CN109472423A - A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration - Google Patents
A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration Download PDFInfo
- Publication number
- CN109472423A CN109472423A CN201811499152.5A CN201811499152A CN109472423A CN 109472423 A CN109472423 A CN 109472423A CN 201811499152 A CN201811499152 A CN 201811499152A CN 109472423 A CN109472423 A CN 109472423A
- Authority
- CN
- China
- Prior art keywords
- unit
- power
- wind
- constraint
- individual
- 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
- 238000000034 method Methods 0.000 title claims abstract description 55
- 230000033228 biological regulation Effects 0.000 title claims abstract description 43
- 230000010354 integration Effects 0.000 title claims abstract description 25
- 230000005611 electricity Effects 0.000 claims abstract description 26
- 230000002068 genetic effect Effects 0.000 claims abstract description 17
- 230000003044 adaptive effect Effects 0.000 claims description 53
- 238000005457 optimization Methods 0.000 claims description 27
- 230000035772 mutation Effects 0.000 claims description 17
- 230000009194 climbing Effects 0.000 claims description 15
- 238000011156 evaluation Methods 0.000 claims description 15
- 238000009987 spinning Methods 0.000 claims description 15
- 239000003245 coal Substances 0.000 claims description 12
- 230000008901 benefit Effects 0.000 claims description 8
- 230000001186 cumulative effect Effects 0.000 claims description 8
- 230000000694 effects Effects 0.000 claims description 6
- 229910002056 binary alloy Inorganic materials 0.000 claims description 5
- 238000003491 array Methods 0.000 claims description 4
- 238000002485 combustion reaction Methods 0.000 claims description 4
- 230000007423 decrease Effects 0.000 claims description 4
- 238000009396 hybridization Methods 0.000 claims description 4
- 230000000977 initiatory effect Effects 0.000 claims description 4
- 238000012804 iterative process Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 206010064571 Gene mutation Diseases 0.000 claims description 3
- 238000009395 breeding Methods 0.000 claims description 3
- 230000001488 breeding effect Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000010353 genetic engineering Methods 0.000 claims description 3
- 230000027272 reproductive process Effects 0.000 claims description 3
- 230000000717 retained effect Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims 1
- 239000000203 mixture Substances 0.000 claims 1
- 230000001105 regulatory effect Effects 0.000 abstract description 5
- 230000029087 digestion Effects 0.000 abstract description 4
- 230000005684 electric field Effects 0.000 abstract description 2
- 238000003860 storage Methods 0.000 description 4
- 241000208340 Araliaceae Species 0.000 description 3
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 3
- 235000003140 Panax quinquefolius Nutrition 0.000 description 3
- 235000008434 ginseng Nutrition 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000567 combustion gas Substances 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000001771 impaired effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000005619 thermoelectricity Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to wind electricity digestion technical field more particularly to a kind of Unit Combination methods for considering fired power generating unit depth peak regulation under the conditions of wind-electricity integration, under the conditions of specifically a kind of considerations wind-electricity integration, the method for raising fired power generating unit peak modulation capacity.It include: the algorithm of Unit Combination；The objective function of Unit Combination；The constraint condition of Unit Combination；The method for solving of Unit Combination；The operation process of Unit Combination.The present invention reserves certain spare variation to balance wind power output under the conditions of large-scale wind power integration, to be thought of as wind power plant.When load is lower, conventional power plant unit has been transferred to lower power output, if output of wind electric field is significantly increased at this time, the receiving ability of wind-powered electricity generation will be restricted, and abandonment is caused to ration the power supply.Globally optimal solution is found using genetic algorithm, and under the conditions of analyzing large-scale wind power integration, the economy of fired power generating unit depth peak regulating plan.The problem of solution never abandonment dissolves, saves fired power generating unit cost of electricity-generating, and " deep to adjust consumption " is made to have feasibility.
Description
Technical field
The present invention relates to fired power generating unit depth under the conditions of wind electricity digestion technical field more particularly to a kind of consideration wind-electricity integration
The Unit Combination method of peak regulation, under the conditions of specifically a kind of consideration wind-electricity integration, the method that improves fired power generating unit peak modulation capacity.
Background technique
After large-scale wind power integration power generation, electric system peak modulation capacity is seriously impaired.In addition, wind power output has interval
Property, fluctuation, demodulate peak character and precision of prediction and volume metering it is low the features such as, be added significantly to the equivalent load of system
Peak-valley difference increases peak-load regulating difficulty.With the continuous adjustment of Industrial Structure of China, Chinese power structure is also constantly becoming
Change.Power grid peak-valley difference is away from increasingly increasing at present, so that the peak regulation range of power grid increases, difficulty is consequently increased.Make with peak regulation
Water power, hydroenergy storage station, gas combustion-gas vapor combined cycle etc. quickly adjust power supply and relatively lag behind, so that new energy source machine
The problems such as group is grid-connected difficult, consumption is difficult becomes increasingly conspicuous, and brings certain influence to power network safety operation and power quality.Northeast ground
Area's heat supply in winter phase abandonment amount is huge particularly problematic, it has also become whole society's focus of attention.Utilize power grid fired power generating unit
The big feature of capacity, depth excavate fired power generating unit peak modulation capacity, break through traditional concept, improve the utilization of renewable energy, become
A kind of inexorable trend.
Optimization of Unit Commitment is an important component of electric power system optimization operation, and traditional unit is dispatched at one
In period, according to Load Forecasting, in the case where meeting the constraint conditions such as electric system line balancing and start and stop limitation, when optimization selectes each
Section participates in the unit of operation, determines the Unit Commitment time, keeps the total burn-off in this period minimum, reaches bigger economic effect
Benefit.Although traditional Optimization of Unit Commitment problem that may occur is that total burn-off is minimum or economic benefit is maximum, it is the failure to
Considering when load is lower, conventional power plant unit has been transferred to lower power output, if output of wind electric field is significantly increased at this time,
Can conventional power unit, which further contribute, allows wind-powered electricity generation to carry out the ability that on-load determines power grid receiving wind-powered electricity generation, that is to say, that conventional power unit
Peak modulation capacity in low ebb load is the key condition that limitation saves that net receives wind-powered electricity generation ability.
