CN106469354A - A kind of user's request response participatory approaches under Load aggregation quotient module formula - Google Patents
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Abstract
The invention provides the user's request response participatory approaches under a kind of Load aggregation quotient module formula, including：Grid company issues demand response message, and signs reduction contract with Load aggregation business；Load aggregation business's demand response server obtain registered users may participate in situation, response quautity, conventional evaluating deg information；Load aggregation business's demand response server, according to above information of registered users, sets up that user may participate in model, Load aggregation business selects user to participate in model and user's selection target function model；User is selected to participate in the optimum combination of demand response using the Load aggregation business under the above-mentioned multiple target of genetic algorithm for solving；Issue demand response message to selecting the user participating in demand response.The present invention selects user to participate in demand response Optimized model by setting up Load aggregation business, achieve the optimal combination participating in demand response user, be that the demand response server under Load aggregation quotient module formula determines that user participates in demand response problem and provides a kind of feasible method.
Description
Technical field
The present invention relates to the demand response technical field in intelligent grid, in particular in intelligent grid, a kind of load gathers
Close the user's request response participatory approaches under quotient module formula.
Background technology
Demand response (demand response, DR) refer to power consumer receive the price signal that supplier of electricity sends or
After incentive mechanism, change intrinsic consumption power mode, reduce or shift the behavior of certain period power load.If user can lead
Move, reasonably participate in DR, then can reach peak load shifting, maintain the purposes such as power supply and demand balance.Traditional demand response signal master
Artificial transmission to be relied on, personnel close down the operation power of equipment or adjusting device manually, and this makes user side cannot obtain in time
The DR information of grid side, the up-to-date power consumption information adjustment DR signal that grid side also cannot in real time according to user, reduce DR
Realize reliability and the efficiency of peak load shifting.Therefore, the very difficult peak clipping truly realizing electrical network of traditional demand response is filled out
Paddy is it is ensured that the equilibrium of supply and demand of electrical network.On the other hand, automatic demand response (automated demand response, ADR) energy
Enough on the basis of there is not any artificial operation, it is automatically obtained the DR response of user it is achieved that electrical network truly supplies
Need to balance.
Based on automatic demand response technology, occur in that the demand response operation mode with Load aggregation business as agent.Should
Pattern mainly realizes interacting between electrical network and user by means of Load aggregation business third party, and this has been carried out a series of
Research work, achieve a lot of achievements.Lawrence Berkeley National development in laboratory supports automatic demand response
Open automatic demand response communication protocol (the open automated demand response of communication information framework
Communications specification, OpenADR), for realizing each reality in automatic demand response under intelligent grid
Information communication between body.Senior measurement system is capable of in intelligent grid power information collection, storage and processes, realize to
Family DR response data etc. gather, information is fed back in real time electrical network and Load aggregation business, for realize electrical network-Load aggregation business-
The real-time interactive of user provides data to support.Honeywell Inc. has been developed based on the automatic demand response service of OpenADR
Device and load management equipment, support that user realizes the management of load equipment by terminal, and have built a set of on this basis
The automatic demand response demonstration formula system of application OpenADR.In terms of demand response user terminal, research and develop the automatic demand of support
The various intelligent energy management system of response, such as wired home energy management system, intelligent building energy management system.But
In existing research, ring with regard to, in automatic demand response implementation process, how Load aggregation business selects user to participate in automatic demand
The problem answered, rarely has the research of this respect at present.
Content of the invention
In order to solve the problems, such as that in the above-mentioned automatic demand response implementation process referring to, Load aggregation business selects user to participate in,
The present invention provides the user's request response participatory approaches under a kind of Load aggregation quotient module formula.
