CN109599894A - A kind of grid-connected Optimal Configuration Method of DG based on improved adaptive GA-IAGA - Google Patents
A kind of grid-connected Optimal Configuration Method of DG based on improved adaptive GA-IAGA Download PDFInfo
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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
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Abstract
The invention discloses a kind of grid-connected Optimal Configuration Methods of the DG based on improved adaptive GA-IAGA, it includes the following steps: (1) with system losses, and variation and economy establish objective function；Step 2 establishes equality constraint according to objective function；Step 3 establishes inequality constraints condition；Step 4, with the reciprocal as fitness function of objective function, calculate using genetic algorithm and seek optimal solution and obtain the installation site and installed capacity of DG；It is based on empirical equation and artificially determining that the prior art, which is solved, for the installation site of DG and the determination significant portion of installed capacity, the voltage of each node in distribution system is not can effectively improve after leading to the installation site and installed capacity investment of DG, reduce system losses, improves the stability of system operation and the safety of power supply.
Description
Technical field:
The invention belongs to network systems to distribute technical field more particularly to a kind of DG based on improved adaptive GA-IAGA rationally simultaneously
Net Optimal Configuration Method.
Background technique
With the rapid development of our country's economy, social progress, Electrical demand further expansion, fossil energy are gradually withered
Exhaust, environmental pressure increases year by year, and environmental pollution is always global problem, face environmental problem and lack of energy, new energy by
More and more concerns are arrived.Original electric system has been broken in the proposition of distributed generation resource, this efficient, cleaning, operation side
The access of the flexible distributed generation resource of formula can be to node voltage, line current, power distribution, and trend distribution etc. impacts, this
Kind influences have inseparable relationship with the position of distributed generation resource and amount of capacity.Distributed energy is incorporated to electric system,
By reasonably distributing rationally, it can reduce via net loss, improve power quality.Therefore the distributed generation resource addressing of power distribution network is fixed
It is very important for appearance problem.
The addressing constant volume problem of distributed generation resource is most important during designing an electricity generation system containing distributed generation resource
Link.The different installation site of distributed generation resource and capacity, by the voltage of each node in largely change system, respectively
The electric current of branch, and then influence the stability and economy of Operation of Electric Systems.Suitable capacity is installed on reasonable position
Distributed generation resource can be effectively improved the voltage of each node in distribution system, reduce system losses, improve the stability of system operation
With the safety of power supply.The installation of distributed generation resource will take into account stability, safety and economy, therefore, the installation site of DG
It just must satisfy many limitations with installed capacity.And in the prior art very for the determination of the installation site of DG and installed capacity
Major part is to be determined based on empirical equation with artificial, not can effectively improve and matches after causing the installation site of DG and installed capacity to put into
The voltage of each node in electric system reduces system losses, improves the stability of system operation and the security requirement of power supply.
Summary of the invention:
The technical problem to be solved by the present invention is providing a kind of grid-connected side of distributing rationally the DG based on improved adaptive GA-IAGA
Method is based on empirical equation and artificial to solve the prior art for the installation site of DG and the determination significant portion of installed capacity
It determines, not can effectively improve the voltage of each node in distribution system after causing the installation site of DG and installed capacity to put into, reduce
System losses improve the stability of system operation and the safety of power supply.Technical solution of the present invention:
A kind of grid-connected Optimal Configuration Method of DG based on improved adaptive GA-IAGA, it includes:
Step 1 establishes objective function with system losses, variation and economy；
Step 2 establishes equality constraint according to objective function；
Step 3 establishes inequality constraints condition；
Step 4, with the reciprocal as fitness function of objective function, constraint condition and inequality constraints condition in equation
For edge-restraint condition, calculate using genetic algorithm and seek optimal solution and obtain the installation site and installed capacity of DG.
