CN103824125A - Scheduling optimal configuration method for smart grid - Google Patents
Scheduling optimal configuration method for smart grid Download PDFInfo
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- CN103824125A CN103824125A CN201410052403.0A CN201410052403A CN103824125A CN 103824125 A CN103824125 A CN 103824125A CN 201410052403 A CN201410052403 A CN 201410052403A CN 103824125 A CN103824125 A CN 103824125A
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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
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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
- 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
Abstract
The invention relates to a scheduling optimal configuration method for a smart grid. The scheduling optimal configuration method for the smart grid is characterized in that the method comprises the following steps that: 1) an equivalent model is established, and with districts adopted as the unit, the number and locations of power supply power stations are determined by a power supply bureau, and the power stations are connected with each other in pairs through straight lines, such that an undirected weighted power station power supply model can be formed; 2) model parameter calculation is performed: according to the undirected weighted power station power supply model formed in the step 1), with distances between every two power stations adopted as parameters, calculation is performed according to a situation that AC power loss is 0.05kVA per km, such that a power loss matrix table between every two power stations can be made; 3) according to the power loss matrix table made in the step 2), a Dijkstra algorithm is adopted to perform smart grid resource scheduling simulation; 4) an ant colony algorithm is adopted to perform visualization calculation on the power loss matrix table made in the step 2) and perform simulation analysis on smart grid resource scheduling; and 5) a scheduling optimal configuration method for the smart grid is determined according to the step 3) and the step 4). With the scheduling optimal configuration method for the smart grid of the invention adopted, the quantity of calculation can be decreased, and acceptable scheduling configuration schemes can be obtained fast. The scheduling optimal configuration method for the smart grid has the advantages of fast convergence and high efficiency.
Description
Technical field
The present invention relates to a kind of optimizing scheduling collocation method of intelligent grid, is a kind of Optimal Configuration Method that intelligent grid monitors, dispatches of realizing based on dijkstra's algorithm and ant group algorithm, belongs to intelligent grid configuration and the dispatching technique field of electric system.
Background technology
Intelligent grid is exactly the intellectuality of electrical network, be also referred to as " electrical network 2.0 ", it is based upon integrated, on the basis of high-speed bidirectional communication network, by advanced sensing and measuring technique, advanced equipment and technology, the application of advanced control method and advanced person's decision support system (DSS) technology, realize the reliable of electrical network, safety, economical, efficiently, environmental friendliness and the safe target of use, its principal character comprises self-healing, excitation and comprise user, resist attack, the quality of power supply that meets 21 century user's request is provided, allow the access of various different forms of electricity generation, the optimization that starts electricity market and assets efficiently moves.
Present stage, intelligent grid occupied more and more consequence gradually in electric system, due to conventional Power Generation Mode, such as thermal power generation, hydropower etc. or environmental pollution are serious, or condition that need to be comparatively strict, and the generating that utilizes new forms of energy has the features such as environmental friendliness, there is very large development potentiality.But there is generated energy be difficult to prediction and be difficult to the features such as accurate control simultaneously, produced thus electric power and adjusted the actual demand of sending, also in electric power development, occupy more and more consequence with regard to power scheduling transportation.
At present, existing intelligent grid structure and control method are by sensing, measuring technique, advanced person's equipment and technology, control by computer control system, need to process mass data.In intelligent grid in operational process, the information of large amount of complex, redundancy constantly increases and has exceeded individual or system can be accepted, process or when the scope of effective utilization, due to these information cannot be by time, effectively integrate, organize and be internalized into needed information is monitored, managed to electrical network, easily cause decision-making to postpone, cannot dispatch electric energy in time, cause electric network fault and fault recovery problem not in time, bring great inconvenience to people's life and commercial production.
The mass data of processing intelligent grid with prior art means, easily causes " information overload " problem." information overload " refers in intelligent grid a large amount of, complicated, redundancy and ever-increasing information has exceeded individual or system can be accepted, process or the scope of effective utilization, these information cannot be by time, effectively integrate, organize and be internalized into needed information is monitored, managed to electrical network, cause decision-making to postpone or error, and may cause electric network fault or fault recovery phenomenon not in time.
