CN104103022A - Reactive compensation multi-target optimizing configuration method of 10kV distribution line - Google Patents
Reactive compensation multi-target optimizing configuration method of 10kV distribution line Download PDFInfo
- Publication number
- CN104103022A CN104103022A CN201410347281.8A CN201410347281A CN104103022A CN 104103022 A CN104103022 A CN 104103022A CN 201410347281 A CN201410347281 A CN 201410347281A CN 104103022 A CN104103022 A CN 104103022A
- Authority
- CN
- China
- Prior art keywords
- reactive
- power loss
- cost
- investment
- compensation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention provides a reactive compensation multi-target optimizing configuration method of a 10kV distribution line. A reactive compensation optimizing multi-target function of the distribution line is constructed, reactive power loss and investment cost are comprehensively considered, and a fuzzy membership degree function of the reactive power loss and the fuzzy membership degree function of the investment cost are each calculated under a condition that the reactive power loss and the investment cost are mutually contradictory, i.e. the reactive power loss and the investment cost are standardized by the fuzzy membership degree function so as to solve a multi-target solving problem that the reactive power loss and the investment cost exhibits different dimensions and different orders of magnitude. In addition, an optimization model is solved in a genetic algorithm, the hybrid coding of a reactive compensation point and a reactive compensation volume corresponding to the reactive compensation point is adopted as a body, and an initial population is generated according to a compensation point constraint condition so as to lower the probability of non-feasible solutions and improve the calculation efficiency of the method.
Description
Technical field
The present invention relates to power technology field, relate in particular to a kind of 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method.
Background technology
Distribution network var compensation optimization is keeping system reactive power equilibrium, improve quality of voltage and a kind of important measures of reducing the wastage.Distribution net work structure complexity, topological scope is large, number of devices is many, is the important component part of electric system.Therefore, the reactive-load compensation of distribution is for the high-quality economical operation important in inhibiting that ensures electric system.
Conventionally the target using the cost of investment sum minimum of electric network active network loss and compensation equipment as the reactive-load compensation of distribution, in the reactive-load compensation of distribution because needs are considered two targets, therefore this distribution idle work optimization belongs to the problem of multiple-objection optimization, in the time solving, need to be translated into single goal, but due to the electric network active network loss in this problem, dimension disunity with cost of investment, cannot rationally sue for peace to both, and in the time that both are sued for peace, do not distinguish both significance levels, this does not have actual directive significance, therefore need now a kind of novel Optimal Configuration Method, to address the above problem.
Summary of the invention
The invention provides a kind of 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method, can reasonably multi-objective problem be converted to single goal problem, and can distinguish both significance levels.
To achieve these goals, the invention provides following technological means:
A kind of 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method, comprising:
The multiple objective function that builds the active power loss of minimum power and the cost of investment of reactive-load compensation equipment, the constraint condition of described multiple objective function comprises the restriction of trend constraint, control variable and state variable and the position constraint of reactive-load compensation point;
A character code being made up of some reactive-load compensation points position and an integer coding that configures compensation capacity on these some reactive-load compensation points are combined as to a coding, the body one by one of the solution space using this coding as described multiple objective function, organizes the position of reactive-load compensation point more and the reactive compensation capacity of its configuration forms multiple individualities;
The random initial population that generates multiple objective function solution space, using initial population as current population;
Calculate the desired value of each individual active power loss and the desired value of cost of investment; Utilization is fallen half line shape formula and is calculated the fuzzy membership function of active power loss and the fuzzy membership function of cost of investment, and wherein independent variable is respectively the desired value of each individual active power loss and the desired value of each individual cost of investment;
Fitness function using the weighted sum of the fuzzy membership function of the membership function of active power loss and cost of investment as current population, using the maximal value of fitness function as optimum results, two weighting coefficients and be 1;
Judge whether to reach termination evolution conditions;
When reaching described termination evolution conditions, export optimum results;
When not reaching described termination evolution conditions, described current population being intersected, made a variation generates new population, using new population as current population, re-starts above-mentioned steps until reach termination evolution conditions.
Preferably, the initial population that generates at random multiple objective function comprises:
According to the load or burden without work curve of compensation point, using the maximum synthetic load of this point as this compensation point maximum reactive compensation capacity, in compensated position constraint and compensation capacity restriction range, produce at random initial population.
