Electricity selling company day-ahead pricing method and system based on master-slave game

Abstract

The invention relates to the technical field of electricity markets, in particular to an electricity selling company day-ahead pricing method and system based on a master-slave game, and the method comprises the following steps: building a virtual battery model based on the charging and discharging behaviors of an electric vehicle; establishing an electricity selling company-electric vehicle master-slave game pricing model; linearizing the master-slave game pricing model through a KKT condition and a dualization principle; and obtaining the maximum charging and discharging power and the electric quantity boundary of the electric vehicle based on Minkowski summation. Compared with a traditional electric vehicle charging and discharging pricing strategy, benefits of an electricity selling company and an electric vehicle owner can be considered, and the impact of disordered charging on a power grid is reduced; a multi-source variable is converted into an integrated decision variable through model construction, electric vehicle cluster charging and discharging behaviors are described, quantification and integration of large-scale electric vehicle flexible scheduling characteristics are achieved, and electricity selling companies are effectively supported to participate in day-ahead spot market bidding.

Description

Technical Field

[0001] This invention relates to the field of electricity market technology, and in particular to a day-ahead pricing method and system for electricity sales companies based on master-slave game theory. Background Technology

[0002] The orderly advancement of electricity market reform has promoted the development of flexible demand response resources, primarily electric vehicles. As an important component of end-user energy consumption, the charging and discharging behavior of electric vehicles has a significant impact on the real-time balance of the power grid. Electricity sales companies can act as agents for users to purchase electricity in the electricity market and provide charging services for electric vehicle users. By purchasing low-priced electricity in the day-ahead market and selling it to users at a charging price higher than the cost but lower than the real-time electricity price during the real-time charging process, both the electricity sales company and the user can obtain certain benefits.

[0003] At present, new energy vehicles are gradually replacing traditional fuel vehicles and becoming the mainstream in the market. However, the existing incentive measures for electric vehicles cannot provide a direct reference for electricity sales companies to participate in day-ahead market bidding. There is an urgent need for an electricity sales company-electric vehicle day-ahead pricing method and system to support electricity sales companies in participating in day-ahead market bidding. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a day-ahead pricing method and system for electricity sales companies based on master-slave game theory.

[0005] A day-ahead pricing method for electricity retailers based on master-slave game theory includes the following steps:

[0006] S1: Constructing a virtual battery model based on the charging and discharging behavior of electric vehicles;

[0007] S2: Establish a master-slave game pricing model between electricity sales companies and electric vehicles;

[0008] S3: Linearize the master-slave game pricing model using KKT conditions and the dualization principle;

[0009] S4: Obtain the maximum charging and discharging power and energy boundary of electric vehicles based on Minkowski summation.

[0010] Furthermore, the virtual battery model in S1 is used to quantify the historical information of the electric vehicle cluster in order to characterize the flexibility of the charging and discharging behavior of the electric vehicles.

[0011] The master-slave game pricing model in S2 optimizes the charging and discharging behavior of electric vehicles by setting day-ahead prices with the goal of maximizing the interests of both the electricity sales company and the electric vehicle.

[0012] The Minkowski summation method in S4 aggregates the optimized electric vehicle clusters to form time-of-use power directly applicable to day-ahead spot market bidding, providing a reference for electricity sales companies' day-ahead spot market bids.

[0013] Furthermore, the historical information of the electric vehicle cluster includes historical travel time, return time, daily mileage, battery capacity, battery charging power, and battery state of charge.

[0014] Furthermore, the virtual battery model is represented as follows:

[0015] Charge and discharge constraints:

[0016]

[0017]

[0018] Electricity change constraints:

[0019]

[0020] Charge and discharge behavior constraints:

[0021]

[0022] Battery capacity constraints:

[0023]

[0024] in, and These represent the charging and discharging power of electric vehicle n during time period t, respectively. and T represents the maximum charge / discharge power of the general VB model. n EV Let s represent the set of times when electric vehicle n connects to the grid. n,t and s n,t-1 η represents the battery charge of car n during time period t and the period preceding it, respectively. ch and η dis These represent the charging and discharging efficiency, respectively, and Δt represents the scheduling time. and Let X be the initial charging time and the time when charging stops for electric vehicle n. n,t X represents the state of electric vehicle n during time period t. n,t =1 indicates that electric vehicle n is in grid-connected state during time period t, s n,t Let n be the battery capacity of electric vehicle n during time period t. and This represents the upper and lower limits of the battery capacity of electric vehicle n.

