CN109871989A - A kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource - Google Patents
A kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource Download PDFInfo
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Abstract
A kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource disclosed by the invention, belongs to system for distribution network of power planning field.Implementation method of the present invention are as follows: establish Model for Multi-Objective Optimization using power distribution network year minimum comprehensive method of investment operating cost as target, hierarchical planning model is converted by Model for Multi-Objective Optimization, upper layer planning establishes objective function with the minimum target of route year comprehensive method of investment operating cost, solve route decision variable, it obtains best grid structure, passes to lower layer；Lower layer plans that under the basis of upper layer rack, environmental pollution improvement's cost accounting minimum that the average annual investment construction of power supply DG and operation expense, circuit network cost depletions, superior network purchases strategies and access DG are avoided in a distributed manner establishes objective function；Upper and lower layer model is solved using PSCO optimization algorithm, obtains final grid structure and the on-position DG and capacity configuration.The present invention has lower year mixed economy cost, and more stable system voltage is horizontal, can be improved power supply reliability.
Description
Technical field
The invention belongs to system for distribution network of power planning fields, and in particular to contain distribution based on Hybrid particle swarm optimization
The distribution network system hierarchical reconfiguration planning method of power supply (Distributed Generation).
Background technique
As maximum discharges of atomic particle such as the depleted and greenhouse gases of traditional fossil energy, pm2.5 etc. are serious
The appearance of problem of environmental pollution is incorporated with the distributed generation resource of a large amount of such as photovoltaic, blower clean type in power distribution network
(Distributed Generation, abbreviation DG), this is by the planning to power distribution network and runs the certain influence of generation, promotes electricity
Network planning draws difficulty, while proposing challenge to power distribution network reliability of operation.In recent years, the distribution network planning research containing DG becomes
Hot spot, many algorithms such as genetic algorithm, Cultural Algorithm, particle swarm algorithm are applied in Electric Power Network Planning, and achieve and centainly grind
Study carefully achievement.But few documents take the distribution of the capacity of hierarchical planning and DG into consideration, lack the mould of a specification, standard
Type, so that it is also not high to solve difficult and accuracy.
Summary of the invention
A kind of the purpose of power distribution network hierarchical reconfiguration planning method containing distributed generation resource disclosed by the invention, is: providing a kind of based on mixed
The power distribution network hierarchical reconfiguration planning method containing distributed generation resource for closing particle group optimizing realizes that optimal grid structure and polynary power supply are held
Amount configuration.
Object of the present invention is to what is be achieved through the following technical solutions.
A kind of power distribution network hierarchical reconfiguration planning method implementation method containing distributed generation resource disclosed by the invention are as follows: with power distribution network year
Minimum comprehensive method of investment operating cost is that target establishes Model for Multi-Objective Optimization, and plurality of optimization aim includes five different expenses
Use cost.Hierarchical planning model is converted by the Model for Multi-Objective Optimization of foundation, upper layer planning is with route year comprehensive method of investment operation
The minimum target of expense establishes objective function, solves route decision variable, obtains best grid structure, pass to lower layer；Lower layer
It plans under the basis of upper layer rack, in a distributed manner the average annual investment construction of power supply DG (Distributed Generation) and fortune
Environmental pollution improvement's cost that row maintenance cost, circuit network cost depletions, superior network purchases strategies and access DG are avoided
Total minimum establishes objective function.Particle velocity meter each in particle swarm algorithm calculation is improved using simulated annealing to be formed
Hybrid particle swarm Cooperative Optimization Algorithm, abbreviation PSCO solve upper and lower layer model using PSCO optimization algorithm, obtain final
Grid structure and the on-position DG and capacity configuration.Planing method proposed by the present invention has lower year than traditional planning method
Mixed economy cost, more stable system voltage is horizontal, improves power supply reliability.
A kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource disclosed by the invention, includes the following steps:
Step 1: Model for Multi-Objective Optimization is established using power distribution network most off year comprehensive method of investment operating cost as target, wherein more
A optimization aim includes grid structure planning annual cost of investment, distributed generation resource DG (Distributed Generation)
What average annual investment construction and operation expense, circuit network cost depletions, superior network purchases strategies and access DG were avoided
Five different expense costs of environmental pollution improvement's cost.
Multiple-objection optimization mathematical model is established using power distribution network most off year comprehensive method of investment operating cost as target.Plurality of mesh
Mark includes that grid structure planning annual cost of investment, the average annual investment construction of DG and operation expense, circuit network are worn to
Originally, five different expense costs of environmental pollution improvement's cost that superior network purchases strategies and access DG are avoided.Model is such as
Under
minCall=Cline+CDG+Closs+Cen-UDG (1)
In formula: ClineFor grid structure annual construction investment expense, CDGFor the annual investment construction and fortune for being incorporated to DG
Row maintenance cost, ClossFor circuit network wear and tear expense, CenFor the power purchase expense of superior network, UDGFor due to distributed generation resource
The environmental pollution improvement's cost for being incorporated to and saving.