Preferably, utilize and fall half line shape formula and calculate the fuzzy membership function of active power loss and the fuzzy membership function of cost of investment and comprise:
In the time of k=1, be the fuzzy membership function of active power loss, the desired value that now x is each individual active power loss is the fuzzy membership function of cost of investment in the time of k=2, the desired value that now x is each individual cost of investment;
Wherein
with
while representing respectively current population for different target, the optimum solution that calculating obtains and inferior solution.
Preferably, output optimum results comprises:
According to decision maker's preference, under a certain weighting coefficient, position and reactive-load compensation amount, the value of active power loss and the value of cost of investment of output compensation point.
Preferably, stopping evolution conditions comprises:
The maximal value that the algebraically of current population reaches default algebraically, fitness function no longer changes.
Preferably, to described current population intersect, make a variation generate new population comprise:
Randomly draw two individualities in current population;
Utilize hereditary crossover and mutation method to produce new individual;
Add current population to obtain new population new individuality.
Preferably, randomly drawing two individualities in current population comprises:
Choose two individualities in current population in the mode of roulette.
The invention provides a kind of 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method, in the present invention, build Reactive Compensation of Distribution Lines and optimize multiple objective function, active power loss and cost of investment are considered, in active power loss and cost of investment conflicting in the situation that, calculate respectively the fuzzy membership function of active power loss and the fuzzy membership function of cost of investment, by fuzzy membership function, active power loss and cost of investment are standardized, solve active power loss and cost of investment has different dimensions, the multiple goal Solve problems of varying number level with this.
And in genetic algorithm, optimize this model, the combination that adopts reactive-load compensation point and put corresponding reactive compensation capacity with reactive-load compensation is encoded as an individuality, generate initial population according to compensation point constraint condition, due to the individuality having omitted without practical significance, so improved the counting yield of this method.
Brief description of the drawings
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the process flow diagram of the disclosed a kind of 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method of the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
As shown in Figure 1, the invention provides a kind of 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method, comprising:
Step S101: build the multiple objective function of the active power loss of minimum power and the cost of investment of reactive-load compensation equipment, the constraint condition of described multiple objective function comprises the restriction of trend constraint, control variable and state variable and the position constraint of reactive-load compensation point;
The present invention needs the target of minimum two targets of the cost of investment of electric network active loss minimization and compensation equipment as the reactive-load compensation of distribution, therefore both are built to multiple objective function, the constraint condition that has electrical network self in multiple objective function is trend constraint, the constraint that has idle compensation point to select in the time of reactive-load compensation, with the restriction of the reactive compensation capacity corresponding with this point, therefore using reactive-load compensation point and reactive compensation capacity also as the constraint condition of multiple objective function.
System active power loss is:
Wherein V is node voltage amplitude, and θ is node voltage phase angle, and G is that internodal electricity is led, and B is internodal susceptance, and n is all nodes; Cost of investment comprises purchase cost, construction cost, operating cost and maintenance cost.Cost of investment is uploaded to database by technician in advance, can from database, obtain the related data of relevant cost of investment in the present invention.
Step S102 a: character code being made up of some reactive-load compensation points position and an integer coding that configures compensation capacity on these some reactive-load compensation points are combined as to a coding, the body one by one of the solution space using this coding as described multiple objective function, organizes the position of reactive-load compensation point more and the reactive compensation capacity of its configuration forms multiple individualities;
Adopt hybrid coding mode to encode to multiple objective function, coding two parts composition, the position that Part I is compensation point, represents with node serial number form, is character code; Part II is the compensation capacity of corresponding compensation point, is integer coding.Encode as the body one by one of multi-objective optimization question using this.In the time that reactive-load compensation point is different, reactive compensation capacity is also thereupon different, produces thus multiple different individualities.
Step S103: generate at random the initial population of multiple objective function solution space, using initial population as current population;
According to the typical load or burden without work curve of compensation point, the maximum reactive compensation capacity using its maximum synthetic load as this point, within the scope of each variable bound and compensation point position constraint, produces initial population at random, and initial population comprises the individuality of some.
