CN104795828A - Wind storage capacity configuration method based on genetic algorithm - Google Patents
Wind storage capacity configuration method based on genetic algorithm Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention relates to a wind storage capacity configuration method based on a genetic algorithm, and belongs to the technical field of automated analysis of an electricity system. The method includes acquiring network parameters and system parameters of the system, and defining algorithm parameters; coding decision variables; randomly generating a population including M initial individuals, and performing feasibility detection on the randomly generated population including the M initial individuals, wherein each individual comprises codes of the decision variables; substituting all individuals in the feasible population and tide solutions into a fitness function, and calculating fitness of each individual; performing heritable variation calculation on the current feasible population to form a next generation of population; judging whether or not a current genetic algebra is identical to a maximum genetic algebra N, and if yes, finishing calculation and taking the value of the decision variable contained in the individual, with the highest fitness, in the last generation of population as a final calculation result. The wind storage capacity configuration method has the advantages that the problem of difficulty in large-scale concentrated grid connection of wind power is solved, a calculation method is simple and application of the actual system is facilitated.
Description
Technical field
The invention belongs to technical field of wind power generation, in particular to utilize genetic algorithm by population by for the optimizing of hereditary variation iteration, taking into account in wind generator system economy situation, obtain the best configuration place in systems in which of energy storage device, configuration capacity and maximum input and output capacity, concentrate grid-connected difficulty to solve large-scale wind power.
Background technology
Since entering 21 century, global environmental pollution and energy crisis facilitate greatly developing of regenerative resource, and wherein, wind-force is with the fastest developing speed.Large-scale wind power concentrates grid-connected convenient scheduling and management, but also brings a series of challenge, comprises power peak regulation problem, abandons wind problem etc.Concentrate grid-connected difficulty to solve wind-powered electricity generation, extensive energy storage technology is developed rapidly.Along with the application of large-scale energy storage device, in conventional electric power system, electric energy can not the characteristic of mass storage will change to a certain extent, simultaneously, because energy storage device can absorb fast or discharge electric energy, enable it effectively make up the shortcoming of regenerative resource fluctuation, thus concentrate grid-connected problem to provide brand-new thinking for solving large-scale wind power.
Mainly contain following several to the research method of capacity of energy storing device configuration at present: based on actual wind farm wind velocity probability density curve, calculate wind energy turbine set and reach the required energy storage energy of the stable output of long-term active power, and then Rational choice capacity of energy storing device; Based on regenerative resource power output result of spectrum analysis, with the target power output pulsation rate after energy storage device compensation for constraint, calculate the capacity of energy storing device meeting constraints; Minimum for optimization aim with capacity of energy storing device, be optimized for constraints with wind generator system stable region and overall convergence rate index, try to achieve the allocation optimum of capacity of energy storing device.
Although above method can obtain the capacity of energy storage device, all do not consider the impact of energy storage device layout on wind generator system.Introduce energy storage device in wind generator system after, the effective power flow of system and the distribution of reactive power flow all can change along with the difference of the layout of energy storage device, likely can cause some economy and safety issue.And in the allocation problem of the layout and capacity that consider energy storage device, decision variable not only comprises integer variable but also comprise continuous variable, belongs to mixed integer programming problem, adds the difficulty of problem, traditional optimized algorithm is difficult to solve.
Summary of the invention
The object of the invention is to the weak point for overcoming prior art, providing a kind of wind based on genetic algorithm to store up capacity collocation method.This method can provide comparatively reasonably the configuration place of energy storage device in wind generator system, configuration capacity and maximum input and output capacity, and computational methods are simple, are convenient to the application of real system.
