CN-115271184-B - Guiding method, system and storage medium for participation of demand side in flexible interaction in consideration of social information influence

CN115271184BCN 115271184 BCN115271184 BCN 115271184BCN-115271184-B

Abstract

The invention discloses a guiding method, a system and a storage medium for a demand side to participate in flexible interaction in consideration of social information influence. The method comprises the steps of determining influence degrees of social information on electricity loads of users respectively, determining electricity consumption of the users according to the social information corresponding to the influence degrees, establishing an electricity seller multi-level market electricity purchasing model and an electricity seller electricity purchasing optimizing decision model, solving the electricity purchasing optimizing decision model to obtain optimal demand side adjustable loads, and guiding the users to participate in flexible interaction according to the optimal demand side adjustable loads. The invention can accurately calculate the relation between the guide information and the user load adjustment amount, and combines an optimization algorithm to obtain the optimal guide strategy, the obtained guide strategy can effectively achieve the optimization target, and meanwhile, plays a role in peak clipping and valley filling to a certain extent, solves the problem that the power load peak climbs to damage the stability of the power grid, and has certain social benefit and strong applicability.

Inventors

XIAO CHUPENG
ZHOU YU
GAO FAN
Mu Zhuowen
JIANG CHENG
WANG ZHONGDONG
WANG ZHENYU
ZHOU CHAO
WANG KUI
HU WENBO
WU KAIBIN
LI YUE

Assignees

国网电力科学研究院武汉能效测评有限公司
国网电力科学研究院有限公司
国网江苏省电力有限公司营销服务中心
国家电网有限公司

Dates

Publication Date: 20260508
Application Date: 20220714

Claims (8)