Summary of the invention
Object of the present invention, in order to address the deficiencies of the prior art, a kind of optimizing scheduling collocation method of intelligent grid is provided, the method can obtain optimal electrical power scheduling scheme within shorter working time, can efficiently and accurately detect dispensing to electrical network, and carry out debugging mode by display window and intuitively show.
Object of the present invention can reach by the following technical programs:
An optimizing scheduling collocation method for intelligent grid, is characterized in that comprising the following steps:
1) set up equivalent model
Be unit take district, determine quantity and the position in power supply power station by power supply administration, described power station is coupled together between two with straight line, form undirected weighting power station power supply model;
2) computation model parameter
The undirected weighting power station power supply model forming according to step 1), take the distance in every two power stations as parameter, calculates according to every kilometer of ac power loss 0.05kVA, produces the amount of power dissipation matrix table between every two power stations;
3) according to step 2) the amount of power dissipation matrix table made, carries out the simulation of intelligent grid resource allocation with dijkstra's algorithm;
4) adopt ant group algorithm to step 2) make amount of power dissipation matrix table carry out Visual calculation, intelligent grid scheduling of resource is carried out to sunykatuib analysis;
5) determine the optimizing scheduling collocation method of intelligent grid according to step 3), step 4).
Object of the present invention can also reach by the following technical programs:
As a kind of preferred version, carry out the simulation of intelligent grid resource allocation with dijkstra's algorithm described in step 3), refer to:
First, pick out the power station that all generated energy are less than power consumption, comprise a region of using of not generating electricity, join queue lack; For first power station of lack queue, use the parameter of equivalent model described in step 1) as the length of path, use dijkstra's algorithm to build the distance of each node to first power station node;
Secondly, arrange from small to large by distance in the power station that can power, and first obtains transmitted power from first power station, required path and the transmitted power of transmission of electricity is joined in current matrix simultaneously; If first power station power supply power is inadequate, then obtain power supply from second power station, repeat this step; After completing, fallen out in this power station from lack queue; If lack queue still has the power station that needs power supply, repeat this step until whole power station output setting power.
As a kind of preferred version, carry out the simulation of intelligent grid resource allocation with dijkstra's algorithm described in step 3), concrete steps are as follows:
3-1), minimum load statistical form the highest take in the actual motion of each power station month is as basis, enter comparison by the output power that can provide with each power station, determine power station load running data, pick out the power station of not generating electricity, and the power station that generated energy is less than power consumption joins calculating queue, form M calculating object, M is 1,2,3 ... M;
3-2) according to step 2) described amount of power dissipation matrix table matrix, arrange from small to large by distance in the power station that can power, and forms N power supply power station;
3-3) from calculating object M=1, in the time that the first calculating object output power is not enough, first obtain transmitted power from nearest supply station N=1; If the 1st supply station output power is inadequate, then obtain from the 2nd supply station, there is no can be even to go out electric energy until the 1st power station obtains enough electric energy or N supply station; Otherwise carry out next step;
3-4) calculating object M=2, execution step 3-2), in the time that the first calculating object output power is not enough, first obtain transmitted power from nearest supply station N=1; If the 1st supply station output power is inadequate, then obtain from the 2nd supply station, there is no can be even to go out electric energy until calculating object obtains enough electric energy or N supply station; Otherwise carry out next step;
3-5) calculating object M=3, execution step 3-2), in the time that the first calculating object output power is not enough, first obtain transmitted power from nearest supply station N=1; If the 1st supply station output power is inadequate, then obtain from the 2nd supply station, there is no can be even to go out electric energy until calculating object obtains enough electric energy or N supply station; Otherwise carry out next step; Until calculating object is M, finish.
As a kind of preferred version, intelligent grid scheduling of resource is carried out to sunykatuib analysis described in step 4), refer to: adopt pheromones to postpone update mode; First carry out parameter initialization, intercity initial information amount is set, distribute ant to each transformer station, it is all 0 that initialization information element distributes; Secondly the random each transformer station's motion that electric energy can be provided towards periphery of ant, and leave pheromones; Each ant k (k=1,2 ..., M) advance side by side according to the distribution of pheromones content on each route, revise pheromones and distribute; The 3rd calculates every loop distance that ant walks, and finds the ant of the transformer station that electric energy can be provided to return to nest, and leaves pheromones; The 4th in the time that test reaches preset value 1000 T.T., EOP (end of program).