Multiple optimization aim constraint conditions include inequality constraints condition and equality constraint:
Inequality constraints condition (one) is as follows:
1) voltage constrains
|Vi min|≤|Vi|≤|Vi max|, i ∈ N (2)
In formula: ViIndicate the voltage value of node i, Vi min,Vi maxRespectively indicate the upper and lower bound of each node voltage；N is
Node set.
2) restriction of current
In formula: IjIndicate the current value of route j,Indicate route maximum allowed current capacity；T is line set.
3) Branch Power Flow constrains
In formula: SjFor the active power of branch j,For capacity of trunk upper limit value on branch j, T is line set.
4) the total installed capacity constraint of DG
In formula:For the DG capacity being incorporated at power distribution network node i, η is DG permeability, PNETo be increased newly after power distribution network extension
Total load capacity.
5) single DG capacity-constrained
In formula:The upper and lower bound for the DG capacity being respectively incorporated at power distribution network node i.
Equality constraint (two) is as follows:
1) it is incorporated to DG posterior nodal point power-balance constraint
In formula: PGiFor the electrical power generators general power for injecting node i by upper layer power distribution network, Vi、VjRespectively node i, j
Node voltage, GijAnd BijRespectively two end nodes are the conductance and susceptance of the route of i, j,For line power factor angle.
2) superior power distribution network power purchase power constraint
Pen=PNE-∑PDGi (8)
In formula: PenFor the total power purchase power of system.
Step 2: hierarchical planning model is converted by the Model for Multi-Objective Optimization that step 1 is established, upper layer planning is comprehensive with year
It closes investment operating cost minimum and establishes objective function, solve route decision variable, obtain best grid structure, pass to lower layer；
Lower layer plans power supply DG (DistributedGeneration) the average annual investment construction in a distributed manner under the basis of upper layer rack
The environmental pollution improvement avoided with operation expense, circuit network cost depletions, superior network purchases strategies and access DG
Cost combination item minimum establishes objective function.
Convert hierarchical planning model for the Model for Multi-Objective Optimization that step 1 is established, i.e., it will be as excellent such as formula (1) multiple target
Changing model conversation is the hierarchical planning model such as formula (9).
minCall=Cline+Cdown (9)
In formula: CallFor the comprehensive method of investment and operating cost year value, Wan Yuan/kW；ClineIt is line construction into this year value, Wan Yuan/
kW；CdownFor the comprehensive cost year value of DG access, Wan Yuan/kW, including the C mentioned in formula (1)DG, Closs, CenAnd UDG。
Upper layer planning establishes objective function, i.e. C with route annual construction cost minimumline, solve route decision variable.
Route annual construction cost ClineExpression formula such as (10) is shown,
In formula: λ1、γ1Respectively track investment return rate and year operation and maintenance rate；β1For the annual of route fixed investment
Expense conversion factor；LjIndicate the construction length of j-th strip route；It is x for j-th strip line construction circuit typesjWhen unit
Length construction cost, xjIt indicates route decision variable, works as xjWhen=0, indicate that route is not planned, xjWhen=1, layout of roads is indicated.
T is that power distribution network need to build route total collection.
β1It calculates according to the following formula
In formula: r is rate of return on investment, n1For the equipment normal use time limit, n2The time limit is built for equipment construction.
Lower layer's planning establishes objective function C with DG access year after next comprehensive method of investment operating cost minimumdown, including formula (1)
In the DG annual investment construction and operation expense C mentionedDG、Closs、CenAnd UDG。CDG、Closs、CenAnd UDGSpecifically ask
It is as follows to solve formula:
A) the average annual fixed investment of DG and O&M cost CDGFor
In formula: λ2、γ2The respectively rate of return on investment of DG and year operation and maintenance rate；β2Indicate the Average Annual Cost of DG investment
Conversion factor；PDGiIndicate the DG rated capacity accessed at node i；XiIndicate the type that DG is incorporated at node i, wherein N expression is matched
Plan node set in electric network composition, XiIt indicates not to be incorporated to DG, X at this node when=0iIt indicates to be incorporated at this node when=1
The DG of first seed type, XiThe DG that second of type is incorporated at this node is indicated when=2, and so on, variable XiDifferent values
Represent different types of DG；fi(Xi) it is that X is accessed at node iiThe unit power generation installation cost and operation expense of type DG,
It is shown below.
In formula:Respectively indicate access X at node iiThe unit installation cost of type DG and unit O&M expense year
Value, the corresponding unit power generation installation cost of different types of DG and unit O&M expense are different, and work as XiWhen=0, fi(Xi)=
0。
B) route year via net loss cost Closs
Closs=τmaxcbPloss (14)
In formula: τmaxIndicate that distribution network system and DG maximum utilize hourage；cbFor unit power purchase price；PlossFor distribution
Network total losses power in net grid structure route, is shown below.
In formula: δ is the total power factor of distribution network system；ΔVijNode voltage difference between node i, j；ZijFor
Endpoint is the impedance of the route of i, j.
C) superior purchases strategies Cen
Cen=τmaxcbPb (16)
In formula: PbThe total power purchase power for indicating power distribution network, is shown below.