1. A guiding control method for the participation of a demand side in flexible interaction in consideration of social information influence is characterized by comprising the following steps: Determining the influence degree of each social information on the electricity load of the user, wherein the social information comprises a coupon coefficient, the historical load of the electricity of the user, the temperature and the humidity; Determining the electricity consumption of the user according to the social information corresponding to the influence degree; establishing a multi-level market electricity purchasing model of an electricity seller; establishing an electricity selling and purchasing optimization decision model based on the social information, the electricity consumption of the user and the multi-level market electricity purchasing model of the electricity seller; solving an electricity selling optimization decision model of electricity selling commercial by adopting a particle swarm algorithm and a CPLEX solver to obtain an optimal demand side adjustable load, and guiding a user to participate in flexible interaction according to the optimal demand side adjustable load; The coupon coefficient is determined by the following formula: Wherein cp i,t is the coupon denomination available to the user i at the time t, k t is the coupon coefficient at the time t, and delta q i,t is the load adjustment quantity of the user i at the time t; obtaining a load adjustment amount required to be achieved by the maximum coupon denomination for a user i at a time t; the maximum load adjustment quantity of the user i at the time t is obtained; for the maximum coupon denomination available to user i at time t, The electricity selling optimization decision model of the electricity selling commercial is as follows: max C=(C sell -C buy -C p -C cp ) C buy ＝B Y +B D The system comprises a power supply system, a power supply system and a power supply system, wherein C is total income of an electric seller, C sell is electricity selling income, C buy is electricity purchasing expenditure, C P is electricity payment settlement of deviation electric quantity paid by an electric company to a power generation company, C cp is coupon cost, I is a user set, p t is peak-valley time-sharing electricity price at T time, and T is a whole-day time set; The electricity consumption of the user i at the time t is calculated, wherein B Y is the electricity purchasing cost of the medium-long-term market, and B D is the electricity purchasing cost of the day-ahead market; the electric quantity is deviated for bilateral contracts of an electricity selling company at the time t; And The price is settled by positive and negative deviation electric quantity of the electric company respectively, alpha 1 and alpha 2 are 0/1 variables, when the deviation electric quantity of the electric company is positive alpha 1 ＝1,α 2 =0, and conversely alpha 1 ＝0,α 2 =1; the electricity price is the electricity price at the time t of the market in the day; L is the number of medium-and-long-term contracts signed by the e-commerce vendor; the electric quantity signed for the middle-long-term contract; the power decomposition ratio of the contract l at the time t is shown.
2. The method for guiding a demand side to participate in flexible interaction taking social information influence into consideration as set forth in claim 1, wherein the influence degree of social information on the user power consumption is quantified by a maximum information coefficient model, the maximum information coefficient model being Wherein MIC is the correlation between social information and user electricity load, X is social information, Y is user electricity load, n x ,n y is the number of grids of X axis and Y axis, G is the grid formed by n x ×n y , I G (X, Y) represents mutual information under grid G, B (n, alpha) =n α (0 < alpha < 1) is a function for limiting the maximum number of grids, and log 2 min(n x ,n y is a standardized term for ensuring MIC is in the range of 0 to 1.
3. The method for guiding the demand side to participate in the flexible interaction taking social information influence into consideration as set forth in claim 1, wherein the user electricity consumption is calculated through an Attention-LSTM load prediction model: x t ＝[Te j (t),h j (t),p j (t),k j (t),q j (t-1),q j (t-2),q j-1 (t),q j-1 (t-1)] Wherein: The method comprises the steps of obtaining electricity consumption of a user at a time t, obtaining f t as an electricity consumption load prediction function, obtaining x t as an input feature vector at the time t, obtaining j as a number of days, obtaining Te j (t) as a prediction temperature at the time of a predicted point, obtaining h j (t) as a prediction humidity at the time of the predicted point, obtaining p j (t) as an electricity price at the time of the predicted point, obtaining k j (t) as a coupon coefficient at the time of the predicted point, obtaining q j (t-1) as a load value at the time of the predicted point, obtaining q j (t-2) as a load value at two times of the predicted point, obtaining q j-1 (t) as a load value at the same time as the time of the predicted point and obtaining q j-1 (t-1) as a load value at the time of the predicted point.
4. The method for guiding the demand side to participate in the flexible interaction taking social information influence into consideration as set forth in claim 1, wherein the multi-level market electricity purchasing model of the seller comprises a medium-long-term market electricity purchasing model and a day-ahead market electricity purchasing model, which are respectively Wherein B Y is the electricity purchasing cost in the medium-long term market, B D is the electricity purchasing cost in the day-ahead market, Y is the medium-long term market, D is the day-ahead market, L is the medium-long term contract number signed by the electricity seller; the electric quantity signed for the middle-long-term contract; the price of the medium-and-long-term contract; for purchase in the market before date the electric quantity at the time t; The electricity price at the time t of the market in the day before.
5. The method for guiding the demand side to participate in the flexible interaction taking social information influence into consideration as set forth in claim 1, wherein the process of solving the electricity selling optimization decision model of the electricity selling commerce comprises the following steps: Step 1, inputting parameters required by an electricity selling optimization decision model of an electricity selling manufacturer; step 2, setting the number of particles and the maximum iteration number, wherein the particles are coupon coefficients; step 3, randomly generating an initial particle swarm, calculating fitness values of all individuals of the initial particle swarm, and simultaneously obtaining an individual extremum and a global extremum; Step 4, each particle follows two extremum to change its own speed and position, then compares with the individual extremum and the global extremum, if the individual extremum or the global extremum is better than the individual extremum or the global extremum, updates the individual extremum or the global extremum, and updates the position and the speed of each particle with the updated individual extremum and the global extremum to form new particles; step 5, calling a user electricity behavior analysis program, and solving the electricity consumption of each user at each moment through the Attention-LSTM based on new particles; step 6, calling an electricity seller optimization subroutine, and solving electricity seller purchase electricity selling benefits by using a CPLEX solver based on an electricity seller purchase electricity seller optimization decision model according to the maximum benefit as an objective function; and 7, adding one to the current iteration number, comparing the current iteration number with the set maximum iteration number, returning to the step 4 if the maximum iteration number is not reached, otherwise ending the iteration process, and determining the optimal demand side adjustable load by using the updated particles in the last iteration number.
6. The method for guiding the demand side to participate in the flexible interaction taking social information influence into consideration as set forth in claim 1, wherein the optimal demand side adjustable load is the electricity consumption of the user at the corresponding time of day determined based on the optimal coupon coefficient, and the user is guided to participate in load regulation according to the electricity consumption.
7. A system for implementing the method for guiding the demand side participation in flexible interaction taking social information influence into consideration according to any of claims 1 to 6, characterized by comprising The data acquisition module is used for acquiring social information data; the user electricity consumption prediction module is used for determining the influence degree of each social information on the user electricity consumption load respectively, and determining the user electricity consumption according to the social information corresponding to the influence degree; the model construction module is used for constructing a multi-level market electricity purchasing model of the electricity seller and constructing an electricity selling optimization decision model of the electricity seller based on the social information, the electricity consumption of the user and the multi-level market electricity purchasing model of the electricity seller; And the flexible interaction module is used for solving the electricity selling optimization decision model of the electricity selling business by adopting a particle swarm algorithm and a CPLEX solver to obtain the optimal demand side adjustable load, and guiding a user to participate in flexible interaction according to the optimal demand side adjustable load.
8. A computer-readable storage medium, in which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