As a kind of preferred version, intelligent grid scheduling of resource is carried out to sunykatuib analysis described in step 4), concrete steps are as follows:
The probability distribution that 4-1) quantity of pheromones draws according to step 3) algorithm at initial time, the transformer station pheromones far away apart from other websites distributes less, and ant is according to the random each transformer station's motion that electric energy can be provided towards periphery of this pheromones distribution;
4-2) to consume the transformer station that is greater than power supply as nest, be food for TV university in the transformer station consuming, determine the parameter in speed radius, path, the transmission line of electricity between the Wei Liang transformer station of path;
4-3) test T.T. longer, program operation is slower, the shorter local optimum that is more easily absorbed in of time is determined test T.T. parameter, is provided with N nest, N=1,2,3 ... N, from N=1;
4-4) all ants are all positioned at nest, and ant to the motion of food point, has food at random in scope, so directly move to food; Every ant is moved according to the guide of pheromones, the Probability p moving according to maximum information element channeling direction is 0.7, it is 1 that every ant is sowed pheromones speed in the time just finding food, after this every time cycle excessively, reduce 10%, until return to nest, and pheromones rate of volatilization in time constant be 0.1;
4-5) calculate the loop distance that every ant walks, find the ant of the transformer station that electric energy can be provided to return to nest, and leave pheromones, now find the ant of food to return to starting point according to former road;
4-6) take N=2 as starting object, execution step 4-4) and step 4-5);
4-7) until take N as starting object, perform step 4-4) and 4-5);
4-8) execution time arrives test T.T. 1000s, finish, otherwise execution step 4-3).
As a kind of preferred version, described in step 4), intelligent grid scheduling of resource is carried out to sunykatuib analysis, refer to: setting ant speed radius parameter is 1km, nest is to consume the transformer station that is greater than power supply, food point is greater than the transformer station of consumption for powering, transmission line of electricity between the Wei Liang transformer station of path, barrier is line loss; Ants all when original state are all positioned at nest, at random to other four food points motion; Determine that parameter is t=1000; It is 1 that every ant is sowed pheromones speed in the time just finding food, after this every time cycle excessively, reduces 10%, until return to nest; Pheromones rate of volatilization is in time constant is 0.1; If there is food in range of observation, so directly move to food, otherwise mobile according to following rule: every ant is moved according to the guide of pheromones, if the probability moving according to maximum information element channeling direction is p, p weighs ant group's initiative, through checking repeatedly, selects p=0.7.
Ant speed radius is larger can find food with faster speed, to reduce program runtime, and the less food point of can not omitting of speed radius.In this problem, consider the distance of working time and 5 transformer stations, determine that speed radius parameter is 1km.In this problem, nest is and consumes the transformer station that is greater than power supply, and food point is greater than the transformer station of consumption for powering, the transmission line of electricity between the Wei Liang transformer station of path, and barrier is line loss.The all ants of original state are all positioned at nest, at random to other four food points motion.Test T.T. is longer, and program operation is slower, the shorter local optimum that is more easily absorbed in of time.Balance determine parameter be every ant of t=1000. to sow pheromones speed be 1 in the time just finding food, after this often spend the time cycle, reduce 10%, until return to nest.And pheromones rate of volatilization in time constant be 0.1.If there is food in range of observation, so directly move to food.Otherwise mobile according to following rule.Every ant is moved according to the guide of pheromones, but also have certain probability not according to pheromones random walk.If the probability moving according to maximum information element channeling direction is p, p weighs ant group's initiative.P Yue great Ze colony more can keep comparatively good route, and p is less is more not easy to be absorbed in local optimum.Through checking repeatedly, select p=0.7.
As a kind of preferred version, in step 3), step 4), carry out display scheduling result with power grid visualization, specifically refer to: by the mouse call back function of OPENCV, realize visual data real-time query function, mouse is moved on the power station, passage that needs inquiry, motor left button can inquire the real time data of power station, passage load on the LISTBOX in left side.