Summary of the invention
To solve above-mentioned problems of the prior art, the present invention provides thermoelectricitys under the conditions of a kind of consideration wind-electricity integration
The Unit Combination method of unit depth peak regulation, its purpose is to further the depth of investigation peak regulations to dissolve energy to clean energy resource is promoted
The specific embodiment of power makes full use of the characteristic of fired power generating unit depth peak regulation in dispatching cycle.Using genetic algorithm to unit
Combination carries out optimizing, proposes a kind of Unit Combination mould of fired power generating unit depth peak regulation under the conditions of the wind-electricity integration based on genetic algorithm
Type.
For achieving the above object, the present invention is achieved through the following technical solutions:
A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration, comprising the following steps:
The algorithm of Unit Combination；
The objective function of Unit Combination；
The constraint condition of Unit Combination；
The method for solving of Unit Combination；
The operation process of Unit Combination.
The algorithm of the Unit Combination uses genetic algorithm, and genetic algorithm (GA) has borrowed the mechanism of biogenetics, leads to
The operation such as natural selection, hybridization and variation is crossed, the raising of adaptive value is realized, natural selection this rule is ground applied to science
Study carefully and various optimization problems in engineering technology；The remarkable advantage of genetic algorithm is many that can be simultaneously scanned in solution space
Point, rather than a point, thus intimate globally optimal solution can be found.
The genetic algorithm key step:
A) initialization coding ":
Operating status variable of the unit on a period is indicated with a binary code, it is single if there is n platform unit
A unit parameter string length is defined as l, then a length of L=(n*l) position of string, thus obtained in a dispatching cycle
Interior (24 hours), the peak capacity of each scheduling instance generating set combination, initialize several group of individuals being randomly generated
At a group, first generation solution group is constituted；In general, the quality of initial solution is all very poor；
B) it decodes:
According to constraint condition, the binary code of initialization can be converted into real number to indicate, convert binary code to pair
The real number answered is known as decoding；
C) fitness and evaluation:
Resulting real number will be decoded, will be narrowed down within the scope of constraint condition by a certain percentage, the reality for meeting constraint condition is obtained
Number.For fitness function, it is taken as the function of each scheduling instance peak modulation capacity, it is also very straight to the adaptive value evaluation of individual in this way
It sees, also there is its specific meaning；Real number corresponding to individual is substituted into fitness function and calculates adaptive value, adaptive value is bigger, indicates
The peak modulation capacity of Unit Combination is bigger, and superiority and inferiority of the adaptive value to evaluate individual provides foundation for Swarm Evolution；
D) selection and breeding:
According to the adaptive value of each individual, after the individual for using random manner pairing selection more outstanding is bred as parent
It generation, can be using the methods of runner selection or sequencing selection；
E) genetic manipulation:
It is selected by the way of roulette wheel selection herein, obtaining the selected probability of the bigger variable of adaptive value will be bigger,
To be retained, as optimal reservation undetermined；
F) single point crossing:
One crossover location is randomly selected according to crossover probability for each of selected individual, this is exchanged and exists to individual
The later binary code in the position forms two new individuals；
G) Mutation:
Mutation operation, will be in individual to simulate the biological gene mutation due to caused by accidental cause in the natural environment
Binary code negated by mutation probability, i.e., 0 and 1 exchange；
H) retain optimal:
In reproductive process, individual adaptive value can be continuously improved；In order to avoid the loss of optimized individual, by optimized individual
It is stored in specified position, all individuals are then substituted into a upper groups of individuals according to hereditary step again, then carry out commenting for adaptive value
Valence loops back and forth like this, and the adaptive value of individual is continuously improved, does not lose optimized individual while selecting adaptive optimal control value；When
When reaching evolutionary generation or finding satisfactory solution, then iteration terminates.
The objective function of the Unit Combination are as follows:
α in formulai=(Pimax-Pimin)/Pimax,
Front portion is cumulative in objective function and reflects the peak modulation capacity of unit, and rear portion is cumulative and illustrates machine
The coal consumption of group and start-up and shut-down costs；XtBigger expression guarantees to meet peak load regulation while unit operating cost minimum in t moment
Ability is maximum；αiIndicate i-th peak load regulation depth coefficient；PitIndicate i-th unit in the active power output of t moment；PiminFor
The minimum load that i-th unit surely fires；PimaxFor i-th unit generation power maximum upper limit；βitIt is set as 0 and 1 two value, βit=
Indicate that i-th unit is in shutdown status, β in t moment when 0itIndicate that i-th unit is in operating status in t moment when=1；F
(Pit) indicate i-th unit in the coal consumption of t moment；ai、bi、ciIndicate the coal consumption coefficient of i-th unit；fistartAnd fishutPoint
Not Wei i-th unit starting and shut down cost.
The constraint condition of the Unit Combination include power-balance constraint, spinning reserve constraint, unit output bound about
Beam, unit climbing and start and stop power constraint, unit depth peak regulation power are about and wind power output constrains.
The power-balance constraint indicates are as follows:
P in formulawtIndicate the grid-connected power output required in t moment wind-powered electricity generation；PltIndicate the supply load power total in t moment；
The spinning reserve constraint representation are as follows:
Spinning reserve capacity, P in formula are considered by the 7% of system total loadxtFor system spinning reserve capacity；PimaxIt is i-th
Platform unit generation power maximum upper limit；
The unit output upper and lower limit indicates are as follows:
Pimin≤Pit≤Pimax
PiminAnd PimaxRespectively indicate the power output upper and lower limit of i-th unit；
The unit climbing and start and stop power constraint indicate are as follows:
Pit-Pit-1≤βit-1Aiup+(βit-βit-1)Aistart
Pit-1-Pit≤βitAidown+(βit-1-βit)Aishut
A in formulaiupAnd AidownThe upper and lower climbing power limit of respectively unit i；AistartAnd AishutRespectively unit i's
Power limit is shut down in starting；
The unit depth peak regulation power constraint indicates are as follows:
Alphabetical meaning is same as above in formula；
It is required that when load power decline is more, deep to adjust unit that load power be followed accordingly to reduce power output, i.e., per a period of time
The sum of all unit minimum steady combustion power outputs are carved no more than the moment load power, is ensured that in this way when load is lower, is continued
Fired power generating unit power output is forced down to meet the needs of load, guarantees the stability of system operation；
The wind power output constraint representation are as follows:
P in formulawtIndicate the practical power output in t moment wind-powered electricity generation；Indicate that wind-powered electricity generation maximum predicted is contributed in t moment.