Description

Guiding method, system and storage medium for participation of demand side in flexible interaction in consideration of social information influence Technical Field The invention belongs to the technical field of flexible interaction guidance on a demand side, and particularly relates to a guidance method, a system and a storage medium for flexible interaction of the demand side in consideration of social information influence. Background With the rapid development of social economy and science and technology, especially the popularization of intelligent equipment such as intelligent household appliances, the electricity consumption on the demand side is greatly increased, and the intelligent household appliances have great adjustable potential. The electronic vendor can adjust the adjustable load of the demand side by issuing reasonable and effective guiding information, and guides the user to participate in flexible interaction. The guiding information refers to a series of information issued by an electronic vendor on the Internet, and aims to guide users to reasonably use electricity and actively participate in flexible interaction. The guiding method for guiding users to reasonably use electricity by exciting guiding information such as time-of-use electricity price, energy-consumption coupons, social activity coupons, energy-saving information pushing and the like is receiving more and more attention. However, in the current research on the flexible interaction guiding method at the requirement side, users are all used as absolute rational individuals to participate in the interaction. However, in reality, the electricity consumption behavior of the user is not completely rational, the rationality degree of the electricity consumption decision is limited by factors such as known information, cognitive limitation and the like, the typical limited rationality characteristics are provided, and in the electricity consumption behavior, the user is a satisfaction but not a maximization, which means that the traditional guiding method cannot necessarily achieve the purpose of guiding the user to adjust the electricity consumption. In addition, the existing guiding method does not consider the influence of social information such as temperature, humidity, electricity price and the like on the electricity load of the user, the precision of the load change quantity under the action of the obtained guiding information is low, and the user may violate the theoretical optimal electricity adjustment quantity in actual life, so that the guiding information is invalid in action. Disclosure of Invention The invention aims to solve the defects in the background technology, and provides a guiding method, a system and a storage medium for taking the influence of social information into consideration and participating in flexible interaction on a demand side, so as to solve the problem that the stability of a power grid is damaged due to the rising of a power load peak. The technical proposal adopted by the invention is that a guiding method for the flexible interaction of the demand side considering the influence of social information, Determining the influence degree of each social information on the power load of the user; Determining the electricity consumption of the user according to the social information corresponding to the influence degree; establishing a multi-level market electricity purchasing model of an electricity seller; establishing an electricity selling and purchasing optimization decision model based on the social information, the electricity consumption of the user and the multi-level market electricity purchasing model of the electricity seller; and solving the electricity selling optimization decision model of the electricity selling business by adopting a particle swarm algorithm and a CPLEX solver to obtain an optimal demand side adjustable load, and guiding a user to participate in flexible interaction according to the optimal demand side adjustable load. Further, the influence degree of social information on the electricity load of the user is quantified through a maximum information coefficient model, wherein the social information comprises coupon coefficients, the historical load of the electricity of the user, temperature and humidity, and the maximum information coefficient model is that Wherein MIC is the correlation between social information and user electricity load, X is social information, Y is user electricity load, n x,ny is the number of grids of X axis and Y axis, G is the grid formed by n x×ny, I G (X, Y) represents mutual information under grid G, B (n, alpha) =n α (0 < alpha < 1) is a function for limiting the maximum number of grids, and log 2 min(nx,ny is a standardized term for ensuring MIC is in the range of 0 to 1. Further, the coupon coefficient is determined by the following formula: Wherein cp i,t is the coupon denomination available to the user i at the time t, k t is