MFC is for 3 BUTTON controls: initialization, operation and end, 1 LISTBOX control and a PICTURE control.Wherein, the function of initialization button is used ifstream stream to read in the initialization data being stored in text, comprises generated energy, the power consumption in 5 power stations, and the resistance value of 10 paths.The function of operation button, for activating DIJSKTRA algorithmic dispatching function, is carried out circuit allocation.The function of conclusion button is terminator.The function of LISTBOX control, for showing interaction data, is observed for user.The function of PICTURE control is the figure that shows that OPENCV draws, and uses the call back function of mouse button simultaneously, provides user in LISTBOX, to inquire about the data of power station and path.
Right side PICTURE has used the map of Pudong, and red point above represents five transformer stations of Pudong, and line represents path.Color is larger by more shallow expression load.RGB color (0,0,0) (black) represents 0 load, and RGB color (200,200,200) (connecing subalbous grey) represents 100% load.
The present invention has following outstanding beneficial effect:
1, the present invention adopts the mode that the ant group algorithm of dijkstra's algorithm guiding combines, according to the actual conditions of each department, electrical network is dispatched to configuration, can reduce calculated amount, comparatively fast try to achieve acceptable scheduling allocation plan, there is fast convergence rate, beneficial effect that efficiency is high.
2, the present invention can carry out visualization procedure computing to algorithm by computing machine, can can obtain fast optimum solution, and can on window, inquire the real time data of power station, passage load, and can show in real time the situation of electrical network debugging, have the advantages that accuracy and efficiency are high.
3, the present invention, according to the characteristic of DIJKSTRA algorithm and ant group algorithm, is applied in a flexible way in the practical problems of dispatching of power netwoks.In DIJKSTRA algorithm application, take minimal path as thinking, each transformer station is analyzed, to make electric power allotment Least-cost be optimum solution.In ant group algorithm application, with the thinking of dynamic similation, electric power is allocated and carried out emulation, constantly find the concocting method of lowest costs.
4, the present invention is for the more saving resource of processing of intelligent electrical network mass data, efficiently solve the problem of the normal information overload occurring of existing method, and employing the visual design, make developer needn't be concerned about the detail that realizes the required each gordian technique of Knowledge Visualization, directly just can be take engine the various visualization systems in core developing intellectual resource electrical network, thereby greatly reduce developer's work difficulty and workload, accelerate the construction speed of power grid visualization.
Accompanying drawing explanation
Fig. 1 is structural representation of the present invention.
Fig. 2 is dijkstra's algorithm schematic flow sheet of the present invention.
Fig. 3 is ant algorithm schematic flow sheet of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail:
Specific embodiment 1:
The present embodiment relates to a kind of optimizing scheduling collocation method of intelligent grid, it is characterized in that comprising the following steps:
1) set up equivalent model
Be unit take district, determine quantity and the position in power supply power station by power supply administration, described power station is coupled together between two with straight line, form undirected weighting power station power supply model;
2) computation model parameter
The undirected weighting power station power supply model forming according to step 1), take the distance in every two power stations as parameter, calculates according to every kilometer of ac power loss 0.05kVA, produces the amount of power dissipation matrix table between every two power stations;
3) according to step 2) the amount of power dissipation matrix table made, carries out the simulation of intelligent grid resource allocation with dijkstra's algorithm;
4) adopt ant group algorithm to step 2) make amount of power dissipation matrix table carry out Visual calculation, intelligent grid scheduling of resource is carried out to sunykatuib analysis;
5) determine the optimizing scheduling collocation method of intelligent grid according to step 3), step 4).