In formula: PNEIndicate newly-increased load total amount；Indicate XiThe capacity coefficient of seed type DG, and XiWhen=0, that is, save
DG is not accessed at point i,Different type DG corresponds to different capacity coefficients.
D) DG power generation social compensation UDG
In formula:For XiThe unit generated energy social compensation expense of middle DG, different types of DG correspond to different allowances
With.
Step 3: particle velocity meter each in particle swarm algorithm calculation is improved to form mangcorn using simulated annealing
Subgroup Cooperative Optimization Algorithm, abbreviation PSCO solve upper and lower layer model using PSCO optimization algorithm, obtain grid structure and
The on-position DG and capacity configuration.
Step 3.1: particle velocity meter each in particle swarm algorithm calculation being improved to form mixing using simulated annealing
Particle swarm collaborative optimization algorithm, abbreviation PSCO.
Step 3.1 realizes that steps are as follows:
Step 3.1.1: the speed of each particle and position in particle swarm algorithm are randomly provided.
Step 3.1.2: each particle fitness value is calculated, the position of particle and fitness value are stored in the individual of particle
Extreme value pbestIn, by all pbestIn optimal value be stored in global extremum gbestIn.
Step 3.1.3: the initial temperature of simulated annealing is determined according to following formula and moves back warm mode.
tk+1=λ tk (20)
Step 3.1.4: each particle p under Current Temperatures is determined according to the following formulaiFitness value, i.e., simulated annealing is calculated
Each particle foundation contacts in temperature and particle swarm algorithm in method.
Step 3.1.5: from all piMiddle determining global optimum pi', particle rapidity and location update formula are as follows:
Wherein,
xij(t+1)=xij(t)+vij(t+1) (24)
Wherein, c1And c2For Studying factors；r1And r2For the uniform random number in [0,1] range；vijFor the speed of particle,
xijFor the position of particle.
Step 3.1.6: each particle p is calculatediTarget extreme value pbestAnd the optimal global extremum g of all particlesbest, and according to step
Rapid 3.1.5 updates pbestAnd gbest, then carry out moving back warm operation.
Step 3.1.7: it when PSCO optimization algorithm reaches its stop condition, then stops search and exports PSCO optimization algorithm
Optimum results；Otherwise it is continued searching back to step 3.1.4.
So far, it realizes and particle velocity meter each in particle swarm algorithm calculation is improved to form mixing using simulated annealing
Particle swarm collaborative optimization algorithm.
Step 3.2: step 2 model built being solved using hybrid particle swarm Cooperative Optimization Algorithm PSCO, obtains DG
Position and capacity.
Upper and lower layer model is solved using PSCO algorithm, obtains the position DG and capacity.In particle swarm algorithm
In the operational process of (Particle Swarm Optimization, hereinafter referred to as PSO), in group particle to global optimum
The continuous tracking of point, to make particle show more homoplasies during evolution, however, the diversity of group can have
Effect improves the global convergence ability of algorithm, and PSCO algorithm proposed by the present invention can make population that diversity be kept not reduce it again
Homoplasy.
Step 3.2 realizes that steps are as follows:
Step 3.2.1: using hybrid particle swarm Cooperative Optimization Algorithm PSCO to upper layer plan with route annual construction at
The objective function that this minimum is established is solved.Random to generate primary group, each particle position is xi, initial velocity is set,
And the initial individuals optimal solution and globally optimal solution of each particle are set, Load flow calculation and net are carried out for particle each in population
Damage calculates, and the fitness value of each particle is assessed by formula (10), is solved later according to described in step 3.1, is obtained route and determine
The value of plan variable.
Step 3.2.2: lower layer is planned with the comprehensive throwing of the DG access year after next using hybrid particle swarm Cooperative Optimization Algorithm PSCO
Money operating cost minimum establish objective function solved, with step 3.2.1 the difference is that, fitness function at this time
It for formula (12), the adduction function of (14), (16), (18), is solved, obtain the position DG and is held according to described in step 3.1 later
Amount.
Step 4: by the position DG solved by step 3 and capacity feed-back to upper layer model, again to upper layer model
It is solved.By iterating between upper and lower layer plan model, the optimal grid structure of final output and the position DG and capacity configuration
Scheme realizes optimal grid structure and polynary electricity to get to the smallest power distribution network planning scheme of year comprehensive method of investment operating cost
Source capacity configuration.
The utility model has the advantages that
1, a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource disclosed by the invention, considers not only polynary distribution
The capacity configuration of formula power supply, it is also contemplated that grid structure planning and influencing each other between it, in order to realize the connection, to containing more
First distributed power distribution network carries out hierarchical planning, and the grid structure that first layer obtains is leader, the polynary power supply of the second layer
Capacity and position, which solve, to need to carry out on the basis of first layer grid structure, while the configuration pair of the polynary power supply capacity of the second layer
Grid structure can also play feedback effect, such double-layer lap generation, until showing that an optimal grid structure and polynary power supply are held
Amount configuration.
2, a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource disclosed by the invention, both has in searching process
Faster convergence rate, and since the introducing of simulated annealing makes it avoid falling into local optimum, stuff and other stuff is optimized and is calculated
Method is applied to the distribution network planning containing distributed generation resource, and obtained programme is more comprehensive with lower year than traditional planning method
Economic cost.
