CN105576709B - A kind of wind based on hybrid algorithm stores the optimization method of fiery cooperation - Google Patents
A kind of wind based on hybrid algorithm stores the optimization method of fiery cooperation Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The present invention relates to the optimization method that a kind of wind based on hybrid algorithm stores fiery cooperation, establish the mathematical modeling for being up to object function with association system economic benefit, drawn water using wind power plant itself capacity limit, hydroenergy storage station, the output bound of the limitation of generated output bound and capacity reservoir and fired power generating unit, climbing power, minimum start-stop time and system reserve and power-balance as constraints, solved by hybrid algorithm.The present invention takes into full account the principle of " dissolving wind-powered electricity generation as far as possible ", using wind-powered electricity generation direct grid-connected power, hydroenergy storage station draw water power and generated output as optimized variable, it is set to meet the situation of wind power plant and hydroenergy storage station constraints, contributed accordingly further according to the switching variable and each moment of equilibrium constraint optimization fired power generating unit, so as to reach the optimal effect of macroeconomic, improve wind power plant, hydroenergy storage station and the cooperation benefit of thermal power station is not high and system is to wind-powered electricity generation " consumption " scarce capacity problem.
Description
【Technical field】
The present invention relates to the optimization method that a kind of wind based on hybrid algorithm stores fiery cooperation, belong to the power system energy
Optimum Scheduling Technology field.
【Background technology】
With the continuous development of human society, the mankind enter the epoch of industrialized production.On the one hand, for the energy
Demand increasingly increases, therefore energy crisis is all over the world；On the other hand, energy crisis is accompanied by environmental pollution, due to master
Pollutant discharge amount is wanted to exceed the ability that environment can carry, fossil energy has very big pollution and harm to environment.To understand
The certainly pollution such as dust, acid rain caused by energy shortage crisis and combustion of fossil fuel sex chromosome mosaicism, China need to greatly develop
The clean energy resourcies such as wind-powered electricity generation, and wind-powered electricity generation is the characteristics of itself due to plus the present situation of China's wind-powered electricity generation industry, easily producing " abandoning wind ", therefore
Using energy-storage system of the hydroenergy storage station as wind-powered electricity generation.Using the optimization method involved by the invention, wind-force can be both reduced
" abandoning wind " phenomenon to generate electricity, the whole mains side that can be given again including wind-powered electricity generation, hydroenergy storage station and conventional fired power generating unit
Great economic benefit is brought, there is certain researching value.
【The content of the invention】
It is an object of the invention to：The defects of for prior art and deficiency, there is provided a kind of wind based on hybrid algorithm
The optimization method of fiery cooperation is stored, based on economic load dispatching and Unit Combination, power network overall efficiency is established and is up to mesh
Target Optimized model, is solved using integrated intelligent algorithm, improves the accuracy and reliability of economic load dispatching.
To achieve the above object, the technical solution adopted by the present invention is：
A kind of wind based on hybrid algorithm of the present invention stores the optimization method of fiery cooperation, is held with wind power plant itself
Amount limitation, hydroenergy storage station draw water, generated output bound and capacity reservoir limitation and fired power generating unit output bound,
Climbing power, minimum start-stop time and system reserve and power-balance are constraints, are established with association system economic benefit
It is up to the mathematical modeling of object function, the object function of the mathematical modeling is：
Wherein,
And object function is divided into two parts,
In formula, M represents the economic benefit that power network is obtained；N is 24,24 periods in representing one day；N represents fired power generating unit
Number；CwtFor t-th of period wind-powered electricity generation rate for incorporation into the power network, ChtFor t-th of period hydroenergy storage station rate for incorporation into the power network；CptFor t-th
The electricity price of drawing water of period；PwtThen represent that windburn is stored under electric power thus supplied, t-th of period Wind turbines direct grid-connected power；Ui,tRepresent
The operation conditions of fired power generating unit t-th of period of i-th unit.