In the present embodiment:
The power station in Yi Moushimou district is object, and the particular content of step 1) is:
As shown in Figure 1, utilize power supply administration to determine quantity and the position of power supply station, the power supply station in certain district of city of search of office, finds that one has five transformer stations, is respectively tree peony transformer station, transformer station of Golden Bridge, Linyi transformer station, He Jin Xiang Nan transformer station of Bei Cai transformer station.To between every two power stations, couple together with straight line, form undirected weighting power station power supply model;
Described step 2) matrix A that obtains is:
| ? | Tree peony | Golden Bridge | Linyi | North Cai | Jin Xiangnan |
| Tree peony | 0 | 1.7 | 2.5 | 2.25 | 1.35 |
| Golden Bridge | 1.7 | 0 | 1.35 | 2.5 | 2.5 |
| Linyi | 2.5 | 1.35 | 0 | 1.75 | 2.5 |
| North Cai | 2.25 | 2.5 | 1.75 | 0 | 1.2 |
| Jin Xiangnan | 1.35 | 2.5 | 2.5 | 1.2 | 0 |
As shown in Figure 2, the concrete steps of described step 3) are as follows:
3-1) pick out the power station that all generated energy are less than power consumption, comprise a region of using of not generating electricity, tree peony station, station, Linyi, northern Cai station, join calculating queue, forms the 3rd calculating object;
3-2) according to step 2) matrix, arrange from small to large by distance in the power station that can power, and south, Ji Jin lane and Golden Bridge station form 2 power supply power stations;
3-3) from calculating object tree peony station, in the time that tree peony station output power is not enough, first obtain transmitted power from southern station, nearest Jin lane; If Jin Xiang southern station output power is inadequate, then obtain from Golden Bridge station supply station, there is no can be even to go out electric energy until tree peony station obtains enough electric energy or Golden Bridge station; Otherwise carry out next step;
3-4) take station, Linyi as calculating object, execution step 3-2), in the time that station, Linyi output power is not enough, first obtain transmitted power from nearest Golden Bridge station; If Golden Bridge station output power is inadequate, southern station, Zai Congjin lane obtains, and there is no can be even to go out electric energy until station, Linyi obtains enough electric energy or Golden Bridge station; Otherwise carry out next step;
3-5), Cai station is calculating object, execution step 3-2), in the time that northern Cai stands output power deficiency, first obtain transmitted power from southern station, nearest Jin lane; If Jin Xiang southern station output power is inadequate, then obtain from Golden Bridge station, until northern Cai stands and obtains enough electric energy or Golden Bridge station and there is no can be even to go out electric energy.
The concrete steps of described step 4) are as follows:
The probability distribution that 4-1) quantity of pheromones draws according to step 3) algorithm at initial time, the transformer station pheromones far away apart from other websites distributes less, and ant is according to the random each transformer station's motion that electric energy can be provided towards periphery of this pheromones distribution;
4-2) take tree peony station, station, Linyi, northern Cai station as nest, south, Yi Jin lane and Golden Bridge station are food, consider the distance of working time and 5 transformer stations, determine that speed radius parameter is 1km;
4-3) test T.T. longer, program operation is slower, the shorter local optimum that is more easily absorbed in of time is determined test T.T. parametric t=1000s, starts calculating from station, tree peony station;
4-4) 5 ants at tree peony station according to the distribution of pheromones content on each route and advance, have food in scope, so directly move to food; Every ant is moved according to the guide of pheromones; The Probability p moving according to maximum information element channeling direction is 0.7; It is 1 that every ant is sowed pheromones speed in the time just finding food, after this often spends the time cycle, reduces 10%, until return to nest, and pheromones rate of volatilization in time constant be 0.1; In the time that ant enters the transformer station of respective paths, deposit this transformer station in taboo list, this transformer station can not pass through again; When the taboo list element number of every ant is 5, after all traveling through, complete the operation in this cycle, taboo list empties
4-5) calculate the loop distance that every ant walks, find the ant of the transformer station that electric energy can be provided to return to nest, and leave pheromones, now find the ant of food to return to starting point according to former road;
4-6) take station, Linyi as starting object, execution step 4.4) and 4.5);
4-7), Cai station is for starting object, execution step 4.4) and 4.5);
4-8) execution time arrives test T.T. 1000s, finish, otherwise execution step 4.3).
Dijkstra's algorithm, ant algorithm are routine techniques.
Specific embodiment 2:
The present embodiment is five transformer stations for certain district of city equally, i.e. tree peony transformer station, transformer station of Golden Bridge, Linyi transformer station, He Jin Xiang Nan transformer station of Bei Cai transformer station.
The present embodiment comprises the following steps:
The first step, set up equivalent model.This program will be set up five power stations, between every two power stations, all will connect, and be abstracted into the undirected weighted graph of five nodes, odd plots of land that can be cultivated.
Second step, computation model parameter.
Approximately 27 kilometers of tree peony Substation Station Dao Jin Xiang Nan transformer station distances in figure, calculate according to every kilometer of ac power loss 0.05kVA. because distance is closer, easy in order to calculate, we by loss with being approximately linearity.Every kilometer of voltage loss:
Can obtain the loss matrix A of power supply mutually of Tu Zhongwusuo transformer station.