The objective function of foundation described in step 1 are as follows:
Objective function 1: establishing function by target of loss minimization, if system losses are Ploss, then network loss is expressed as follows:
In formula: PlossThe active loss of system is also cried for system losses；N is system branch sum；UiIt is branch b end node
Voltage magnitude；rbFor the resistance of branch b；PiActive power, Q for branch iiFor the reactive power of branch i；
Function is then established with the minimum target of system losses are as follows:
f1=min (Ploss) (2)
Objective function 2: intrinsic expense is minimum constructor, if total cost is C, then C is indicated are as follows:
In formula: μ is unit capacity cost, value 2000/KW, CDGiWhat is indicated is the capacity of the DG of i point access, and n is indicated
Be DG access number；
The then the smallest objective function of DG access fee are as follows:
f2=min (C) (4)
Objective function 3: with the minimum objective function of node voltage offset, if Δ UmaxFor voltage maximum offset, formula
It is as follows:
ΔUmax=max (| Ui-UNi|) (5)
In formula: UiIt is the voltage value of DG access node i, unit is KV.For the load voltage value of node i, unit is KV；
Then with the minimum objective function of node voltage offset are as follows:
f3=min (Δ Umax) (6)
With system losses, voltage deviation, intrinsic expense is objective function, and the weight by setting up three indexs is converted into
Single object of planning function are as follows:
F=α f1+βf2+λf3 (7)
α, β, λ respectively represent network loss, variation, the weight coefficient of intrinsic expense.
Equality constraint is established according to objective function described in step 2 are as follows:
In formula: Pi、Qi、PDGi、QDGiIt is the active reactive power and DG difference of node i input respectively
The active reactive power injected to node i.PLi、QLiIt is the active nothing of i-node institute on-load respectively
Function power.Vi、VjThe respectively voltage of i, j point, Y are the admittance matrixs of branch.
Inequality constraints condition is established described in step 3 are as follows:
Node voltage constraint
In formula, UiFor the voltage of node i,WithFor the maximum value and minimum value of node i voltage；
Tributary capacity constraint；
In formula, SiFor the power that node i flows through,For the upper overpowering maximum value of branch i.
The active constraint of distributed generation resource
In formula,It is distributed generation resource maximum active power value.
Constraint that distributed generation resource is idle
In formula,It is the maximum value and minimum value of distributed generation resource reactive power.
Carrying out the method that optimal solution is sought in calculating using genetic algorithm described in step 4 includes:
6) binary coding is carried out to chromosome and generates initialization population；
7) determination of fitness function；The inverse of objective function is as fitness function；
8) selection strategy screens initialization population by improved selection operator；
9) genetic operation, so-called genetic operation, just refers to intersection and variation, both operations are the marrow of genetic algorithm,
By improving to variation, expand variation type；
10) according to the degree of convergence of population, i.e., the consistency of individual fitness is to determine whether terminate operation in population.
Beneficial effects of the present invention:
The present invention is with network loss, and variation and economy are objective function, establishes equality constraint and inequality constraints item
Part is then based on improved adaptive GA-IAGA and is solved, progress constant volume addressing grid-connected to DG；The present invention is mainly by genetic algorithm
Selection and variation improve, and have fully considered excellent individual and remaining influence of individual to population diversity, ensure that remaining
Individual also has an opportunity to enter next-generation progress cross and variation operation, considerably increases population region of search, ensure that initialization kind
The population diversity of group's extreme case.
The invention has the advantages that
1) it improves selection operation and has fully considered excellent individual and remaining influence of individual to population diversity, ensure that it
Remaining individual also has an opportunity to enter next-generation progress cross and variation operation, and population diversity is improved.
2) Improving Genetic Algorithm has fully considered the diversity of population, significantly reduces genetic algorithm and falls into part most
The probability of excellent solution is suitable for quick global optimizing, and decreases relative to tradition selection complexity.
3) variation type is enriched, the superiority-inferiority of initialization population can be taken into account, considerably increase population region of search, is guaranteed
The population diversity of initialization population extreme case.