Step S104: calculate the desired value of each individual active power loss and the desired value of cost of investment; Utilization is fallen half line shape formula and is calculated the membership function of active power loss and the membership function of cost of investment, and wherein independent variable is respectively the desired value of each individual active power loss and the desired value of each individual cost of investment;
The present invention wishes that the desired value of multiple objective function is the smaller the better, and cost is the smaller the better, so select to fall the fuzzy membership function of half line shape function as multiple objective function, cost is less, and fuzzy membership function value is larger.Specifically comprise:
In the time of k=1, be the membership function of active power loss, the desired value that now x is each individual active power loss is the membership function of cost of investment in the time of k=2, the desired value that now x is each individual cost of investment;
Wherein
with
while representing respectively current population for different target, the optimum solution of the different target degree of membership of all individualities and inferior solution.
Step S105: the fitness function using the weighted sum of the fuzzy membership function of the membership function of active power loss and cost of investment as current population, using the maximal value of fitness function as optimum results, two weighting coefficients and be 1;
Multiply each other with weighting coefficient separately by the fuzzy membership function of active power loss and the fuzzy membership function of cost of investment, again by two product summations, using with value as the fitness function of current population, using the maximal value of fitness function as optimum results, two weighting coefficients and be 1;
The object of multiple objective function is: the minimum value that obtains active power loss and cost of investment, in the present invention using the weighted sum of two fuzzy membership functions as objective function, owing to falling the effect of half line shape formula, choose the maximal value of membership function, be the minimum value of objective function, so the weighted sum to active power loss and cost of investment is got maximal value, using maximal value as optimum results.
In the present invention, can, according to technician's the attention degree to active power loss and cost of investment, distribute respectively different weighting coefficients to both, the weighting coefficient that attention degree is high is large, and the low weighting coefficient of attention degree is little, and both weighting coefficient sums are 1.
Step S106: judge whether to reach termination evolution conditions;
Termination evolution conditions in the present invention comprises: the maximal value that the algebraically of current population reaches default algebraically, fitness function no longer changes.
The first: current population reaches default algebraically; Initial population is often more of new generation, increase algebraically one time, when current population reaches after the algebraically of default regulation, stop evolving, think in theory, upgrade after default algebraically, just can there is optimization individuality, so reach after default algebraically, just stop evolving, default algebraically is artificially to draw according to prior art experience.
The second: the maximal value of fitness function no longer changes;
When the maximal value of fitness function no longer changes, show to reach optimum, so now just without upgrading again and optimizing.
Step S107: when reaching described termination evolution conditions, export optimum results;
Output optimum individual comprises: the value of system active power loss and the value of cost of investment under individual corresponding reactive-load compensation point position and corresponding reactive-load compensation amount and this configuration.
Step S108: when not reaching described termination evolution conditions, described current population being intersected, made a variation generates new population, using new population as current population, enters step S104.
The mode that generates new population comprises: randomly draw two individualities in current population; Utilize hereditary crossover and mutation method to produce new individual; Add current population to obtain new population new individuality.The invention provides a kind of 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method, in the present invention, build Reactive Compensation of Distribution Lines and optimize multiple objective function, active power loss and cost of investment are considered, in active power loss and cost of investment conflicting in the situation that, calculate respectively the membership function of active power loss and the membership function of cost of investment, by membership function, active power loss and cost of investment are standardized, solve active power loss and cost of investment has different dimensions, the multiple goal Solve problems of varying number level with this.
And in genetic algorithm, optimize this model, the combination that adopts reactive-load compensation point and put corresponding reactive compensation capacity with reactive-load compensation is encoded as an individuality, generate initial population according to compensation point constraint condition, due to the individuality having omitted without practical significance, so improved the counting yield of this method.
A kind of specific embodiments of the invention are provided below, comprise:
1, gathering power distribution network operational factor comprises: the each node load parameter under power network topology, line parameter circuit value, transformer station's parameter, the various method of operation, generator parameter, node voltage allow range of operation, reactive-load compensation equipment parameter, the constraint condition of all control variable, the constraint condition of state variable;
2, setting up this problem Optimized model is multi-objective optimization question;
Target is f=min (P
loss(V, Q), Cost (Q)), represent electric energy active power loss P
lossminimum and reactive-load compensation equipment cost of investment Cost minimum are as multiple goal, and cost of investment comprises purchase cost, construction cost, operating cost and maintenance cost.Be constrained to system load flow constraint, variable bound comprises control variable and state variable, and the constraint of reactive-load compensation position, and compensation point can not be too much, generally determines according to actual conditions such as line lengths.