Obviously, A is symmetric matrix.
Each transformer station is 110kV level,, minimum load statistical form the highest according to each power station moon, and calculating it can provide load as following table:
Biao2-1Wu Ge transformer station provides load meter
| Name of station | Tree peony | Golden Bridge | Linyi | North Cai | Jin Xiangnan |
| Load is provided | 90kVA | 130kVA | 88kVA | 100kVA | 140kVA |
Consumed power changed with season and time, can not accurately provide parameter.Herein take January 25 in 2013 Ge transformer station load calculate as example.[9]
Biao2-2Wu Ge transformer station daily consumption load meter January 25 in 2013
| Name of station | Tree peony | Golden Bridge | Linyi | North Cai | Jin Xiangnan |
| Load | 95kVA | 130kVA | 120kVA | 110kVA | 120kVA |
The 3rd step, carry out lexical analysis with dijkstra's algorithm
This program is used following methods to realize the scheduling of intelligent grid.Five power stations that use take program are as example.
1. pick out the power station that all generated energy are less than power consumption, comprise a region of using of not generating electricity, tree peony station, station, Linyi, northern Cai station, join queue lack.
2. for first power station of lack queue, the parameter that uses a model, as the length of path, is used dijkstra's algorithm to build the distance of each node to first power station node.As shown in matrix A.
3. arrange from small to large by distance in the power station that can power, and first obtains transmitted power from first power station, and south, Ji Jin lane joins current matrix current[5,5 by required path and the transmitted power of transmission of electricity simultaneously] in.
If 4. first power station power supply power of the 3rd step is inadequate, then obtain power supply from second power station, repeat the 3rd step.Owing to not having other can be even to go out the power station of electric energy in this example, therefore enter next step, fallen out in power station from lack queue.
5.lack queue still has the power station that needs power supply, i.e. Linyi, northern Cai station.Repeat the 4th, the 5th step.
The 4th step, dispatching of power netwoks is analyzed with ant group algorithm
First carry out parameter setting:
(1) speed radius
Ant speed radius is larger can find food with faster speed, to reduce program runtime, and the less food point of can not omitting of speed radius.In this problem, consider the distance of working time and 5 transformer stations, determine that speed radius parameter is 1km.
(2) food point, nest and path
In this problem, nest is and consumes the transformer station that is greater than power supply, and food point is greater than the transformer station of consumption for powering, the transmission line of electricity between the Wei Liang transformer station of path, and barrier is line loss.
(3) original state
The all ants of original state are all positioned at nest, at random to other four food points motion.
(4) test T.T.
Test T.T. is longer, and program operation is slower, the shorter local optimum that is more easily absorbed in of time.Balance determines that parameter is t=1000.
(5) pheromones is sowed and the rule of volatilizing
It is 1 that every ant is sowed pheromones speed in the time just finding food, after this every time cycle excessively, reduces 10%, until return to nest.And pheromones rate of volatilization in time constant be 0.1.
(6) movement rule
If there is food in range of observation, so directly move to food.Otherwise mobile according to following rule.
Every ant is moved according to the guide of pheromones, but also have certain probability not according to pheromones random walk.If the probability moving according to maximum information element channeling direction is p, p weighs ant group's initiative.P Yue great Ze colony more can keep comparatively good route, and p is less is more not easy to be absorbed in local optimum.Through checking repeatedly, select p=0.7.
(7) other problems discussion
When ant group algorithm is applied in this problem, there are certain special circumstances to need to process.When the electric energy that can outwards provide when a transformer station is consumed completely, this transformer station should remove from food list, otherwise cannot provide our station required electric energy.
What in this problem, adopt is that pheromones postpones update mode.Algorithm performing step is as follows:
Parameter initialization, arranges intercity initial information amount.
Distribute ant to each transformer station, initialization information element distributes and is 0.
The random each transformer station's motion that electric energy can be provided towards periphery of ant, and leave pheromones.
Each ant k (k=1,2 ..., M) according to the distribution of pheromones content on each route and advance, revise pheromones and distribute.