The method for solving of the Unit Combination includes:
(1) setting of control parameter:
Evolutionary generation: T=100；
Population size: N=80；
Crossover probability: pc=0.8；
Mutation probability: pm=0.05；
Single parameter string length: L=10；
(2) coding method:
β is indicated with a binary codeit；One shares 47 units, so by PitIt is divided into 47 sections, every section respectively with ten
Binary code indicates that population size is set as 80, ultimately generates the binary system initial population of 80 row (47*10) column；
(3) multi-period optimization problem is reduced to the optimization problem of day part；Optimization of Unit Commitment is more than one
Period decision process, it is contemplated that multi-period dimension restrains slow or convergence difficulties even if will also result in using the solution of GA method；
Therefore be broken down into a series of single period optimal decision problems to solve, i.e., multi-period optimization problem is reduced to day part
Optimization problem, solved by the period, the variable number of per period is greatly reduced than multi-period variable number；This will make total calculating
Amount also greatly reduces；
(4) each scheduling slot βitThe initialization of value uses heuristic to generate relatively good initial solution, the method is as follows: if
A period is reduced system loading earlier above, and when initialization does not shut down the unit run generally；If system loading period earlier above
Increase, when initialization does not start the unit stopped transport generally；The state that processing not only reduces unit in this way shifts number, accelerates
Solving speed can also reduce the cost that unit generates during switching；Due in GA there is hybridize and variation link, it is this
Way has no effect on the generation of globally optimal solution；
(5) adaptive optimal control degree initial value is set as-∞, and three arrays are in addition arranged, stores produce in each iterative process respectively
Raw adaptive optimal control angle value Xt, every unit output P when choosing maximum peak modulation capacityitWith state array βit；
(6) unit capacity is constrained, by the position (47*10) binary code generated in (2), according to every ten binary codes
A decimal number is generated, in total available 47 decimal numbers, this 47 decimal number Ri(i=1,2,3...47) table
Show, the power output expression in every unit of each scheduling slot can be obtained are as follows:
Pi=(Pimax-Pimin)*Ri/(2^L-1)+Pimin
Alphabetical meaning is the same as noted earlier in formula；
The decimal number being converted into this guarantees the binary code being randomly generated in initial population is all by according to above-mentioned
Formula constraint, to avoid infeasible initial solution is generated, helps to improve GA solution to meet the constraint of every unit capacity
Efficiency and solution quality；
(7) for unit climbing power constraint and respective start and stop power constraint, it is all made of hard constraint, i.e., when beyond constraint
When condition, fitness is directly set 0；
The operation process of the Unit Combination includes:
(1) initiation parameter, input evolutionary generation, population size, cross and variation probability, parameter value range, single ginseng
Digital string length；
(2) parent population is randomly generated in initialization population, and adaptive optimal control degree initial value is arranged；
(3) iteration starts, and decodes and calculate fitness value, and evaluation individual stores adaptive optimal control angle value and mesh so far
Before until optimized parameter and optimum state array so far；
(4) heredity, single point crossing and Mutation is carried out to operate to form next-generation group；
(5) calculate fitness value, evaluation individual, storage so far adaptive optimal control angle value and so far optimized parameter with
And optimum state array so far；
(6) calculating if having reached evolutionary generation terminates, and otherwise jumps (4).
The invention has the advantages and beneficial effects that:
The present invention makes full use of the characteristic of fired power generating unit depth peak regulation in dispatching cycle, using genetic algorithm to Unit Combination
Optimizing is carried out, not only increases wind electricity digestion capability, additionally it is possible to which the peak modulation capacity for improving fired power generating unit improves the flexible of power supply
Property, electricity needs and clean energy resource power producing characteristics are preferably matched, efficiently solve the problems, such as that clean energy resource dissolves, alleviate unit tune
Peak pressure power.The present invention solves production of energy and using mismatch in time, in the peak regulation energy of each scheduling instance unit
Guarantee maximization of economic benefit while power maximum, improves system wind electricity digestion capability, be conducive to system in bigger space-time model
Enclose interior access and consumption wind-powered electricity generation.
Specific embodiment
The present invention is a kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration, including following
Step:
1, the algorithm of Unit Combination；
The algorithm of the Unit Combination uses genetic algorithm, and genetic algorithm (GA) has borrowed the mechanism of biogenetics, leads to
The operation such as natural selection, hybridization and variation is crossed, the raising of adaptive value is realized, natural selection this rule is ground applied to science
Study carefully and various optimization problems in engineering technology.The remarkable advantage of genetic algorithm is many that can be simultaneously scanned in solution space
Point, rather than a point, thus intimate globally optimal solution can be found.
The genetic algorithm the following steps are included:
A) initialization coding:
Operating status variable of the unit on a period is indicated with a binary code.It is single if there is n platform unit
A unit parameter string length is defined as l, then a length of L=(n*l) position of string, thus obtained in a dispatching cycle
Interior (24 hours), the peak capacity of each scheduling instance generating set combination, initialize several group of individuals being randomly generated
At a group, first generation solution group is constituted.In general, the quality of initial solution is all very poor.
B) it decodes:
According to constraint condition, the binary code of initialization can be converted into real number to indicate, convert binary code to pair
The real number answered is known as decoding.
C) fitness and evaluation:
Resulting real number will be decoded, will be narrowed down within the scope of constraint condition by a certain percentage, the reality for meeting constraint condition is obtained
Number.For fitness function, it is taken as the function of each scheduling instance peak modulation capacity, it is also very straight to the adaptive value evaluation of individual in this way
It sees, also there is its specific meaning.Real number corresponding to individual is substituted into fitness function and calculates adaptive value, adaptive value is bigger, indicates
The peak modulation capacity of Unit Combination is bigger, and superiority and inferiority of the adaptive value to evaluate individual provides foundation for Swarm Evolution.
D) selection and breeding:
According to the adaptive value of each individual, after the individual for using random manner pairing selection more outstanding is bred as parent
It generation, can be using the methods of runner selection or sequencing selection.
E) genetic manipulation:
It is selected by the way of roulette wheel selection herein, obtaining the selected probability of the bigger variable of adaptive value will be bigger,
To be retained, as optimal reservation undetermined.
F) single point crossing:
One crossover location is randomly selected according to crossover probability for each of selected individual, this is exchanged and exists to individual
The later binary code in the position forms two new individuals.
G) Mutation:
Mutation operation, will be in individual to simulate the biological gene mutation due to caused by accidental cause in the natural environment
Binary code negated by mutation probability, i.e., 0 and 1 exchange.
