CN-121981785-A - Day-ahead-real-time rolling pricing method and system for electricity selling company

CN121981785ACN 121981785 ACN121981785 ACN 121981785ACN-121981785-A

Abstract

The invention relates to the technical field of electric power markets, in particular to a day-ahead real-time rolling pricing method and system for an electric company, comprising the following steps of S1, constructing an evolution game replication dynamic equation for developing vehicle network interaction of the electric company-electric vehicle; S2, constructing an evolution game gain matrix, S3, constructing a day-ahead two-stage pricing optimization model, and S4, constructing a real-time rolling optimization model. According to the invention, the interactive behavior of the electric vehicle and the electric vehicle can be simulated through the replication dynamic equation of the evolution game, so that the economic benefits of the electric company and the electric vehicle users under different cooperation strategies can be effectively predicted and adjusted, a powerful theoretical support is provided through establishing a stable cooperation relationship through the evolution game benefit matrix, and an effective price and load management strategy is provided for the electric company in a complex and changeable spot market through constructing a day-ahead two-stage pricing optimization model and a real-time rolling optimization model.

Inventors

CHENG QILIN
LUO GUOZHONG
ZHU MING
ZHU GANGYI
CHEN SHIJUN
LI XIAOLU

Assignees

贵州电力交易中心有限责任公司

Dates

Publication Date: 20260505
Application Date: 20240416

Claims (9)

