CN-121998672-A - Electricity selling company day-ahead pricing method and system based on master-slave gaming

CN121998672ACN 121998672 ACN121998672 ACN 121998672ACN-121998672-A

Abstract

The invention relates to the technical field of electric power markets, in particular to a day-ahead pricing method and system for an electricity selling company based on master-slave gaming, comprising the following steps of constructing a virtual battery model based on charging and discharging behaviors of an electric vehicle; the method comprises the steps of establishing a master-slave game pricing model of an electric company-electric vehicle, linearizing the master-slave game pricing model through KKT conditions and a dual-coupling principle, and obtaining the maximum charge and discharge power and electric quantity boundary of the electric vehicle based on Minkowski summation. Compared with the traditional electric vehicle charging and discharging pricing strategy, the method can consider the benefits of an electric company and an electric vehicle owner, reduce impact of disordered charging on a power grid, convert a multisource variable into an integrated decision variable through a built model, describe electric vehicle cluster charging and discharging behaviors, realize quantification and integration of flexible scheduling characteristics of large-scale electric vehicles, and effectively support the participation of the electric company in daily spot market bidding.

Inventors

LUO GUOZHONG
CHENG QILIN
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: 20260508
Application Date: 20240408

Claims (9)

1. A day-ahead pricing method of an electricity selling company based on master-slave gaming is characterized by comprising the following steps: s1, constructing a virtual battery model based on charging and discharging behaviors of an electric vehicle; s2, establishing a master-slave game pricing model of an electric company-electric vehicle; s3, linearizing a master-slave game pricing model through KKT conditions and a dual-coupling principle; and S4, obtaining the maximum charge and discharge power and electric quantity boundary of the electric vehicle based on the Minkowski summation.
2. The day-ahead pricing method for the electric company based on master-slave gaming according to claim 1, wherein the virtual battery model in S1 is used for quantifying historical information of an electric vehicle cluster so as to characterize flexibility of charging and discharging behaviors of the electric vehicle; the master-slave game pricing model in the S2 optimizes the charging and discharging behaviors of the electric vehicle by day-ahead pricing with the aim of maximizing benefits of both electric company and electric vehicle; The Minkowski summation method in the S4 forms the time-sharing power directly used for bidding the current spot market in the day through the aggregation of the electric vehicle clusters after the optimization, and provides a reference for the current spot market declaration of the electric company.
3. The method for pricing day-ahead of an electric company based on master-slave gaming according to claim 2, wherein 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. A method for day-ahead pricing of electricity-selling companies based on master-slave gaming according to claim 3, wherein the virtual battery model is expressed as: and (3) charge and discharge constraint: electric quantity change constraint: and (3) constraint of charge and discharge behaviors: battery capacity constraints: Wherein, the And Respectively representing the charge and discharge power of the electric vehicle n in the period t, And For the maximum charge and discharge power of the general VB model, T n EV represents a grid-connected time set of the electric vehicle n, s n,t and s n,t-1 respectively represent the battery electric quantity of the electric vehicle n in the period T and the last period thereof, eta ch and eta dis respectively represent charge and discharge efficiency, delta T represents scheduling time, T n arrival and T n leave represent initial charge time and stop charge time of the electric vehicle n, X n,t represents the state of the electric vehicle n in the period T, X n,t =1 represents the grid-connected state of the electric vehicle n in the period T, s n,t represents the battery capacity of the electric vehicle n in the period T, And The upper and lower limits of the battery capacity of the electric vehicle n are indicated.
5. The day-ahead pricing method of the electric company based on master-slave gaming of claim 4, wherein in the master-slave gaming pricing model of the electric company-electric vehicle, the decision variables of the electric company are charging prices formulated for users of the electric vehicle and electricity purchasing and selling decisions in the market, the electric company is taken as a leader of master-slave gaming, benefits come from charging fees of the users of the electric vehicle, the objective function is maximization of benefits of the electric company, and the calculation formula is as follows: Wherein P t is the charging price of the electricity selling company in the t period, Q it is the charging power of the ith electric vehicle, P d,t and Q d,t are the electricity purchasing price and electricity purchasing quantity of the electricity selling company in the t period of the market in the day before, and P r,t and Q r,t are the electricity purchasing price and electricity purchasing quantity of the electricity purchasing company in the t period of the real-time market in the first stage; the calculation formula of the price constraint is as follows: Wherein, P t min and P t max are respectively the lower limit and the upper limit of pricing for the electric company, and T is the schedulable period; The calculation formula of the electric quantity constraint is as follows: m is a normal number, and the maximum electricity purchasing quantity of an electricity selling company in the market is taken.
6. The method for pricing the day ahead of the electric company based on the master-slave game according to claim 5, wherein the electric vehicle user is used as a follower of the master-slave game, the charging strategy of the electric vehicle user is adjusted along with the pricing strategy of the electric company, the objective function of the electric vehicle user is total cost minimization, and the calculation formula is as follows: Electric quantity constraint of electric vehicle: Wherein, the For the maximum battery capacity of the ith electric vehicle, And The expected state of charge and the initial state of charge of the ith electric vehicle user when off-grid are respectively determined.
7. The master-slave gaming-based day-ahead pricing method of an electric company of claim 6, wherein the linearization of the master-slave gaming pricing model comprises: Constructing a Lagrangian function according to the user objective function: Wherein α i 、β it 、γ it and δ it are the dual variables of the lagrangian function; the bias of Q it and its equality to 0 yields: Respectively solving bias derivatives for four dual variables and enabling the bias derivatives to be equal to 0 to obtain: ; Introduction of Boolean variable And Building an inequality: the dual function of the nonlinear section is expressed as: Replacing the nonlinear part thereof to obtain a final objective function as follows:
8. A method for front-of-day pricing of an electric utility based on master-slave gaming as claimed in claim 7, wherein said minkowski summation method comprises: the minkowski summation can be expressed as: Wherein A and B represent 2 variable spaces, a and B are elements therein, respectively, A is a Minkowski sum of the 2 variable spaces; The loose Minkowski summation is adopted, the charging and discharging boundary and the electric quantity change boundary of a single electric vehicle are expanded to the charging and discharging boundary and the electric quantity change boundary of an electric vehicle group, and the calculation formula is as follows: N EV is the number of clusters of the electric vehicles, P t ch,max ,P t dis,max is the maximum charge and discharge power of the virtual battery model, S t is the electric quantity boundary of the virtual battery model, and delta S t is the electric quantity change quantity of the virtual battery caused by the grid-connected state change of the electric vehicles.
9. A front-of-day pricing system of an electric company based on master-slave gaming for realizing the front-of-day pricing method of the electric company based on master-slave gaming as claimed in any one of claims 1-8, characterized by comprising a data collection and processing module, a virtual battery model building module, a master-slave gaming pricing model building module, a linearization processing module and a minkowski summation module, wherein; The data collection and processing module is used for collecting historical information of the electric vehicle cluster, including travel time, return time, daily driving mileage and battery capacity; The virtual battery model building module builds a virtual battery model based on the charging and discharging behaviors of the electric vehicles to quantify the charging and discharging flexibility of the electric vehicle clusters; The master-slave game pricing model building module is used for building a master-slave game model between an electricity selling company and an electric vehicle, and day-ahead pricing is carried out through an optimization strategy, so that benefits of the two parties are maximized; The linearization processing module linearizes the master-slave game model by using KKT conditions and a dual-coupling principle so as to simplify problem solving; The Minkowski summation module determines the maximum charge and discharge power and electric quantity boundary of the electric vehicle based on the Minkowski summation method and is used for forming time-sharing power so as to participate in day-ahead spot market bidding.