[0025] Furthermore, in the aforementioned master-slave game pricing model between the electricity sales company and electric vehicles, the electricity sales company's decision variables are setting charging prices for electric vehicle users and its electricity purchase and sale decisions in the market. As the leader in the master-slave game, the electricity sales company's revenue comes from the charging fees charged by electric vehicle users. The objective function is to maximize the electricity sales company's revenue, calculated using the following formula:

[0026]

[0027] Among them, P t The charging price for the electricity sales company during period t, Q it Let P be the charging power of the i-th electric vehicle. d,t and Q d,t These represent the electricity purchase price and purchase volume of the electricity sales company during the day-ahead market period t, respectively. r,t and Q r,t These represent the electricity purchase price and purchase volume for the first phase of the real-time market during time period t.

[0028] The formula for calculating price constraints is:

[0029]

[0030] Among them, P t min and P t max These represent the lower and upper limits of the electricity sales company's pricing, respectively, where T is the dispatchable time period;

[0031] The formula for calculating the power constraint is:

[0032]

[0033] M is a positive number, representing the maximum electricity purchase volume that the electricity sales company can acquire in the market.

[0034] Furthermore, the electric vehicle users, acting as followers in a master-slave game, adjust their charging strategies according to the electricity sales company's pricing strategy. The objective function for electric vehicle users is to minimize total cost, calculated using the following formula:

[0035]

[0036] Electric vehicle battery limit:

[0037]

[0038] in, Let be the maximum battery capacity of the i-th electric vehicle. and These represent the expected state of charge and the initial state of charge of the i-th electric vehicle user when the user leaves the network.

[0039] Furthermore, the linearization of the master-slave game pricing model includes:

[0040] Construct the Lagrange function based on the user's objective function:

[0041]

[0042] Where, α i β it γ it and δ it Let be the dual variable of the Lagrange function;

[0043] Q it Taking the partial derivative and setting it to 0, we get:

[0044]

[0045]

[0046] Taking the partial derivatives with respect to each of the four dual variables and setting them equal to 0, we get:

[0047] ;

[0048]

[0049]

[0050]

[0051]

[0052] Introducing Boolean variables and Constructing inequalities:

[0053]

[0054]

[0055]

[0056]

[0057] The dual function of the nonlinear part is expressed as:

[0058]

[0059] Replacing the nonlinear part yields the final objective function:

[0060]

[0061] Furthermore, the Minkowski summation method specifically includes:

[0062] Minkowski summation can be expressed as:

[0063]

[0064] Where A and B represent two variable spaces, a and b are elements in them respectively, and A⊕B represents the Minkowski sum of the two variable spaces;

[0065] Using relaxed Minkowski summation, the charging / discharging boundaries and energy change boundaries of a single electric vehicle are extended to the charging / discharging boundaries and energy change boundaries of a group of electric vehicles. The calculation formula is as follows:

[0066]

[0067] N EV P represents the number of electric vehicle clusters. t ch,max P t dis,max S represents the maximum charge / discharge power of the virtual battery model. t For the charge boundary of the virtual battery model, ΔS t This represents the change in virtual battery charge caused by changes in the grid connection status of the electric vehicle.

[0068] A day-ahead pricing system for electricity retailers based on master-slave game theory, used to implement the aforementioned day-ahead pricing method for electricity retailers based on master-slave game theory, includes a data collection and processing module, a virtual battery model construction module, a master-slave game pricing model establishment module, a linearization processing module, and a Minkowski summation module, wherein;

[0069] The data collection and processing module is used to collect historical information of the electric vehicle cluster, including travel time, return time, daily mileage, and battery capacity.