The individuality coding of 3, constructing this optimization problem, produces initial population at random.
A, individual coding
For this problem, adopt hybrid coding mode, coding two parts composition, the position that Part I is compensation point, is expressed as character code with node serial number form; Part II is the compensation capacity of corresponding compensation point, is integer coding.Encode as the body one by one of this optimization problem using this.
B, initial population produce
According to the typical load or burden without work curve of all load point of system, maximum reactive compensation capacity using the maximum synthetic load of each load point as this point, the random initial population that produces in variable bound and compensated position restriction range, each individuality is carried out to trend calculating, as in finite iteration number of times, convergence, is not eliminated.
4, in Reactive Power Optimization Algorithm for Tower process, need to carry out electric power system tide calculating solving, before individual fitness function value is calculated, must do trend and calculate and just can provide system active power loss and the out-of-limit statistics of state variable etc.Trend is calculated according to given power network topology, line parameter circuit value, transformer station's parameter, generator parameter, load parameter, determines meritorious, idle, voltage and the phase angle of the each node of electrical network by mathematical computations;
5, in population each individuality carry out fitness evaluation
The fuzzy conversion of a, multiple goal objective function;
Calculate respectively each individual corresponding two desired values (network loss and cost of investment) of separating, this optimization problem is objective function minimum, therefore, chooses and falls the membership function μ of half line shape as each objective function
k(x) (k=1,2),
with
represent respectively in population individual in different target situation, the optimum solution obtaining and inferior solution.
C, determine fitness function
If μ is the weighted sum in all target membership functions, weighting coefficient and be 1.Former like this multi-objective problem is converted into and solves the maximized single goal nonlinear optimal problem of Satisfaction index μ that meets institute's Prescribed Properties.μ is defined as to the fitness function of genetic algorithm.
Target is converted into f=max (μ), wherein μ=α μ
1+ (1-α) μ
2, α is weighting coefficient, can reflect decision maker's goal orientation.
6, judge whether to meet termination evolution conditions, if obtained satisfied solution or reached termination condition, end, go to step 9, if do not met, go to step 7 continuation; Stop evolution conditions can be taken as evolutionary process reach certain algebraically or all individual fitnesses all consistent or continuous some generation target function value do not improve
7, with the mode of the roulette individuality of regenerating, the selected probability of individuality that fitness is high is high, and the individuality that fitness is low may be eliminated.
8, carry out crossover and mutation according to certain probability, produce new individuality.Go to step 5 continuation
9, output optimum results comprises value, system load flow level and the system active loss cost of investment etc. of each control variable, state variable, finishes.
If the function described in the present embodiment method realizes and during as production marketing independently or use, can be stored in a computing equipment read/write memory medium using the form of SFU software functional unit.Based on such understanding, the part that the embodiment of the present invention contributes to prior art or the part of this technical scheme can embody with the form of software product, this software product is stored in a storage medium, comprise that some instructions (can be personal computers in order to make a computing equipment, server, mobile computing device or the network equipment etc.) carry out all or part of step of method described in each embodiment of the present invention.And aforesaid storage medium comprises: USB flash disk, portable hard drive, ROM (read-only memory) (ROM, Read-Only Memory), the various media that can be program code stored such as random access memory (RAM, Random Access Memory), magnetic disc or CD.
In this instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is and the difference of other embodiment, between each embodiment same or similar part mutually referring to.