Description

Electricity selling company day-ahead pricing method and system based on master-slave gaming Technical Field The invention relates to the technical field of electric power markets, in particular to a day-ahead pricing method and system for an electricity selling company based on master-slave gaming. Background The ordered pushing of the electric power market reform promotes the development of flexible demand response resources mainly of electric vehicles, and as an important component of terminal energy consumption, the charging and discharging behaviors of the electric vehicles have important influence on the real-time balance of a power grid, an electricity selling company can take the place of a user to purchase electricity in an electric energy market and provide charging service for the user of the electric vehicles, the user can purchase low-price electric quantity in the market before the day and sell the electric vehicles to the user for profit at a charging price which is higher than the cost and lower than the real-time electricity price in the real-time charging process, and at the moment, both the electricity selling company and the user can obtain certain profits. At present, new energy automobiles gradually replace traditional fuel automobiles and become the main stream of the market, but the existing electric vehicle excitation means cannot provide direct reference for the participation of electric companies in day-ahead market bidding, and an electric company-electric vehicle day-ahead pricing method and system are urgently needed to provide support for the participation of electric companies in day-ahead market bidding. Disclosure of Invention Based on the purposes, the invention provides a day-ahead pricing method and system for an electricity selling company based on master-slave gaming. A day-ahead pricing method for an electricity selling company based on master-slave gaming comprises the following steps: s1, constructing a virtual battery model based on charging and discharging behaviors of an electric vehicle; s2, establishing a master-slave game pricing model of an electric company-electric vehicle; s3, linearizing a master-slave game pricing model through KKT conditions and a dual-coupling principle; and S4, obtaining the maximum charge and discharge power and electric quantity boundary of the electric vehicle based on the Minkowski summation. Further, the virtual battery model in the S1 is used for quantifying history information of the electric vehicle cluster, so as to characterize flexibility of charging and discharging behaviors of the electric vehicle; the master-slave game pricing model in the S2 optimizes the charging and discharging behaviors of the electric vehicle by day-ahead pricing with the aim of maximizing benefits of both electric company and electric vehicle; The Minkowski summation method in the S4 forms the time-sharing power directly used for bidding the current spot market in the day through the aggregation of the electric vehicle clusters after the optimization, and provides a reference for the current spot market declaration of the electric company. Further, the historical information of the electric vehicle cluster comprises historical trip time, return time, daily driving mileage, battery capacity, battery charging power and battery charge state. Further, the virtual battery model is expressed as: and (3) charge and discharge constraint: electric quantity change constraint: and (3) constraint of charge and discharge behaviors: battery capacity constraints: Wherein, the AndRespectively representing the charge and discharge power of the electric vehicle n in the period t,AndFor the maximum charge and discharge power of the general VB model, T nEV represents a grid-connected time set of the electric vehicle n, s n,t and s n,t-1 respectively represent the battery electric quantity of the vehicle n in the period T and the last period thereof, eta ch and eta dis respectively represent the charge and discharge efficiency, delta T represents the scheduling time,AndFor the initial charging time and the stop charging time of electric vehicle n, X n,t represents the state of electric vehicle n in the t period, X n,t =1 represents the grid-connected state of electric vehicle n in the t period, s n,t is the battery capacity of electric vehicle n in the t period,AndThe upper and lower limits of the battery capacity of the electric vehicle n are indicated. Furthermore, in the master-slave game pricing model of the electric vehicle and the electric vehicle, the decision variables of the electric vehicle are the charging price formulated for the users of the electric vehicle and the electricity purchasing and selling decision in the market, the electric vehicle is taken as the leader of master-slave game, the benefits are derived from the charging cost of the users of the electric vehicle, the objective function is that the benefits of the electric vehicle are maximized, a