[0070] The virtual battery model building module constructs a virtual battery model based on the charging and discharging behavior of electric vehicles to quantify the charging and discharging flexibility of electric vehicle clusters.

[0071] The master-slave game pricing model building module is used to establish a master-slave game model between electricity sales companies and electric vehicles, and to optimize day-ahead pricing through optimization strategies, aiming to maximize the interests of both parties.

[0072] The linearization module uses KKT conditions and the dualization principle to linearize the master-slave game model, thereby simplifying the problem-solving process.

[0073] The Minkowski summation module, based on the Minkowski summation method, determines the maximum charging and discharging power and energy boundary of the electric vehicle, which is used to form time-of-use power and then participate in the day-ahead spot market bidding.

[0074] The beneficial effects of this invention are:

[0075] This invention, compared with traditional electric vehicle charging and discharging pricing strategies, can fully consider the interests of electricity sales companies and electric vehicle owners, and reduce the impact of disorderly charging on the power grid.

[0076] This invention transforms multi-source variables into a single integrated decision variable by constructing a model, which can precisely characterize the charging and discharging behavior of electric vehicle clusters, quantify and integrate the flexible scheduling characteristics of large-scale electric vehicles, reduce the risk of leakage of personal information of individual electric vehicle owners, and effectively support electricity sales companies in participating in day-ahead spot market bidding. Attached Figure Description

[0077] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 This is a schematic diagram of the pricing method flow according to an embodiment of the present invention;

[0079] Figure 2 This is a schematic diagram of the functional modules of the pricing system according to an embodiment of the present invention. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0081] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0082] like Figure 1 As shown, a day-ahead pricing method for electricity retailers based on master-slave game theory includes the following steps:

[0083] A virtual battery model is constructed based on the charging and discharging behavior of electric vehicles;

[0084] Establish a master-slave game pricing model between electricity sales companies and electric vehicles;

[0085] The master-slave game pricing model is linearized using KKT conditions and the dualization principle;

[0086] The maximum charging and discharging power and energy boundary of electric vehicles are obtained based on the Minkowski summation.

[0087] Preferably, in the constructed virtual battery model of electric vehicle charging and discharging behavior, the historical information of the electric vehicle cluster, such as historical travel time, return time, daily mileage, battery capacity, battery charging power, and battery state of charge, is quantified through the virtual battery model to characterize the flexibility of electric vehicle charging and discharging behavior.

[0088] The constructed master-slave game pricing model between electricity sales companies and electric vehicles aims to maximize the interests of both parties by setting day-ahead prices in order to optimize the charging and discharging behavior of electric vehicles.

[0089] The constructed KKT conditions and dualization theorem are used to linearize the master-slave game pricing model, thereby improving the algorithm speed.

[0090] The constructed Minkowski summation method aggregates electric vehicle clusters after optimization to form time-of-use power that can be directly used for day-ahead spot market bidding, providing a reference for electricity sales companies' day-ahead spot market declarations.

[0091] Preferably, in the process of constructing the virtual battery model of electric vehicle charging and discharging behavior, the virtual battery model is represented as follows:

[0092] (1) Charge and discharge constraints:

[0093]

[0094] (2) Constraints on changes in electricity quantity:

[0095]

[0096] (3) Constraints on charging and discharging behavior:

[0097]

[0098] (4) Battery capacity constraints:

[0099]

[0100] and These represent the charging and discharging power of electric vehicle n during time period t, respectively. and T represents the maximum charge / discharge power of the general VB model. n EV Let s represent the set of times when electric vehicle n is connected to the grid; n,t and s n,t-1 η represents the battery charge of car n during time period t and the period preceding it, respectively; ch and η dis These represent the charging and discharging efficiency, respectively; Δt represents the scheduling time. and Let X be the initial charging time and the time when charging stops for electric vehicle n. n,t X represents the state of electric vehicle n during time period t. n,t =1 indicates that electric vehicle n is in grid-connected state during time period t; s n,t Let n be the battery capacity of electric vehicle n during time period t. and This represents the upper and lower limits of the battery capacity of electric vehicle n.