3, a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource disclosed by the invention, due to DG it is grid-connected after to present
Line transimission power reduces, while the idle injection of DG is so that the sub-load node voltage along feeder line is elevated, this planing method
So that voltage is rationally raised, to obtain more stable system voltage level, power supply reliability is improved.
Detailed description of the invention
A kind of Fig. 1 power distribution network hierarchical reconfiguration planning method flow chart based on stuff and other stuff optimization disclosed by the invention；
Fig. 2 is PSCO optimization algorithm flow chart of the invention；
Fig. 3 is the comparison diagram for not accessing distributed generation resource and accessing distributed generation resource planning posterior nodal point voltage；
Fig. 4 is the influence accessed after distributed generation resource planning to power distribution network active power loss；
Fig. 5 is the influence accessed after distributed generation resource planning to the idle network loss of power distribution network；
Fig. 6 is initial IEEE33 network structure；
Fig. 7 is the IEEE33 network structure after accessing distributed generation resource and planning grid structure.
Specific embodiment
The present invention is described in more detail presently in connection with attached drawing, specific embodiment is as follows.
Embodiment 1:
This method is verified based on IEEE33 node system.Initial network structure is as shown in Figure 6: solid line is to have branch
Road, dotted line are that can build branch, and the total burden with power of system is 3715kW, and total load or burden without work is 2300kVar.DG installation node to be selected
For Isosorbide-5-Nitrae, 5,6,7,8,10,12,18,20,21,23,25,28,30.PSCO algorithm parameter setting are as follows: maximum number of iterations 100
It is secondary, population scale 50, Studying factors C1=C2=1, weight limit ωmax=1.2, minimal weight ωmin=0.5, anneal constant
σ=0.6.
Each projecting parameter that objective function is related to is as follows: the average annual expense conversion factor β of the fixed investment of route1=
0.131, rate of return on investment λ1=0.5, year operation and maintenance rate γ1=0.655；The fixed investment of DG construction average annual expense conversion system
Number β2=0.138, rate of return on investment λ2=0.5, year operation and maintenance rate γ2=0.655；Assuming that system and DG power factor δ=
0.9.System maximum utilizes hourage τmax=4200h.Superior unit power purchase price Cb=0.4 yuan/kWh.Newly-built route selection
Following three types: LGJ-185, LGJ-120, LGJ-95.
As shown in Figure 1, a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource disclosed in the present embodiment, specific implementation
Steps are as follows:
Step 1: Model for Multi-Objective Optimization is established using power distribution network most off year comprehensive method of investment operating cost as target, wherein more
A optimization aim includes grid structure planning annual cost of investment, distributed generation resource DG (Distributed Generation)
What average annual investment construction and operation expense, circuit network cost depletions, superior network purchases strategies and access DG were avoided
Five different expense costs of environmental pollution improvement's cost.
Multiple-objection optimization mathematical model is established using power distribution network most off year comprehensive method of investment operating cost as target.Plurality of mesh
Mark includes that grid structure planning annual cost of investment, the average annual investment construction of DG and operation expense, circuit network are worn to
Originally, five different expense costs of environmental pollution improvement's cost that superior network purchases strategies and access DG are avoided.Model is such as
Under
minCall=Cline+CDG+Closs+Cen-UDG (1)
In formula: ClineFor grid structure annual construction investment expense, CDGFor the annual investment construction and fortune for being incorporated to DG
Row maintenance cost, ClossFor circuit network wear and tear expense, CenFor the power purchase expense of superior network, UDGFor due to distributed generation resource
The environmental pollution improvement's cost for being incorporated to and saving.
Multiple optimization aim constraint conditions include inequality constraints condition and equality constraint:
Inequality constraints condition (one) is as follows:
1) voltage constrains
|Vi min|≤|Vi|≤|Vi max|, i ∈ N (2)
In formula: ViIndicate the voltage value of node i, Vi min,Vi maxRespectively indicate the upper and lower bound of each node voltage；N is
Node set.
2) restriction of current
In formula: IjIndicate the current value of route j,Indicate route maximum allowed current capacity；T is line set.
3) Branch Power Flow constrains
In formula: SjFor the active power of branch j,For capacity of trunk upper limit value on branch j, T is line set.
4) the total installed capacity constraint of DG
In formula:For the DG capacity being incorporated at power distribution network node i, η is DG permeability, PNETo be increased newly after power distribution network extension
Total load capacity.
5) single DG capacity-constrained
In formula:The upper and lower bound for the DG capacity being respectively incorporated at power distribution network node i.
Equality constraint (two) is as follows:
1) it is incorporated to DG posterior nodal point power-balance constraint
In formula: PGiFor the electrical power generators general power for injecting node i by upper layer power distribution network, Vi、VjRespectively node i, j
Node voltage, GijAnd BijRespectively two end nodes are the conductance and susceptance of the route of i, j,For line power factor angle.