To the above-mentioned explanation of the disclosed embodiments, make professional and technical personnel in the field can realize or use the present invention.To be apparent for those skilled in the art to the multiple amendment of these embodiment, General Principle as defined herein can, in the situation that not departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention will can not be restricted to these embodiment shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (7)
1. a 10kV Reactive Compensation of Distribution Lines multiple-objection optimization collocation method, is characterized in that, comprising:
The multiple objective function that builds the active power loss of minimum power and the cost of investment of reactive-load compensation equipment, the constraint condition of described multiple objective function comprises the restriction of trend constraint, control variable and state variable and the position constraint of reactive-load compensation point;
A character code being made up of some reactive-load compensation points position and an integer coding that configures compensation capacity on these some reactive-load compensation points are combined as to a coding, the body one by one of the solution space using this coding as described multiple objective function, organizes the position of reactive-load compensation point more and the reactive compensation capacity of its configuration forms multiple individualities;
The random initial population that generates multiple objective function solution space, using initial population as current population;
Calculate the desired value of each individual active power loss and the desired value of cost of investment; Utilization is fallen half line shape formula and is calculated the fuzzy membership function of active power loss and the fuzzy membership function of cost of investment, and wherein independent variable is respectively the desired value of each individual active power loss and the desired value of each individual cost of investment;
Fitness function using the weighted sum of the fuzzy membership function of the membership function of active power loss and cost of investment as current population, using the maximal value of fitness function as optimum results, two weighting coefficients and be 1;
Judge whether to reach termination evolution conditions;
When reaching described termination evolution conditions, export optimum results;
When not reaching described termination evolution conditions, described current population being intersected, made a variation generates new population, using new population as current population, re-starts above-mentioned steps until reach termination evolution conditions.
2. the method for claim 1, is characterized in that, the initial population that generates at random multiple objective function comprises:
According to the load or burden without work curve of compensation point, using the maximum synthetic load of this point as this compensation point maximum reactive compensation capacity, in compensated position constraint and compensation capacity restriction range, produce at random initial population.
3. the method for claim 1, is characterized in that, the half line shape formula calculating fuzzy membership function of active power loss falls in utilization and the fuzzy membership function of cost of investment comprises:
In the time of k=1, be the fuzzy membership function of active power loss, the desired value that now x is each individual active power loss is the fuzzy membership function of cost of investment in the time of k=2, the desired value that now x is each individual cost of investment;
Wherein
with
while representing respectively current population for different target, the optimum solution that calculating obtains and inferior solution.
4. the method for claim 1, is characterized in that, output optimum results comprises:
According to decision maker's preference, under a certain weighting coefficient, position and reactive-load compensation amount, the value of active power loss and the value of cost of investment of output compensation point.
5. the method for claim 1, is characterized in that, stops evolution conditions and comprises:
The maximal value that the algebraically of current population reaches default algebraically, fitness function no longer changes.
6. the method for claim 1, is characterized in that, to described current population intersect, make a variation generate new population comprise:
Randomly draw two individualities in current population;
Utilize hereditary crossover and mutation method to produce new individual;
Add current population to obtain new population new individuality.
7. method as claimed in claim 6, is characterized in that, randomly draws two individualities in current population and comprises:
Choose two individualities in current population in the mode of roulette.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410347281.8A CN104103022A (en) | 2014-07-21 | 2014-07-21 | Reactive compensation multi-target optimizing configuration method of 10kV distribution line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410347281.8A CN104103022A (en) | 2014-07-21 | 2014-07-21 | Reactive compensation multi-target optimizing configuration method of 10kV distribution line |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104103022A true CN104103022A (en) | 2014-10-15 |
Family
ID=51671153
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410347281.8A Pending CN104103022A (en) | 2014-07-21 | 2014-07-21 | Reactive compensation multi-target optimizing configuration method of 10kV distribution line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104103022A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104934990A (en) * | 2015-07-13 | 2015-09-23 | 国家电网公司 | Method of realizing coordination control of power-distribution-network static var compensator and static synchronous compensator |