[0101] Preferably, in the process of constructing the master-slave game pricing model between the electricity sales company and the electric vehicle, the main decision variables for the electricity sales company are setting the charging price for electric vehicle users and making electricity purchase and sales decisions in the market.

[0102] As the leader in this master-slave game, the electricity sales company's revenue comes from the charging fees charged by electric vehicle users. Its objective function is to maximize its own revenue, which includes the following process:

[0103]

[0104] P t The charging price for the electricity sales company during period t; Q it P represents the charging power of the i-th electric vehicle. d,t and Q d,t These represent the electricity purchase price and purchase volume of the electricity company during the day-ahead market period t, respectively; P r,t and Q r,t These represent the electricity purchase price and volume for the first phase of the real-time market during time period t.

[0105] Price constraints:

[0106]

[0107] P t min and P t max These represent the lower and upper limits of the electricity sales company's pricing, respectively. T represents the dispatchable time period.

[0108] Power constraints:

[0109]

[0110] M is a sufficiently large positive number that can be taken as the maximum amount of electricity that the electricity sales company can purchase in the market.

[0111] Preferably, electric vehicle users act as followers in a master-slave game, adjusting their charging strategies according to the electricity company's pricing strategy. The objective function for electric vehicle users is to minimize total cost:

[0112]

[0113] Electric vehicle battery limit:

[0114]

[0115] Let be the maximum battery capacity of the i-th electric vehicle; and These represent the expected state of charge and the initial state of charge of the i-th electric vehicle user when the user leaves the network.

[0116] Preferably, in constructing the master-slave game pricing model between electricity sales companies and electric vehicles, it is necessary to use KKT conditions and dualization theorem to linearize the master-slave game pricing model to improve algorithm speed. Specifically, this includes the following process:

[0117] Construct the Lagrange function based on the user's objective function:

[0118]

[0119] α i β it γ it and δ it Let be the dual variable of the Lagrange function;

[0120] Q it Taking the partial derivative and setting it to 0, we get:

[0121]

[0122]

[0123] Taking the partial derivatives with respect to each of the four dual variables and setting them equal to 0, we get:

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] Introducing Boolean variables and Constructing inequalities:

[0130]

[0131]

[0132]

[0133]

[0134] Preferably, the first term of the objective function in the upper-level model is still nonlinear. The duality theorem of linear programming shows that the optimal solution of the dual function is the optimal solution of the original function. The dual function of the nonlinear part can be expressed as:

[0135]

[0136] Replacing the nonlinear part yields the final objective function:

[0137]

[0138] At this point, the entire master-slave game model has become a linear function.

[0139] Preferably, the constructed Minkowski summation method, through the aggregation of the optimized electric vehicle clusters, specifically includes the following process:

[0140] Minkowski addition is a type of addition applicable to Euclidean space. Its physical essence is an expansion set of multiple spaces, which can be represented as:

[0141]

[0142] In the formula, A and B represent two variable spaces; a and b are elements in these spaces; and A⊕B represents their Minkowski sum.

[0143] By employing relaxed Minkowski summation, the charging / discharging boundaries and energy change boundaries of a single electric vehicle are further extended to the charging / discharging boundaries and energy change boundaries of a group of electric vehicles, namely:

[0144] ;

[0145] N EV P represents the number of electric vehicle clusters. t ch,max P t dis,max S represents the maximum charge / discharge power of the virtual battery model.t For the charge boundary of the virtual battery model, ΔS t This represents the change in virtual battery charge caused by changes in the grid connection status of the electric vehicle.

