CN111080069A - Resident demand response model based on multi-dimensional factor game - Google Patents
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Abstract
The invention discloses a resident demand response model based on multidimensional factor game, which relates to the technical field of power systems and comprises the following steps: s1, simulating a residential user utility function model; s2, simulating a profit function model of the electricity vendor; s3, the strategy of the electricity vendor and the residential user is interacted; s4, establishing an evolutionary game model among the residential users; s5, establishing a non-cooperative price competition game model among the electricity vendors; s6, the optimal load reduction input quantity and the most reasonable load scheduling strategy are obtained by solving the non-cooperative game model, and the method can perform optimal real-time user load scheduling, so that the profit of the aggregator is maximized under the condition of ensuring the comfort of the user.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a resident demand response model based on multi-dimensional factor gaming.
Background
The demand side is used as an important component of the smart power grid, the regulation and control function of demand side resources in the power grid is gradually shown, and bidirectional interaction is carried out with the power grid in a demand response mode. In the demand-side resource, the electricity quantity of the residential users accounts for 36.6% of the total electricity consumption of the society, but the load elasticity level of the single user is low and cannot reach the minimum level participating in demand response. Thus, customer flexible load resources may be aggregated by the load aggregator to reach a minimum level of participation in demand response, thereby participating in grid dispatch. As an emerging independent electricity selling organization, an aggregator responds to resource selling to a power company dispatching department by integrating demands and obtains certain profit from the resource selling. The appearance of the aggregator can introduce the demand response resources of the resident user side into market trading, improve the benefit of demand response, help the user to form an efficient power utilization mode and improve the power utilization efficiency of the terminal.
Demand charges are also implemented as an alternative to residential users with distributed photovoltaics. For an electric power company, how to reasonably optimize a demand electricity price scheme makes it important that the electric power company can obtain the maximum benefit while meeting the income requirement. The prior art considers a two-part electricity rate with a fixed access fee imposed on each user, which is only suitable for residential users and does not control the load factor.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a resident demand response model based on multidimensional factor gaming.
In order to achieve the purpose, the invention adopts the following technical scheme:
a resident demand response model based on multi-dimensional factor game comprises the following steps:
s1, simulating a residential user utility function model;
s2, simulating a profit function model of the electricity vendor;
s3, the strategy of the electricity vendor and the residential user is interacted;
s4, establishing an evolutionary game model among the residential users;
s5, establishing a non-cooperative price competition game model among the electricity vendors;
and S6, solving the non-cooperative game model and the real-time scheduling model to obtain the optimal load reduction input quantity and the most reasonable load scheduling strategy by the load aggregator.
Preferably, the specific algorithm of S1 is that the power demand of the user i after selecting an electricity vendor for himself/herself in the time period k is xi,k,xi,min,k≤xi,k≤xi,max,k,xi,min,kAnd xi,max,kRespectively representing the minimum and maximum electricity consumption of the user i in the time period k, and the real-time electricity demand vectors of all the users in the time period k are available
The user power utilization personal preference and satisfaction degree are expressed by adopting a quadratic utility function ui,k(xi,k,ωi,k) In the course of the description, it is,
in the formula: omegai,kRepresenting the value of electricity consumed by different users for parameters which differ over time and by user αkIs a parameter preset in advance.
Preferably, the specific algorithm of S2 is a cost function C of the electricity vendor jj,kThe real-time electricity quantity cost of purchasing from the electric power wholesale market is defined as a time period k and is recorded as: cj,k＝pLj,kIn the formula: p is the power wholesale price, here set to a constant; l isj,kFor the electric quantity purchased by the electricity seller j in the wholesale market in the time period k, the profit function of the electricity seller j is as follows: rj,k(pj,k,sj,k)＝pj,ksj,k-pLj,kIn the formula: sj,kThe amount of electricity sold by the electricity vendor j in the retail market for time period k, sj,k＝min(Lj,k,Dj,k),Dj,k，Dj,kAnd (4) the total electric quantity demand of the power users serving the electricity vendor j.
Preferably, the specific process of S3 assumes that the electricity vendor obtains a large profit at a low cost, and the user obtains maximum satisfaction and welfare at a low payment, so that an appropriate strategy needs to be taken to maintain the power supply and demand balance between the electricity vendor and the user.
Preferably, the specific algorithm of S4 is that, after each user receives the electricity prices announced by all electricity vendors, each user selects one electricity vendor to purchase the electricity, then gradually observes and copies the policy of others to adjust its behavior, and considering the personal privacy factor, each residential user does not know the electricity consumption selection behavior of other users, so that each user selects the policy of electricity vendor to purchase the electricity as a hybrid policy, and if the probability that the user selects electricity vendor j in time period k is
Then the population status is available
And (4) showing.
