CN109167359A - It is a kind of meter and power distribution network reconfiguration solid-state transformer site selecting method - Google Patents
It is a kind of meter and power distribution network reconfiguration solid-state transformer site selecting method Download PDFInfo
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- CN109167359A CN109167359A CN201811316552.8A CN201811316552A CN109167359A CN 109167359 A CN109167359 A CN 109167359A CN 201811316552 A CN201811316552 A CN 201811316552A CN 109167359 A CN109167359 A CN 109167359A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The solid-state transformer site selecting method of a kind of meter and power distribution network reconfiguration, the method includes the steps the solid-state transformer addressing bi-level optimal models that: A. establishes meter and power distribution network reconfiguration, including objective function and constraint condition；B. the design parameter of power distribution network, the modified bacterial foraging algorithm for merging speed change social learning is determined；C. it is solved using the modified bacterial foraging algorithm of fusion speed change social learning；D. result is analyzed, obtains the optimal installation site of solid-state transformation and Distribution system result.The solid-state transformer site selecting method of meter and power distribution network reconfiguration through the invention, it can be realized the optimal addressing of solid-state transformer, improve the operation stability and economy of power distribution network, simultaneously, modified bacterial foraging algorithm is when calculating the network that looped network is more, topology is more complex, fast convergence rate, low optimization accuracy are high.
Description
Technical field
The present invention relates to field of power systems, especially relate to the optimization running technology of power distribution network reconfiguration.
Background technique
Solid-state transformer as a kind of new and effective Intelligent electric power electronic equipment, to the development of energy internet play into
One step impetus.It executes equipment as the energy flow of energy internet, and conventional transformer voltage transformation not only may be implemented
With the function of electrical isolation, effective management of distributed energy, energy storage device and load can also be achieved, the two-way flow of energy,
The functions such as the trend distribution in control system.Power grid is added in solid-state transformer if choosing suitable position, system damage can be reduced
Consumption improves ability to transmit electricity and voltage stability, so it is no matter all aobvious from economy or the solid Optimizing Site Selection of stability angle
It obtains particularly important.
Power distribution network reconfiguration is the important means of power distribution network optimization, by changing block switch and interconnection switch in power distribution network
State is cut-off, power distribution network network structure is adjusted, reaching reduces network loss, improves the purpose of quality of voltage, balanced load.Currently, asking
The method of solution power distribution network reconfiguration is broadly divided into three classes: 1) optimization algorithm, and 2) heuritic approach, 3) intelligent algorithm.Its
In, intelligent algorithm has the characteristics that encode simple and global convergence, has been widely used in solving power distribution network reconfiguration, such as
Genetic algorithm, particle swarm algorithm, binary system crossover algorithm etc. in length and breadth.Bacterial foraging algorithm is a kind of novel bionic colony intelligence optimization
Algorithm, because of its group's parallel search, optimizing ability is strong, realizes the advantages that simple has obtained answering extensively in field of power system
With, Distribution system application aspect still in its infancy,
Currently, studying more blank on load flow regulation and planning problem to the research of solid-state transformation both at home and abroad.It is general
Bacterium loses algorithm when solving Distribution system problem, it may appear that the problems such as algorithm is precocious, and computational efficiency is low is unable to satisfy reality
Demand.The present invention has carried out detailed analysis and research for solid-state transformer addressing and bacterial foraging algorithm optimization, by double
Layer Optimized model has determined solid-state transformer optimal location, operation efficiency is improved by innovatory algorithm, in answering for Distribution system
There is good development prospect with aspect.
Summary of the invention
The object of the present invention is to provide a kind of meter and the solid-state transformer site selecting methods of power distribution network reconfiguration, realize solid-state transformation
The Optimizing Site Selection of device, and solve the problems, such as that bacterial foraging algorithm computational efficiency when solving reconstruction is low.
In order to realize that this purpose, the technical solution adopted by the present invention are as follows.
It is a kind of meter and power distribution network reconfiguration solid-state transformer site selecting method, the method includes the steps:
A. the solid-state transformer addressing bi-level optimal model of meter and power distribution network reconfiguration, including objective function and constraint item are established
Part；
B. the design parameter of power distribution network, the modified bacterial foraging algorithm for merging speed change social learning is determined；
C. it is solved using the modified bacterial foraging algorithm of fusion speed change social learning；
D. result is analyzed, obtains the optimal installation site of solid-state transformation and Distribution system result.