In the present invention：The expression formula of the wind power plant constraints is：
In formula, Pwt represents wind power plant t direct grid-connected power；Represent the wind-powered electricity generation direct grid-connected upper limit；Pvt is represented
Original wind power；Ppt represents that hydroenergy storage station t is drawn water power；PDLtRepresent wind power plant t abandons wind
Power.
In the present invention：The expression formula of the hydroenergy storage station constraints is：
Reservoir capacity constrains：
In formula,PpWithRepresent that drawing water for water pump and draws water power most at power minimum in hydroenergy storage station respectively
Big value；PhWithThe lower and upper limit of output of hydraulic turbine in hydroenergy storage station are represented respectively；η p and η h represent the storage that draws water respectively
Draw water efficiency and the generating efficiency of Reversible Machinery Group in energy power station；Et represents the reservoir energy storage of t periods；EWithRepresent respectively
The minimum value and maximum of reservoir stored energy;Δ t takes 1h.
In the present invention：The expression formula of thermal power station's constraints is included with lower part, wherein：
（1）, system spinning reserve constraint expression formula：；
（2）, unit output bound constraint expression formula：；
（3）, unit climbing rate constraint expression formula：；
（4）, unit minimum run time constraint expression formula：；
（5）, unit minimum idle time constraint expression formula：；
In formula, RtThe spinning reserve capacity of t period systems is represented, takes the 7% of Lt；PGi WithI-th fire is represented respectively
The lower and upper limit that group of motors is contributed；Pi,downAnd Pi,upRepresent i-th fired power generating unit descending about beam power respectively and go up a slope to constrain
Power；Ti,onAnd Ti,offThe minimum run time of i-th fired power generating unit of expression and minimum idle time respectively.
In the present invention：The expression formula of the power-balance：
In formula, PwtRepresent t Wind turbines direct grid-connected power；Ui,tWhen representing i-th unit t of fired power generating unit
The operation conditions at quarter；PGi,tRepresent the output of i-th unit t of fired power generating unit；Pht、PptRepresent that t is drawn water respectively
The generated output of storage station and the power that draws water.
In the present invention:In the object function of the mathematical modelingFor the adaptation of genetic algorithm
Function is spent, the wherein population scale of genetic algorithm is 200, and termination algebraically was 100 generations, and the offspring individual of the genetic algorithm is adopted
With single-point interleaved mode, crossover probability 0.65, and the mutation probability of genetic algorithm are 0.01.
In the present invention：In the object function of the mathematical modelingFor
The population scale of the fitness function of particle cluster algorithm, wherein particle cluster algorithm is 100, and termination algebraically was 100 generations.
After the above method, the present invention has the beneficial effect that：The present invention is established based on economic load dispatching and Unit Combination
Power network overall efficiency is up to the Optimized model of target, is solved using integrated intelligent algorithm, improves the accurate of economic load dispatching
Property and reliability." consumption " ability of system to wind-powered electricity generation is not only increased, and improves the economic benefit of electricity power enterprise.
【Brief description of the drawings】
Accompanying drawing described herein be for providing a further understanding of the present invention, forming the part of the application, but
Inappropriate limitation of the present invention is not formed, in the accompanying drawings：
Fig. 1 is the principle schematic of the present invention；
Fig. 2 is the schematic flow sheet of the present invention.
【Embodiment】
The present invention is described in detail below in conjunction with accompanying drawing and specific embodiment, illustrative examples therein and is said
It is bright to be only used for explaining the present invention but not as a limitation of the invention.