The technical solution used in the present invention is：
A kind of user's request response participatory approaches under Load aggregation quotient module formula, comprise the steps：
(1) grid company issues demand response message, and signs reduction contract with Load aggregation business；
(2) Load aggregation business demand response server obtains may participate in situation, response quautity, evaluating in the past of registered users
Degree information；
(3) Load aggregation business demand response server is according to above information of registered users, set up user may participate in model,
Load aggregation business selects user to participate in model and user's selection target function model；
(4) user is selected to participate in the optimum of demand response using the Load aggregation business under the above-mentioned multiple target of genetic algorithm for solving
Combination；
(5) issue demand response message to the user selecting participation demand response；
Preferably, in described step (4), described model is set up as follows：
(4-1), one day 24 hours discrete are turned to 24 time intervals；
(4-2), situation be may participate in the user in each time interval, set up 0-1 model；
(4-3), Load aggregation business is selected user participate in the demand response situation in certain time interval, set up 0-1 model；
(4-4), setting up Load aggregation business selects user to participate in the target function model of demand response.
Further, in described step (4-2), in described sign user's time interval, may participate in demand response situation
0-1 model is：
L represents Customs Assigned Number；Segment number when t represents；Represent that the user that numbering is l participates in the shape of t period demand response
Condition.
Further, in described step (4-3), described expression Load aggregation business selects user to participate in certain time interval
The 0-1 model of demand response situation is：
L is Customs Assigned Number；Segment number when t is；Represent that the user that numbering is l is selected to participate in the t period by Load aggregation business
The situation of demand response.
Further, in described step (4-4), described Load aggregation business selects user to participate in the object function of demand response
1 model is：
N (t) represents that t period Load aggregation business selects to participate in the total number of users of demand response service；M represents total number of users；l
Represent Customs Assigned Number, its value is 1～m；Represent that the user that numbering is l is selected participation t period demand to ring by Load aggregation business
The situation answered.
Described Load aggregation business select user participate in demand response object function 2 model be：
Cost (t) represents the profit of Load aggregation business in t period demand response；Represent t period Load aggregation business and electricity
The reduction contracted quantity that net company signs；priceaRepresent that grid company is promised to undertake (every by cutting down to the subsidized price of Load aggregation business
KW subsidizes)；pricecRepresent the subsidized price to user response demand response for the Load aggregation business；Represent Load aggregation business
The user l drafting response quautity in t period demand response.
Described Load aggregation business select user participate in demand response object function 3 model be：
M is total number of users；Represent the reduction contracted quantity that t period Load aggregation business is signed with grid company；Table
Show that Load aggregation business drafts the user that numbering is l response quautity in t period demand response；Score (t) represents that t period demand is rung
User's evaluating deg (comprehensive satisfaction) of unit response amount in answering；Represent the conventional evaluating deg of the user that numbering is l.
Described Load aggregation business selects user to participate in demand response all of bound for objective function model：
Represent the reduction contracted quantity that t period Load aggregation business is signed with grid company；Represent Load aggregation business
The user l drafting response quautity in t period demand response；Represent that the user that numbering is l participates in the shape of t period demand response
Condition；Represent that the user that numbering is l is selected to participate in the situation of t period demand response by Load aggregation business；Value be 0
Or 1.
The invention has the advantages that：
The present invention may participate in demand response situation by modelling user, Load aggregation business selects user to participate in demand response
Situation, by selecting, number of users is minimum, makes a profit maximum, obtains user and is expected the best principle of satisfaction, sets up Load aggregation business choosing
Select the object module that user participates in demand response, successfully solve the demand response operation mode with Load aggregation business as agent
Under Load aggregation business select user participate in problem.
Brief description
The flow chart of the method that Fig. 1 provides for the present invention；
Fig. 2 is that the time discretization adopting in the inventive method processes schematic diagram；
Fig. 3 is that the demand response under Load aggregation quotient module formula realizes Organization Chart；
Fig. 4 is the algorithm flowchart of the inventive method.
Specific embodiment
It is to make embodiments of the invention technical problem to be solved, technical scheme and advantage clearer below, below will
It is described in detail in conjunction with accompanying drawing, subordinate list and specific embodiment.
