CN106469354B - User demand response participation method in load aggregation business mode - Google Patents
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Abstract
The invention provides a user demand response participation method in a load aggregator mode, which comprises the following steps: the power grid company issues a demand response message and makes a reduction contract with the load aggregator; the load aggregator demand response server acquires the participation condition, the response quantity and the past evaluation degree information of the registered user; the load aggregator demand response server establishes a user participatable model, a load aggregator selected user participated model and a user selected objective function model according to the registered user information; solving the optimal combination of the load aggregator selected user to participate in demand response under the multiple targets by adopting a genetic algorithm; and issuing a demand response message to the user who selects to participate in the demand response. The invention realizes the optimized combination of users participating in demand response by establishing a load aggregator selection user participation demand response optimization model, and provides a feasible method for a demand response server in a load aggregator mode to determine the problem of user participation demand response.
Description
Technical Field
The invention relates to the technical field of demand response in a smart power grid, in particular to a user demand response participation method in a load aggregator mode in the smart power grid.
Background
Demand Response (DR) means that after a power consumer receives a price signal or an incentive mechanism sent by a power supplier, the power consumer changes an inherent power consumption mode and reduces or shifts the behavior of a power load for a certain period of time. If the user can actively and reasonably participate in DR, the purposes of peak clipping, valley filling, power supply and demand balance maintaining and the like can be achieved. The traditional demand response signal mainly depends on manual transmission, and personnel manually shut down equipment or adjust the running power of the equipment, so that a user side cannot obtain DR information of a power grid side in time, the power grid side cannot adjust the DR signal in real time according to the latest power consumption information of the user, and the reliability and efficiency of realizing peak clipping and valley filling by DR are reduced. Therefore, the traditional demand response is difficult to realize peak clipping and valley filling of the power grid in the true sense, and the supply and demand balance of the power grid is ensured. On the other hand, Automatic Demand Response (ADR) can automatically realize DR response of a user on the basis of no manual operation, and thus, real power grid supply and demand balance is realized.
Based on the automatic demand response technology, a demand response operation mode with a load aggregator as an intermediary appears. The mode mainly realizes the interaction between the power grid and the user by means of a third party of a load aggregator, and a series of research works have been carried out on the interaction, so that a lot of results are obtained. The U.S. Lorentsbackley national laboratory develops a communication information architecture supporting automatic demand response, namely an open automatic demand response communication protocol (OpenADR), which is used for realizing information communication among entities in automatic demand response under an intelligent power grid. The advanced measurement system can realize the collection, storage and processing of power utilization information in the smart grid, realize the collection of DR response data of users and the like, feed back the information to the power grid and the load aggregators in real time, and provide data support for realizing the real-time interaction of the power grid, the load aggregators and the users. The automated demand response server and the load management device based on the OpenADR have been developed by the honeywell company, the management of the load device is realized by a user through a terminal, and a set of automated demand response paradigm system applying the OpenADR has been established on the basis. In terms of demand response user terminals, various intelligent energy management systems supporting automatic demand response, such as an intelligent home energy management system and an intelligent building energy management system, have been developed. However, in the existing research, there is currently little research on how a load aggregator chooses a user to participate in automatic demand response during the implementation of automatic demand response.
Disclosure of Invention
In order to solve the problem that the load aggregator selects user participation in the automatic demand response implementation process, the invention provides a user demand response participation method in a load aggregator mode.
The technical scheme adopted by the invention is as follows:
a user demand response participation method in a load aggregator mode comprises the following steps:
(1) the power grid company issues a demand response message and makes a reduction contract with the load aggregator;
(2) the load aggregator demand response server acquires the participation condition, the response quantity and the past evaluation degree information of the registered user;
(3) the load aggregator demand response server establishes a user participatable model, a load aggregator selected user participated model and a user selected objective function model according to the registered user information;
(4) solving the optimal combination of the load aggregator selected user to participate in demand response under the multiple targets by adopting a genetic algorithm;
(5) issuing a demand response message to a user who selects to participate in demand response;
preferably, in the step (4), the model is established as follows:
(4-1), discretizing 24 hours a day into 24 time intervals;
(4-2) establishing a 0-1 model for the participatable situation of the user in each time interval;
(4-3) selecting a demand response condition of a user participating in a certain time interval for the load aggregation provider, and establishing a 0-1 model;
and (4-4) establishing an objective function model of the load aggregator for selecting the user to participate in the demand response.