As illustrated, a kind of wind based on hybrid algorithm stores the optimization method of fiery cooperation, with wind power plant itself capacity
Limitation, hydroenergy storage station draw water, generated output bound and capacity reservoir limitation and fired power generating unit output bound, climb
Slope power, minimum start-stop time and system reserve and power-balance are constraints, are established with association system economic benefit most
The greatly mathematical modeling of object function, the object function of the mathematical modeling are：
Wherein,
And object function is divided into two parts,
In formula, M represents the economic benefit that power network is obtained；N is 24,24 periods in representing one day；N represents fired power generating unit
Number；CwtFor t-th of period wind-powered electricity generation rate for incorporation into the power network, ChtFor t-th of period hydroenergy storage station rate for incorporation into the power network；CptFor t-th
The electricity price of drawing water of period；PwtThen represent that windburn is stored under electric power thus supplied, t-th of period Wind turbines direct grid-connected power；Ui,tRepresent
The operation conditions of fired power generating unit t-th of period of i-th unit.The expression formula of the wind power plant constraints is：
In formula, Pwt represents wind power plant t direct grid-connected power；Represent the wind-powered electricity generation direct grid-connected upper limit；Pvt is represented
Original wind power；Ppt represents that hydroenergy storage station t is drawn water power；PDLtRepresent wind power plant t abandons wind
Power.The expression formula of the hydroenergy storage station constraints is：
Reservoir capacity constrains：
In formula,PpWithRepresent that drawing water for water pump and draws water power most at power minimum in hydroenergy storage station respectively
Big value；PhWithThe lower and upper limit of output of hydraulic turbine in hydroenergy storage station are represented respectively；η p and η h represent the storage that draws water respectively
Draw water efficiency and the generating efficiency of Reversible Machinery Group in energy power station；Et represents the reservoir energy storage of t periods；EWithRepresent respectively
The minimum value and maximum of reservoir stored energy;Δ t takes 1h.The expression formula of thermal power station's constraints is included with lower part,
Wherein：
（1）, system spinning reserve constraint expression formula：；
（2）, unit output bound constraint expression formula：；
（3）, unit climbing rate constraint expression formula：；
（4）, unit minimum run time constraint expression formula：；
（5）, unit minimum idle time constraint expression formula：；
In formula, RtThe spinning reserve capacity of t period systems is represented, takes the 7% of Lt；PGi WithI-th fire is represented respectively
The lower and upper limit that group of motors is contributed；Pi,downAnd Pi,upRepresent i-th fired power generating unit descending about beam power respectively and go up a slope to constrain
Power；Ti,onAnd Ti,offThe minimum run time of i-th fired power generating unit of expression and minimum idle time respectively.The power-balance
Expression formula：
In formula, PwtRepresent t Wind turbines direct grid-connected power；Ui,tWhen representing i-th unit t of fired power generating unit
The operation conditions at quarter；PGi,tRepresent the output of i-th unit t of fired power generating unit；Pht、PptRepresent that t is drawn water respectively
The generated output of storage station and the power that draws water.In the object function of the mathematical modelingFor heredity
The population scale of the fitness function of algorithm, wherein genetic algorithm is 200, and termination algebraically was 100 generations, the genetic algorithm
It is 0.01 that offspring individual, which uses single-point interleaved mode, crossover probability 0.65, and the mutation probability of genetic algorithm,.The mathematics
In the object function of modelFor the fitness function of particle cluster algorithm, wherein
The population scale of particle cluster algorithm is 100, and termination algebraically was 100 generations.
As shown in figure 1, the invention belongs to the method for three layers of optimization, distributing rationally for resource is divided into three bulks, respectively to wind
Electric field, hydroenergy storage station and fired power generating unit carry out sharing of load.The present invention is set up to wind-powered electricity generation, water-storage and thermal motor
After founding corresponding mathematical modeling, based on this object function of the economic benefit of entirety.First to wind-powered electricity generation and hydroenergy storage station, i.e.,
To wind-powered electricity generation direct grid-connected power, hydroenergy storage station draw water power and generated output optimizes, by wind farm grid-connected plan
Slightly understand, it is all grid-connected when original wind power is less than a threshold value；When original wind power is more than the limit value, wind-powered electricity generation
Grid-connected power is the limit value, and redundance draws water for hydroenergy storage station；Secondly, according to priority method's (i.e. generating efficiency
High fired power generating unit preferentially gives system power supply) and traversal search method (to meet minimum start-off time constraints) backward, to each thermoelectricity
Each moment switching variable of unit optimizes；Finally, by account load balancing constraints, Climing constant, bound constraint and spinning reserve
Constraint under the unit output at fired power generating unit each moment is optimized, three layers of optimization are mutually nested, pass through intelligent algorithm so that
Whole economic efficiency is optimal.