As shown in figure 1, being the user's request response participant under a kind of Load aggregation quotient module formula in intelligent grid of the present invention
The flow chart of method, comprises the steps：
(1) grid company issues demand response message, and signs reduction contract with Load aggregation business；
(2) Load aggregation business demand response server obtains may participate in situation, response quautity, evaluating in the past of registered users
Degree information；
(3) Load aggregation business demand response server is according to above information of registered users, set up user may participate in model,
Load aggregation business selects user to participate in model and user's selection target function model；
(4) user is selected to participate in the optimum of demand response using the Load aggregation business under the above-mentioned multiple target of genetic algorithm for solving
Combination；
(5) issue demand response message to the user selecting participation demand response；
(6) evaluation score according to user feedback, updates the conventional evaluating deg information of user.
As shown in figure 3, taking cut down electrical network peak as a example, the demand response told about under whole Load aggregation quotient module formula was realized
Journey and the problem of present invention solution.When transformer station detects regional power grid and faces peak of power consumption, send to grid dispatching center and cut
Peak early warning signal.Grid dispatching center assigns peak clipping demand to the DR service centre of grid company, and DR service centre bears to related
Lotus polymerization business issues demand response message.Through the negotiation of both sides, grid company and Load aggregation business reach reduction contract, contract
Content includes cutting down capacitySubsidized price pricea, cut down period t etc..
After Load aggregation business and grid company are signed a contract, execution DR participates in logic control, i.e. load proposed by the present invention
User's request response participatory approaches under polymerization quotient module formula, determine which user participates in demand response.Set according to registered users
Fixed participation period information, the user in peak clipping period t may participate in situation and is represented by：
L represents Customs Assigned Number；Segment number when t represents；Represent that the user that numbering is l participates in the shape of t period demand response
Condition.
Load aggregation business selects the situation that user participates in demand response the t period to be represented by：
L is Customs Assigned Number；Segment number when t is；Represent that the user that numbering is l is selected to participate in the t period by Load aggregation business
The situation of demand response.
Next Load aggregation business determines the multiple object functions selecting user to participate in demand response, sets each target letter
The expected value of number.Participate in the optimum combination of demand response using genetic algorithm for solving user, and issue demand to the user selecting
Response command.
As shown in figure 4, Load aggregation business determines that the specific algorithm flow process of user's participation demand response is as follows：
(1) the multiple object functions selecting user to participate in demand response are determined：
Object function 1：
N (t) represents that t period Load aggregation business selects to participate in the total number of users of demand response service；M represents total number of users；l
Represent Customs Assigned Number, its value is 1～m；Represent that the user that numbering is l is selected participation t period demand to ring by Load aggregation business
The situation answered.
Object function 2：
Cost (t) represents the profit of Load aggregation business in t period demand response；Represent t period Load aggregation business and electricity
The reduction contracted quantity that net company signs；priceaRepresent that grid company is promised to undertake (every by cutting down to the subsidized price of Load aggregation business
KW subsidizes)；pricecRepresent the subsidized price to user response demand response for the Load aggregation business；Represent Load aggregation business
The user l drafting response quautity in t period demand response.
Object function 3：
M is total number of users；Represent the reduction contracted quantity that t period Load aggregation business is signed with grid company；Table
Show that Load aggregation business drafts the user that numbering is l response quautity in t period demand response；Score (t) represents that t period demand is rung
User's evaluating deg (comprehensive satisfaction) of unit response amount in answering；Represent the conventional evaluating deg of the user that numbering is l.
All bounds for objective function are：
Represent the reduction contracted quantity that t period Load aggregation business is signed with grid company；Represent Load aggregation business
The user l drafting response quautity in t period demand response；Represent that the user that numbering is l participates in the shape of t period demand response
Condition；Represent that the user that numbering is l is selected to participate in the situation of t period demand response by Load aggregation business；Value be 0
Or 1.
(2) set with the deviation of actual value and Load aggregation business according to each target desired value that Load aggregation business sets
Each object function weight, multiple target Solve problems are turned to single goal Solve problems：
λ1,λ2,λ3It is respectively object function N (t) that Load aggregation business sets, the weight of Cost (t), Score (t)；N*
(t),Cost*(t),Score*T () is respectively the phase that Load aggregation business sets to object function N (t), Cost (t), Score (t)
Prestige value.