A kind of storage of the wind based on genetic algorithm capacity collocation method that the present invention proposes, it is characterized in that, the method specifically comprises the following steps:
1) initialization: the network parameter and the system parameters that obtain system, definition algorithm parameter;
Network parameter comprises the constraint of wind generator system Static Power Flow, node voltage constraint, the constraint of power supply units limits, capacity of energy storing device, line transmission capacity-constrained; System parameters is the operating cost of the cost of investment of energy storage device, wind generator system; It is N that algorithm parameter comprises maximum genetic algebra Gen;
2) decision variable is encoded:
Decision variable comprises the configuration place of energy storage device, configuration capacity and maximum input, power output; Adopt gray encoding mode to energy storage device configuration place, the maximum input of energy storage device, power output and configuration capacity adopt binary coding mode; Random generation comprises the population of M initial individuals, and each individuality is made up of the coding of described decision variable, the value stochastic generation of decision variable, and M is positive integer;
3) feasibility detection is carried out to the population that stochastic generation contains M initial individuals:
Utilize the constraints of wind generator system to carry out feasibility detection to M initial individuals, the condition of detection is the constraints of wind generator system; The concrete steps that feasibility detects are:
31) check: the condition that the decision variable value in each individuality is calculated as Static Power Flow, calculate the trend of wind generator system current section, judge whether trend can separate; If intangibility, then this individuality is infeasible; Otherwise, judge whether trend solution meets Static Power Flow constraint, node voltage constraint, the constraint of power supply units limits, capacity of energy storing device, line transmission capacity-constrained, if do not meet, then this individuality is infeasible; For feasible individual, record its trend solution;
32) reject: after M initial individuals is carried out feasibility detection one by one, reject infeasible initial individuals;
33) supplement: the random individuality producing and reject the identical number of number of individuals again, adds in original seed group and form the population after upgrading;
34) population after described renewal is carried out to the operation of " detection-reject-supplement ", until M individuality in population is all detected by feasibility, now population is feasible population, and initialization genetic algebra Gen is 0;
4) individual fitness function value is calculated:
By step 3) in all individualities in the feasible population that obtains and trend solution thereof substitute in fitness function, calculate the fitness of each individuality;
5) hereditary variation computing is carried out to present feasible population, forms population of future generation: concrete grammar is:
51) utilize the fitness function value of the Different Individual in present feasible population, Selecting operation is carried out to feasible individual; Crossing operation is carried out to the feasible population of Selecting operation generation and produces new individuality;
52) feasibility detection is carried out to the new individuality produced, reject infeasible individuality also supplements number identical with the individuality of rejecting again new individuality by crossing operation, until M individuality in population is all feasible, become feasible population;
53) mutation operator is carried out to the feasible population that crossing operation produces, and feasibility detection is carried out to the new individuality produced, reject infeasible individuality also supplements number identical with the individuality of rejecting again new individuality by mutation operator, until M individuality in population is all feasible, form feasible population of future generation;
6) judge whether hereditary variation has terminated and result exports:
Judge whether current genetic algebra reaches maximum genetic algebra N, if, then calculate end, using the value of decision variable that comprises in individuality the highest for fitness in last population as final calculation result, thus determine configuration place and the capacity of energy storage device in generation; Otherwise genetic algebra Gen increases by 1, gets back to step 4).
Feature of the present invention and beneficial effect:
The present invention from economy point, based on genetic algorithm by population by for the optimizing of hereditary variation iteration, obtain the best configuration place of energy storage device, configuration capacity and maximum input and output capacity, concentrate grid-connected difficulty to solve large-scale wind power.
The method that the present invention proposes can realize energy-storage system for improving the complex optimum application of wind power integration ability, be applicable to various dissimilar energy-storage system and various application scenarios, for the application of large-scale energy storage system in electric power system is laid a good foundation, contribute to solving extensive renewable resource and concentrate grid-connected problem, have significant social value and economic worth.
Accompanying drawing explanation
Fig. 1 is overall procedure block diagram of the present invention.
Embodiment
A kind of wind based on the genetic algorithm storage capacity collocation method that the present invention proposes by reference to the accompanying drawings and embodiment be described as follows:
The present invention is from wind generator system operating cost minimum, feasible initial population is formed by carrying out decision variable encoding, the variation of recycling population genetic improves the fitness function value of population by generation, until reach maximum genetic algebra, and then carry out the configuration of energy storage device according to final population.