As shown in Fig. 2
Step 1: input initial data, wherein, including the original power of load value and wind-powered electricity generation；
Step 2: the parameter that input is related to wind power plant, hydroenergy storage station and thermal power station；
Step 3: wind power plant direct grid-connected power is optimized using particle cluster algorithm, hydroenergy storage station is drawn water power and hair
Electrical power；Wherein, wind-powered electricity generation direct grid-connected power and the power that draws water are learnt by wind-electricity integration strategy, generated output is in prescribed limit
Random generation, in the case where reservoir constrains so that f1 is maximum；
Step 4: T=1 represents that iterations is 1；Optimized using genetic algorithm, produce initial population, population scale
For 200；
Step 5: according to balancing the load equation, the total output of fired power generating unit can obtain；
Step 6: according to priority method and minimum start-off time constraints, switching variable is primarily determined that；
Step 7: the switching variable according to determined by step 6, again using particle cluster algorithm, optimizes and is meeting climbing about
Fired power generating unit under beam, spinning reserve constraint is contributed；
Step 8: overall economic benefit is primarily determined that by step 3 and step 7；
Step 9: so that initial population is by selection, intersection, mutation operation；
Step 10: T=T+1 represents that evolution number adds one；
Step 11: judge whether evolutionary generation is more than 100, if so, then exporting corresponding unit output；Otherwise, go to
Step 5.
Described above is only the better embodiment of the present invention, therefore all constructions according to described in present patent application scope,
The equivalent change or modification that feature and principle are done, is included in the range of present patent application.
Claims (7)
1. a kind of wind based on integrated intelligent algorithm stores the optimization method of fiery cooperation, it is characterised in that：Establish to combine and be
System economic benefit is up to the mathematical modeling of object function, is drawn water, generated electricity with wind power plant itself capacity limit, hydroenergy storage station
Power bound and the output bound of capacity reservoir limitation and fired power generating unit, climbing and are at power, minimum start-stop time
Standby and power-balance of uniting is constraints, is solved by integrated intelligent algorithm, the mathematical modeling object function is：
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<msub>
<mi>U</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>U</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>T</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
</mrow>
In formula, M represents the economic benefit that whole electric network source side is obtained；F1 represents the economy that wind-storage association system is obtained
Benefit；F2 represents the economic benefit that fired power generating unit is obtained；N is 24,24 periods in representing one day；N represents fired power generating unit
Number；CwtFor t-th of period wind-powered electricity generation rate for incorporation into the power network, ChT is t-th of period hydroenergy storage station rate for incorporation into the power network；CptFor t-th when
The electricity price of drawing water of section；CtFor t-th of period fired power generating unit rate for incorporation into the power network；II, tUsed for the payment for initiation of i-th unit t；PwtThen
Represent under fire-wind-storage electric power thus supplied, t-th of period Wind turbines direct grid-connected power；UI, tRepresent i-th unit of fired power generating unit
The operation conditions of t-th of period；Ui,t-1Then represent the operation shape of previous moment, i.e. fired power generating unit the t-1 period of i-th unit
Condition；When value is 1, represent that the unit is under operation conditions, and when value is 0, represent that the unit is under stoppage in transit state；
PGi, tRepresent in the case of fire-wind-storage association system power supply, the output of i-th unit t period of fired power generating unit；Pht、PptPoint
The generated output of t-th of period hydroenergy storage station and the power that draws water are not represented；CE is the unit price of coal；f(PGi, t) represent thermal motor
The coal consumption amount characterisitic function of i-th unit t period of group；a0、a1、a2F (P are represented respectivelyGi, t) consumption characterisitic function quadratic term
Coefficient, Monomial coefficient and constant term.
