are set based on the preset target price tier interval. Based on the objective function of the park and the price level constraints, an optimization decision model for the park is constructed. The park optimization decision model is solved by a preset heuristic algorithm to obtain the target charging and discharging price information of the target park.

2. A device for pricing of charging and discharging prices of an electric vehicle charging station in a business park, characterized by The method for setting charging and discharging prices for electric vehicle charging stations in industrial and commercial parks as described in claim 1 includes: The determination module is used to determine the charging and discharging energy cost and battery aging cost for each type of vehicle in the target park. The modeling module is used to construct a decision model for the owner's charge and discharge power curve of each target electric vehicle under each vehicle type based on the charge and discharge price information, the charge and discharge energy cost and the battery aging cost. The pricing module is used to determine the electricity market purchase cost, vehicle owner electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle type in the target park. Based on the number of vehicles, the vehicle owner charging and discharging power curve decision model, the electricity market purchase cost, and the vehicle owner electricity interaction cost, the module constructs the park optimization decision model for the target park and solves the park optimization decision model to obtain the target charging and discharging price information for the target park. The determining module includes: The first acquisition unit is used to acquire the set of dwell times corresponding to any model among all models, and to obtain the dwell time corresponding to each target electric vehicle through the set of dwell times. 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 dwell time; The second calculation unit is used to determine the battery degradation ratio, battery capacity degradation information, and total cost of battery purchase and replacement for each target electric vehicle, and to calculate the battery aging cost using the dwell time, the battery degradation ratio, the battery capacity degradation information, and the total cost of battery purchase and replacement. The modeling module includes: The second acquisition unit is used to acquire the maximum charging and discharging power, upper and lower limits of battery capacity, initial battery capacity and target battery capacity for any model among all models. The first setting unit is used to determine the owner objective function corresponding to each target electric vehicle based on the charging and discharging energy cost, the battery aging cost, the preset power fluctuation penalty and energy shortage penalty; The first construction unit is used to determine the charge-discharge complementary constraints and battery capacity upper and lower limit constraints corresponding to the vehicle owner's objective function based on the set of dwell times, the maximum charge-discharge power, the upper and lower limits of battery capacity, the initial battery capacity, and the target battery capacity, so as to construct the vehicle owner's charge-discharge power curve decision model according to the charge-discharge complementary constraints, the upper and lower limit constraints of battery capacity, and the vehicle owner's objective function.

3. An electronic device, comprising: include: The device includes 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 pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks as described in claim 1.

4. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks as described in claim 1.

5. A computer program product comprising a computer program, characterized in that, The computer program is executed to implement the pricing method for charging and discharging electric vehicle charging stations in industrial and commercial parks as described in claim 1.

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