CN104700162A - Electricity load dispatching method for residential users based on time coupling constraint - Google Patents

Electricity load dispatching method for residential users based on time coupling constraint Download PDF

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CN104700162A
CN104700162A CN201510102050.5A CN201510102050A CN104700162A CN 104700162 A CN104700162 A CN 104700162A CN 201510102050 A CN201510102050 A CN 201510102050A CN 104700162 A CN104700162 A CN 104700162A
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frog
subgroup
user
dispatching method
electricity consumption
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CN104700162B (en
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徐雷
钱芳
李千目
杨余旺
张小飞
李亚平
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Nanjing University of Science and Technology
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention discloses an electricity load dispatching method for residential users based on time coupling constraint. The method comprises the steps: firstly, collecting the available resources of all physical nodes by working nodes in a smart distributing power grid; collecting task requests of the users; and finally distributing recourses in the smart power grid by adopting an SFLA (Shuffled Frog Leaping Algorithm). According to the efficient and reliable electricity load dispatching method for residential users based on time coupling constraint provided by the invention, the available resources in the intelligent power grid can be fully excavated, and the resources can be distributed as required by taking the revenue maximization of a user as a target; furthermore, the resources can be utilized efficiently, and the service quality requirements of a user terminal can be ensured; in addition, by adopting the SFLA, the method has the advantages of being simple in algorithm model and fast in solving speed, and being likely to realize and the like.