(3) determine the code form of the solution of this problem of genetic algorithm for solving：
M is user's number；Value be 0 or 1.
(4) according to size N setting population, produce the initial population of meet the constraint condition；
(5) using the fitness assignment method based on sequence, calculate each individual fitness value in population：
(6) according to setting genetic algebra MAXGEN, judge：If current algebraically is more than MAXGEN, algorithm terminates, and output is
Excellent user's combination；If current algebraically is less than MAXGEN, skip to step (7)；
(7) according to setting Replica Selection probability, Replica Selection operation is carried out to whole population；
(8) according to setting crossover probability, carry out crossover operation to replicating the population obtaining；
(9) according to setting mutation probability, carry out mutation operation to intersecting the population obtaining, obtain new population, then jump
Go to step (5).
In order to the effectiveness of the invention described above method is described, herein simulating, verifying is carried out using following example：In example,
Load aggregation business is t=10 (10 with the reduction period of agreement in electrical network contract:00～11:00), cutting down total amount isUser may participate in condition parameter the t period and sets as shown in table 2, and user can cut down capacity ginseng the t period
Number sets as shown in table 3, and as shown in table 4, user is to Load aggregation for user's evaluating deg parameter setting conventional to Load aggregation business
The evaluation rule of business is as shown in table 1；Electrical network is price to the subsidized price of Load aggregation businessa=120 (unit is unit), load
Polymerization business is price to the subsidized price of userc=80 (unit is unit)；The expectation of the object function 1 that Load aggregation business sets
It is worth for N*T ()=10, the expected value of the object function 2 setting is as Cost*(t)=240, the expected value of the object function 3 setting as
Score*(t)=4, the object function 1 of setting, object function 2, weight λ of object function 31,λ2,λ3Be respectively 0.2,0.5,
0.3；Population number is set to 10, maximum genetic algebra MAXGEN=100, and Replica Selection probability is 0.95, and crossover probability is
0.7, mutation probability is 0.05.
Simulation Example shows, example is withheld 24 and held back, and the optimal user of output participates in combination ηt=[0 001101
00 1], that is, Load aggregation business selects the optimal user participating in t period demand response to be combined as：User 4, user 5, user 7, use
Family 10.In now corresponding step (2), single-goal function value is Z=0.0113, Load aggregation business in corresponding step (1)
Determine that the multiple objective function value selecting user to participate in demand response is N (t)=4, Cost (t)=240, Score (t)=3.6, with
Each object function expected value that Load aggregation business sets is closely it was demonstrated that the inventive method is effective.
The evaluation rule to Load aggregation business for table 1 user
| Opinion rating | Very satisfied | Satisfied | Typically | Relatively more dissatisfied | Very dissatisfied |
| Evaluation score | 5 | 4 | 3 | 2 | 1 |
User in table 2 example may participate in condition parameter
| Customs Assigned Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| May participate in situation | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
In table 3 example, user can cut down capacity parameter the t period
| Customs Assigned Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| The t period can cut down capacity (kW) | 0.5 | 1 | 2 | 1.5 | 3 | 2.5 | 0.5 | 2.5 | 1.5 | 1 |
User in table 4 example is to Load aggregation business conventional evaluating deg parameter
| Customs Assigned Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| The conventional evaluating deg (dividing) to Load aggregation business for the user | 4 | 3.5 | 2.8 | 3 | 3.5 | 4 | 4 | 5 | 2 | 4.5 |
Embodiment described above is only the preferred embodiment lifted for absolutely proving the present invention, the protection model of the present invention
Enclose not limited to this.Equivalent substitute or conversion that those skilled in the art are made on the basis of the present invention, all in the present invention
Protection domain within.Protection scope of the present invention is defined by claims.