H) retain optimal:
In reproductive process, individual adaptive value can be continuously improved.In order to avoid the loss of optimized individual, by optimized individual
It is stored in specified position, all individuals are then substituted into a upper groups of individuals according to hereditary step again, then carry out commenting for adaptive value
Valence loops back and forth like this, and the adaptive value of individual is continuously improved, does not lose optimized individual while selecting adaptive optimal control value.When
When reaching evolutionary generation or finding satisfactory solution, then iteration terminates.
2, the objective function of Unit Combination；
The objective function of the Unit Combination are as follows:
α in formulai=(Pimax-Pimin)/Pimax,
Front portion is cumulative in objective function and reflects the peak modulation capacity of unit, and rear portion is cumulative and illustrates machine
The coal consumption of group and start-up and shut-down costs；XtBigger expression guarantees to meet peak load regulation while unit operating cost minimum in t moment
Ability is maximum.αiIndicate i-th peak load regulation depth coefficient；PitIndicate i-th unit in the active power output of t moment；PiminFor
The minimum load that i-th unit surely fires；PimaxFor i-th unit generation power maximum upper limit；βitIt is set as 0 and 1 two value, βit=
Indicate that i-th unit is in shutdown status, β in t moment when 0itIndicate that i-th unit is in operating status in t moment when=1；F
(Pit) indicate i-th unit in the coal consumption of t moment；ai、bi、ciIndicate the coal consumption coefficient of i-th unit；fistartAnd fishutPoint
Not Wei i-th unit starting and shut down cost.
Constraint condition corresponding to the objective function of the Unit Combination include power-balance constraint, spinning reserve constraint,
The constraint of unit output bound, unit climbing and start and stop power constraint, unit depth peak regulation power constraint, wind power output constraint.
(1) power-balance constraint indicates are as follows:
P in formulawtIndicate the grid-connected power output required in t moment wind-powered electricity generation；PltIndicate the supply load power total in t moment.
(2) the spinning reserve constraint representation are as follows:
Spinning reserve capacity, P in formula are considered by the 7% of system total loadxtFor system spinning reserve capacity；PimaxIt is i-th
Platform unit generation power maximum upper limit.
(3) the unit output bound constraint representation are as follows:
Pimin≤Pit≤Pimax
PiminAnd PimaxRespectively indicate the power output upper and lower limit of i-th unit.
(4) the unit climbing and start and stop power constraint indicate are as follows:
Pit-Pit-1≤βit-1Aiup+(βit-βit-1)Aistart
Pit-1-Pit≤βitAidown+(βit-1-βit)Aishut
A in formulaiupAnd AidownThe upper and lower climbing power limit of respectively unit i；AistartAnd AishutRespectively unit i's
Power limit is shut down in starting.
(5) the unit depth peak regulation power constraint indicates are as follows:
Alphabetical meaning is same as above in formula.
It is required that when load power decline is more, deep to adjust unit that load power be followed accordingly to reduce power output, i.e., per a period of time
The sum of all unit minimum steadies combustion power output is carved no more than the moment load power, is ensured that in this way when load is lower, it can be with
Continue to force down fired power generating unit power output to meet the needs of load, the stability of guarantee system operation.
3, the constraint condition of Unit Combination；
The wind power output constraint representation are as follows:
P in formulawtIndicate the practical power output in t moment wind-powered electricity generation；Indicate that wind-powered electricity generation maximum predicted is contributed in t moment.
4, the method for solving of Unit Combination；
The method for solving of the Unit Combination includes:
(1) setting of control parameter:
Evolutionary generation: T=100；
Population size: N=80；
Crossover probability: pc=0.8；
Mutation probability: pm=0.05；
Single parameter string length: L=10；
(2) coding method indicates β with a binary code hereinit；One shares 47 units, so by PitIt is divided into 47 sections,
Every section indicates that population size is set as 80 with ten binary codes respectively, ultimately generates the binary system of 80 row (47*10) column
Initial population.
(3) multi-period optimization problem is reduced to the optimization problem of day part.Optimization of Unit Commitment is more than one
Period decision process, it is contemplated that multi-period dimension restrains slow or convergence difficulties even if will also result in using the solution of GA method.
Therefore be broken down into a series of single period optimal decision problems to solve, i.e., multi-period optimization problem is reduced to day part
Optimization problem, solved by the period, the variable number of per period is greatly reduced than multi-period variable number.This will make total calculating
Amount also greatly reduces.
(4) each scheduling slot βitThe initialization of value uses heuristic to generate relatively good initial solution, the method is as follows: if
A period is reduced system loading earlier above, and when initialization does not shut down the unit run generally；If system loading period earlier above
Increase, when initialization does not start the unit stopped transport generally.The state that processing not only reduces unit in this way shifts number, accelerates
Solving speed can also reduce the cost that unit generates during switching.Due in GA there is hybridize and variation link, it is this
Way has no effect on the generation of globally optimal solution.
(5) adaptive optimal control degree initial value is set as-∞, and three arrays are in addition arranged, stores produce in each iterative process respectively
Raw adaptive optimal control angle value Xt, every unit output P when choosing maximum peak modulation capacityitWith state array βit。
(6) unit capacity is constrained, by the position (47*10) binary code generated in (2), according to every ten binary codes
A decimal number is generated, in total available 47 decimal numbers, this 47 decimal number Ri(i=1,2,3...47) table
Show, the power output expression in every unit of each scheduling slot can be obtained are as follows:
Pi=(Pimax-Pimin)*Ri/(2^L-1)+Pimin
Alphabetical meaning is the same as noted earlier in formula.
The decimal number being converted into this guarantees the binary code being randomly generated in initial population is all by according to above-mentioned
Formula constraint, to avoid infeasible initial solution is generated, helps to improve GA solution to meet the constraint of every unit capacity
Efficiency and solution quality.
(7) for unit climbing power constraint and respective start and stop power constraint, it is all made of hard constraint, i.e., when beyond constraint
When condition, fitness is directly set 0.
5, the operation process of Unit Combination.
The operation process of the Unit Combination includes:
(1) initiation parameter, input evolutionary generation, population size, cross and variation probability, parameter value range, single ginseng
Digital string length；
(2) parent population is randomly generated in initialization population, and adaptive optimal control degree initial value is arranged；
(3) iteration starts, and decodes and calculate fitness value, and evaluation individual stores adaptive optimal control angle value and mesh so far
Before until optimized parameter and optimum state array so far；
(4) heredity, single point crossing and Mutation is carried out to operate to form next-generation group；
(5) calculate fitness value, evaluation individual, storage so far adaptive optimal control angle value and so far optimized parameter with
And optimum state array so far；
(6) calculating if having reached evolutionary generation terminates, and otherwise jumps to (4) step.