Description

Based on resident's electricity consumption load dispatching method of time coupling constraint
Technical field
The invention belongs to technical field of the computer network, particularly a kind of user power utilization load dispatching method based on time coupling constraint.
Background technology
Electric power networks is a large-scale interconnected infrastructure, is responsible for electric energy to be transported to huge numbers of families from power house.In order to tackle the disadvantage of traditional energy shortage and traditional electrical network, intelligent grid arises at the historic moment, and has become the focus of current international and domestic new technology and NPD projects.In the past few decades, although infotech and control technology have a very large change, day by day aging electric power networks does not catch up with the paces of technological change.As the electric power networks of a new generation, intelligent grid is application message and the communication technology in an automated manner, realize flexibly, reliable, effectively, safety, ecnomics and enviroment close friend target.Adapted electrical network is the throat of intelligent grid, ensures that the load dispatch of reliable information access and reasonably optimizing in real time in intelligent adapted electrical network is also the foundation stone building China's " strong intelligent grid ".Demand Side Response is an important component part at following intelligent grid, and it is fast that it has response, and discharge is few, low cost and other advantages.System peak period electricity price can be reduced, reduce Electricity price fluctuation risk, optimize allocation of resources and ensure that market stability is run, have important strategy function to power industry and the aspect such as economic development and environmental protection.Meter reading data from intelligent electric meter will reach ten hundreds of terabytes, and this collects for intelligent grid communication network, transmit and store so large-scale data brings huge challenge.Need advanced wireless communication technology badly, as cognitive radio technology, to ensure that meter reading data transmits in real time reliably.Along with the further propelling of current intelligent grid, because it incorporates senior information, control and the communication technology, in the responsive measures of user side, technical foundation has been established in the enforcement of Spot Price.The simplification mutual contact mode of intelligent grid employing plug and play can realize the seamless connection between family's energy storage device and electrical network.And family's end waits widely using of energy storage device, bring new challenge also to the ustomer premises access equipment scheduling under following Spot Price.On the other hand, in the decades in future, continuation also increases by the power consumption of user.In addition, the widespread use of electric automobile also may make electrical energy demands amount double, and rational load dispatch is extremely urgent.
Chinese invention patent CN201210431915.9 discloses a kind of intelligent grid load Dynamic controlling based on wireless sensor network and analytical approach, comprise the following steps: communication network analysis: according to the data of the cyclical transmission of the load data in conjunction with Real-time Collection, the COMMUNICATION NETWORK PERFORMANCES of intelligent grid is analyzed, obtains the current performance affecting the key element of intelligent grid performance; Dynamic load analysis and control model is set up: according to the acquisition information of load and the performance of communication network, set up corresponding dynamic load analysis and control model, the current data of load is analyzed with the historical data stored, is predicted the electricity consumption situation in load future; Load process: based on the result of described dynamic load analysis and control model prediction, is optimized control to the electricity consumption allotment of load.But the method has just carried out dynamic load analysis, do not consider the impact that time coupling constraint may cause system.
Summary of the invention
The object of the present invention is to provide a kind of efficient, reliable resident's electricity consumption load dispatching method based on time coupling constraint, from computational resource and network bandwidth resources two dimension dynamic on-demand Resources allocation, fully to excavate hardware and software resource available in intelligent adapted electrical network.
The technical solution realizing the object of the invention is: a kind of resident's electricity consumption load dispatching method based on time coupling constraint, comprises the following steps:
In step 1, intelligent adapted electrical network, working node collects the available resources of user;
In step 2, intelligent adapted electrical network, working node collects the task requests of user;
In step 3, intelligent adapted electrical network, working node adopts shuffled frog leaping algorithm to distribute the resource in intelligent grid.
The present invention compared with prior art, its remarkable advantage is: (1) resident's electricity consumption load dispatching method based on time coupling constraint of the present invention carries out Resourse Distribute, consider the constraint of Game Relationship between user and time coupling simultaneously, effective transfer peak load and reduction peak-to-average force ratio, and can apply when multiple user, the resident's electricity consumption load dispatch requirement based on time coupling constraint can be met; (2) the present invention is in solution based in the residential electricity consumption load dispatch problem of time coupling constraint, have employed shuffled frog leaping algorithm, has that algorithm model is simple, solving speed is fast, be easy to the advantages such as realization; (3) globally optimal solution can be obtained, for ensureing that all terminal user's maximize revenue provide technical support in the residential electricity consumption load dispatch that the present invention is based on time coupling constraint.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the resident's electricity consumption load dispatching method that the present invention is based on time coupling constraint.
Fig. 2 is the resident's electricity consumption load dispatching method schematic diagram that the present invention is based on time coupling constraint.
Fig. 3 is the resource allocation methods process flow diagram that the present invention is based on shuffled frog leaping algorithm.
Embodiment
Below in conjunction with drawings and the specific embodiments, the present invention is described in further detail.
Composition graphs 1, a kind of resident's electricity consumption load dispatching method based on time coupling constraint, comprises the following steps:
In step 1, intelligent adapted electrical network, working node collects the available resources of user; Electric energy and the restriction relation between user and user needed for the consumer that described available resources comprise terminal user;
In step 2, intelligent adapted electrical network, working node collects the task requests of user; Described task requests is that user wishes the highest satisfaction and minimum electricity cost;
In step 3, intelligent adapted electrical network, working node adopts shuffled frog leaping algorithm to distribute the resource in intelligent grid; Composition graphs 3, concrete steps are:
The parameter of step 3.1, initialization shuffled frog leaping algorithm; The following parameter of initialization: candidate solution number n, maximal subgroup iterations M, maximum iteration time N in frog population at individual quantity N, subgroup quantity k, subgroup g.
Step 3.2, random initializtion frog population; Be specially:
Random generation N number of frog composition initial population, frog need meet formula (1) and formula (2):
x i ‾ ≤ x i t ≤ x i ‾ , ∀ t ∈ T - - - ( 1 )
Σ t ∈ T x i t ≥ Y i - - - ( 2 )
Wherein, represent the power consumption of user i at time slot t, 1≤i≤N, x i , represent the minimum and maximum electricity consumption level of user i respectively, Y irepresent that user i has been the electric energy that Given task needs altogether, T represents the time slot collection that the cycle of one day is divided into, T=24.
Step 3.3, frog population is divided into some subgroups by fitness, record globally optimal solution; Be specially:
N frog is become k subgroup by fitness descending sort, first frog enters first subgroup, second frog enters second subgroup, a kth frog enters a kth subgroup, kth+1 candidate solution enters again the 1st subgroup, kth+2 frogs enter the 2nd subgroup, repeat until N number of frog is assigned successively; The evaluation of individuality is undertaken by formula (3):
f ( x i ) = max W i ( x i ; p ) = max [ Σ t ∈ T ( U i t ( x i t ) - p t x i t ) ] - - - ( 3 )
Wherein, a candidate solution of this optimization problem of vector representation of every frog; If i-th frog is expressed as wherein represent the power consumption of user i at time slot t; x irepresent user i, 1≤i≤N, p represents electricity price vector, W i(x i; P) represent the user i income of a day, T represents the time slot collection that the cycle of one day is divided into, T=24, represent utility function: user i in the electricity consumption satisfaction of time slot t, p tfor Spot Price, then for user i is in the electricity charge of time slot t.
Step 3.4, the poorest individuality upgraded in each subgroup, until maximal subgroup iterations; Be specially:
Upgrade the frog that in each subgroup, fitness is the poorest, namely according to the search strategy shown in formula (4):
X'＝X w+R×(X b-X w) (4)
Wherein, X bbe the candidate solution that in a subgroup, fitness is best, X wbe the candidate solution that in a subgroup, fitness is the poorest, X' is the new explanation that formula (4) produces, and R is the random number of 0 to 1;
If X' fitness is better than X w, then X w=X'; Otherwise formula substitutes X with globally optimal solution in (4) b, repeat search strategy, if still do not improve, namely X' fitness still can not be better than X w, then from whole population, the new candidate solution of random generation one replaces X w; Repeat above-mentioned steps, stop when searching times is greater than the maximal subgroup inner search number of times of setting.
Step 3.5, repetition step 3.3 ~ step 3.4, until maximum iteration time, export optimum individual solution.
Below in conjunction with specific embodiment, the present invention will be further described.
Embodiment 1
Composition graphs 1, Fig. 2, the present embodiment adopts shuffled frog leaping algorithm to carry out load dispatch to the resource in intelligent grid, and step is as follows:
In step 1, intelligent adapted electrical network working node collect user consumer needed for electric energy and the restriction relation between user and user;
Have 16 working nodes in described intelligent adapted electrical network, the consumer of user comprises the household electrical appliance that electric automobile, washing machine, dryer etc. do not need whole day electricity consumption; Electric automobile needs the electric energy of 16kWh to ensure second day stroke of 40 miles; The load that restriction relation between user and user is embodied in user side is selected to need the Spot Price according to distribution side issue, and distribution side needs the user side information on load setting electricity price according to receiving.For ease of illustrating, considering the intelligent adapted electrical network be simply made up of 1 electrical supplier and 3 users, also can obtain similar result for more user;
In step 2, intelligent adapted electrical network, working node collects the task requests of user;
In intelligence adapted electrical network, working node collects the task requests of user, and described task requests comprises user and wishes the highest satisfaction and minimum electricity cost; User i is at the extent function of time slot t U i t ( x i t ) = - ( x i t - y i t ) 2 , x i t ≤ y i t 0 , x i t ≥ y i t , Wherein, the target electrical energy demands amount of this user at time slot t; Electricity price function p (t)=(t+1) 2/ 2;
In step 3, intelligent adapted electrical network, working node adopts shuffled frog leaping algorithm to distribute the resource in intelligent grid; Composition graphs 3, shuffled frog leaping algorithm step is as follows:
The first step, initiation parameter, candidate solution number n=10, maximal subgroup iterations M=10, maximum iteration time N in frog population at individual quantity N=300, subgroup quantity k=30, subgroup g=100;
Second step, produce individuality at random, and each individuality is assessed, sorted, find out globally optimal solution; The assessment of individuality is undertaken by formula (3);
3rd step, the poorest individuality upgraded in each subgroup, until maximal subgroup iterations; The renewal of the poorest individuality is undertaken by formula (4);
4th step, repeats second step and the 3rd step, until maximum iteration time, exports optimum individual solution.
In sum, the present invention is efficient, the reliable electricity consumption load dispatching method based on time coupling constraint, fully to excavate the resource in intelligent grid, turns to target distribution according to need resource from multiple dimension to make each user's Income Maximum; In solution based in the residential electricity consumption load dispatch problem of time coupling constraint, adopt shuffled frog leaping algorithm, have that algorithm model is simple, solving speed is fast, be easy to the advantages such as realization.