4) when initialize population at individual it is poor when, can be corrected by whole variation, when initialization population at individual compared with
When good, it can be made a variation and be finely adjusted by single-point, improved the fitness of population, faster more accurately find optimum point.
5) prominent DG optimizes emphasis, expands ability of searching optimum, improve the fitness of population, effective solution algorithm
The problem of being easily trapped into local optimum.
It is based on empirical equation that the prior art, which is solved, for the installation site of DG and the determination significant portion of installed capacity
It is determined with artificial, not can effectively improve the electricity of each node in distribution system after causing the installation site of DG and installed capacity to put into
Pressure reduces system losses, improves the stability of system operation and the safety of power supply.
Detailed description of the invention:
Fig. 1 is the calculation flow chart of improved adaptive GA-IAGA；
Fig. 2 is IEEE33 node power distribution net test macro topological diagram；
Fig. 3 is that improved adaptive GA-IAGA optimizes network loss result；
Fig. 4 is classical genetic algorithm optimization network loss result；
Fig. 5 is the DG of access optimization and does not access the node voltage variation of DG.
Specific embodiment:
The present invention is described in detail below by specific steps.
In order to make present invention solves the technical problem that, technical solution and control effect be more clearly understood, below in conjunction with attached
Figure and specific embodiment, are the present invention and are further described in detail.
The purpose of the present invention is achieved through the following technical solutions:
Step 1:DG addressing constant volume is a multiple target, the Combinatorial Optimization global question of multiple constraint, according to purpose and is wanted
The difference asked can construct different objective function and inequality constraints condition, main herein to consider with network loss, variation and
Economy is objective function, and practical DG access capacity and practical mountable place is combined to do comprehensive eye exam.
Objective function 1: establishing function by target of loss minimization, if system losses are Ploss, then network loss is expressed as follows:
In formula: PlossFor the active loss of system；N is system branch sum；UiIt is branch b end node voltage magnitude；rbFor
The resistance of branch b；PiActive power, Q for branch iiFor the reactive power of branch i.
Then function is established by target of loss minimization are as follows:
f1=min (Ploss) (2)
Objective function 2: intrinsic expense is minimum constructor, if total cost is C, then C may be expressed as:
In formula: μ is unit capacity cost, value 2000/KW, CDGiWhat is indicated is the capacity of the DG of i point access, and n is indicated
Be DG access number.
The then the smallest objective function of DG access fee are as follows:
f2=min (C) (4)
Objective function 3: the minimum objective function of node voltage offset, if Δ UmaxFor voltage maximum offset, formula is such as
Under:
ΔUmax=max (| Ui-UNi|) (5)
In formula: UiIt is the voltage value of DG access node i, unit is KV.For the load voltage value of node i, unit is KV.
Then with the minimum objective function of node voltage offset are as follows:
f3=min (Δ Umax) (6)
In conclusion voltage deviation, intrinsic expense is objective function, by the weight for setting up three indexs with network loss
It is translated into single object of planning function are as follows:
F=α f1+βf2+λf3 (7)
α, β, λ respectively represent network loss, variation, the weight coefficient of intrinsic expense.
Equality constraint:
In formula: Pi、Qi、PDGi、QDGiIt is that the active reactive power of node i input and DG are injected to node i respectively respectively
Active reactive power.PLi、QLiIt is the active reactive power of i-node institute on-load respectively.Vi、VjRespectively i, j point
Voltage, Y are the admittance matrixs of branch.
Inequality constraints condition:
Node voltage constraint
In formula, UiFor the voltage of node i,WithFor the maximum value and minimum value of node i voltage.
Tributary capacity constraint
In formula, SiFor the power that node i flows through,For the upper overpowering maximum value of branch i.
The active constraint of distributed generation resource
In formula,It is distributed generation resource maximum active power value.
Constraint that distributed generation resource is idle
In formula,It is the maximum value and minimum value of distributed generation resource reactive power.