Calculate the loop distance that every ant walks, find the ant of the transformer station that electric energy can be provided to return to nest, and leave pheromones.
In the time that test reaches preset value 1000 T.T., EOP (end of program).
Implementation result
With respect to the intelligent algorithm such as ant group algorithm, genetic algorithm, not necessarily can obtain optimum solution although use dijkstra's algorithm to carry out distribution.But for the mass data of intelligent grid, use dijkstra's algorithm simple, can obtain the approximate solution of optimum solution.And when in the sufficient situation of the delivery of each power house, i.e. power station power consumption can not affect the electricity consumption in other power stations, uses dijkstra's algorithm can obtain optimum solution.
By the mouse call back function of OPENCV, realize visual data real-time query function.Mouse is moved on the power station, passage that needs inquiry, motor left button can inquire the real time data of power station, passage load on the LISTBOX in left side.
From this result, take 5 transformer stations when example is analyzed, the result that ant group algorithm and dijkstra's algorithm obtain is basic identical, also the feasibility of two kinds of algorithms of checking each other just.
The above; it is only preferably specific embodiment of the present invention; but protection scope of the present invention is not limited to this; any be familiar with those skilled in the art the present invention disclose scope in; be equal to replacement or changed according to technical scheme of the present invention and inventive concept thereof, all being belonged to protection scope of the present invention.
Claims (7)
1. an optimizing scheduling collocation method for intelligent grid, is characterized in that comprising the following steps:
1) set up equivalent model
Be unit take district, determine quantity and the position in power supply power station by power supply administration, described power station is coupled together between two with straight line, form undirected weighting power station power supply model;
2) computation model parameter
The undirected weighting power station power supply model forming according to step 1), take the distance in every two power stations as parameter, calculates according to every kilometer of ac power loss 0.05kVA, produces the amount of power dissipation matrix table between every two power stations;
3) according to step 2) the amount of power dissipation matrix table made, carries out the simulation of intelligent grid resource allocation with dijkstra's algorithm;
4) adopt ant group algorithm to step 2) make amount of power dissipation matrix table carry out Visual calculation, intelligent grid scheduling of resource is carried out to sunykatuib analysis;
5) determine the optimizing scheduling collocation method of intelligent grid according to step 3), step 4).
2. the optimizing scheduling collocation method of intelligent grid according to claim 1, is characterized in that: described in step 3), carry out the simulation of intelligent grid resource allocation with dijkstra's algorithm, refer to:
First, pick out the power station that all generated energy are less than power consumption, comprise a region of using of not generating electricity, join queue lack; For first power station of lack queue, use the parameter of equivalent model described in step 1) as the length of path, use dijkstra's algorithm to build the distance of each node to first power station node;
Secondly, arrange from small to large by distance in the power station that can power, and first obtains transmitted power from first power station, required path and the transmitted power of transmission of electricity is joined in current matrix simultaneously; If first power station power supply power is inadequate, then obtain power supply from second power station, repeat this step; After completing, fallen out in this power station from lack queue; If lack queue still has the power station that needs power supply, repeat this step until whole power station output setting power.
3. the optimizing scheduling collocation method of intelligent grid according to claim 1, is characterized in that: described in step 3), carry out the simulation of intelligent grid resource allocation with dijkstra's algorithm, concrete steps are as follows:
3-1), minimum load statistical form the highest take in the actual motion of each power station month is as basis, enter comparison by the output power that can provide with each power station, determine power station load running data, pick out the power station of not generating electricity, and the power station that generated energy is less than power consumption joins calculating queue, form M calculating object, M is 1,2,3 ... M;
3-2) according to step 2) described amount of power dissipation matrix table matrix, arrange from small to large by distance in the power station that can power, and forms N power supply power station;
3-3) from calculating object M=1, in the time that the first calculating object output power is not enough, first obtain transmitted power from nearest supply station N=1; If the 1st supply station output power is inadequate, then obtain from the 2nd supply station, there is no can be even to go out electric energy until the 1st power station obtains enough electric energy or N supply station; Otherwise carry out next step;
3-4) calculating object M=2, execution step 3-2), in the time that the first calculating object output power is not enough, first obtain transmitted power from nearest supply station N=1; If the 1st supply station output power is inadequate, then obtain from the 2nd supply station, there is no can be even to go out electric energy until calculating object obtains enough electric energy or N supply station; Otherwise carry out next step;
3-5) calculating object M=3, execution step 3-2), in the time that the first calculating object output power is not enough, first obtain transmitted power from nearest supply station N=1; If the 1st supply station output power is inadequate, then obtain from the 2nd supply station, there is no can be even to go out electric energy until calculating object obtains enough electric energy or N supply station; Otherwise carry out next step; Until calculating object is M, finish.