Further, in the step (4-2), the 0-1 model characterizing the condition that the user can participate in the demand response within a certain time interval is:
l represents a user number; t represents a period number;
indicating that the user with the number l participates in the demand response condition in the period t.
Further, in the step (4-3), the 0-1 model representing the condition that the load aggregator selects the user to participate in the demand response within a certain time interval is as follows:
l is a user number; t is a time interval number;
the user denoted by the number l is selected by the load aggregator to participate in the condition of the demand response during the period t.
Further, in the step (4-4), the load aggregator selects an objective function 1 model of the user participating in the demand response as follows:
n (t) represents the total number of users selected by the load aggregator to participate in the demand response service in the period t; m represents the total number of users; l represents a user number, and the value of l is 1-m;
the user denoted by the number l is selected by the load aggregator to participate in the condition of the demand response during the period t.
The load aggregator selects an objective function 2 model of the user participating in the demand response as follows:
cost (t) represents the profit of the load aggregator in the demand response in the period t;
representing the reduction contract amount signed by the load aggregator and the power grid company in the t period; priceaRepresents the subsidy price (subsidy per kW cut) the grid company promises to the load aggregator; pricecRepresenting a subsidy price of the load aggregator to the customer response demand response;
and the response quantity of the load aggregation planned user l in the demand response in the time period t is represented.
The load aggregator selects an objective function 3 model of the user participating in the demand response as follows:
m is the total number of users;
representing the load aggregate quotient andreduction contract amount signed by a power grid company;
representing the response quantity of a user with a load aggregation drawn-out number l in the demand response in the period t; score (t) represents the user evaluation degree (comprehensive satisfaction degree) of unit response quantity in the demand response in the t period;
the past evaluation degree of the user of the number l is shown.
The constraint condition model of the load aggregator for selecting all objective functions of the user participating in the demand response is as follows:
The invention has the following beneficial effects:
the invention establishes the target model of the load aggregator for selecting the user to participate in the demand response according to the principle that the number of selected users is the least, the profit is the greatest and the expected satisfaction of the user is the best by modeling the demand response condition that the user can participate in the demand response condition and the load aggregator selects the user to participate in the demand response condition, thereby successfully solving the problem that the load aggregator selects the user to participate in the demand response under the demand response operation mode with the load aggregator as an intermediate.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
FIG. 2 is a schematic diagram of the time discretization process employed in the method of the present invention;
FIG. 3 is a diagram of a demand response implementation architecture in a load aggregator mode;
FIG. 4 is a flow chart of an algorithm implementation of the method of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved by the embodiments of the present invention clearer, the following detailed description will be given with reference to the accompanying drawings, the accompanying tables and specific embodiments.
As shown in fig. 1, the invention is a flowchart of a user demand response participation method in a load aggregator mode in a smart grid, and the method includes the following steps:
(1) the power grid company issues a demand response message and makes a reduction contract with the load aggregator;
(2) the load aggregator demand response server acquires the participation condition, the response quantity and the past evaluation degree information of the registered user;
(3) the load aggregator demand response server establishes a user participatable model, a load aggregator selected user participated model and a user selected objective function model according to the registered user information;
(4) solving the optimal combination of the load aggregator selected user to participate in demand response under the multiple targets by adopting a genetic algorithm;
(5) issuing a demand response message to a user who selects to participate in demand response;
(6) and updating the past evaluation degree information of the user according to the evaluation score fed back by the user.
As shown in fig. 3, toThe reduction of the peak of the power grid is taken as an example, and the demand response implementation process in the whole load aggregator mode and the problem solved by the invention are described. And when detecting that the regional power grid faces a power utilization peak, the transformer substation sends a peak clipping early warning signal to the power grid dispatching center. The power grid dispatching center issues peak clipping requirements to a DR service center of a power grid company, and the DR service center issues requirement response messages to relevant load aggregators. After the negotiation of the two parties, the power grid company and the load aggregation company reach a reduction contract, and the contract content comprises the reduction capacity
Price of patchaA curtailment period t, etc.
After the load aggregator contracts with the power grid company, DR participation logic control is executed, namely, the user demand response participation method in the load aggregator mode provided by the invention determines which users participate in demand response. According to the participation period information set by the registered user, the user participation status in the peak clipping period t can be expressed as:
l represents a user number; t represents a period number;
indicating that the user with the number l participates in the demand response condition in the period t.
The condition that the load aggregator selects the user to participate in the demand response in the t period can be represented as follows:
l is a user number; t is a time interval number;
the user denoted by the number l is selected by the load aggregator to participate in the condition of the demand response during the period t.