Step 2: classic algorithm usually carries out selection operation with roulette Operator Method, and adaptive value is high to be held in selection operation
Easy to be selected into the next generation, on the one hand the certain probability of meeting loses defect individual to this method, next high meeting of some adaptive values
It is chosen to repeatedly in iteration, it is excellent difference and moderate to be unable to complete evolution, it is easily trapped into the trap of close-race, therefore
It walks self-styled, the diversity of population is caused to decline.The high individual of adaptive value can constantly survive during population genetic is survived
It continues.The low individual of adaptive value will face the destiny being gradually eliminated.Therefore adaptive value is set in genetic algorithm
It seems very important.The present invention is mainly the inverse with objective function as fitness function；With the equality constraint item of foundation
Part and inequality constraints condition improve the accuracy of result as boundary constraint；The present invention improves selection operation and fully considers
Excellent individual and remaining influence of individual to population diversity, ensure that remaining individual also has an opportunity to intersect into the next generation
Mutation operation, and operating procedure is simple.Steps are as follows:
1. generating ideal adaptation angle value according to initialization population and the fitness function of setting, it is assumed here that population invariable number is
15.
2. by each individual fitness according to sequence from small to large and three sections of equal part.
3. first segment fitness is poor from the point of view of subsection efect, third section defect individual is more.Consider in line with emphasis excellent
Individual will also consider influence of other individuals to population, i.e., excellent how bad few principle, three sections of selected probability are 0.6,0.8,1.It should
Ratio be verified by numerous experiments come optimal selection ratio.Ensure that the poor certain probability of individual can entrance under
A generation, population diversity are improved and decrease relative to tradition selection complexity.
4. preceding two sections of individuals being eliminated there will be the highest individual of every section of fitness to supplement, new population is generated.
Step 3: abundant variation type can take into account the superiority-inferiority of initialization population, this chapter will be to classical genetic algorithm
Three kinds of modes of inheritance are executed, are operated as follows:
In formula: i value is 1,2,3, and representative is three kinds of variation modes, and nun is population number, the value of a be -1,0,
1}。Population1Indicate that only one a takes -1 or 1, remaining whole takes 0, and here plus one judges that k is random number.
Population2Indicate part a take -1 or 1, part a takes 0, here plus judgement it is as above.Population3It indicates
All a take -1 or 1, and determining program is same as above.
By operation above, steps are as follows for hereditary variation:
1. the random chance generated is made comparisons with mutation probability, random chance < mutation probability, three kinds of variation modes are calculated
Fitness value.
2. replacing population with maximum adaptive value chromosome if three kinds of variation modes obtain population maximum adaptation value chromosome
In the smallest adaptive value chromosome, then go to 6., otherwise perform the following steps in sequence.
3. pass-through mode 1 is obtained if the maximum adaptation angle value that variation mode 1 obtains is greater than population minimum fitness value
The maximum adaptation degree chromosome value arrived replaces the smallest fitness value chromosome of population, and Population Regeneration.
4. will pass through if the maximum adaptation angle value that variation mode 2 obtains is greater than population minimum fitness value after update
The maximum adaptation degree chromosome value that mode 2 obtains replaces the smallest fitness value chromosome of population after updating, and Population Regeneration.
5. will pass through if the maximum adaptation angle value that variation mode 3 obtains is greater than population minimum fitness value after update
The maximum adaptation degree chromosome value that mode 3 obtains replaces the smallest fitness value chromosome of population after updating, and Population Regeneration.
6. completing variation whole process.
In order to make present invention solves the technical problem that, technical solution and control effect be more clearly understood, below in conjunction with attached
Figure and specific embodiment, are the present invention and are further described in detail.