1. A day-ahead-real-time rolling pricing method for an electricity selling company, comprising the steps of: s1, constructing an evolution game replication dynamic equation of an electricity selling company-an electric vehicle for carrying out vehicle network interaction; S2, constructing an evolution game income matrix; S3, constructing a day-ahead two-stage pricing optimization model; And S4, constructing a real-time rolling optimization model.
2. The day-ahead real-time rolling pricing method for the electric company according to claim 1, wherein the evolution game replication dynamic equation reflects the income situation of the electric company and the electric vehicle under different participation strategies in a vehicle network interaction mode; The evolution game income matrix is combined with the Lyapunov stability theory to solve the probability expression of the trend cooperation result of both sides of the electric company-electric automobile user, and analyzes the influence of various factors on the cooperation of both sides to obtain the charge and discharge electricity price which is profitable for both sides of the electric company-electric automobile; The first stage of the day-ahead two-stage pricing optimization model is a pricing stage, an electricity selling company obtains charge and discharge prices of each period based on an evolution game income matrix according to load curve requirements, meanwhile obtains day-ahead market electricity purchasing quantity and electric vehicle owners electricity of each period, the second stage of the day-ahead two-stage pricing optimization model is a charge and discharge optimization stage, the electricity selling company formulates a charge and discharge trending strategy according to a first-stage electricity purchasing strategy and a charging plan of a user by taking benefits as objective functions to maximum, calculates required electricity purchasing quantity, supplements electricity purchasing in the day-ahead market, and distributes obtained benefits to the user according to vehicle network interaction cost and spot market profit conditions in a second stage according to preset proportions; and the real-time rolling optimization model takes the real-time electricity price as a reference, and the charging and discharging behaviors of the second stage of the day-ahead two-stage pricing optimization model are opposite to the real-time electricity price risk.
3. The day-ahead-real-time rolling pricing method for electric utility companies according to claim 2, wherein the evolving game replication dynamic equations include replication dynamic equations for electric utility companies to select collaboration strategies and replication dynamic equations for electric vehicle users to select collaboration strategies, wherein; the dynamic equation for copying the cooperation strategy selected by the electricity selling company specifically comprises the following steps: Expected benefits when the electricity selling company and the electric automobile user cooperate with the strategy are calculated, and the calculation formula is as follows: I prc1 ＝y[R prc1 -(pr d Q d +qr c Q c )-C k (pQ d +qQ c )-C o ]+(1-y)(R prc1 -B prc1 -B prc2 ); wherein, R prc1 is the service charge earned by the electric selling company, C k electric automobile provides the electric network loss cost caused by unit electric energy, C o electric selling company is used for guiding the announced cost of unit electric automobile cooperation, B prc1 electric selling company selects cooperation and electric automobile selects the cost of extra purchasing service for the electric selling company when not in cooperation, B prc2 electric selling company selects cooperation and electric automobile selects the cost of operating electric automobile behavior for the electric selling company when not in cooperation, R d electric selling company gives the electric automobile discharge patch, R c electric selling company gives the electric automobile charge patch, K b electric automobile user consumes the unit battery in the charging and discharging process, Q d electric selling company needs the electric automobile discharge electric quantity to be guided, Q c electric selling company needs the electric automobile charge electric quantity to be guided; The profit obtained by the electric company when the electric company and the electric automobile users select the cooperation strategy is R prc1 -(pr d Q d +qr c Q c )-C k N e -C o N e ,R prc1 -(pr d Q d +qr c Q c )-C k N e -C o N e , and the operation cost is formed by subtracting the operation cost from the service fee profit obtained by the electric company from the power grid, wherein the operation cost comprises charging and discharging interaction subsidy pr d Q d +qr c Q c given by the electric automobile by the electric company, and the service cost C k (pQ d +qQ c given by the electric automobile after the electric automobile is connected to the power grid), and the releasing cost C o for the electric automobile users to conduct charging and discharging actions is guided by the electric automobile; The profit obtained by the electric company when the electric vehicle user selects the cooperative strategy is R prc1 -B prc ,R prc1 -B prc , which is formed by subtracting the loss brought to the electric company when the electric vehicle is selected to be not in cooperation from the service fee profit obtained by the electric company from the power grid; The expected benefits when the electricity selling company selects the non-cooperative strategy are calculated, and the calculation formula is as follows: I prc2 ＝y(R prc1 -B prc1 )+(1-y)(R prc1 -B prc1 ); the average income of the electricity selling company is calculated, and the calculation formula is as follows: I prc ＝xI prc1 +(1-x)I prc2 ; Defining a replication dynamic equation of the electricity selling company selecting cooperation strategy, expressed as: F(x)=dx/dt=x(I prc1 -I prc )＝x(1-x)(I prc1 -I prc2 ) ＝x(1-x){-y[pr d Q d +qr c Q c +C k (pQ d +qQ c )+C o -B prc1 -B prc2 ]-B prc2 }; when the electric company issues the charging and discharging requirements of the electric automobile, p is 1, q is 0, and when the electric company issues the charging requirements of the electric automobile, p is 0, q is 1; The copying dynamic equation of the electric automobile user selection cooperation strategy specifically comprises the following steps: The expected benefits when the electric automobile user selects the cooperation strategy are calculated, and the calculation formula is as follows: I ev1 ＝x(p*(r d Q d -K b *Q d )+q*r c Q c )+(1-x)*0; The method comprises the steps that when an electric company and an electric automobile user select a cooperation strategy, the income obtained by the electric