Embodiment 1:
The present invention is a kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration, including following
Step:
Step A. establishes the Unit Combination model of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration；
Step B. analyzes the Unit Combination method for solving of fired power generating unit depth peak regulation under the conditions of wind-electricity integration；
Step C. analyzes the Unit Combination operation process of fired power generating unit depth peak regulation under the conditions of wind-electricity integration；
Step D. considers the Unit Combination model of fired power generating unit depth peak regulation under the conditions of wind-electricity integration according to step A
Determine objective function:
α in formulai=(Pimax-Pimin)/Pimax,
Front portion is cumulative in objective function and reflects the peak modulation capacity of unit, and rear portion is cumulative and illustrates machine
The coal consumption of group and start-up and shut-down costs；XtBigger expression guarantees to meet peak load regulation while unit operating cost minimum in t moment
Ability is maximum.αiIndicate i-th peak load regulation depth coefficient；PitIndicate i-th unit in the active power output of t moment；PiminFor
The minimum load that i-th unit surely fires；PimaxFor i-th unit generation power maximum upper limit；βitIt is set as 0 and 1 two value, βit=
Indicate that i-th unit is in shutdown status, β in t moment when 0itIndicate that i-th unit is in operating status in t moment when=1；F
(Pit) indicate i-th unit in the coal consumption of t moment；ai、bi、ciIndicate the coal consumption coefficient of i-th unit；fistartAnd fishutPoint
Not Wei i-th unit starting and shut down cost.
Step E. objective function according to step D determines constraint condition:
Power-balance constraint:
P in formulawtIndicate the grid-connected power output required in t moment wind-powered electricity generation；PltIndicate the supply load power total in t moment.
Spinning reserve constraint:
Spinning reserve capacity, P in formula are considered by the 7% of system total loadxtFor system spinning reserve capacity；PimaxIt is i-th
Platform unit generation power maximum upper limit.
The constraint of unit output upper and lower limit:
Pimin≤Pit≤Pimax
PiminAnd PimaxRespectively indicate the power output upper and lower limit of i-th unit.
Unit climbing and start and stop power constraint:
Pit-Pit-1≤βit-1Aiup+(βit-βit-1)Aistart
Pit-1-Pit≤βitAidown+(βit-1-βit)Aishut
A in formulaiupAnd AidownThe upper and lower climbing power limit of respectively unit i；AistartAnd AishutRespectively unit i's
Power limit is shut down in starting.
Unit depth peak regulation power constraint:
Alphabetical meaning is same as above in formula.
It is required that when load power decline is more, deep to adjust unit that load power be followed accordingly to reduce power output, i.e., per a period of time
The sum of all unit minimum steadies combustion power output is carved no more than the moment load power, is ensured that in this way when load is lower, it can be with
Continue to force down fired power generating unit power output to meet the needs of load, the stability of guarantee system operation.
Wind power output constraint:
P in formulawtIndicate the practical power output in t moment wind-powered electricity generation；Indicate that wind-powered electricity generation maximum predicted is contributed in t moment.
The Unit Combination method for solving packet of fired power generating unit depth peak regulation under the conditions of step F. wind-electricity integration according to step B
It includes:
(1) setting of control parameter:
Evolutionary generation: T=100
Population size: N=80
Crossover probability: pc=0.8
Mutation probability: pm=0.05
Single parameter string length: L=10
(2) coding method:
The present invention indicates β with a binary codeit；One shares 47 units, so by PitIt is divided into 47 sections, every section of difference
It indicates that population size is set as 80 with ten binary codes, ultimately generates initial kind of binary system of 80 row (47*10) column
Group.
(3) multi-period optimization problem is reduced to the optimization problem of day part.Optimization of Unit Commitment is more than one
Period decision process, it is contemplated that multi-period dimension restrains slow or convergence difficulties even if will also result in using the solution of GA method.
Therefore be broken down into a series of single period optimal decision problems to solve, i.e., multi-period optimization problem is reduced to day part
Optimization problem, solved by the period, the variable number of per period is greatly reduced than multi-period variable number.This will make total calculating
Amount also greatly reduces.
(4) each scheduling slot βitThe initialization of value uses heuristic to generate relatively good initial solution, the method is as follows: if being
Uniting, a period is reduced load earlier above, and when initialization does not shut down the unit run generally；If a period increases system loading earlier above
Add, when initialization does not start the unit stopped transport generally.The state that processing not only reduces unit in this way shifts number, accelerates and asks
Speed is solved, the cost that unit generates during switching can be also reduced.Due in GA there is hybridize and variation link, it is this to do
Method has no effect on the generation of globally optimal solution.
(5) adaptive optimal control degree initial value is set as-∞, and three arrays are in addition arranged, stores produce in each iterative process respectively
Raw adaptive optimal control angle value Xt, every unit output P when choosing maximum peak modulation capacityitWith state array βit。
(6) unit capacity is constrained, by the position (47*10) binary code generated in (2), according to every ten binary codes
A decimal number is generated, in total available 47 decimal numbers, this 47 decimal number Ri(i=1,2,3...47) table
Show, the power output expression in every unit of each scheduling slot can be obtained are as follows:
Pi=(Pimax-Pimin)*Ri/(2^L-1)+Pimin
Alphabetical meaning is the same as noted earlier in formula.
The decimal number being converted into this guarantees the binary code being randomly generated in initial population is all by according to above-mentioned
Formula constraint, to avoid infeasible initial solution is generated, helps to improve GA solution to meet the constraint of every unit capacity
Efficiency and solution quality.
(7) for unit climbing power constraint and respective start and stop power constraint, it is all made of hard constraint, i.e., when beyond constraint
When condition, fitness is directly set 0.
The Unit Combination operation process of fired power generating unit depth peak regulation under the conditions of step G. wind-electricity integration according to step C
Include:
(1) initiation parameter, input evolutionary generation, population size, cross and variation probability, parameter value range, single ginseng
Digital string length；
(2) parent population is randomly generated in initialization population, and adaptive optimal control degree initial value is arranged；
(3) iteration starts, and decodes and calculate fitness value, and evaluation individual stores adaptive optimal control angle value and mesh so far
Before until optimized parameter and optimum state array so far；
(4) heredity, single point crossing and Mutation is carried out to operate to form next-generation group；
(5) calculate fitness value, evaluation individual, storage so far adaptive optimal control angle value and so far optimized parameter with
And optimum state array so far；
(6) calculating if having reached evolutionary generation terminates, and otherwise go to step (4).