2) superior power distribution network power purchase power constraint
Pen=PNE-∑PDGi (8)
In formula: PenFor the total power purchase power of system.
Step 2: hierarchical planning model is converted by the Model for Multi-Objective Optimization that step 1 is established, upper layer planning is comprehensive with year
It closes investment operating cost minimum and establishes objective function, solve route decision variable, obtain best grid structure, pass to lower layer；
Lower layer plans power supply DG (Distributed Generation) the average annual investment construction in a distributed manner under the basis of upper layer rack
The environmental pollution improvement avoided with operation expense, circuit network cost depletions, superior network purchases strategies and access DG
Cost accounting minimum establishes objective function.
Convert hierarchical planning model for the Model for Multi-Objective Optimization that step 1 is established, i.e., it will be as excellent such as formula (1) multiple target
Changing model conversation is the hierarchical planning model for such as formula (9).
minCall=Cline+Cdown (9)
In formula: CallFor the comprehensive method of investment and operating cost year value, Wan Yuan/kW；ClineIt is line construction into this year value, Wan Yuan/
kW；CdownFor the comprehensive cost year value of DG access, Wan Yuan/kW, including the C mentioned in formula (1)DG, Closs, CenAnd UDG。
Upper layer planning establishes objective function, i.e. C with route annual construction cost minimumline, solve route decision variable.
Route annual construction cost ClineExpression formula such as (10) is shown,
In formula: λ1、γ1Respectively track investment return rate and year operation and maintenance rate；β1For the annual of route fixed investment
Expense conversion factor；LjIndicate the construction length of j-th strip route；It is x for j-th strip line construction circuit typesjWhen unit
Length construction cost, xjIt indicates route decision variable, works as xjWhen=0, indicate that route is not planned, xjWhen=1, layout of roads is indicated.
T is that power distribution network need to build route total collection.
β1It calculates according to the following formula
In formula: r is rate of return on investment, n1For the equipment normal use time limit, n2The time limit is built for equipment construction.
Lower layer's planning establishes objective function C with DG access year after next comprehensive method of investment operating cost minimumdown, including formula (1)
In the annual investment construction and operation expense C of DG mentionedDG、Closs、CenAnd UDG。CDG、Closs、CenAnd UDGSpecifically
Solution formula is as follows:
A) the average annual fixed investment of DG and O&M cost CDGFor
In formula: λ2、γ2The respectively rate of return on investment of DG and year operation and maintenance rate；β2Indicate the Average Annual Cost of DG investment
Conversion factor；PDGiIndicate the DG rated capacity accessed at node i；XiIndicate the type that DG is incorporated at node i, wherein N expression is matched
Plan node set in electric network composition, XiIt indicates not to be incorporated to DG, X at this node when=0iIt indicates to be incorporated at this node when=1
The DG of first seed type, XiThe DG that second of type is incorporated at this node is indicated when=2, and so on, variable XiDifferent values
Represent different types of DG；fi(Xi) it is that X is accessed at node iiThe unit power generation installation cost and operation expense of type DG,
It is shown below.
In formula:Respectively indicate access X at node iiThe unit installation cost of type DG and unit O&M expense year
Value, the corresponding unit power generation installation cost of different types of DG and unit O&M expense are different, and work as XiWhen=0, fi(Xi)=
0。
B) route year via net loss cost Closs
Closs=τmaxcbPloss (14)
In formula: τmaxIndicate that distribution network system and DG maximum utilize hourage；cbFor unit power purchase price；PlossFor distribution
Network total losses power in net grid structure route, is shown below.
In formula: δ is the total power factor of distribution network system；ΔVijNode voltage difference between node i, j；ZijFor
Endpoint is the impedance of the route of i, j.
C) superior purchases strategies Cen
Cen=τmaxcbPb (16)
In formula: PbThe total power purchase power for indicating power distribution network, is shown below.
In formula: PNEIndicate newly-increased load total amount；Indicate XiThe capacity coefficient of seed type DG, and XiWhen=0, that is, save
DG is not accessed at point i,Different type DG corresponds to different capacity coefficients.
D) DG power generation social compensation UDG
In formula:For XiThe unit generated energy social compensation expense of middle DG, different types of DG correspond to different allowances
With.
Step 3: particle velocity meter each in particle swarm algorithm calculation is improved to form mangcorn using simulated annealing
Subgroup Cooperative Optimization Algorithm, abbreviation PSCO solve upper and lower layer model using PSCO optimization algorithm, obtain grid structure and
The on-position DG and capacity configuration.
Step 3.1: particle velocity meter each in particle swarm algorithm calculation being improved to form mixing using simulated annealing
Particle swarm collaborative optimization algorithm, abbreviation PSCO.
Step 3.1 realizes that steps are as follows:
Step 3.1.1: the speed of each particle and position in particle swarm algorithm are randomly provided.
Step 3.1.2: each particle fitness value is calculated, the position of particle and fitness value are stored in the individual of particle
Extreme value pbestIn, by all pbestIn optimal value be stored in global extremum gbestIn.