And then the load aggregator determines a plurality of objective functions for selecting users to participate in demand response, and sets a desired value of each objective function. And solving the optimal combination of the user participation demand response by using a genetic algorithm, and issuing a demand response command to the selected user.
As shown in fig. 4, the specific algorithm flow for the load aggregator to determine the participation of the user in the demand response is as follows:
(1) determining a plurality of objective functions for selecting a user to participate in a demand response:
objective function 1:
n (t) represents the total number of users selected by the load aggregator to participate in the demand response service in the period t; m represents the total number of users; l represents a user number, and the value of l is 1-m;
the user denoted by the number l is selected by the load aggregator to participate in the condition of the demand response during the period t.
The objective function 2:
cost (t) represents the profit of the load aggregator in the demand response in the period t;
representing the reduction contract amount signed by the load aggregator and the power grid company in the t period; priceaRepresents the subsidy price (subsidy per kW cut) the grid company promises to the load aggregator; pricecRepresenting a subsidy price of the load aggregator to the customer response demand response;
and the response quantity of the load aggregation planned user l in the demand response in the time period t is represented.
The objective function 3:
m is the total number of users;
representing the reduction contract amount signed by the load aggregator and the power grid company in the t period;
representing the response quantity of a user with a load aggregation drawn-out number l in the demand response in the period t; score (t) represents the user evaluation degree (comprehensive satisfaction degree) of unit response quantity in the demand response in the t period;
the past evaluation degree of the user of the number l is shown.
The constraints for all objective functions are:
(2) And solving the multi-target problem into a single-target problem according to the deviation between each target expected value and each actual value set by the load aggregation quotient and the weight of each target function set by the load aggregation quotient:
λ1,λ2,λ3weights of objective functions N (t), cost (t), and score (t) set for the load aggregators, respectively; n is a radical of*(t),Cost*(t),Score*(t) expected values of the load aggregation quotient for the objective functions N (t), cost (t), and score (t) are set, respectively.
(3) Determining the code form of the solution of the genetic algorithm to solve the problem:
m is the number of users;
is 0 or 1.
(4) Generating an initial population meeting constraint conditions according to the size N of the set population;
(5) calculating the fitness value of each individual in the population by adopting a fitness distribution method based on sorting:
(6) judging according to a set genetic algebra MAXGEN: if the current algebra is larger than the MAXGEN, finishing the algorithm and outputting an optimal user combination; if the current algebra is smaller than MAXGEN, jumping to the step (7);
(7) performing copy selection operation on the whole population according to the set copy selection probability;
(8) performing cross operation on the copied population according to the set cross probability;
(9) and (5) carrying out mutation operation on the population obtained by crossing according to the set mutation probability to obtain a new population, and then skipping to the step (5).
To illustrate the effectiveness of the above-described method of the present invention, the following examples were used for simulation verification: in the calculation example, the reduction period agreed in the contract between the load aggregator and the power grid is t 10(10: 00-11: 00), and the total reduction amount is
The parameter setting of the participatable condition in the user t period is shown in table 2, the parameter setting of the reducible capacity in the user t period is shown in table 3, the parameter setting of the past evaluation degree of the user to the load aggregator is shown in table 4, and the evaluation rule of the user to the load aggregator is shown in table 1; the subsidy price of the power grid to the load aggregator is pricea120 (element), the subsidy price given to the user by the load aggregator is pricec80 (units are yuan); the load aggregation quotient sets the desired value of the objective function 1 to be N*(t) 10, and the expected value of the set objective function 2 is Cost*(t) 240, and the expected value of the objective function 3 is set to Score*(t) 4, and the weight λ of the set objective function 1, objective function 2, and objective function 31,λ2,λ30.2, 0.5 and 0.3 respectively; the number of the populations is set to be 10, the maximum genetic algebra MAXGEN is 100, the replication selection probability is 0.95, the cross probability is 0.7, and the mutation probability is 0.05.
The example simulation shows that the example converges in 24 generations, and the output optimal user participation combination etat＝[0 0 0 1 1 0 1 0 0 1]Namely, the optimal user combination selected by the load aggregator to participate in the demand response in the t period is as follows: user 4, user 5, user 7, user 10. In this case, the single objective function value Z in the corresponding step (2) is 0.0113, and the load aggregator determines that the multiple objective function values n (t) 4, cost (t) 240, and score (t) 3.6 for the selected user to participate in the demand response are very close to the desired values of the respective objective functions set by the load aggregator, which proves that the method of the present invention is effective.
