CN104700162B - Resident's electricity consumption load dispatching method based on time coupling constraint - Google Patents

Resident's electricity consumption load dispatching method based on time coupling constraint Download PDF

Info

Publication number
CN104700162B
CN104700162B CN201510102050.5A CN201510102050A CN104700162B CN 104700162 B CN104700162 B CN 104700162B CN 201510102050 A CN201510102050 A CN 201510102050A CN 104700162 B CN104700162 B CN 104700162B
Authority
CN
China
Prior art keywords
lt
gt
mo
mi
amp
Prior art date
Application number
CN201510102050.5A
Other languages
Chinese (zh)
Other versions
CN104700162A (en
Inventor
徐雷
钱芳
李千目
杨余旺
张小飞
李亚平
Original Assignee
南京理工大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 南京理工大学 filed Critical 南京理工大学
Priority to CN201510102050.5A priority Critical patent/CN104700162B/en
Publication of CN104700162A publication Critical patent/CN104700162A/en
Application granted granted Critical
Publication of CN104700162B publication Critical patent/CN104700162B/en

Links

Classifications

    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of electrical power generation, transmission or distribution, i.e. smart grids as climate change mitigation technology in the energy generation sector
    • Y02E40/76Computing methods or systems for efficient or low carbon management or operation of electric power systems
    • YGENERAL 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
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS 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
    • Y04S10/00Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/54Management of operational aspects
    • Y04S10/545Computing methods or systems for efficient or low carbon management or operation of electric power systems

Abstract

The invention discloses a kind of resident's electricity consumption load dispatching method based on time coupling constraint, step is:Working node collects the available resources of each physical node in adapted power network intelligent first;Collect the task requests of user;Finally use and the resource in intelligent grid is allocated based on shuffled frog leaping algorithm;The present invention is efficient, reliable resident's electricity consumption load dispatching method based on time coupling constraint, has fully excavated the available resources in intelligent grid, and target distribution according to need resource is turned to from user's Income Maximum;Resource can be efficiently utilized, and ensures the quality of service requirement of user terminal;Using shuffled frog leaping algorithm, have the advantages that algorithm model is simple, solving speed is fast, be easily achieved.

Description

Resident's electricity consumption load dispatching method based on time coupling constraint

Technical field

The invention belongs to technical field of the computer network, particularly a kind of user power utilization load based on time coupling constraint Dispatching method.

Background technology

Electric power networks are the infrastructure of a large-scale interconnection, are responsible for electric energy to be transported to huge numbers of families from power station. In order to tackle the disadvantage of the short and traditional power network of traditional energy, intelligent grid arises at the historic moment, it has also become current international and domestic new skill The focus of art and NPD projects.Between in the past few decades, although information technology and control technology have a very large change, day Gradually the electric power networks of aging do not have the paces for keeping up with technological change.As the electric power networks of a new generation, intelligent grid is with automatic The mode application message and the communication technology of change, realize flexible, reliable, effective, safety, economic and environment-friendly target.Adapted Power network is the throat of intelligent grid, ensures the load dispatch of real-time reliable information access and reasonably optimizing in intelligent adapted power network And build the foundation stone of China " strong intelligent grid ".Demand Side Response is in an important composition portion of following intelligent grid Point, it has response fast, and discharge is few, low cost and other advantages.Can reduce system peak period electricity price, reduce Electricity price fluctuation risk, Optimize allocation of resources and ensure that market stability is run, power industry and economic development and environmental protection etc. are suffered from important Strategy function.Meter reading data from intelligent electric meter will be up to ten hundreds of terabyte, this is for intelligent grid communication network Network is collected, transmission and the so large-scale data of storage bring huge challenge.Need advanced wireless communication technology badly, such as recognize Radiotechnics is known, to ensure that meter reading data reliably transmits in real time.With being pushed further into for current intelligent grid, due to it Advanced information, control and the communication technology are incorporated, is that technology base has been established in the implementation of Spot Price in user side responsive measures Plinth.Intelligent grid can realize the seamless rank between family's energy storage device and power network using the simplification mutual contact mode of plug and play Connect.And widely using for the energy storage device such as family end, also bring new challenge to the ustomer premises access equipment scheduling under following Spot Price. On the other hand, in the decades in future, the power consumption of user will also continue to increase.In addition, the extensive use of electric automobile is also It may make it that electrical energy demands amount is double, rational load dispatch is extremely urgent.