[0146] like Figure 2 As shown, a day-ahead pricing system for electricity retailers based on master-slave game theory is used to implement the aforementioned day-ahead pricing method for electricity retailers based on master-slave game theory. It includes a data collection and processing module, a virtual battery model construction module, a master-slave game pricing model establishment module, a linearization processing module, and a Minkowski summation module, wherein;

[0147] The data collection and processing module is used to collect historical information of the electric vehicle cluster, including travel time, return time, daily mileage, and battery capacity, to provide necessary data support for the virtual battery model and subsequent pricing model.

[0148] The virtual battery model construction module constructs a virtual battery model based on the charging and discharging behavior of electric vehicles to quantify the charging and discharging flexibility of electric vehicle clusters. The virtual battery model is used to simulate the comprehensive charging behavior and power changes of electric vehicle groups.

[0149] The master-slave game pricing model building module is used to establish a master-slave game model between electricity sales companies and electric vehicles. It optimizes day-ahead pricing through strategies to maximize the interests of both parties. Electricity sales companies, as leaders, set charging prices and market electricity purchase and sales strategies, while electric vehicle users, as followers, adjust their charging strategies.

[0150] The linearization module uses KKT conditions and the dualization principle to linearize the master-slave game model, thereby simplifying the problem-solving process and improving the efficiency of the solution.

[0151] The Minkowski summation module, based on the Minkowski summation method, determines the maximum charging and discharging power and energy boundary of electric vehicles to form time-of-use power, which then participates in day-ahead spot market bidding, providing a reference for electricity sales companies and optimizing their day-ahead market bidding strategies.

[0152] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0153] This invention is intended to cover all such substitutions, modifications, and variations falling within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A day-ahead pricing method for electricity sales companies based on master-slave game theory, characterized in that, Includes the following steps: S1: Constructing a virtual battery model based on the charging and discharging behavior of electric vehicles; S2: Establish a master-slave game pricing model between electricity sales companies and electric vehicles; S3: Linearize the master-slave game pricing model using KKT conditions and the dualization principle; S4: Obtain the maximum charging and discharging power and energy boundary of electric vehicles based on Minkowski summation.

2. The day-ahead pricing method for electricity sales companies based on master-slave game theory as described in claim 1, characterized in that, The virtual battery model in S1 is used to quantify the historical information of the electric vehicle cluster in order to characterize the flexibility of the charging and discharging behavior of electric vehicles. The master-slave game pricing model in S2 optimizes the charging and discharging behavior of electric vehicles by setting day-ahead prices with the goal of maximizing the interests of both the electricity sales company and the electric vehicle. The Minkowski summation method in S4 aggregates the optimized electric vehicle clusters to form time-of-use power directly applicable to day-ahead spot market bidding, providing a reference for electricity sales companies' day-ahead spot market bids.

3. The day-ahead pricing method for electricity sales companies based on master-slave game theory as described in claim 2, characterized in that, The historical information of the electric vehicle cluster includes historical travel time, return time, daily mileage, battery capacity, battery charging power, and battery state of charge.

4. The day-ahead pricing method for electricity sales companies based on master-slave game theory as described in claim 3, characterized in that, The virtual battery model is represented as follows: Charge and discharge constraints: Electricity change constraints: Charge and discharge behavior constraints: Battery capacity constraints: in, and These represent the charging and discharging power of electric vehicle n during time period t, respectively. and T represents the maximum charge / discharge power of the general VB model. n EV Let s represent the set of times when electric vehicle n connects to the grid. n,t and s n,t-1 η represents the battery charge of car n during time period t and the period preceding it, respectively. ch and η dis They represent the charge / discharge efficiency, Δt represents the scheduling time, and T represents the discharge efficiency. n arrival and T n leave Let X be the initial charging time and the time when charging stops for electric vehicle n. n,t X represents the state of electric vehicle n during time period t. n,t =1 indicates that electric vehicle n is in grid-connected state during time period t, s n,t Let n be the battery capacity of electric vehicle n during time period t. and This represents the upper and lower limits of the battery capacity of electric vehicle n.