Particularly, in the step A, propose it is a kind of meter and power distribution network reconfiguration solid-state transformer addressing dual-layer optimization
Model, upper layer carry out power distribution network reconfiguration and determine optimal rack, and lower layer realizes the optimal addressing of solid-state transformer, phase between upper and lower level
It can be calculated to independence and by alternating iteration, finally acquire optimal solid-state transformer position and network when system losses minimum
Topology.
Particularly, in the step C, improved optimum individual moving direction is speed change social learning direction, specifically
It is as follows that direction adjusts formula:
Wherein, PHIbestFor speed change social learning direction, gbestPFor the position of group's optimum individual in the secondary chemotactic, P
(:, i, j, k, l) it is position of the bacterium i in the secondary chemotactic.
Particularly, in the step C, the location updating of bacterium is made of three parts after improvement: bacterium present position,
Chemotactic in random reverses direction, speed change social learning direction, calculating process are as follows:
P (:, i, j+1, k, l)=P (:, i, j, k, l)+Ci(:).*PHI+PHIbest,
Wherein, Ci(:) is chemotactic step-length of the bacterium i in this time, and PHI is random reverses direction of the bacterium i in this time.
By using the solid-state transformer site selecting method of meter and power distribution network reconfiguration of the invention, the technical effect of acquirement are as follows:
1. solid-state transformer addressing is nested with power distribution network reconfiguration to establish bi-level optimal model, a variety of fortune of power distribution network can be being considered
The optimal addressing that solid-state transformer is realized under line mode, increases so that the network loss of system is further decreased with minimum node voltage,
It is greatly improved the operation stability and economy of power distribution network.
2. speed change social learning is introduced, bacterium learning ability is enhanced on the basis of conventional bacteria foraging algorithm, it can
It breaks through bacterium and falls into local optimum bring limitation, its neighborhood search space is widened, to increase the convergence of total algorithm
Speed and convergence efficiency.
Therefore, meter of the invention and the solid-state transformer site selecting method of power distribution network reconfiguration, can be realized solid-state transformer
Optimal addressing improves the operation stability and economy of power distribution network, meanwhile, modified bacterial foraging algorithm calculate looped network compared with
When the more complex network of more, topology, fast convergence rate, low optimization accuracy is high.
Detailed description of the invention
Fig. 1 is the distribution net work structure schematic diagram of a specific application example of the invention.
Fig. 2 falls into a trap for embodiment of the present invention and the solid-state transformer addressing bi-level optimal model of power distribution network reconfiguration.
Fig. 3 is bi-level optimal model calculation flow chart in embodiment of the present invention.
Specific embodiment
1-3 with reference to the accompanying drawing, invention is further described in detail.
The present invention provides a kind of technical solution: it is a kind of meter and power distribution network reconfiguration solid-state transformer site selecting method, the side
Method comprising steps of
A. the solid-state transformer addressing bi-level optimal model of meter and power distribution network reconfiguration, including objective function and constraint item are established
Part；
B. the design parameter of power distribution network, the modified bacterial foraging algorithm for merging speed change social learning is determined；
C. it is solved using the modified bacterial foraging algorithm of fusion speed change social learning；
D. result is analyzed, obtains the optimal installation site of solid-state transformation and Distribution system result.
For step A, for the network shown in Fig. 1, the solid-state transformer addressing dual-layer optimization mould of meter and power distribution network reconfiguration
Type are as follows:
In formula, i is branch number, and N is the sum of circuitry number, k in networkiIndicate that branch is disconnected for the state of cut-offfing of branch i, 0
It opens, 1 indicates branch closure, riFor the resistance value of branch i, Pi,QiFor the active and reactive power of the end branch i, ViFor branch i
The node voltage of end.
In addition, constraint condition are as follows:
1) (1) Ai=I
Formula (1) indicates trend equality constraint, and A is branch/node incidence matrix, and i is branch telegram in reply flow vector, and I is node
Telegram in reply stream injection vector
2)Si,min≤Si≤Si,max (2)
Formula (2) indicates tributary capacity constraint, SiExpression flows through the performance number of i-th branch, Si,max,Si,minRespectively indicate branch
The permission maxima and minima of road i；
3)Ui,min≤Ui≤Ui,max (3)
Formula (3) indicates node voltage constraint, Ui,max,Ui,minRespectively indicate the voltage bound of node i；
4)gk∈Gk (4)
Formula (4) indicates the radial constraint of power grid, gkExist Network Structure, GkFor the collection for meeting network radial pattern structural constraint
It closes.