4. the optimizing scheduling collocation method of intelligent grid according to claim 1, is characterized in that: described in step 4), intelligent grid scheduling of resource is carried out to sunykatuib analysis, refer to: adopt pheromones to postpone update mode; First carry out parameter initialization, intercity initial information amount is set, distribute ant to each transformer station, it is all 0 that initialization information element distributes; Secondly the random each transformer station's motion that electric energy can be provided towards periphery of ant, and leave pheromones; Each ant k (k=1,2 ..., M) advance side by side according to the distribution of pheromones content on each route, revise pheromones and distribute; The 3rd calculates every loop distance that ant walks, and finds the ant of the transformer station that electric energy can be provided to return to nest, and leaves pheromones; The 4th in the time that test reaches preset value 1000 T.T., EOP (end of program).
5. the optimizing scheduling collocation method of intelligent grid according to claim 1, is characterized in that: described in step 4), intelligent grid scheduling of resource is carried out to sunykatuib analysis, concrete steps are as follows:
The probability distribution that 4-1) quantity of pheromones draws according to step 3) algorithm at initial time, the transformer station pheromones far away apart from other websites distributes less, and ant is according to the random each transformer station's motion that electric energy can be provided towards periphery of this pheromones distribution;
4-2) to consume the transformer station that is greater than power supply as nest, be food for TV university in the transformer station consuming, determine the parameter in speed radius, path, the transmission line of electricity between the Wei Liang transformer station of path;
4-3) test T.T. longer, program operation is slower, the shorter local optimum that is more easily absorbed in of time is determined test T.T. parameter, is provided with N nest, N=1,2,3 ... N, from N=1;
4-4) all ants are all positioned at nest, and ant to the motion of food point, has food at random in scope, so directly move to food; Every ant is moved according to the guide of pheromones, the Probability p moving according to maximum information element channeling direction is 0.7, it is 1 that every ant is sowed pheromones speed in the time just finding food, after this every time cycle excessively, reduce 10%, until return to nest, and pheromones rate of volatilization in time constant be 0.1;
4-5) calculate the loop distance that every ant walks, find the ant of the transformer station that electric energy can be provided to return to nest, and leave pheromones, now find the ant of food to return to starting point according to former road;
4-6) take N=2 as starting object, execution step 4-4) and step 4-5);
4-7) until take N as starting object, perform step 4-4) and 4-5);
4-8) execution time arrives test T.T. 1000s, finish, otherwise execution step 4-3).
6. the optimizing scheduling collocation method of intelligent grid according to claim 4, it is characterized in that: described in step 4), intelligent grid scheduling of resource is carried out to sunykatuib analysis, refer to: setting ant speed radius parameter is 1km, nest is to consume the transformer station that is greater than power supply, food point is greater than the transformer station of consumption for powering, transmission line of electricity between the Wei Liang transformer station of path, barrier is line loss; Ants all when original state are all positioned at nest, at random to other four food points motion; Determine that parameter is t=1000; It is 1 that every ant is sowed pheromones speed in the time just finding food, after this every time cycle excessively, reduces 10%, until return to nest; Pheromones rate of volatilization is in time constant is 0.1; If there is food in range of observation, so directly move to food, otherwise mobile according to following rule: every ant is moved according to the guide of pheromones, if the probability moving according to maximum information element channeling direction is p, p weighs ant group's initiative, through checking repeatedly, selects p=0.7.
7. the optimizing scheduling collocation method of intelligent grid according to claim 1, it is characterized in that: in step 3), step 4), carry out display scheduling result with power grid visualization, specifically refer to: by the mouse call back function of OPENCV, realize visual data real-time query function, mouse is moved on the power station, passage that needs inquiry, motor left button can inquire the real time data of power station, passage load on the LISTBOX in left side.
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