Chinese invention patent CN201210431915.9 discloses a kind of intelligent grid based on wireless sensor network and born Dynamic control and analysis method are carried, is comprised the following steps:Communication network analysis:Combined according to the data of the cyclical transmission of load The data gathered in real time, the COMMUNICATION NETWORK PERFORMANCES of intelligent grid is analyzed, obtain wanting for current influence intelligent grid performance The performance of element;Dynamic load is analyzed and Controlling model is established:According to the information and the performance of communication network for obtaining load, phase is established The dynamic load analysis answered and Controlling model, the current data of load is analyzed with the historical data stored, prediction is negative Carry following electricity consumption situation;Load is handled:Result based on dynamic load analysis and Controlling model prediction, to the use of load Electricity allotment optimizes control.But this method has simply carried out dynamic load analysis, not accounting for time coupling constraint may The influence to caused by system.

The content of the invention

It is an object of the invention to provide it is a kind of efficiently, reliable resident's electric loading based on time coupling constraint Dispatching method, from two dimension dynamic on-demand distribution resources of computing resource and network bandwidth resources, fully to excavate intelligent adapted Available hardware and software resource in power network.

The technical solution for realizing the object of the invention is:A kind of resident's electric loading based on time coupling constraint Dispatching method, comprise the following steps:

Working node collects the available resources of user in step 1, intelligent adapted power network;

Working node collects the task requests of user in step 2, intelligent adapted power network;

Working node is divided the resource in intelligent grid using shuffled frog leaping algorithm in step 3, intelligent adapted power network Match somebody with somebody.

Compared with prior art, its remarkable advantage is the present invention:(1) resident based on time coupling constraint of the invention uses Electricity consumption load dispatching method in family carries out resource allocation, while considers the pact of the Game Relationship between user and time coupling Beam, it is effective to shift peak load and reduce peak-to-average force ratio, and can be applied in the case of multiple users, it can meet to be based on time coupling Resident's electricity consumption load dispatch requirement of contract beam;(2) present invention is born in residential electricity consumption of the solution based on time coupling constraint Carry in scheduling problem, employ shuffled frog leaping algorithm, have the advantages that algorithm model is simple, solving speed is fast, be easily achieved;(3) Residential electricity consumption load dispatch interior energy of the invention based on time coupling constraint obtains globally optimal solution, to ensure all terminal users Maximum revenue provides technical support.

Brief description of the drawings

Fig. 1 is the flow chart of resident's electricity consumption load dispatching method of the invention based on time coupling constraint.

Fig. 2 is resident's electricity consumption load dispatching method schematic diagram of the invention based on time coupling constraint.

Fig. 3 is the resource allocation methods flow chart of the invention based on shuffled frog leaping algorithm.

Embodiment

Below in conjunction with the accompanying drawings and specific embodiment is described in further detail to the present invention.

With reference to Fig. 1, a kind of resident's electricity consumption load dispatching method based on time coupling constraint, comprise the following steps:

Working node collects the available resources of user in step 1, intelligent adapted power network;The available resources are used including terminal Restriction relation needed for the electrical equipment at family between electric energy and user and user;

Working node collects the task requests of user in step 2, intelligent adapted power network;The task requests are wished for user Highest satisfaction and minimum electricity cost;

Working node is divided the resource in intelligent grid using shuffled frog leaping algorithm in step 3, intelligent adapted power network Match somebody with somebody;With reference to Fig. 3, concretely comprise the following steps:

Step 3.1, the parameter for initializing shuffled frog leaping algorithm;Initialize parameters described below:Frog population at individual quantity N, son Candidate solution number n, maximal subgroup iterations M, maximum iteration N in group's quantity k, subgroupg

Step 3.2, random initializtion frog population;Specially:

N number of frog composition initial population is randomly generated, frog need to meet formula (1) and formula (2):

Wherein,User i is represented in time slot t power consumption, 1≤i≤N,xi Represent that user i is minimum and maximum respectively Electricity consumption level, YiIt is to complete the electric energy that Given task needs altogether to represent user i, and T represents the time slot being divided into the cycle of one day Collection, T=24.