2. the wind according to claim 1 based on integrated intelligent algorithm stores the optimization method of fiery cooperation, its feature exists
In：The expression formula of the constraints of the wind power plant：
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>w</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
<mover>
<msub>
<mi>P</mi>
<mi>w</mi>
</msub>
<mo>&OverBar;</mo>
</mover>
</mrow>
Pvt=Pwt+Ppt+PDLt
PDLt≥0
In formula, PwtRepresent wind power plant t direct grid-connected power；Represent the wind-powered electricity generation direct grid-connected upper limit；PvtRepresent original wind
Electric field power；PptRepresent that hydroenergy storage station t is drawn water power；PDLtRepresent wind power plant t abandons wind power.
3. the wind according to claim 1 based on integrated intelligent algorithm stores the optimization method of fiery cooperation, its feature exists
In：The expression formula of the constraints of the hydroenergy storage station：
<mrow>
<munder>
<msub>
<mi>P</mi>
<mi>p</mi>
</msub>
<mo>&OverBar;</mo>
</munder>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>p</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
<mover>
<msub>
<mi>P</mi>
<mi>p</mi>
</msub>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mrow>
<munder>
<msub>
<mi>P</mi>
<mi>h</mi>
</msub>
<mo>&OverBar;</mo>
</munder>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>h</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
<mover>
<msub>
<mi>P</mi>
<mi>h</mi>
</msub>
<mo>&OverBar;</mo>
</mover>
</mrow>
1
Ppt×Pht=0
Reservoir capacity constrains：
<mrow>
<munder>
<mi>E</mi>
<mo>&OverBar;</mo>
</munder>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mi>t</mi>
</msub>
<mo>&le;</mo>
<mover>
<mi>E</mi>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>E</mi>
<mi>t</mi>
</msub>
<mo>+</mo>
<mi>&Delta;</mi>
<mi>t</mi>
<mo>&times;</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>&eta;</mi>
<mi>p</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>p</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<mfrac>
<msub>
<mi>P</mi>
<mrow>
<mi>h</mi>
<mi>t</mi>
</mrow>
</msub>
<msub>
<mi>&eta;</mi>
<mi>h</mi>
</msub>
</mfrac>
<mo>)</mo>
</mrow>
</mrow>
In formula,Pp WithDraw water power minimum and the maximum for the power that draws water of water pump in hydroenergy storage station are represented respectively；
Ph WithThe lower and upper limit of output of hydraulic turbine in hydroenergy storage station are represented respectively；η p and η h represent water-storage respectively
Draw water efficiency and the generating efficiency of Reversible Machinery Group in power station；Et represents the reservoir energy storage of t periods；EWithWater is represented respectively
The minimum value and maximum of place energy storage capacity；Δ t takes 1h.
4. the wind according to claim 1 based on integrated intelligent algorithm stores the optimization method of fiery cooperation, its feature exists
In：The expression formula of the constraints of the thermal power station：
System spinning reserve constrains：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mrow>
<mo>(</mo>
<mover>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>U</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>R</mi>
<mi>t</mi>
</msub>
</mrow>
Unit output bound constraint：
<mrow>
<msub>
<mi>U</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<munder>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&OverBar;</mo>
</munder>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>U</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mover>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&OverBar;</mo>
</mover>
</mrow>
Unit climbing rate constrains：
PGi,t-1-PGi,down≤PGi,t≤PGi,t-1+PGi,up
Unit minimum run time constrains：
(Ui,t-1-Ui,t)(Ti,t-1-Ti,on)≥0
Unit minimum idle time constrains：
(Ui,t-Ui,t-1)(Ri,t-1-Ti,off)≥0
In formula, Rt represents the spinning reserve capacity of t period systems, takes PLt7%, wherein PLtRepresent the load of t user；PGi WithThe lower and upper limit that i-th fired power generating unit is contributed are represented respectively；PGi, downAnd PGi, upI-th thermal motor is represented respectively
Group descending about beam power and the about beam power that goes up a slope；UI, tRepresent the operation conditions of fired power generating unit t-th of period of i-th unit；UI, t-1
Then represent the operation conditions of previous moment, i.e. fired power generating unit the t-1 period of i-th unit；When value is 1, the unit is represented
Under operation conditions, and when value is 0, represent that the unit is under stoppage in transit state；TI, t-1Represent conventional fired power generating unit i units
The time continuously run within the preceding t-1 periods, and Ri,t-1Then represent that conventional fired power generating unit i units continuously close within the preceding t-1 periods
The time of machine, to determine t start and stop state；TI, onAnd TI, offWhen representing that the minimum of i-th fired power generating unit is run respectively
Between and minimum idle time.