Step 3.1.3: the initial temperature of simulated annealing is determined according to following formula and moves back warm mode.
tk+1=λ tk (20)
Step 3.1.4: each particle p under Current Temperatures is determined according to the following formulaiFitness value, i.e., simulated annealing is calculated
Each particle foundation contacts in temperature and particle swarm algorithm in method.
Step 3.1.5: from all piMiddle determining global optimum pi', particle rapidity and location update formula are as follows:
Wherein,
xij(t+1)=xij(t)+vij(t+1) (24)
Wherein, c1And c2For Studying factors；r1And r2For the uniform random number in [0,1] range；vijFor the speed of particle,
xijFor the position of particle.
Step 3.1.6: each particle p is calculatediTarget extreme value pbestAnd the optimal global extremum g of all particlesbest, and according to step
Rapid 3.1.5 updates pbestAnd gbest, then carry out moving back warm operation.
Step 3.1.7: it when PSCO optimization algorithm reaches its stop condition, then stops search and exports PSCO optimization algorithm
Optimum results；Otherwise it is continued searching back to step 3.1.4.
So far, it realizes and particle velocity meter each in particle swarm algorithm calculation is improved to form mixing using simulated annealing
Particle swarm collaborative optimization algorithm.
Step 3.2: step 2 model built being solved using hybrid particle swarm Cooperative Optimization Algorithm PSCO.
Upper and lower layer model is solved using PSCO algorithm.In particle swarm algorithm (Particle Swarm
Optimization, hereinafter referred to as PSO) operational process in, with continuous tracking of the particle to globe optimum in group, thus
Particle is set to show more homoplasies during evolution, however, the diversity of group can effectively improve the overall situation of algorithm
Convergence capabilities, PSCO optimization algorithm proposed by the present invention can make population that diversity be kept not reduce its homoplasy again.
Step 3.2 realizes that steps are as follows:
Step 3.2.1: using hybrid particle swarm Cooperative Optimization Algorithm PSCO to upper layer plan with route annual construction at
The objective function that this minimum is established is solved.Random to generate primary group, each particle position is xi, initial velocity is set,
And the initial individuals optimal solution and globally optimal solution of each particle are set, Load flow calculation and net are carried out for particle each in population
Damage calculates, and the fitness value of each particle is assessed by formula (10), is solved later according to described in step 3.1, is obtained route and determine
The value of plan variable.
Step 3.2.2: lower layer is planned with the comprehensive throwing of the DG access year after next using hybrid particle swarm Cooperative Optimization Algorithm PSCO
Money operating cost minimum establish objective function solved, with step 3.2.1 the difference is that, fitness function at this time
It for formula (12), the adduction function of (14), (16), (18), is solved, obtain the position DG and is held according to described in step 3.1 later
Amount.
Step 4: by the position DG solved by step 3 and capacity feed-back to upper layer model, again to upper layer model
It is solved.By iterating between upper and lower layer plan model, the optimal grid structure of final output and the position DG and capacity configuration
Scheme realizes optimal grid structure and polynary electricity to get to the smallest power distribution network planning scheme of year comprehensive method of investment operating cost
Source capacity configuration.
Using different DG access schemes, its influence to power distribution network is analyzed, including the influence to voltage's distribiuting, to route
The influence of network loss.Firstly, analysis does not access the influence that DG and three kinds of difference DG access schemes are distributed distribution network voltage, as a result such as
Shown in Fig. 3；Secondly, analysis does not access the influence of DG and three kinds of difference DG access schemes to distribution network loss, as a result such as Fig. 4, Fig. 5
It is shown；In conjunction with the above two aspects analysis, (it is 100/200/500/ that access capacity is distinguished at node 1/12/6/8 to Choice two
The distributed generation resource of 200kVA) it is used as final DG access scheme, to as shown in Figure 7 after the planning of distribution network system shown in Fig. 6.Docking
Year overall economic efficiency after entering DG carries out analysis as can be seen that the access of DG is so that year comprehensive method of investment expense reduces 91.74
Wan Yuan, remarkable in economical benefits.
Above-described specific descriptions have carried out further specifically the purpose of invention, technical scheme and beneficial effects
It is bright, it should be understood that above is only a specific embodiment of the present invention, being used to explain the present invention, it is not used to limit this
The protection scope of invention, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all
It is included within protection scope of the present invention.
Claims (6)
1. a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource, it is characterised in that: include the following steps,
Step 1: establishing Model for Multi-Objective Optimization using power distribution network most off year comprehensive method of investment operating cost as target, plurality of excellent
Changing target includes that grid structure plans that annual cost of investment, distributed generation resource DG (Distributed Generation) are average annual
The environment that investment construction and operation expense, circuit network cost depletions, superior network purchases strategies and access DG are avoided
Five different expense costs of pollution control cost；
Step 2: hierarchical planning model is converted by the Model for Multi-Objective Optimization that step 1 is established, upper layer planning is with year comprehensive throwing
Money operating cost minimum establishes objective function, solves route decision variable, obtains best grid structure, pass to lower layer；Lower layer
It plans under the basis of upper layer rack, in a distributed manner the average annual investment construction of power supply DG (Distributed Generation) and fortune
Environmental pollution improvement's cost that row maintenance cost, circuit network cost depletions, superior network purchases strategies and access DG are avoided
Group item minimum establishes objective function；
Step 3: particle velocity meter each in particle swarm algorithm calculation is improved to form hybrid particle swarm using simulated annealing
Cooperative Optimization Algorithm, abbreviation PSCO solve upper and lower layer model using PSCO optimization algorithm, obtain grid structure and DG connects
Enter position and capacity configuration；
Step 4: by the position DG solved by step 3 and capacity feed-back to upper layer model, upper layer model is carried out again
It solves；By iterating between upper and lower layer plan model, the optimal grid structure of final output and the position DG and capacity configuration side
Case realizes optimal grid structure and polynary power supply to get to the smallest power distribution network planning scheme of year comprehensive method of investment operating cost
Capacity configuration.