Step 3.3, frog population is divided into some subgroups by fitness, records globally optimal solution;Specially:

N frog is arranged in k subgroup by fitness descending, first frog enters first subgroup, second green grass or young crops The frog enters second subgroup, and k-th of frog enters k-th of subgroup, and+1 candidate solution of kth enters the 1st subgroup ,+2 green grass or young crops of kth again The frog enters the 2nd subgroup, is repeated in until N number of frog is assigned;Evaluation to individual is carried out by formula (3):

Wherein, a candidate solution of the vector representation of the every frog optimization problem;If i-th frog is expressed asWhereinRepresent power consumptions of the user i in time slot t;xiRepresent that user i, 1≤i≤N, p are represented Electricity price vector, Wi(xi;P) user's i incomes of mono- day are represented, T represents the time slot collection being divided into cycle of one day, T=24,Represent utility function:User i is in time slot t electricity consumption satisfaction, ptFor Spot Price, thenIt is user i in time slot t The electricity charge.

Worst individual in step 3.4, each subgroup of renewal, until maximal subgroup iterations;Specially:

Update the frog that fitness is worst in each subgroup, i.e., the search strategy shown according to formula (4):

X'=Xw+R×(Xb-Xw) (4)

Wherein, XbFor the best candidate solution of fitness in a subgroup, XwFor the worst candidate of fitness in a subgroup Solution, X' is new explanation caused by formula (4), and R is 0 to 1 random number;

If X' fitness is better than Xw, then Xw=X';Otherwise, X is substituted with globally optimal solution in formula (4)b, repeat and search Rope strategy, if still without improvement, i.e. X' fitness still can not be better than Xw, then a new candidate is randomly generated from whole population Solution substitution Xw;Repeat the above steps, terminated when being more than the maximal subgroup inner search number of setting to searching times.

Step 3.5,3.3~step 3.4 of repeat step, until maximum iteration, export optimum individual solution.

With reference to specific embodiment, the present invention will be further described.

Embodiment 1

With reference to Fig. 1, Fig. 2, the present embodiment carries out load dispatch, step to the resource in intelligent grid using shuffled frog leaping algorithm It is rapid as follows:

In step 1, intelligent adapted power network working node collect electric energy and user and user needed for the electrical equipment of user it Between restriction relation;

There are 16 working nodes in the intelligent adapted power network, the electrical equipment of user includes electric automobile, washing machine, baking Dry machine etc. does not need the household electrical appliance of whole day electricity consumption;Electric automobile needs 16kWh electric energy to ensure second day 40 miles of row Journey;The load selection that restriction relation between user and user is embodied in user side needs the real-time electricity issued according to distribution side Valency, and distribution side is needed according to the user side information on load setting electricity price received.For purposes of illustration only, consider simply by 1 The intelligent adapted power network of electrical supplier and 3 user's compositions, similar result can be also obtained for more users;

Working node collects the task requests of user in step 2, intelligent adapted power network;

Working node collects the task requests of user in intelligent adapted power network, and the task requests wish highest including user Satisfaction and minimum electricity cost;Extent functions of the user i in time slot tIts In,It is target electrical energy demands amount of the user in time slot t;Electricity price function p (t)=(t+1)2/2;

Working node is divided the resource in intelligent grid using shuffled frog leaping algorithm in step 3, intelligent adapted power network Match somebody with somebody;With reference to Fig. 3, shuffled frog leaping algorithm step is as follows:

The first step, initiation parameter, frog population at individual quantity N=300, subgroup quantity k=30, candidate solution in subgroup Number n=10, maximal subgroup iterations M=10, maximum iteration Ng=100;

Second step, individual is randomly generated, and each individual is assessed, sorted, find out globally optimal solution;To individual Assess and carried out by formula (3);

Worst individual in 3rd step, each subgroup of renewal, until maximal subgroup iterations;To worst individual renewal Carried out by formula (4);

4th step, second step and the 3rd step are repeated, until maximum iteration, export optimum individual solution.