TABLE 1 evaluation rules of users for load aggregators
| Rating of evaluation | Is very satisfactory | Is relatively satisfied | In general | Is less satisfactory | Is very unsatisfactory |
| Evaluation score | 5 | 4 | 3 | 2 | 1 |
User engageable status parameters in the table 2 example
| User number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Participatable status | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
TABLE 3 exemplary user t period reduced capacity parameter
| User number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Capacity reduction (kW) at time t | 0.5 | 1 | 2 | 1.5 | 3 | 2.5 | 0.5 | 2.5 | 1.5 | 1 |
Table 4 previous evaluation degree parameter of user to load aggregator in calculation example
| User number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Past degree of evaluation (point) of user to load aggregator | 4 | 3.5 | 2.8 | 3 | 3.5 | 4 | 4 | 5 | 2 | 4.5 |
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (6)
1. A user demand response participation method in a load aggregator mode is characterized by comprising the following steps:
step A, a power grid company issues a demand response message to a load aggregator;
b, the load aggregator obtains the information of the registered user;
step C, the load aggregation businessman establishes a plurality of objective functions and sets the expected value and the weight of the objective functions;
step D, according to the expected value and the actual value of each objective function and the weight of each objective function, a plurality of objective functions are functionalized into a single objective function;
step E: calculating the optimal solution of the single objective function by utilizing a genetic algorithm in combination with the registered user information, and simultaneously obtaining the corresponding optimal user participation combination under the optimal solution condition;
step F: issuing a demand response message to a user who selects to participate in demand response;
in the step A, the demand response message comprises a load reduction amount, a reduction time period and a subsidy price;
in step C, the plurality of objective functions are respectively as follows:
wherein N (t) selects to participate in the demand response service for the load aggregator for the period tThe total number of users, m is the total number of users feeding back information, l is the number of users, the value is 1-m,
selecting a condition for participating in the demand response in the t period for the user with the number l by the load aggregation provider;
cost (t) is the profit of the load aggregator in the demand response for period t,
for load reduction at time t, priceaPrice of subsidy to load aggregator for grid companycRepresents the subsidy price given to the user by the load aggregator,
responding quantity of a user l drawn for load aggregation in demand response in a time period t;
score (t) is the user's rating of unit response in the demand response over time t,
the evaluation degree of the user is the user's past evaluation degree.
2. The method of claim 1, wherein the period of the curtailment period is a discretized representation of time: the 24 hours a day are scattered into 24 periods, every 1 hour is taken as a whole period, and each period is numbered.
3. The method as claimed in claim 1, wherein in the step B, the information of the registered user includes an available participation status, a response amount and a past evaluation degree of the user.
4. The method of claim 1, wherein the objective functions further satisfy constraints as follows:
wherein the content of the first and second substances,
for the load reduction amount for the period t,
the response quantity of the user l drawn up for the load aggregation in the demand response in the period t,
for the situation where the user numbered l participates in the demand response for the period t,
the user numbered l is selected by the load aggregator to participate in the condition of demand response for time t,
5. The method according to claim 1, wherein in step D, the single objective function is as follows:
wherein Z is a single objective function value, λ1,λ2,λ3Respectively setting an objective function N (t), cost (t), the weight of score (t), the total number of users who participate in the demand response service selected by the load aggregator in the period of t, cost (t) the profit of the load aggregator in the demand response in the period of t, score (t) the user evaluation degree of unit response quantity in the demand response in the period of t, and N*(t),Cost*(t),Score*(t) expected values of the load aggregation quotient for the objective functions N (t), cost (t), and score (t) are set, respectively.
6. The method for participating in user demand response in load aggregator mode according to claim 1, wherein in said step E, the genetic algorithm solving method is as follows:
e1, determining the code form of the solution of the genetic algorithm for solving the problem;
e2, generating an initial population meeting the constraint condition according to the size N of the set population;
e3, calculating the fitness value of each individual in the population by adopting a fitness distribution method based on sorting;
e4, judging according to the set genetic algebra MAXGEN: if the current algebra is larger than the MAXGEN, finishing the algorithm and outputting an optimal user combination; if the current algebra is smaller than the MAXGEN, performing a step E5;
e5, performing copy selection operation on the whole population according to the set copy selection probability;
e6, performing cross operation on the copied population according to the set cross probability;
e7, carrying out mutation operation on the crossed population according to the set mutation probability to obtain a new population, and then returning to the step E3.
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