2. a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource as described in claim 1, it is characterised in that: step 1
Implementation method is,
Multiple-objection optimization mathematical model is established using power distribution network most off year comprehensive method of investment operating cost as target；Plurality of target packet
Include grid structure planning annual cost of investment, the average annual investment construction of DG and operation expense, circuit network cost depletions, to
The upper network purchases strategies expense cost different with environmental pollution improvement's cost five that access DG is avoided；Model is as follows
minCall=Cline+CDG+Closs+Cen-UDG (1)
In formula: ClineFor grid structure annual construction investment expense, CDGFor the annual investment construction and operation dimension for being incorporated to DG
Protect cost, ClossFor circuit network wear and tear expense, CenFor the power purchase expense of superior network, UDGFor due to distributed generation resource and
The environmental pollution improvement's cost for entering and saving；
Multiple optimization aim constraint conditions include inequality constraints condition and equality constraint:
Inequality constraints condition (one) is as follows:
1) voltage constrains
|Vi min|≤|Vi|≤|Vi max|, i ∈ N (2)
In formula: ViIndicate the voltage value of node i, Vi min,Vi maxRespectively indicate the upper and lower bound of each node voltage；N is node
Set；
2) restriction of current
In formula: IjIndicate the current value of route j,Indicate route maximum allowed current capacity；T is line set；
3) Branch Power Flow constrains
In formula: SjFor the active power of branch j,For capacity of trunk upper limit value on branch j, T is line set；
4) the total installed capacity constraint of DG
In formula:For the DG capacity being incorporated at power distribution network node i, η is DG permeability, PNEIt is total negative to be increased newly after power distribution network extension
Lotus capacity；
5) single DG capacity-constrained
In formula:The upper and lower bound for the DG capacity being respectively incorporated at power distribution network node i；
Equality constraint (two) is as follows:
1) it is incorporated to DG posterior nodal point power-balance constraint
In formula: PGiFor the electrical power generators general power for injecting node i by upper layer power distribution network, Vi、VjRespectively node i, j node
Voltage, GijAnd BijRespectively two end nodes are the conductance and susceptance of the route of i, j,For line power factor angle；
2) superior power distribution network power purchase power constraint
Pen=PNE-∑PDGi (8)
In formula: PenFor the total power purchase power of system.
3. a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource as claimed in claim 2, it is characterised in that: step 2
Implementation method is,
Convert hierarchical planning model for the Model for Multi-Objective Optimization that step 1 is established, i.e., it will be such as formula (1) multiple-objection optimization mould
Type is converted into the hierarchical planning model such as formula (9)；
minCall=Cline+Cdown (9)
In formula: CallFor the comprehensive method of investment and operating cost year value, Wan Yuan/kW；ClineIt is line construction into this year value, Wan Yuan/kW；
CdownFor the comprehensive cost year value of DG access, Wan Yuan/kW, including the C mentioned in formula (1)DG, Closs, CenAnd UDG；
Upper layer planning establishes objective function, i.e. C with route annual construction cost minimumline, solve route decision variable；Route
Annual construction cost ClineExpression formula such as (10) is shown,
In formula: λ1、γ1Respectively track investment return rate and year operation and maintenance rate；β1For the Average Annual Cost of route fixed investment
Conversion factor；LjIndicate the construction length of j-th strip route；It is x for j-th strip line construction circuit typesjWhen unit length
Construction cost, xjIt indicates route decision variable, works as xjWhen=0, indicate that route is not planned, xjWhen=1, layout of roads is indicated；T is
Power distribution network need to build route total collection；
β1It calculates according to the following formula
In formula: r is rate of return on investment, n1For the equipment normal use time limit, n2The time limit is built for equipment construction；
Lower layer's planning establishes objective function C with DG access year after next comprehensive method of investment operating cost minimumdown, including mention in formula (1)
DG annual investment construction and operation expense CDG、Closs、CenAnd UDG；CDG、Closs、CenAnd UDGSpecific solution formula
It is as follows:
A) the average annual fixed investment of DG and O&M cost CDGFor
In formula: λ2、γ2The respectively rate of return on investment of DG and year operation and maintenance rate；β2Indicate the Average Annual Cost conversion of DG investment
Coefficient；PDGiIndicate the DG rated capacity accessed at node i；XiIndicate the type that DG is incorporated at node i, wherein N indicates power distribution network
Plan node set in structure, XiIt indicates not to be incorporated to DG, X at this node when=0iIt indicates to be incorporated to first at this node when=1
The DG of seed type, XiThe DG that second of type is incorporated at this node is indicated when=2, and so on, variable XiDifferent values represents
Different types of DG；fi(Xi) it is that X is accessed at node iiThe unit power generation installation cost and operation expense of type DG, it is as follows