5)Pc1=Pc2 (5)
Formula (5) indicates the radial constraint of network, Pc1,Pc2Respectively pass through solid-state transformer primary side and secondary side
Active power need to guarantee the balance of active power inside solid-state transformer to keep DC voltage constant.
6) -45 ° of < θ1,θ245 ° of < (6)
When formula (6) indicates that constant voltage converter station operates in safety and stability range, it need to guarantee that it modulates angle less than 45 °.
In a specific embodiment, for step A, bi-level optimal model is as shown in Figure 2.
Specifically, improved optimum individual moving direction is speed change social learning direction for step C, specific side
It is as follows to adjustment formula:
Wherein, PHIbestFor speed change social learning direction, gbestPFor the position of group's optimum individual in the secondary chemotactic, P
(:, i, j, k, l) it is position of the bacterium i in the secondary chemotactic.
Further, the location updating of bacterium is made of three parts after improvement: bacterium present position, in random reverses direction
Chemotactic, speed change social learning direction, calculating process is as follows:
P (:, i, j+1, k, l)=P (:, i, j, k, l)+Ci(:).*PHI+PHIbest,
Wherein, Ci(:) is chemotactic step-length of the bacterium i in this time, and PHI is random reverses direction of the bacterium i in this time.
In a specific embodiment, for step C, operational flowchart is as shown in Figure 3.
In conclusion bilayer site selection model proposed by the present invention carries out power distribution network reconfiguration and solid-state transformer addressing simultaneously,
Optimum efficiency can be reached, improve the operation stability and economy of power distribution network, meanwhile, modified bacterial foraging algorithm have compared with
Strong convergence capabilities and stability, can adapt to the reconstruct in Complicated Distribution Network.
Finally it should be noted that: above embodiment is only the preferable embodiment of the present invention, fields it is common
Technical staff still can be with modifications or equivalent substitutions are made to specific embodiments of the invention referring to above-described embodiment, these
Without departing from any modification of spirit and scope of the invention or equivalent replacement, applying for pending claim guarantor of the invention
Within the scope of shield.
Claims (4)
1. the solid-state transformer site selecting method of a kind of meter and power distribution network reconfiguration, which is characterized in that the method includes the steps:
A. the solid-state transformer addressing bi-level optimal model of meter and power distribution network reconfiguration, including objective function and constraint condition are established；
B. the design parameter of power distribution network, the modified bacterial foraging algorithm for merging speed change social learning is determined；
C. it is solved using the modified bacterial foraging algorithm of fusion speed change social learning；
D. result is analyzed, obtains the optimal installation site of solid-state transformation and Distribution system result.
2. the solid-state transformer site selecting method of a kind of meter according to claim 1 and power distribution network reconfiguration, which is characterized in that
In the step A, propose it is a kind of meter and power distribution network reconfiguration solid-state transformer addressing bi-level optimal model, upper layer carry out distribution
Net reconstruct determines optimal rack, and lower layer realizes the optimal addressing of solid-state transformer, relatively independent between upper and lower level and can pass through
Alternating iteration calculates, and finally acquires optimal solid-state transformer position and network topology when system losses minimum.
3. the solid-state transformer site selecting method of a kind of meter according to claim 1 and power distribution network reconfiguration, which is characterized in that
In the step C, bacterium is allowed to be speed change social learning direction towards the mobile direction of optimum individual in chemotactic, specific direction adjustment
Formula is as follows:
Wherein, PHIbestFor speed change social learning direction, gbestPFor the position of group's optimum individual in the secondary chemotactic, P (:, i,
J, k, l) it is position of the bacterium i in the secondary chemotactic.
4. the solid-state transformer site selecting method of meter according to claim 1 and power distribution network reconfiguration, which is characterized in that described
In step C, the solid-state transformation of meter and power distribution network reconfiguration is solved using the modified bacterial foraging algorithm of fusion speed change social learning
Device location problem, and improve after bacterium location updating calculating process it is as follows:
P (:, i, j+1, k, l)=P (:, i, j, k, l)+Ci(:).*PHI+PHIbest,
Wherein, Ci(:) is chemotactic step-length of the bacterium i in this time, and PHI is random reverses direction of the bacterium i in this time.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109861199A (en) * | 2019-03-20 | 2019-06-07 | 湖南大学 | A kind of fault recovery method in DC distribution net |
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2018
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109861199A (en) * | 2019-03-20 | 2019-06-07 | 湖南大学 | A kind of fault recovery method in DC distribution net |
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Application publication date: 20190108 |