Wind based on genetic algorithm storage capacity collocation method flow process of the present invention as shown in Figure 1, specifically comprises the following steps:
1) initialization: the network parameter and the system parameters that obtain system, definition algorithm parameter;
Network parameter comprises the constraint of wind generator system Static Power Flow, node voltage constraint, the constraint of power supply units limits, capacity of energy storing device, line transmission capacity-constrained; System parameters is the cost of investment of energy storage device, the operating cost (given by wind energy turbine set) of wind generator system; It is N (can sets itself, recommend N to choose 50 ~ 100) that algorithm parameter comprises maximum genetic algebra Gen;
2) decision variable is encoded:
The decision variable of the storage of the wind based on the genetic algorithm capacity planning algorithms that the present invention proposes comprises the configuration place of energy storage device, configuration capacity and maximum input, power output; Adopt gray encoding mode to energy storage device configuration place, the maximum input-output power of energy storage device and configuration capacity adopt binary coding mode; Random generation comprises M, and (M is positive integer, can sets itself, M is larger, and calculating effect is better, but it is time-consuming longer, recommendation gets 50 ~ 200) population of initial individuals, each individuality is made up of the coding of above-mentioned decision variable, the value stochastic generation of decision variable, M is positive integer (M can specify voluntarily, recommends to get 100);
3) feasibility detection is carried out to the population that stochastic generation contains M initial individuals:
The constraints of wind generator system is utilized to carry out feasibility detection to M initial individuals, the condition detected is the constraints of wind generator system, comprising: Static Power Flow constraint, node voltage constraint, the constraint of power supply units limits, capacity of energy storing device, line transmission capacity-constrained; The concrete steps that feasibility detects are:
31) check: the condition that the decision variable value in each individuality is calculated as Static Power Flow, calculate the trend of wind generator system current section, judge whether trend can separate; If intangibility, then this individuality is infeasible; Otherwise, judge whether trend solution meets the constraint of electricity generation system Static Power Flow, node voltage constraint, the constraint of power supply units limits, capacity of energy storing device, line transmission capacity-constrained, if do not meet, then this individuality is infeasible; For feasible individual, record its trend solution; (when the present invention is applied to concrete scene, for electric jam scene, need to check the Line Flow of current trend solution whether to exceed through-put power restriction, if exceed restriction, then adjust unit output, recalculate, and record feasible trend solution; And for power peak regulation scene, then can omit the inspection to Line Flow in current trend solution).
32) reject; After M initial individuals is carried out feasibility detection one by one, reject infeasible initial individuals;
33) supplement: the random individuality producing and reject the identical number of number of individuals again, adds in original seed group and form the population after upgrading;
34) population after described renewal is carried out to the operation of " detection-reject-supplement ", until M individuality in population is all detected by feasibility, now population is feasible population, and initialization genetic algebra Gen is 0;
4) individual fitness function value is calculated:
By step 3) in all individualities in the feasible population that obtains and trend solution thereof to substitute in fitness function (this method using the cost of investment sum of system operation cost and energy storage device as fitness function), calculate the fitness of each individuality; .
5) hereditary variation computing is carried out to present feasible population, forms population of future generation: concrete grammar is:
51) utilize the fitness function value of the Different Individual in present feasible population, Selecting operation (because Selecting operation does not produce new individuality, therefore detecting without the need to carrying out individual feasibility after Selecting operation) is carried out to feasible individual; Crossing operation is carried out to the feasible population of Selecting operation generation and produces new individuality;
52) feasibility detection is carried out to the new individuality produced, reject infeasible individuality and again supplemented the new individuality of number identical with the individuality of rejecting by crossing operation, until M individuality in population is all feasible become feasible population;
53) mutation operator is carried out to the feasible population that crossing operation produces, and feasibility detection is carried out to the new individuality produced, reject infeasible individuality also supplements number identical with the individuality of rejecting again new individuality by mutation operator, until M individuality in population is all feasible, formed feasible population of future generation (method that wherein Selecting operation, crossing operation, mutation operator are existing ripe at present, this method adopt fitness value ratio method, linear precedence to intersect respectively and non-uniform mutation as the method for Selecting operation, crossing operation, mutation operator).