In summary, the present invention is efficient, the reliable electricity consumption load dispatching method based on time coupling constraint, with abundant The resource in intelligent grid is excavated, from multiple dimensions so that each user's Income Maximum turns to target distribution according to need resource;Solving In residential electricity consumption load dispatch problem certainly based on time coupling constraint, using shuffled frog leaping algorithm, have algorithm model it is simple, Solving speed is fast, the advantages that being easily achieved.

Claims (1)

1. a kind of resident's electricity consumption load dispatching method based on time coupling constraint, it is characterised in that comprise the following steps:
Working node collects the available resources of user in step 1, intelligent adapted power network;The available resources include terminal user's Restriction relation needed for electrical equipment between electric energy and user and user;
Working node collects the task requests of user in step 2, intelligent adapted power network;The task requests are wished most including user High satisfaction and minimum electricity cost;
Working node is allocated using shuffled frog leaping algorithm to the resource in intelligent grid in step 3, intelligent adapted power network;Tool Body step is as follows:
Step 3.1, the parameter for initializing shuffled frog leaping algorithm;Specially:
Initialize parameters described below:Frog population at individual quantity N, subgroup quantity k, candidate solution number n, maximal subgroup iteration in subgroup Number M, maximum iteration Ng
Step 3.2, random initializtion frog population;Specially:
N number of frog composition initial population is randomly generated, frog need to meet formula (1) and formula (2):
<mrow> <munder> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </munder> <mo>&amp;le;</mo> <msubsup> <mi>x</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>&amp;le;</mo> <mover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>t</mi> <mo>&amp;Element;</mo> <mi>T</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <mi>T</mi> </mrow> </munder> <msubsup> <mi>x</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>&amp;GreaterEqual;</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
Wherein,User i is represented in time slot t power consumption, 1≤i≤N,xi Represent that user i is minimum with maximum electricity consumption water respectively It is flat, YiIt is to complete the electric energy that Given task needs altogether to represent user i, and T represents the time slot collection being divided into the cycle of one day, T= 24;
Step 3.3, frog population is divided into some subgroups by fitness, each individual is evaluated;Specially:
N frog is arranged in k subgroup by fitness descending:First frog enters first subgroup, and second frog enters Enter second subgroup, k-th of frog enters k-th of subgroup, and+1 candidate solution of kth enters the 1st subgroup again, and+2 frogs of kth enter Enter the 2nd subgroup, be repeated in until N number of frog is assigned;Evaluation to individual is carried out by formula (3):
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>maxW</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>;</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>&amp;lsqb;</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>&amp;Element;</mo> <mi>T</mi> </mrow> </munder> <mrow> <mo>(</mo> <msubsup> <mi>U</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>(</mo> <msubsup> <mi>x</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>)</mo> <mo>-</mo> <msup> <mi>p</mi> <mi>t</mi> </msup> <msubsup> <mi>x</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
Wherein, a candidate solution of the vector representation of the every frog optimization problem;If i-th frog is expressed as Represent power consumptions of the user i in time slot t;xiRepresent user i, 1≤i≤N, p represent electricity price to Amount, Wi(xi;P) user's i incomes of mono- day are represented, T represents the time slot collection being divided into cycle of one day, T=24,Represent Utility function:User i is in time slot t electricity consumption satisfaction, ptFor Spot Price, thenFor user i time slot t the electricity charge;
Worst individual in step 3.4, each subgroup of renewal, until maximal subgroup iterations;Specially:
Update the frog that fitness is worst in each subgroup, i.e., the search strategy shown according to formula (4):
X'=Xw+R×(Xb-Xw) (4)
Wherein, XbFor the best candidate solution of fitness in a subgroup, XwFor the worst candidate solution of fitness in a subgroup, X' For new explanation caused by formula (4), R is 0 to 1 random number;
If X' fitness is better than Xw, then Xw=X';Otherwise, X is substituted with globally optimal solution in formula (4)b, repeat search plan Slightly, if X' fitness still can not be better than Xw, then a new candidate solution substitution X is randomly generated from whole populationw;Repeat above-mentioned Step, terminated when being more than the maximal subgroup inner search number of setting to searching times;
Step 3.5,3.3~step 3.4 of repeat step, until maximum iteration, export optimum individual solution.
CN201510102050.5A 2015-03-09 2015-03-09 Resident's electricity consumption load dispatching method based on time coupling constraint CN104700162B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510102050.5A CN104700162B (en) 2015-03-09 2015-03-09 Resident's electricity consumption load dispatching method based on time coupling constraint