The present invention makes the maximized angle of fired power generating unit peak modulation capacity from wind-powered electricity generation is dissolved as far as possible, proposes a kind of wind-powered electricity generation simultaneously
Depth regulating units are optimized scheduling by the mathematical model of fired power generating unit depth peak regulation under the conditions of net.Based on a province in China grade
The real data of power grid finds globally optimal solution using genetic algorithm, and under the conditions of analyzing large-scale wind power integration, thermal motor
The economy of group depth peak regulating plan.Research achievement shows that not only can solve abandonment by using unit depth peak regulating plan disappears
The problem of receiving, moreover it is possible to save fired power generating unit cost of electricity-generating, make " deep to adjust consumption " that there is feasibility.
The present invention is powered using the big fired power generating unit of preferential selection peak load regulation ability, improves the peak regulation of fired power generating unit
Ability further by wind-powered electricity generation can not only carry out on-load by extrusion pressure, improve and receive wind-powered electricity generation ability, moreover it is possible to reach when load is lower
To bigger economic benefit.
Claims (8)
1. a kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration, which is characterized in that the packet
Include following steps:
The algorithm of Unit Combination；
The objective function of Unit Combination；
The constraint condition of Unit Combination；
The method for solving of Unit Combination；
The operation process of Unit Combination.
2. the Unit Combination side of fired power generating unit depth peak regulation under the conditions of a kind of consideration wind-electricity integration according to claim 1
Method, which is characterized in that the algorithm of the Unit Combination uses genetic algorithm, and genetic algorithm (GA) has borrowed the machine of biogenetics
Reason is operated by natural selection, hybridization and variation etc., realizes the raising of adaptive value, this rule is applied to by natural selection
Various optimization problems in scientific research and engineering technology；The remarkable advantage of genetic algorithm can simultaneously scan in solution space
It is many, rather than a point, thus intimate globally optimal solution can be found.
3. the Unit Combination side of fired power generating unit depth peak regulation under the conditions of a kind of consideration wind-electricity integration according to claim 2
Method, which is characterized in that the genetic algorithm key step:
A) initialization coding
Operating status variable of the unit on a period is indicated with a binary code, if there is n platform unit, single machine
Group parameter string length is defined as l, then a length of L=(n*l) position of string, thus obtained within a dispatching cycle
(24 hours), the peak capacity of each scheduling instance generating set combination initialize several individual compositions being randomly generated
One group constitutes first generation solution group；In general, the quality of initial solution is all very poor；
B) it decodes
According to constraint condition, the binary code of initialization can be converted into real number to indicate, convert binary code to corresponding
Real number is known as decoding；
C) fitness and evaluation
Resulting real number will be decoded, will be narrowed down within the scope of constraint condition by a certain percentage, the real number for meeting constraint condition is obtained.It is right
In fitness function, it is taken as the function of each scheduling instance peak modulation capacity, it is also very intuitive to the adaptive value evaluation of individual in this way,
There is its specific meaning；Real number corresponding to individual is substituted into fitness function and calculates adaptive value, adaptive value is bigger, indicates unit group
The peak modulation capacity of conjunction is bigger, and superiority and inferiority of the adaptive value to evaluate individual provides foundation for Swarm Evolution；
D) selection and breeding:
According to the adaptive value of each individual, uses random manner pairing to choose more outstanding individual and raises up seed as parent,
It can be using the methods of runner selection or sequencing selection；
E) genetic manipulation:
It is selected by the way of roulette wheel selection herein, obtaining the selected probability of the bigger variable of adaptive value will be bigger, thus
It is retained, as optimal reservation undetermined；
F) single point crossing:
One crossover location is randomly selected according to crossover probability for each of selected individual, exchanges this to individual in the position
Later binary code is set, two new individuals are formed；
G) Mutation:
Mutation operation is to simulate the biological gene mutation due to caused by accidental cause in the natural environment, by two in individual
Ary codes are negated by mutation probability, i.e., 0 and 1 exchanges；
H) retain optimal:
In reproductive process, individual adaptive value can be continuously improved；In order to avoid the loss of optimized individual, optimized individual is stored
In specified position, all individuals are then substituted into a upper groups of individuals according to hereditary step again, then carry out the evaluation of adaptive value, such as
This moves in circles, and the adaptive value of individual is continuously improved, does not lose optimized individual while selecting adaptive optimal control value；When reach into
When changing algebra or finding satisfactory solution, then iteration terminates.
4. the Unit Combination side of fired power generating unit depth peak regulation under the conditions of a kind of consideration wind-electricity integration according to claim 1
Method, which is characterized in that the objective function of the Unit Combination are as follows:
α in formulai=(Pi max-Pi min)/Pi max,
Front portion is cumulative in objective function and reflects the peak modulation capacity of unit, and rear portion is cumulative and illustrates unit
Coal consumption and start-up and shut-down costs；XtBigger expression guarantees to meet peak load regulation ability while unit operating cost minimum in t moment
It is maximum；αiIndicate i-th peak load regulation depth coefficient；PitIndicate i-th unit in the active power output of t moment；Pi minIt is i-th
The minimum load that unit surely fires；Pi maxFor i-th unit generation power maximum upper limit；βitIt is set as 0 and 1 two value, βitWhen=0
Indicate that i-th unit is in shutdown status, β in t momentitIndicate that i-th unit is in operating status in t moment when=1；F
(Pit) indicate i-th unit in the coal consumption of t moment；ai、bi、ciIndicate the coal consumption coefficient of i-th unit；fisiartAnd fishutPoint
Not Wei i-th unit starting and shut down cost.
5. the Unit Combination side of fired power generating unit depth peak regulation under the conditions of a kind of consideration wind-electricity integration according to claim 1
Method, which is characterized in that the constraint condition of the Unit Combination includes power-balance constraint, spinning reserve constraint, on unit output
Lower limit constraint, unit climbing and start and stop power constraint, unit depth peak regulation power are about and wind power output constrains.