5. the wind according to claim 1 based on integrated intelligent algorithm stores the optimization method of fiery cooperation, its feature exists
In：The expression formula of the power-balance：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>U</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>w</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>h</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>p</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>L</mi>
<mi>t</mi>
</mrow>
</msub>
</mrow>
2
In formula, PwtRepresent t Wind turbines direct grid-connected power；UI, tRepresent i-th unit t of fired power generating unit
Operation conditions；PGi, tRepresent the output of i-th unit t of fired power generating unit；Pht、PptT water-storage is represented respectively
The generated output in power station and the power that draws water；PLtRepresent the load of t user.
6. a kind of wind based on hybrid algorithm according to claim 1 stores the optimization method of fiery cooperation, its feature exists
In：M=f1+f2 in the object function of the mathematical modeling is the kind of the fitness function, wherein genetic algorithm of genetic algorithm
Group's scale is 200, and termination algebraically was 100 generations, and the offspring individual of the genetic algorithm uses single-point interleaved mode, crossover probability
For 0.65, and the mutation probability of genetic algorithm is 0.01.
7. a kind of wind based on hybrid algorithm according to claim 1 stores the optimization method of fiery cooperation, its feature exists
In：In the object function of the mathematical modelingFor particle cluster algorithm
The population scale of fitness function, wherein particle cluster algorithm is 100, and termination algebraically was 100 generations.
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| CN106485352B (en) * | 2016-09-30 | 2019-06-25 | 国电南瑞科技股份有限公司 | A kind of multiclass power supply generation schedule combination decision-making method |
| CN106875068B (en) * | 2017-03-03 | 2019-12-10 | 风脉能源（武汉）股份有限公司 | optimization method and system for wind driven generator configuration and model selection |
| CN106934545B (en) * | 2017-03-13 | 2021-03-16 | 广东工业大学 | Power station group joint economic dispatching method and system |
| CN109386429B (en) * | 2017-08-04 | 2021-01-15 | 中国电力科学研究院 | Coordinated operation control method and device for wind power and photo-thermal power generation complementary system |
| CN108648096B (en) * | 2018-04-06 | 2022-03-18 | 东北电力大学 | Pumped storage power station capacity planning method based on life cycle cost theory |
| CN108667077A (en) * | 2018-04-10 | 2018-10-16 | 燕山大学 | A kind of wind storage association system Optimization Scheduling |
| CN109472423A (en) * | 2018-12-08 | 2019-03-15 | 国网辽宁省电力有限公司电力科学研究院 | A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration |
| CN110266058B (en) * | 2019-05-31 | 2023-07-14 | 国网山东省电力公司济南供电公司 | Modeling and solving method of unit combination model based on interval optimization |
| CN114243737A (en) * | 2021-12-13 | 2022-03-25 | 国网青海省电力公司经济技术研究院 | Pumped storage power station capacity benefit calculation method based on production operation simulation |
| CN116231767B (en) * | 2023-05-11 | 2023-07-14 | 南方电网调峰调频发电有限公司储能科研院 | Multi-energy complementary scheduling method and system for cascade hydropower station |
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