6) judge whether hereditary variation has terminated and result exports:
Judge whether current genetic algebra reaches maximum genetic algebra N, if, then calculate end, using the value of decision variable that comprises in individuality the highest for fitness in last population as final calculation result, thus determine configuration place and the capacity of energy storage device in generation; Otherwise genetic algebra Gen increases by 1, gets back to step 4).
Claims (1)
1., based on a wind storage capacity collocation method for genetic algorithm, it is characterized in that, the method specifically comprises the following steps:
1) initialization: the network parameter and the system parameters that obtain system, definition algorithm parameter;
Network parameter comprises the constraint of wind generator system Static Power Flow, node voltage constraint, the constraint of power supply units limits, capacity of energy storing device, line transmission capacity-constrained; System parameters is the operating cost of the cost of investment of energy storage device, wind generator system; It is N that algorithm parameter comprises maximum genetic algebra Gen;
2) decision variable is encoded:
Decision variable comprises the configuration place of energy storage device, configuration capacity and maximum input, power output; Adopt gray encoding mode to energy storage device configuration place, the maximum input of energy storage device, power output and configuration capacity adopt binary coding mode; Random generation comprises the population of M initial individuals, and each individuality is made up of the coding of described decision variable, the value stochastic generation of decision variable, and M is positive integer;
3) feasibility detection is carried out to the population that stochastic generation contains M initial individuals:
Utilize the constraints of wind generator system to carry out feasibility detection to M initial individuals, the condition of detection is the constraints of wind generator system; The concrete steps that feasibility detects are:
31) check: the condition that the decision variable value in each individuality is calculated as Static Power Flow, calculate the trend of wind generator system current section, judge whether trend can separate; If intangibility, then this individuality is infeasible; Otherwise, judge whether trend solution meets Static Power Flow constraint, node voltage constraint, the constraint of power supply units limits, capacity of energy storing device, line transmission capacity-constrained, if do not meet, then this individuality is infeasible; For feasible individual, record its trend solution;
32) reject: after M initial individuals is carried out feasibility detection one by one, reject infeasible initial individuals;
33) supplement: the random individuality producing and reject the identical number of number of individuals again, adds in original seed group and form the population after upgrading;
34) population after described renewal is carried out to the operation of " detection-reject-supplement ", until M individuality in population is all detected by feasibility, now population is feasible population, and initialization genetic algebra Gen is 0;
4) individual fitness function value is calculated:
By step 3) in all individualities in the feasible population that obtains and trend solution thereof substitute in fitness function, calculate the fitness of each individuality;
5) hereditary variation computing is carried out to present feasible population, forms population of future generation: concrete grammar is:
51) utilize the fitness function value of the Different Individual in present feasible population, Selecting operation is carried out to feasible individual; Crossing operation is carried out to the feasible population of Selecting operation generation and produces new individuality;
52) feasibility detection is carried out to the new individuality produced, reject infeasible individuality also supplements number identical with the individuality of rejecting again new individuality by crossing operation, until M individuality in population is all feasible, become feasible population;
53) mutation operator is carried out to the feasible population that crossing operation produces, and feasibility detection is carried out to the new individuality produced, reject infeasible individuality also supplements number identical with the individuality of rejecting again new individuality by mutation operator, until M individuality in population is all feasible, form feasible population of future generation;
6) judge whether hereditary variation has terminated and result exports:
Judge whether current genetic algebra reaches maximum genetic algebra N, if, then calculate end, using the value of decision variable that comprises in individuality the highest for fitness in last population as final calculation result, thus determine configuration place and the capacity of energy storage device in generation; Otherwise genetic algebra Gen increases by 1, gets back to step 4).