| CN105896580A (en) * | 2016-05-24 | 2016-08-24 | 广东电网有限责任公司电力科学研究院 | Multi-objective optimization control method and device of microgrid |
| CN107706901A (en) * | 2017-11-07 | 2018-02-16 | 国网安徽省电力公司经济技术研究院 | A kind of optimization dispersion compensation method of arc suppression coil of power distribution network |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102170137A (en) * | 2011-04-26 | 2011-08-31 | 华北电力大学 | ORP (optimal reactive power) method of distribution network of electric power system |
| CN102611119A (en) * | 2012-03-14 | 2012-07-25 | 华北电力大学 | Multi-target reactive power optimization method for electric system |
| US20150051744A1 (en) * | 2013-08-19 | 2015-02-19 | Board Of Trustees Of Michigan State University | Linear Optimal Power Flow System and Method |
-
2014
- 2014-07-21 CN CN201410347281.8A patent/CN104103022A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102170137A (en) * | 2011-04-26 | 2011-08-31 | 华北电力大学 | ORP (optimal reactive power) method of distribution network of electric power system |
| CN102611119A (en) * | 2012-03-14 | 2012-07-25 | 华北电力大学 | Multi-target reactive power optimization method for electric system |
| US20150051744A1 (en) * | 2013-08-19 | 2015-02-19 | Board Of Trustees Of Michigan State University | Linear Optimal Power Flow System and Method |
Non-Patent Citations (2)
| Title |
|---|
| 张平: "风电场无功优化补偿技术研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑（月刊）》 * |
| 贾德香: "基于改进遗传算法的配电网电压无功多目标优化", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑（季刊）》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104934990A (en) * | 2015-07-13 | 2015-09-23 | 国家电网公司 | Method of realizing coordination control of power-distribution-network static var compensator and static synchronous compensator |
| CN105896580A (en) * | 2016-05-24 | 2016-08-24 | 广东电网有限责任公司电力科学研究院 | Multi-objective optimization control method and device of microgrid |
| CN105896580B (en) * | 2016-05-24 | 2019-02-12 | 广东电网有限责任公司电力科学研究院 | A kind of micro-capacitance sensor multiobjective optimization control method and device |
| CN107706901A (en) * | 2017-11-07 | 2018-02-16 | 国网安徽省电力公司经济技术研究院 | A kind of optimization dispersion compensation method of arc suppression coil of power distribution network |
| CN107706901B (en) * | 2017-11-07 | 2019-08-16 | 国网安徽省电力公司经济技术研究院 | A kind of optimization dispersion compensation method of arc suppression coil of power distribution network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Singh et al. | Optimal allocation of capacitors in distribution systems using particle swarm optimization | |
| CN107301470B (en) | Double-layer optimization method for power distribution network extension planning and optical storage location and volume fixing | |
| Akorede et al. | Effective method for optimal allocation of distributed generation units in meshed electric power systems | |
| Fang et al. | A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling | |
| Ganguly | Multi-objective distributed generation penetration planning with load model using particle swarm optimization | |
| CN111342461B (en) | Power distribution network optimal scheduling method and system considering dynamic reconfiguration of network frame | |
| Cai et al. | A fuzzy adaptive chaotic ant swarm optimization for economic dispatch | |
| CN104794541B (en) | A kind of power distribution network running optimizatin method based on simulated annealing and cone optimization | |
| CN105488593A (en) | Constant capacity distributed power generation optimal site selection and capacity allocation method based on genetic algorithm | |
| Tang et al. | Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques | |
| CN101242103B (en) | Intelligent optimization method for power system stabilizer parameter | |
| CN106374513B (en) | A kind of more microgrid dominant eigenvalues optimization methods based on leader-followers games | |
| CN102915472A (en) | Comprehensive power distribution network optimization planning method based on gene modified chaos genetic algorithm | |
| CN113036772A (en) | Power distribution network topology voltage adjusting method based on deep reinforcement learning | |
| Nguyen et al. | Improved coyote optimization algorithm for optimally installing solar photovoltaic distribution generation units in radial distribution power systems | |
| CN104037761A (en) | AGC power multi-objective random optimization distribution method | |
| CN105488589A (en) | Genetic simulated annealing algorithm based power grid line loss management evaluation method | |
| CN104103022A (en) | Reactive compensation multi-target optimizing configuration method of 10kV distribution line | |
| CN104484517A (en) | Method for optimizing parameters of bridge arm reactors of MMC (modular multi-level converters) | |
| Vitor et al. | Operation planning and decision-making approaches for Volt/Var multi-objective optimization in power distribution systems | |
| Mahdavi et al. | Transmission expansion planning considering network adequacy and investment cost limitation using genetic algorithm | |
| CN115841187A (en) | Method, device, equipment and storage medium for optimizing operation strategy of flexible power distribution network | |
| CN110611305B (en) | Photovoltaic access planning method considering distribution network voltage out-of-limit risk | |
| CN104598657A (en) | Gene die body reconstruction technology based on memtic algorithm | |
| CN104091214A (en) | Reactive power optimization method for 10 kV distribution network on basis of quantum genetic algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20141015 |
|
| RJ01 | Rejection of invention patent application after publication |