Preferably, the specific algorithm of S5 is that, at the electric power retail end, each electricity seller is both profit-oriented and rational, and the goal is to maximize the profit by selling electric power to the users, so that the non-cooperative game is used to simulate price competition among the electricity sellers, and in the non-cooperative game generated among the electricity sellers, the person in the game is the electricity seller, and the price p isj,kIs the strategy of the electricity seller j in the time period k, and the profit function of the electricity seller j is
The invention has the beneficial effects that:
the invention provides a demand response model based on principal and subordinate game and dynamic real-time electricity price for users in an intelligent residential district with a plurality of electricity vendors, proves and analyzes the existence of game behavior balance among all participants in the provided demand response model, designs an algorithm to realize a balance strategy, and shows a numerical simulation result, the demand response model can quickly form demand response behaviors based on dynamic pricing between electricity vendors and users, verify the reasonability and the effectiveness of the demand response behaviors, finally reach a supply and demand balance state, simultaneously solve a non-cooperative game model, namely, a Nash equilibrium solution is searched, the aggregators obtain the optimal load reduction input quantity in the peak load period, and according to the physical characteristics of the electricity consumption of the three types of loads of the users, the optimal real-time scheduling of the user loads is carried out based on the bid amount, so that the profit of the aggregator is the maximum under the condition of ensuring the comfort of the users.
Detailed Description
The following will clearly and completely describe the technical solutions in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
The embodiment provides a resident demand response model based on multi-dimensional factor game, which comprises the following steps:
s1, simulating a utility function model of the residential user, wherein the power demand of the user i in the time period k after selecting an electricity vendor is xi,k,xi,min,k≤xi,k≤xi,max,k,xi,min,kAnd xi,max,kRespectively representing the minimum and maximum electricity consumption of the user i in the time period k, and the real-time demand vectors of the electricity of all the users in the time period k are available
The user power utilization personal preference and satisfaction degree adopt a secondary utility function ui,k(xi,k,ωi,k) In the course of the description, it is,
in the formula: omegai,kRepresenting the value of electricity consumed by different users for parameters which differ over time and by user αkIs a preset parameter;
s2, simulating profit function model of electricity vendor, cost function C of electricity vendor jj,kIs defined as a period k of purchasing real-time electricity from the electric power wholesale marketCost, noted as: cj,k＝pLj,kIn the formula: p is the power wholesale price, here set to a constant; l isj,kFor the electric quantity purchased by the electricity seller j in the wholesale market in the time period k, the profit function of the electricity seller j is as follows: rj,k(pj,k,sj,k)＝pj,ksj,k-pLj,kIn the formula: sj,kThe amount of electricity sold by the electricity vendor j in the retail market for time period k, sj,k＝min(Lj,k,Dj,k),Dj,k，Dj,kThe total electric quantity requirement of the power consumer serving the electricity vendor j;
s3, the electricity vendor interacts with the policy of the residential user, assuming that the electricity vendor gets a large profit at a low cost and the user gets maximum satisfaction and welfare at a low payment, so it is necessary to adopt an appropriate policy to maintain the balance between electricity supply and demand between the electricity vendor and the user;
s4, establishing an evolutionary game model among residential users, selecting one electricity vendor to purchase electricity after each user receives electricity prices announced by all electricity vendors, gradually observing and copying strategies of others to adjust the behaviors of the electricity vendors, considering personal privacy factors, each residential user does not know electricity consumption selection behaviors of other users, therefore, the strategy that each user selects electricity vendors to purchase electricity is a mixed strategy, and if the probability that the user selects electricity vendor j in a time period k is
Then the population status is available
Represents;
s5, establishing a non-cooperative price competition game model among the electricity vendors, wherein each electricity vendor is convergent and rational at the electricity retail end, the aim is to maximize the profit by selling electricity to the users, therefore, the non-cooperative game is used for simulating the price competition among the electricity vendors, in the non-cooperative game generated among the electricity vendors, the person in the office is the electricity vendor, and the price p isj,kIs the strategy of the electricity seller j in the time period k, and the profit function of the electricity seller j is shown in the formulaNumber is
And S6, solving the non-cooperative game model and the real-time scheduling model to obtain the optimal load reduction input quantity and the most reasonable load scheduling strategy by the load aggregator.
Meanwhile, the power consumption parameters of the power grid monitored in real time are shown in the following table:
| time of day | 1 | 2 | 3 | 4 | 5 | 6 |
| Load (kWh) | 0 | 0 | 0 | 0 | 0 | 0 |
| Time of day | 8 | 9 | 10 | 11 | 12 | 13 |
| Load (kWh) | 0 | 2.371 | 14.238 | 26.318 | 33.492 | 37.492 |
| Time of day | 14 | 16 | 17 | 18 | 19 | 20 |
| Load (kWh) | 35.324 | 28.341 | 19.234 | 9.323 | 3.291 | 0.134 |
| Time of day | 21 | 22 | 23 | 24 | 25 | 26 |
| Load (kWh) | 0 | 0 | 0 | 0 | 0 | 0 |
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and equivalent substitutions or changes according to the technical solution and the inventive concept of the present invention should be covered by the scope of the present invention.