automobile user group is p (r d Q d -2*K b *Q d )+q*r c Q c , wherein p multipliers are the income obtained by electric automobile discharging and the battery loss cost when the electric company issues electric quantity demands, q multipliers are the income obtained by electric automobile charging when the electric company issues electric quantity demands, and when the electric automobile user selects the cooperation strategy and the electric company does not have cooperation demands, the income obtained by the electric automobile user group is 0; The expected benefit when the electric automobile user selects the non-cooperative strategy is calculated, and the calculation formula is as follows: I ev2 =x*0+(1-x)*0; The average income of the electric automobile user group is calculated, and the calculation formula is as follows: I ev ＝yI ev1 +(1-y)I ev2 ; defining a replication dynamic equation of the electric automobile user selection cooperation strategy, which is expressed as: F(y)=dy/dt=y(I ev1 -I ev )＝y(1-y)(I ev1 -I ev2 ) =y(1-y)x[p(r d -K b )Q d +qr c Q c ]。
4. The day-ahead real-time rolling pricing method for electric power selling companies according to claim 3, wherein the game evolution stabilization strategy of the evolution game income matrix is a cooperation strategy selected by the electric power selling companies and electric vehicles or a non-cooperation strategy selected by the electric power selling companies and electric vehicles, the relevant evolution equilibrium convergence of the game evolution stabilization strategy is ensured by the lyapunov method, both parties are benefited, and a stable cooperation interaction relation is formed, and the charge and discharge prices satisfy the following conditions:
5. the day-ahead real-time rolling pricing method for electricity vending companies according to claim 4, wherein the objective function in the second phase of the day-ahead two-phase pricing optimization model is expressed as: wherein eta is the proportion of the income obtained by the electricity selling company, And The electricity purchase price and the electricity purchase quantity of the second-stage electricity selling company in the time period t of the real-time market are respectively, And The electricity selling price and the electricity selling quantity of the second-stage electricity selling company in the time period t of the real-time market are respectively, And The electricity purchase price and the electricity purchase quantity are respectively the electricity purchase price and the electricity purchase quantity of the market t period before the second stage.
6. The day-ahead real-time rolling pricing method for electricity vending companies according to claim 5, wherein the constraint of the day-ahead two-stage pricing optimization model comprises: And (3) charge and discharge power constraint: Wherein, the And Respectively charging and discharging power of the ith electric automobile in the t period; 0-1 constraint of charge and discharge: Purchase electricity 0-1 constraint: purchase electricity balance constraint: Electric quantity balance constraint: Electric quantity continuity constraint: e 2,it is the electric quantity of the ith electric automobile user in the t period.
7. The day-ahead-real-time rolling pricing method for electricity-selling companies according to claim 6, wherein the real-time rolling optimization model specifically comprises: Setting constraint and target that the electricity selling company re-formulates the charge and discharge strategy of the second stage by taking the strategy result of the first two stages of days as constraint condition and taking the maximum income of the electricity selling company as objective function; Real-time market transaction decision, namely, carrying out electricity purchasing and selling decision in the real-time market to balance electric quantity, and carrying out transfer transaction of a contract in the future by an electricity selling company in the real-time market so as to improve the flexibility of strategies and optimize resource allocation; The rolling optimization execution is carried out, namely the rolling optimization execution is repeated when the charging and discharging actions of the user are completed before the T period and the charging and discharging strategies before the T period are reserved as constraint conditions and rolled to the T+1 period, and the rolling optimization execution is finished after the rolling update is completed for a plurality of times; Optimizing the objective function, namely optimizing the objective function to obtain the maximum income of the optimized electricity selling company, wherein the maximum income is expressed as: wherein, eta is EVLA, the obtained benefits are proportional, For the real value of the real-time electricity price, And The electricity purchasing quantity and the electricity selling quantity of the second stage EVLA after the real-time rolling optimization in the period t of the real-time market are respectively, And The electricity purchase price and the electricity purchase quantity are respectively the electricity purchase price and the electricity purchase quantity of the market t period before the second stage.
8. The day-ahead-real-time rolling pricing method for electricity-selling companies according to claim 7, wherein the constraints of the real-time rolling optimization model comprise: And (3) charge and discharge power constraint: Wherein, the And Charging and discharging power of the ith V2G user EV in the t-th period after real-time rolling optimization is respectively calculated; 0-1 constraint of charge and discharge: Purchase electricity 0-1 constraint: purchase electricity balance constraint: Electric quantity balance constraint: Electric quantity continuity constraint:
9. A day-ahead real-time roll pricing system for electric power selling companies for implementing a day-ahead real-time roll pricing method for electric power selling companies according to any one of claims 1-8, comprising the following modules: The evolution game copy dynamic equation construction module is used for simulating the interaction behavior of the electric company and the vehicle network of the electric vehicle; The evolution game income matrix construction module is used for evaluating the income under different interaction strategies; The day-ahead two-stage pricing optimization module is used for setting charge and discharge prices and electricity purchasing quantity in advance according to predicted load demands and market conditions; And the real-time rolling optimization module is responsible for adjusting the charging and discharging strategy according to the real-time market information so as to counter the price fluctuation and the operation risk.