Improved adaptive GA-IAGA flow chart in conjunction with the grid-connected addressing constant volume of DG as shown in Fig. 2, comprise steps that:
11) binary coding is carried out to chromosome and generates initialization population；
12) determination of value function is adapted to；
13) selection strategy screens initialization population by improved selection operator；
14) genetic operation, so-called genetic operation, just refers to intersection and variation, both operations are the marrow of genetic algorithm.
It is improved herein by variation, expands variation type；
15) according to the degree of convergence of population, i.e., the consistency of individual fitness is to determine whether fortune should be terminated in population
It calculates.
Further detailed description is done to the present invention with reference to the accompanying drawing.
Fig. 3 Fig. 4 is that improved adaptive GA-IAGA and classical genetic algorithm network loss optimum results can be seen that and pass through innovatory algorithm
Optimization, initial network loss are 139KW, are 14.94KW after DG access optimization, have dropped 89.25%, after classical genetic algorithm optimization under
Drop 87.62%.As can be seen that optimization DG access, can reduce the line loss per unit of power distribution network, the line loss per unit of power distribution network is in entire electric power
The access that system accounts for 7%~8%, DG can play the role of the saving energy and decreasing loss of entire electric system very big.
By Fig. 3 Fig. 4 comparison as can be seen that Revised genetic algorithum is better than classical genetic algorithm, improved adaptive GA-IAGA
Start to restrain in or so 40 generations, classical genetic algorithm starts to restrain in or so 74 generations, and improved adaptive GA-IAGA plans that minimum network loss is
14.94KW, classical genetic algorithm plans that minimum network loss is 17.21KW, and the capacity planned is also slightly larger than improved adaptive GA-IAGA.
In conclusion the network loss that improved adaptive GA-IAGA is not only planned is smaller, but also classical genetic algorithm is easily trapped into locally optimal solution, non-
Initialization population is often relied on, Revised genetic algorithum is more efficient for planning DG.
As seen in Figure 5, DG accesses power grid compared with no DG situation, and voltage has significant raising, to entire power distribution network
System play good supporting role.19 node voltage of distribution network voltage minimum point is 0.9131 (per unit value), passes through DG
Access, is increased to 0.979, there is great improvement, and the improvement of the access of the DG node low to voltage is particularly evident, increases well
Strong bearing load ability, improves the power quality of system.
Claims (5)
1. a kind of grid-connected Optimal Configuration Method of DG based on improved adaptive GA-IAGA, it includes:
Step 1 establishes objective function with system losses, variation and economy；
Step 2 establishes equality constraint according to objective function；
Step 3 establishes inequality constraints condition；
Step 4, with the reciprocal as fitness function of objective function, constraint condition and inequality constraints condition are side in equation
Bound constrained condition calculate using genetic algorithm and seeks optimal solution and obtain the installation site and installed capacity of DG.
2. the grid-connected Optimal Configuration Method of a kind of DG based on improved adaptive GA-IAGA according to claim 1, it is characterised in that:
The objective function of foundation described in step 1 are as follows:
Objective function 1: establishing function by target of loss minimization, if system losses are Ploss, then network loss is expressed as follows:
In formula: PlossThe active loss of system is also cried for system losses；N is system branch sum；UiIt is branch b end node voltage
Amplitude；rbFor the resistance of branch b；PiActive power, Q for branch iiFor the reactive power of branch i；
Function is then established with the minimum target of system losses are as follows:
f1=min (Ploss) (2)
Objective function 2: intrinsic expense is minimum constructor, if total cost is C, then C is indicated are as follows:
In formula: μ is unit capacity cost, value 2000/KW, CDGiWhat is indicated is the capacity of the DG of i point access, and what n was indicated is
The number of DG access；
The then the smallest objective function of DG access fee are as follows:
f2=min (C) (4)
Objective function 3: with the minimum objective function of node voltage offset, if Δ UmaxFor voltage maximum offset, formula is such as
Under:
ΔUmax=max (| Ui-UNi|) (5)
In formula: UiIt is the voltage value of DG access node i, unit is KV.For the load voltage value of node i, unit is KV；
Then with the minimum objective function of node voltage offset are as follows:
f3=min (Δ Umax) (6)
With system losses, voltage deviation, intrinsic expense is objective function, and the weight by setting up three indexs is converted into single
Object of planning function are as follows:
F=α f1+βf2+λf3 (7)
α, β, λ respectively represent network loss, variation, the weight coefficient of intrinsic expense.