Claims (8)
1. the user's request response participatory approaches under a kind of Load aggregation quotient module formula are it is characterised in that comprise the steps：
Step A:Grid company issues demand response message to Load aggregation business；
Step B:Load aggregation business obtains the information of registered users；
Step C:Load aggregation business sets up multiple object functions, and target setting function expected value and weight；
Step D:According to the weight of each object function expected value and object function actual value and each object function, will be multiple
Object function turns to single-goal function；
Step E：In conjunction with registered users information, calculate described single-goal function optimal solution using genetic algorithm, obtain the most simultaneously
Corresponding optimal user under the conditions of excellent solution participates in combination；
Step F：Issue demand response message to selecting the user participating in demand response.
2. the user's request response participatory approaches under Load aggregation quotient module formula according to claim 1 are it is characterised in that institute
State in step A, demand response message includes load reduction, cuts down period and subsidized price.
3. the user's request response participatory approaches under Load aggregation quotient module formula according to claim 2 are it is characterised in that institute
State the discretization that the period in the abatement period is the time to represent：One day 24 hours are discrete to be 24 periods, every 1 hour whole as one
The individual period, all there is numbering each period.
4. the user's request response participatory approaches under Load aggregation quotient module formula according to claim 1 are it is characterised in that institute
State in step B, the information of registered users includes the conventional evaluation that may participate in situation, response quautity and user of user's setting
Degree.
5. the user's request response participatory approaches under Load aggregation quotient module formula according to claim 2 are it is characterised in that institute
State in step C, multiple object functions are as follows respectively：
Wherein, N (t) is that t period Load aggregation business selects to participate in the total number of users of demand response service, and m is the use of feedback information
Family sum, l is Customs Assigned Number, and its value is 1～m,The user being l for numbering is selected the participation t period to need by Load aggregation business
Seek the situation of response；
Cost (t) is the profit of Load aggregation business in t period demand response,For t period load reduction, priceaFor electrical network
Company is to the subsidized price of Load aggregation business, pricecRepresent the subsidized price to user for the Load aggregation business,Gather for load
The user l that conjunction business drafts response quautity in t period demand response；
Score (t) is user's evaluating deg of unit response amount in t period demand response,The user being l for numbering
Evaluating deg in the past.
6., according to the user's request response participatory approaches under Load aggregation quotient module formula according to claim 5, its feature exists
In the plurality of object function also needs meet the constraint condition, as follows：
Wherein,For t period load reduction,Ring in t period demand response for the user l that Load aggregation business drafts
Ying Liang,The user being l for numbering participates in the situation of t period demand response,Selected by Load aggregation business for the user that numbering is l
Select the situation participating in t period demand response,
Value be 0 or 1, as follows：
.
7. the user's request response participatory approaches under Load aggregation quotient module formula according to claim 1 are it is characterised in that institute
State in step D, single-goal function is as follows：
Wherein, Z is single-goal function value, λ1,λ2,λ3Object function N (t) that respectively Load aggregation business sets, Cost (t),
The weight of Score (t), N (t) is that t period Load aggregation business selects to participate in the total number of users of demand response service, and Cost (t) is t
The profit of Load aggregation business in period demand response, Score (t) is that the user of unit response amount in t period demand response evaluates
Degree, N*(t),Cost*(t),Score*T () is respectively Load aggregation business to object function N (t), Cost (t), Score (t) set
Expected value.
8. the user's request response participatory approaches under Load aggregation quotient module formula according to claim 1 are it is characterised in that institute
State in step E, genetic algorithm for solving method is as follows：
E1, determine this problem of genetic algorithm for solving solution code form；
E2, size N according to setting population, produce the initial population of meet the constraint condition；
E3, the fitness assignment method based on sequence for the employing, calculate each individual fitness value in population；
E4, according to set genetic algebra MAXGEN, judge：If current algebraically is more than MAXGEN, algorithm terminates, and output is optimum to be used
Family is combined；If current algebraically is less than MAXGEN, carry out step E5；
E5, according to setting Replica Selection probability, Replica Selection operation is carried out to whole population；
E6, according to setting crossover probability, carry out crossover operation to replicating the population that obtains；
E7, according to setting mutation probability, carry out mutation operation to intersecting the population that obtains, obtain new population, be then back to walk
Rapid E3.
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