Claims (8)

1., based on resident's electricity consumption load dispatching method of time coupling constraint, it is characterized in that, comprise the following steps:
In step 1, intelligent adapted electrical network, working node collects the available resources of user;
In step 2, intelligent adapted electrical network, working node collects the task requests of user;
In step 3, intelligent adapted electrical network, working node adopts shuffled frog leaping algorithm to distribute the resource in intelligent grid.
2. the resident's electricity consumption load dispatching method based on time coupling constraint according to claim 1, is characterized in that, electric energy and the restriction relation between user and user needed for the consumer that available resources described in step 1 comprise terminal user.
3. the resident's electricity consumption load dispatching method based on time coupling constraint according to claim 1, it is characterized in that, task requests described in step 2 comprises user and wishes the highest satisfaction and minimum electricity cost.
4. the resident's electricity consumption load dispatching method based on time coupling constraint according to claim 1, it is characterized in that, in intelligent adapted electrical network described in step 3, working node adopts shuffled frog leaping algorithm to distribute the resource in intelligent grid, and concrete steps are as follows:
The parameter of step 3.1, initialization shuffled frog leaping algorithm;
Step 3.2, random initializtion frog population;
Step 3.3, frog population is divided into some subgroups by fitness, each individuality is evaluated;
Step 3.4, the poorest individuality upgraded in each subgroup, until maximal subgroup iterations;
Step 3.5, repetition step 3.3 ~ step 3.4, until maximum iteration time, export optimum individual solution.
5. the resident's electricity consumption load dispatching method based on time coupling constraint according to claim 4, it is characterized in that, in step 3.1, the parameter of initialization shuffled frog leaping algorithm, is specially:
The following parameter of initialization: candidate solution number n, maximal subgroup iterations M, maximum iteration time N in frog population at individual quantity N, subgroup quantity k, subgroup g.
6. the resident's electricity consumption load dispatching method based on time coupling constraint according to claim 5, is characterized in that, in step 3.2, random initializtion frog population, is specially:
Random generation N number of frog composition initial population, frog need meet formula (1) and formula (2):
x i ‾ ≤ x i t ≤ x i ‾ , ∀ t ∈ T - - - ( 1 )
Σ t ∈ T x i t ≥ Y i - - - ( 2 )
Wherein, represent the power consumption of user i at time slot t, 1≤i≤N, x i , represent the minimum and maximum electricity consumption level of user i respectively, Y irepresent that user i has been the electric energy that Given task needs altogether, T represents the time slot collection that the cycle of one day is divided into, T=24.
7. the resident's electricity consumption load dispatching method based on time coupling constraint according to claim 6, is characterized in that, in step 3.3, frog population is divided into some subgroups by fitness, evaluates each individuality; Be specially:
N frog is become k subgroup by fitness descending sort: first frog enters first subgroup, second frog enters second subgroup, a kth frog enters a kth subgroup, kth+1 candidate solution enters again the 1st subgroup, kth+2 frogs enter the 2nd subgroup, repeat until N number of frog is assigned successively; The evaluation of individuality is undertaken by formula (3):
f ( x i ) = max W i ( x i ; p ) = max [ Σ t ∈ T ( U i t ( x i t ) - p t x i t ) ] - - - ( 3 )
Wherein, a candidate solution of this optimization problem of vector representation of every frog; If i-th frog is expressed as represent the power consumption of user i at time slot t; x irepresent user i, 1≤i≤N, p represents electricity price vector, W i(x i; P) represent the user i income of a day, T represents the time slot collection that the cycle of one day is divided into, T=24, represent utility function: user i in the electricity consumption satisfaction of time slot t, p tfor Spot Price, then for user i is in the electricity charge of time slot t.
8. the resident's electricity consumption load dispatching method based on time coupling constraint according to claim 7, is characterized in that, in step 3.4, the poorest individuality in each subgroup of described renewal, until maximal subgroup iterations; Be specially:
Upgrade the frog that in each subgroup, fitness is the poorest, namely according to the search strategy shown in formula (4):
X'＝X w+R×(X b-X w) (4)
Wherein, X bbe the candidate solution that in a subgroup, fitness is best, X wbe the candidate solution that in a subgroup, fitness is the poorest, X' is the new explanation that formula (4) produces, and R is the random number of 0 to 1;
If X' fitness is better than X w, then X w=X'; Otherwise formula substitutes X with globally optimal solution in (4) b, X' fitness repeats search strategy, if still can not be better than X w, then from whole population, the new candidate solution of random generation one replaces X w; Repeat above-mentioned steps, stop when searching times is greater than the maximal subgroup inner search number of times of setting.
CN201510102050.5A 2015-03-09 2015-03-09 Resident's electricity consumption load dispatching method based on time coupling constraint Expired - Fee Related CN104700162B (en)

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