6. the Unit Combination side of fired power generating unit depth peak regulation under the conditions of a kind of consideration wind-electricity integration according to claim 5
Method, which is characterized in that the power-balance constraint indicates are as follows:
P in formulawtIndicate the grid-connected power output required in t moment wind-powered electricity generation；PltIndicate the supply load power total in t moment；
The spinning reserve constraint representation are as follows:
Spinning reserve capacity, P in formula are considered by the 7% of system total loadxtFor system spinning reserve capacity；Pi maxFor i-th machine
Group generated output maximum upper limit；
The unit output upper and lower limit indicates are as follows:
Pi min≤Pit≤Pi max
Pi minAnd Pi maxRespectively indicate the power output upper and lower limit of i-th unit；
The unit climbing and start and stop power constraint indicate are as follows:
Pit-Pit-1≤βit-1Aiup+(βit-βit-1)Aistart
Pit-1-Pit≤βitAidown+(βit-1-βit)Aishut
A in formulaiupAnd AidownThe upper and lower climbing power limit of respectively unit i；AistartAnd AishutRespectively the starting of unit i,
Shut down power limit；
The unit depth peak regulation power constraint indicates are as follows:
Alphabetical meaning is same as above in formula；
It is required that when load power decline is more, deep to adjust unit that load power be followed accordingly to reduce power output, i.e., each moment institute
There is the sum of unit minimum steady combustion power output no more than the moment load power, is ensured that in this way when load is lower, continue to force down
Fired power generating unit contributes meet the needs of load, guarantees the stability of system operation；
The wind power output constraint representation are as follows:
P in formulawtIndicate the practical power output in t moment wind-powered electricity generation；Indicate that wind-powered electricity generation maximum predicted is contributed in t moment.
7. the Unit Combination side of fired power generating unit depth peak regulation under the conditions of a kind of consideration wind-electricity integration according to claim 1
Method, which is characterized in that the method for solving of the Unit Combination includes:
(1) setting of control parameter:
Evolutionary generation: T=100；
Population size: N=80；
Crossover probability: pc=0.8；
Mutation probability: pm=0.05；
Single parameter string length: L=10；
(2) coding method:
β is indicated with a binary codeit；One shares 47 units, so by PitIt is divided into 47 sections, every section respectively with ten binary systems
Code indicates that population size is set as 80, ultimately generates the binary system initial population of 80 row (47*10) column；
(3) multi-period optimization problem is reduced to the optimization problem of day part；Optimization of Unit Commitment is one multi-period
Decision process, it is contemplated that multi-period dimension restrains slow or convergence difficulties even if will also result in using the solution of GA method；Therefore it will
It is decomposed into a series of single period optimal decision problems to solve, i.e., multi-period optimization problem is reduced to day part most
Optimization problem, solves by the period, and the variable number of per period is greatly reduced than multi-period variable number；This will make total calculation amount
It greatly reduces；
(4) each scheduling slot βitThe initialization of value uses heuristic to generate relatively good initial solution, the method is as follows: if system
A period is reduced load earlier above, and when initialization does not shut down the unit run generally；If a period increases system loading earlier above,
The unit stopped transport is not started generally when initialization；The state that processing not only reduces unit in this way shifts number, accelerates solution
Speed can also reduce the cost that unit generates during switching；Since there is hybridization and variation link, this ways in GA
Have no effect on the generation of globally optimal solution；
(5) adaptive optimal control degree initial value is set as-∞, and three arrays are in addition arranged, and stores generate in each iterative process respectively
Adaptive optimal control angle value Xt, every unit output P when choosing maximum peak modulation capacityitWith state array βit；
(6) unit capacity is constrained, by the position (47*10) binary code generated in (2), is generated according to every ten binary codes
One decimal number, in total available 47 decimal numbers, this 47 decimal number Ri(i=1,2,3...47) is indicated,
The power output expression in every unit of each scheduling slot can be obtained are as follows:
Pi=(Pi max-Pi min)*Ri/(2^L-1)+Pi min
Alphabetical meaning is the same as noted earlier in formula；
The decimal number being converted into this guarantees the binary code being randomly generated in initial population is all by according to above-mentioned formula
Constraint, to avoid infeasible initial solution is generated, helps to improve the effect of GA solution to meet the constraint of every unit capacity
The quality of rate and solution；
(7) for unit climbing power constraint and respective start and stop power constraint, it is all made of hard constraint, i.e., when beyond constraint condition
When, fitness is directly set 0.
8. the Unit Combination side of fired power generating unit depth peak regulation under the conditions of a kind of consideration wind-electricity integration according to claim 1
Method, which is characterized in that the operation process of the Unit Combination includes:
(1) initiation parameter inputs evolutionary generation, population size, cross and variation probability, parameter value range, single parameter word
String length；
(2) parent population is randomly generated in initialization population, and adaptive optimal control degree initial value is arranged；
(3) iteration starts, and decodes and calculate fitness value, and evaluation individual stores adaptive optimal control angle value so far and is at present
Only optimized parameter and so far optimum state array；
(4) heredity, single point crossing and Mutation is carried out to operate to form next-generation group；
(5) fitness value is calculated, evaluation individual stores adaptive optimal control angle value so far and so far optimized parameter and mesh
Before until optimum state array；
(6) calculating if having reached evolutionary generation terminates, and otherwise jumps (4).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811499152.5A CN109472423A (en) | 2018-12-08 | 2018-12-08 | A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811499152.5A CN109472423A (en) | 2018-12-08 | 2018-12-08 | A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109472423A true CN109472423A (en) | 2019-03-15 |
Family
ID=65675030
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811499152.5A Pending CN109472423A (en) | 2018-12-08 | 2018-12-08 | A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109472423A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110112726A (en) * | 2019-04-30 | 2019-08-09 | 国家电网公司华中分部 | The multiple-energy-source short-term economic dispatching method and system evolved based on difference-gradient |
| CN110866632A (en) * | 2019-10-17 | 2020-03-06 | 杭州电子科技大学 | Method for optimizing generator set |