5. The day-ahead pricing method for electricity sales companies based on master-slave game theory as described in claim 4, characterized in that, In the aforementioned master-slave game pricing model between electricity sales companies and electric vehicles, the decision variables for the electricity sales company are setting charging prices for electric vehicle users and making electricity purchase and sales decisions in the market. As the leader in the master-slave game, the electricity sales company's revenue comes from the charging fees charged by electric vehicle users. The objective function is to maximize the electricity sales company's revenue, calculated using the following formula: Among them, P t The charging price for the electricity sales company during period t, Q it Let P be the charging power of the i-th electric vehicle. d,t and Q d,t These represent the electricity purchase price and purchase volume of the electricity sales company during the day-ahead market period t, respectively. r,t and Q r,t These represent the electricity purchase price and purchase volume for the first phase of the real-time market during time period t. The formula for calculating price constraints is: Among them, P t min and P t max These represent the lower and upper limits of the electricity sales company's pricing, respectively, where T is the dispatchable time period; The formula for calculating the power constraint is: M is a positive number, representing the maximum electricity purchase volume that the electricity sales company can acquire in the market.

6. The day-ahead pricing method for electricity sales companies based on master-slave game theory as described in claim 5, characterized in that, The electric vehicle users, acting as followers in a master-slave game, adjust their charging strategies according to the electricity sales company's pricing strategy. Their objective function is to minimize total cost, calculated using the following formula: Electric vehicle battery limit: in, Let be the maximum battery capacity of the i-th electric vehicle. and These represent the expected state of charge and the initial state of charge of the i-th electric vehicle user when the user leaves the network.

7. The day-ahead pricing method for electricity sales companies based on master-slave game theory as described in claim 6, characterized in that, The linearization of the master-slave game pricing model includes: Construct the Lagrange function based on the user's objective function: Where, α i β it γ it and δ it Let be the dual variable of the Lagrange function; Q it Taking the partial derivative and setting it to 0, we get: Taking the partial derivatives with respect to each of the four dual variables and setting them equal to 0, we get: ； Introducing Boolean variables and Constructing inequalities: The dual function of the nonlinear part is expressed as: Replacing the nonlinear part yields the final objective function:

8. The day-ahead pricing method for electricity sales companies based on master-slave game theory as described in claim 7, characterized in that, The Minkowski summation method specifically includes: Minkowski summation can be expressed as: Where A and B represent two variable spaces, a and b are elements in them respectively, and A⊕B represents the Minkowski sum of the two variable spaces; Using relaxed Minkowski summation, the charging / discharging boundaries and energy change boundaries of a single electric vehicle are extended to the charging / discharging boundaries and energy change boundaries of a group of electric vehicles. The calculation formula is as follows: N EV P represents the number of electric vehicle clusters. t ch,max P t dis,max S represents the maximum charge / discharge power of the virtual battery model. t For the charge boundary of the virtual battery model, ΔS t This represents the change in virtual battery charge caused by changes in the grid connection status of the electric vehicle.

9. A day-ahead pricing system for electricity retailers based on master-slave game theory, used to implement the day-ahead pricing method for electricity retailers based on master-slave game theory as described in any one of claims 1-8, characterized in that, It includes a data collection and processing module, a virtual battery model construction module, a master-slave game pricing model establishment module, a linearization processing module, and a Minkowski summation module, among which; The data collection and processing module is used to collect historical information of the electric vehicle cluster, including travel time, return time, daily mileage, and battery capacity. The virtual battery model building module constructs a virtual battery model based on the charging and discharging behavior of electric vehicles to quantify the charging and discharging flexibility of electric vehicle clusters. The master-slave game pricing model building module is used to establish a master-slave game model between electricity sales companies and electric vehicles, and to optimize day-ahead pricing through optimization strategies, aiming to maximize the interests of both parties. The linearization module uses KKT conditions and the dualization principle to linearize the master-slave game model, thereby simplifying the problem-solving process. The Minkowski summation module, based on the Minkowski summation method, determines the maximum charging and discharging power and energy boundary of the electric vehicle, which is used to form time-of-use power and then participate in the day-ahead spot market bidding.

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