3. the grid-connected Optimal Configuration Method of a kind of DG based on improved adaptive GA-IAGA according to claim 1, it is characterised in that:
Equality constraint is established according to objective function described in step 2 are as follows:
In formula: Pi、Qi、PDGi、QDGiIt is that the active reactive power of node i input and DG are injected to node i respectively respectively
Active reactive power.PLi、QLiIt is the active reactive power of i-node institute on-load respectively.Vi、VjThe respectively voltage of i, j point,
Y is the admittance matrix of branch.
4. the grid-connected Optimal Configuration Method of a kind of DG based on improved adaptive GA-IAGA according to claim 1, it is characterised in that:
Inequality constraints condition is established described in step 3 are as follows:
Node voltage constraint
In formula, UiFor the voltage of node i,WithFor the maximum value and minimum value of node i voltage；
Tributary capacity constraint；
In formula, SiFor the power that node i flows through,For the upper overpowering maximum value of branch i.
The active constraint of distributed generation resource
In formula,It is distributed generation resource maximum active power value.
Constraint that distributed generation resource is idle
In formula,It is the maximum value and minimum value of distributed generation resource reactive power.
5. the grid-connected Optimal Configuration Method of a kind of DG based on improved adaptive GA-IAGA according to claim 1, it is characterised in that:
Carrying out the method that optimal solution is sought in calculating using genetic algorithm described in step 4 includes:
1) binary coding is carried out to chromosome and generates initialization population；
2) determination of fitness function；The inverse of objective function is as fitness function；
3) selection strategy screens initialization population by improved selection operator；
4) genetic operation, so-called genetic operation, just refers to intersection and variation, both operations are the marrow of genetic algorithm, passes through
Variation is improved, variation type is expanded；
5) according to the degree of convergence of population, i.e., the consistency of individual fitness is to determine whether terminate operation in population.
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| CN110955971A (en) * | 2019-11-27 | 2020-04-03 | 南京工程学院 | Power spring optimal configuration method based on improved genetic algorithm |
| CN110955971B (en) * | 2019-11-27 | 2023-09-22 | 南京工程学院 | Power spring optimal configuration method based on improved genetic algorithm |
| CN111523204A (en) * | 2020-03-31 | 2020-08-11 | 杭州鸿晟电力设计咨询有限公司 | Optimization configuration solving method for grid-connected type comprehensive energy grid electricity-gas energy storage system |
| CN111523204B (en) * | 2020-03-31 | 2023-09-22 | 杭州鸿晟电力设计咨询有限公司 | Optimal configuration solving method for grid-connected comprehensive energy grid electricity-gas energy storage system |
| CN111985598A (en) * | 2020-07-28 | 2020-11-24 | 国网山东省电力公司禹城市供电公司 | Configuration method of distributed power supply |
| CN113128761A (en) * | 2021-04-19 | 2021-07-16 | 天津大学 | Elastic supply chain network optimization method based on genetic algorithm |
| CN113128761B (en) * | 2021-04-19 | 2022-09-16 | 天津大学 | Elastic supply chain network optimization method based on genetic algorithm |
| CN117114315A (en) * | 2023-08-25 | 2023-11-24 | 普宁市九鼎聚投新能源有限公司 | High-efficiency power generation installation method for solar photovoltaic power station |
| CN117114315B (en) * | 2023-08-25 | 2024-01-30 | 普宁市九鼎聚投新能源有限公司 | High-efficiency power generation installation method for solar photovoltaic power station |
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