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Cited By (8)
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| CN105930980A (en) * | 2016-06-08 | 2016-09-07 | 河海大学 | Multi-point linearized probability energy flow method of integrated energy system with electricity converting to natural gas |
| CN109830990A (en) * | 2019-01-08 | 2019-05-31 | 南京工程学院 | A kind of energy storage Optimal Configuration Method based on Congestion access containing scene |
| CN110365007A (en) * | 2019-05-28 | 2019-10-22 | 国网江苏省电力有限公司盐城供电分公司 | A kind of battery energy storage system method for planning capacity for IEEE-33 node system |
| CN111008769A (en) * | 2019-11-26 | 2020-04-14 | 国电南瑞科技股份有限公司 | Energy transformation optimization method and system considering power blockage |
| CN111817313A (en) * | 2020-07-14 | 2020-10-23 | 国网山东省电力公司电力科学研究院 | Optical storage capacity optimal configuration method and system based on sub-band mixed energy storage |
| CN112487710A (en) * | 2020-11-25 | 2021-03-12 | 国网安徽省电力有限公司 | Power distribution network protection configuration optimization method and system |
| CN113162091A (en) * | 2021-05-13 | 2021-07-23 | 北方工业大学 | Energy storage system configuration method for improving wind power smoothness |
| CN117522061A (en) * | 2023-11-23 | 2024-02-06 | 国网冀北电力有限公司秦皇岛供电公司 | Energy storage configuration optimization method based on multi-source data fusion |
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105930980A (en) * | 2016-06-08 | 2016-09-07 | 河海大学 | Multi-point linearized probability energy flow method of integrated energy system with electricity converting to natural gas |
| CN105930980B (en) * | 2016-06-08 | 2019-10-15 | 河海大学 | A kind of electricity turns the integrated energy system linear multi likelihood energy stream method of gas |
| CN109830990A (en) * | 2019-01-08 | 2019-05-31 | 南京工程学院 | A kind of energy storage Optimal Configuration Method based on Congestion access containing scene |
| CN109830990B (en) * | 2019-01-08 | 2022-09-20 | 南京工程学院 | Energy storage optimization configuration method based on wind and light access contained in output resistor plug |
| CN110365007B (en) * | 2019-05-28 | 2022-08-19 | 国网江苏省电力有限公司盐城供电分公司 | Battery energy storage system capacity planning method for IEEE-33 node system |
| CN110365007A (en) * | 2019-05-28 | 2019-10-22 | 国网江苏省电力有限公司盐城供电分公司 | A kind of battery energy storage system method for planning capacity for IEEE-33 node system |
| CN111008769A (en) * | 2019-11-26 | 2020-04-14 | 国电南瑞科技股份有限公司 | Energy transformation optimization method and system considering power blockage |
| CN111008769B (en) * | 2019-11-26 | 2022-09-06 | 国电南瑞科技股份有限公司 | Energy transformation optimization method and system considering power blockage |
| CN111817313A (en) * | 2020-07-14 | 2020-10-23 | 国网山东省电力公司电力科学研究院 | Optical storage capacity optimal configuration method and system based on sub-band mixed energy storage |
| CN111817313B (en) * | 2020-07-14 | 2022-05-06 | 国网山东省电力公司电力科学研究院 | Optical storage capacity optimal configuration method and system based on sub-band mixed energy storage |
| CN112487710A (en) * | 2020-11-25 | 2021-03-12 | 国网安徽省电力有限公司 | Power distribution network protection configuration optimization method and system |
| CN112487710B (en) * | 2020-11-25 | 2024-05-21 | 国网安徽省电力有限公司 | Power distribution network protection configuration optimization method and system |
| CN113162091A (en) * | 2021-05-13 | 2021-07-23 | 北方工业大学 | Energy storage system configuration method for improving wind power smoothness |
| CN113162091B (en) * | 2021-05-13 | 2022-09-02 | 北方工业大学 | Energy storage system configuration method for improving wind power smoothness |
| CN117522061A (en) * | 2023-11-23 | 2024-02-06 | 国网冀北电力有限公司秦皇岛供电公司 | Energy storage configuration optimization method based on multi-source data fusion |
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