Shown in formula；
In formula:Respectively indicate access X at node iiThe unit installation cost and unit O&M expense year value of type DG,
The corresponding unit power generation installation cost of different types of DG and unit O&M expense are different, and work as XiWhen=0, fi(Xi)=0；
B) route year via net loss cost Closs
Closs=τmaxcbPloss (14)
In formula: τmaxIndicate that distribution network system and DG maximum utilize hourage；cbFor unit power purchase price；PlossFor power distribution network net
Network total losses power in frame structure route, is shown below；
In formula: δ is the total power factor of distribution network system；ΔVijNode voltage difference between node i, j；ZijIt is for endpoint
I, the impedance of the route of j；
C) superior purchases strategies Cen
Cen=τmaxcbPb (16)
In formula: PbThe total power purchase power for indicating power distribution network, is shown below；
In formula: PNEIndicate newly-increased load total amount；Indicate XiThe capacity coefficient of seed type DG, and XiWhen=0, i.e. node i
Place does not access DG,Different type DG corresponds to different capacity coefficients；
D) DG power generation social compensation UDG
In formula:For XiThe unit generated energy social compensation expense of middle DG, different types of DG correspond to different subsidy expenses.
4. a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource as claimed in claim 3, it is characterised in that: step 3
Implementation method is,
Step 3.1: particle velocity meter each in particle swarm algorithm calculation being improved to form stuff and other stuff using simulated annealing
Group's Cooperative Optimization Algorithm, abbreviation PSCO；
Step 3.2: step 2 model built being solved using hybrid particle swarm Cooperative Optimization Algorithm PSCO, obtains the position DG
With capacity；
Upper and lower layer model is solved using PSCO algorithm, obtains the position DG and capacity；In particle swarm algorithm (Particle
Swarm Optimization, hereinafter referred to as PSO) operational process in, in group particle to globe optimum constantly chasing after
Track, to make particle show more homoplasies during evolution, however, the diversity of group can effectively improve algorithm
Global convergence ability.
5. a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource as claimed in claim 4, it is characterised in that: step
3.1 realize that steps are as follows,
Step 3.1.1: the speed of each particle and position in particle swarm algorithm are randomly provided；
Step 3.1.2: calculating each particle fitness value, and the position of particle and fitness value are stored in the individual extreme value of particle
pbestIn, by all pbestIn optimal value be stored in global extremum gbestIn；
Step 3.1.3: the initial temperature of simulated annealing is determined according to following formula and moves back warm mode；
tk+1=λ tk (20)
Step 3.1.4: each particle p under Current Temperatures is determined according to the following formulaiFitness value, i.e., will be in simulated annealing
Temperature and particle swarm algorithm in the foundation of each particle contact；
Step 3.1.5: from all piMiddle determining global optimum pi', particle rapidity and location update formula are as follows:
Wherein,
xij(t+1)=xij(t)+vij(t+1) (24)
Wherein, c1And c2For Studying factors；r1And r2For the uniform random number in [0,1] range；vijFor the speed of particle, xijFor
The position of particle；
Step 3.1.6: each particle p is calculatediTarget extreme value pbestAnd the optimal global extremum g of all particlesbest, and according to step
3.1.5 p is updatedbestAnd gbest, then carry out moving back warm operation；
Step 3.1.7: when PSCO optimization algorithm reaches its stop condition, then stop search and export the optimization of PSCO optimization algorithm
As a result；Otherwise it is continued searching back to step 3.1.4；
So far, it realizes and particle velocity meter each in particle swarm algorithm calculation is improved to form stuff and other stuff using simulated annealing
Group's Cooperative Optimization Algorithm.
6. a kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource as claimed in claim 5, it is characterised in that: step
3.2 realize that steps are as follows,
Step 3.2.1: upper layer is planned with route annual construction cost most using hybrid particle swarm Cooperative Optimization Algorithm PSCO
The objective function of small foundation is solved；Random to generate primary group, each particle position is xi, initial velocity is set, and sets
The initial individuals optimal solution and globally optimal solution of fixed each particle carry out Load flow calculation and network loss meter for particle each in population
It calculates, the fitness value of each particle is assessed by formula (10), is solved later according to described in step 3.1, obtain the change of route decision
The value of amount；
Step 3.2.2: lower layer is planned using hybrid particle swarm Cooperative Optimization Algorithm PSCO and is transported with the DG access year after next comprehensive method of investment
The objective function that row expense minimum is established is solved, the difference is that, fitness function at this time is formula with step 3.2.1
(12), the adduction function of (14), (16), (18), is solved according to described in step 3.1 later, obtains the position DG and capacity.
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Application publication date: 20190611 |