| CN111799778A (en) * | 2020-06-11 | 2020-10-20 | 国网山东省电力公司经济技术研究院 | Wind power-containing power system energy storage capacity optimization method considering peak shaving requirements |
| CN112072709A (en) * | 2020-07-29 | 2020-12-11 | 国网甘肃省电力公司电力科学研究院 | Coordinated optimization method for joint peak regulation system under given outgoing power curve |
| CN112290564A (en) * | 2020-10-20 | 2021-01-29 | 湖南大学 | Method and system for reducing power mismatching degree between power system subregions |
| CN112365101A (en) * | 2020-12-09 | 2021-02-12 | 国网山东省电力公司电力科学研究院 | Thermal power generating unit peak regulation combined total coal consumption optimization method and system based on genetic algorithm |
| CN113240547A (en) * | 2021-05-21 | 2021-08-10 | 河北工业大学 | Scheduling method of hydrogen production unit array under wind power consumption |
| CN113255228A (en) * | 2021-06-09 | 2021-08-13 | 国网山东省电力公司电力科学研究院 | Thermal power generating unit peak regulation combined total coal consumption optimization method and system based on genetic algorithm |
| CN115528736A (en) * | 2022-10-20 | 2022-12-27 | 山东大学 | Electric power system forward-looking scheduling method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105576709A (en) * | 2016-01-06 | 2016-05-11 | 南京工程学院 | Hybrid algorithm based optimization method for wind power-pumped unified operation |
| CN106712075A (en) * | 2016-04-26 | 2017-05-24 | 武汉大学 | Peaking strategy optimization method considering safety constraints of wind power integration system |
-
2018
- 2018-12-08 CN CN201811499152.5A patent/CN109472423A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105576709A (en) * | 2016-01-06 | 2016-05-11 | 南京工程学院 | Hybrid algorithm based optimization method for wind power-pumped unified operation |
| CN106712075A (en) * | 2016-04-26 | 2017-05-24 | 武汉大学 | Peaking strategy optimization method considering safety constraints of wind power integration system |
Non-Patent Citations (2)
| Title |
|---|
| 向红吉等: "考虑低谷时刻负调峰能力及风电预测区间的多目标机组组合优化研究", 《电网技术》 * |
| 梁子鹏等: "考虑风电不确定度的风-火-水-气-核-抽水蓄能多源协同旋转备用优化", 《电网技术》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110112726A (en) * | 2019-04-30 | 2019-08-09 | 国家电网公司华中分部 | The multiple-energy-source short-term economic dispatching method and system evolved based on difference-gradient |
| CN110112726B (en) * | 2019-04-30 | 2022-12-09 | 国家电网公司华中分部 | Multi-energy short-term economic dispatching method and system based on differential-gradient evolution |
| CN110866632B (en) * | 2019-10-17 | 2022-05-06 | 杭州电子科技大学 | Method for optimizing generator set |
| CN110866632A (en) * | 2019-10-17 | 2020-03-06 | 杭州电子科技大学 | Method for optimizing generator set |
| CN111799778A (en) * | 2020-06-11 | 2020-10-20 | 国网山东省电力公司经济技术研究院 | Wind power-containing power system energy storage capacity optimization method considering peak shaving requirements |
| CN112072709A (en) * | 2020-07-29 | 2020-12-11 | 国网甘肃省电力公司电力科学研究院 | Coordinated optimization method for joint peak regulation system under given outgoing power curve |
| CN112290564B (en) * | 2020-10-20 | 2022-07-01 | 湖南大学 | Method and system for reducing power mismatching degree between power system subregions |
| CN112290564A (en) * | 2020-10-20 | 2021-01-29 | 湖南大学 | Method and system for reducing power mismatching degree between power system subregions |
| CN112365101A (en) * | 2020-12-09 | 2021-02-12 | 国网山东省电力公司电力科学研究院 | Thermal power generating unit peak regulation combined total coal consumption optimization method and system based on genetic algorithm |
| CN113240547A (en) * | 2021-05-21 | 2021-08-10 | 河北工业大学 | Scheduling method of hydrogen production unit array under wind power consumption |
| CN113240547B (en) * | 2021-05-21 | 2022-04-12 | 河北工业大学 | Scheduling method of hydrogen production unit array under wind power consumption |
| CN113255228A (en) * | 2021-06-09 | 2021-08-13 | 国网山东省电力公司电力科学研究院 | Thermal power generating unit peak regulation combined total coal consumption optimization method and system based on genetic algorithm |
| CN115528736A (en) * | 2022-10-20 | 2022-12-27 | 山东大学 | Electric power system forward-looking scheduling method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109472423A (en) | A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration | |
| Arya | Impact of hydrogen aqua electrolyzer-fuel cell units on automatic generation control of power systems with a new optimal fuzzy TIDF-II controller | |
| CN108173283B (en) | Operation method of combined heat and power system containing wind and light renewable energy | |
| Amer et al. | Optimization of hybrid renewable energy systems (HRES) using PSO for cost reduction | |
| CN112949188B (en) | Optimizing system and method for parameter configuration of wave energy device | |
| CN103580061A (en) | Microgrid operating method | |
| CN109390981B (en) | Combined operation control method for new energy participating in electric quantity balance of sending-end power grid unit | |
| CN106159944B (en) | Multi-stage transmission expansion planning method under low-carbon environment based on bilevel programming model | |
| CN114400698B (en) | Optimal carbon reduction operation method for high-proportion clean energy power grid power supply | |
| CN102780235A (en) | Pumped storage power station dispatching method on basis of genetic algorithm | |
| CN114154744A (en) | Capacity expansion planning method and device of comprehensive energy system and electronic equipment | |
| CN104616071B (en) | A kind of wind-light storage complementary power generation system Optimal Configuration Method | |
| CN116070857A (en) | Water plant secondary pump house scheduling method and device based on genetic algorithm | |
| CN108734349A (en) | Distributed generation resource addressing constant volume optimization method based on improved adaptive GA-IAGA and system | |
| CN109472716B (en) | Multi-factor-considered capacity end power allocation method based on genetic algorithm | |
| CN112583003A (en) | Multi-energy power system power supply planning method based on evolutionary game theory | |
| Soedibyo et al. | Optimal design of fuel-cell, wind and micro-hydro hybrid system using genetic algorithm | |
| CN115940282A (en) | Wind power hydrogen production energy storage system capacity optimal configuration method considering hydrogen doping ratio constraint | |
| Bakanos et al. | Optimal operation scheduling of multipurpose pumped storage hydro power plant with high penetration of renewable energy resources | |
| CN112884191A (en) | Thermoelectric day-ahead scheduling model based on network source coordination and calculation method | |
| CN110472364A (en) | A kind of optimization method for the off-grid type thermoelectricity gas co-generation system considering renewable energy | |
| CN104484756B (en) | A kind of enterprise's efficiency method for improving the microgrid of generating containing wind-light storage | |
| CN115986744B (en) | Power distribution network power flow optimization method containing shared energy storage | |
| CN117713154A (en) | Power grid side green hydrogen energy storage peak shaving capacity optimization configuration model and efficient solving method thereof | |
| Shekhar et al. | Automatic generation control of a hybrid power system in deregulated environment utilizing GA, DE and CA tuned PID controller |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190315 |