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510102050.5A CN104700162B (en) 2015-03-09 2015-03-09 Resident's electricity consumption load dispatching method based on time coupling constraint

Publications (2)

Publication Number Publication Date
CN104700162A CN104700162A (en) 2015-06-10
CN104700162B true CN104700162B (en) 2018-03-09

Family

ID=53347259

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510102050.5A CN104700162B (en) 2015-03-09 2015-03-09 Resident's electricity consumption load dispatching method based on time coupling constraint

Country Status (1)

Country Link
CN (1) CN104700162B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1198079A2 (en) * 2000-10-14 2002-04-17 Lg Electronics Inc. Method for implementing system information broadcasting function in asynchronous mobile communication system
CN103377084A (en) * 2012-04-11 2013-10-30 李涛 Renewable energy based green data center load scheduling method and device

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1198079A2 (en) * 2000-10-14 2002-04-17 Lg Electronics Inc. Method for implementing system information broadcasting function in asynchronous mobile communication system
CN103377084A (en) * 2012-04-11 2013-10-30 李涛 Renewable energy based green data center load scheduling method and device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
《基于混合蛙跳算法的电力系统经济负荷分配》;张友华等;《传感器与微系统》;20121231;第31卷(第6期);第58-60页 *
《智能电网配用电信息接入与负载调度研究》;邓瑞龙;《中国博士学位论文全文数据库 工程科技II辑》;20140815;论文第20、59-67、78页 *

Also Published As

Publication number Publication date
CN104700162A (en) 2015-06-10

Similar Documents

Publication Publication Date Title
Rastegar et al. A probabilistic energy management scheme for renewable-based residential energy hubs
Parra et al. Optimum community energy storage system for PV energy time-shift
Kok et al. PowerMatcher: multiagent control in the electricity infrastructure
Meng et al. Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system
Wang et al. Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm
US9660450B2 (en) Monitoring system and method for megawatt level battery energy storage power plant
Park et al. Concurrent simulation platform for energy-aware smart metering systems
CN102546059B (en) Non-supervision clustering-based distributed cooperative spectrum sensing method for cognitive self-organizing network
CN101751761B (en) Efficient wireless meter reading method for automatic network router
CN103024825B (en) Method and device of distributing network source among multiple applications of terminal
US20140365419A1 (en) Adaptation of a power generation capacity and determining of an energy storage unit size
CN104170355B (en) A kind of creation method of virtual base station and base station cloud equipment
CN102591276A (en) Intelligent electric meter system for residents for intelligent smart grid
CN103052134B (en) Renewable energy supply base station access selection method and system
CN102694391B (en) Day-ahead optimal scheduling method for wind-solar storage integrated power generation system
Zhang et al. The NS2-based simulation and research on wireless sensor network route protocol
CN102411838A (en) Wireless power meter reading system and control method thereof
CN103945395B (en) The rapid Optimum dispositions method of a kind of wireless network sensor based on population
CN103280821A (en) Multi-period dynamic reactive power optimization method of intelligent power distribution system
Wang et al. Privacy-preserving energy scheduling in microgrid systems
CN103197633B (en) Method and platform and system for remote control of electrical equipment
CN104734631B (en) The distribution priority controller of photovoltaic generating system and control method
CN104578427B (en) Fault self-healing method for power distribution network containing microgrid power source
CN202434038U (en) Wireless power meter reading system
Pagani et al. Generating realistic dynamic prices and services for the smart grid

Legal Events

Date Code Title Description
PB01 Publication
C06 Publication
SE01 Entry into force of request for substantive examination
C10 Entry into substantive examination
GR01 Patent grant
GR01 Patent grant