Claims (6)
1. A resident demand response model based on multi-dimensional factor gaming is characterized by comprising the following steps:
s1: simulating a residential user utility function model, and calculating values brought by electricity consumption of different users through the function model;
s2: simulating a profit function model of the electricity vendor, and calculating a profit value of the electricity vendor according to the profit function model;
s3: the electric power vendor interacts with the strategy of the residential user to maintain the power supply and demand balance between the electric power vendor and the user;
s4: establishing an evolutionary game model among residential users;
s5: establishing a non-cooperative price competition game model among the electricity vendors so that a user can select a strategy of purchasing electric quantity by the electricity vendors;
s6: by solving the non-cooperative game model and the real-time scheduling model, the load aggregator obtains the optimal load reduction input quantity and the most reasonable load scheduling strategy.
2. The resident demand response model based on multi-dimensional factor gaming according to claim 1, wherein the specific algorithm of S1 is that users in time period ki is the power demand x after selecting an electricity vendor for oneselfi,k,xi,min,k≤xi,k≤xi,max,k,xi,min,kAnd xi,max,kRespectively representing the minimum and maximum electricity consumption of the user i in the time period k, and the real-time demand vectors of the electricity of all the users in the time period k are available
The user power utilization personal preference and satisfaction degree are expressed by adopting a quadratic utility function ui,k(xi,k,ωi,k) In the course of the description, it is,
in the formula: omegai,kRepresenting the value of electricity consumed by different users for parameters which differ over time and by user αkIs a parameter preset in advance.
3. The resident demand response model based on multi-dimensional factor gaming according to claim 1, wherein the specific algorithm of S2 is a cost function C of electricity vendor jj,kThe real-time electricity quantity cost of purchasing from the electric power wholesale market is defined as a time period k and is recorded as: cj,k＝pLj,kIn the formula: p is the power wholesale price, here set to a constant; l isj,kFor the electric quantity purchased by the electricity seller j in the wholesale market in the time period k, the profit function of the electricity seller j is as follows: rj,k(pj,k,sj,k)＝pj,ksj,k-pLj,kIn the formula: sj,kThe amount of electricity sold by the electricity vendor j in the retail market for time period k, sj,k＝min(Lj,k,Dj,k),Dj,k，Dj,kAnd (4) the total electric quantity demand of the power users serving the electricity vendor j.
4. The resident demand response model based on multi-dimensional factor gaming according to claim 1, wherein the specific process of S3 is that the electricity seller obtains greater profit at lower cost, and the user obtains maximum satisfaction and benefits at lower payment, so that proper strategies are adopted to maintain the balance between electricity supply and demand between the electricity seller and the user.
5. The residential demand response model based on multidimensional factor gaming as claimed in claim 1, wherein the specific algorithm of S4 is that, after each user receives the electricity prices announced by all electricity vendors, each user selects one electricity vendor to purchase electricity, then gradually observes and copies the policy of others to adjust its behavior, and considering the personal privacy factor, each residential user does not know the electricity consumption selection behavior of other users, so each user selects the policy of electricity vendor to purchase electricity as a mixed policy, if the probability that the user selects electricity vendor j in time period k is
Then the population status is available
And (4) showing.
6. The resident demand response model based on multi-dimensional factor game as claimed in claim 1, wherein the specific algorithm of S5 is that each electricity seller is both interest-seeking and rational at the electric power retail end, and the goal is to maximize the profit by selling electric power to the user, so the non-cooperative game is used to simulate the price competition between the electricity sellers, and in the non-cooperative game generated between the electricity sellers, the person in the office is the electricity seller, and the price p is the price pj,kIs the strategy of the electricity seller j in the time period k, and the profit function of the electricity seller j is
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985783A (en) * | 2020-07-21 | 2020-11-24 | 国网河北省电力有限公司 | Power load demand response game modeling method |
| CN115330144A (en) * | 2022-05-17 | 2022-11-11 | 国网江苏省电力有限公司淮安供电分公司 | Demand response mechanism model establishment method considering real-time carbon emission reduction |
| CN117610888A (en) * | 2024-01-17 | 2024-02-27 | 武汉科技大学 | Demand response scheduling method based on user preference and mixed integer linear programming |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985783A (en) * | 2020-07-21 | 2020-11-24 | 国网河北省电力有限公司 | Power load demand response game modeling method |
| CN115330144A (en) * | 2022-05-17 | 2022-11-11 | 国网江苏省电力有限公司淮安供电分公司 | Demand response mechanism model establishment method considering real-time carbon emission reduction |
| CN115330144B (en) * | 2022-05-17 | 2023-11-28 | 国网江苏省电力有限公司淮安供电分公司 | Method for establishing demand response mechanism model considering real-time carbon emission reduction |
| CN117610888A (en) * | 2024-01-17 | 2024-02-27 | 武汉科技大学 | Demand response scheduling method based on user preference and mixed integer linear programming |
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