Description

Day-ahead-real-time rolling pricing method and system for electricity selling company Technical Field The invention relates to the technical field of electric power markets, in particular to a day-ahead real-time rolling pricing method and system for an electricity selling company. Background At present, a novel power system taking new energy as a main body is actively constructed, the power system is driven to balance the transition from source following to load following, flexible resources on the demand side such as an electric automobile and the like are used as a part of power terminal consumption, a huge effect is exerted, an electricity selling company is used as an intermediate main body for connecting the wholesale side and the user side of the electric market, and benefits can be obtained by guiding the charging and discharging behaviors of the electric automobile, and low buying and high selling welfare is available in the real-time spot market in the day. With the gradual replacement of traditional fuel automobiles by new energy electric automobiles, how to guide electric automobile users to respond to price signals of electric companies, receive charge and discharge behavior scheduling, acquire additional charge benefits, and have important significance for the management of the electric companies and the reduction of the cost of the electric automobiles, but the current electric market construction and the vehicle network interaction are still in a starting stage, the current research cannot effectively combine the charge and discharge cost of the electric automobiles and the price risk of spot markets, so that certain challenges are brought to the development of the vehicle network interaction of the electric companies, and a method for real-time rolling pricing of the electric vehicles, namely the electric vehicles in the future, is urgently needed, and support is provided for the electric companies to proxy the electric vehicles to participate in the spot markets. At present, new energy automobiles gradually replace traditional fuel automobiles, become the main stream of the market, how to guide electric automobiles to accept price signals of electric companies and orderly conduct charging and discharging actions has important significance for safe and stable operation of power grids and operation of the electric companies, but at present, the response of the demand side of China is still in a starting stage, the conventional electric automobile excitation means cannot provide direct reference for the electric companies to participate in day-ahead market bidding, and a day-ahead pricing method and system for the electric companies and electric automobiles are urgently needed to provide support for the electric companies to participate in day-ahead market bidding. Disclosure of Invention Based on the above purpose, the invention provides a day-ahead real-time rolling pricing method and system for an electricity selling company. A day-ahead-real-time rolling pricing method for an electricity selling company, comprising the steps of: s1, constructing an evolution game replication dynamic equation of an electricity selling company-an electric vehicle for carrying out vehicle network interaction; S2, constructing an evolution game income matrix; S3, constructing a day-ahead two-stage pricing optimization model; And S4, constructing a real-time rolling optimization model. Furthermore, the evolution game copy dynamic equation reflects the income conditions of the electric company and the electric automobile under different participation strategies in the vehicle network interaction mode; The evolution game income matrix is combined with the Lyapunov stability theory to solve the probability expression of the trend cooperation result of both sides of the electric company-electric automobile user, and analyzes the influence of various factors on the cooperation of both sides to obtain the charge and discharge electricity price which is profitable for both sides of the electric company-electric automobile; The first stage of the day-ahead two-stage pricing optimization model is a pricing stage, an electricity selling company obtains charge and discharge prices of each period based on an evolution game income matrix according to load curve requirements, meanwhile obtains day-ahead market electricity purchasing quantity and electric vehicle owners electricity of each period, the second stage of the day-ahead two-stage pricing optimization model is a charge and discharge optimization stage, the electricity selling company formulates a charge and discharge trending strategy according to a first-stage electricity purchasing strategy and a charging plan of a user by taking benefits as objective functions to maximum, calculates required electricity purchasing quantity, supplements electricity purchasing in the day-ahead market, and distributes obtained benefits to the user according to vehicle network inter