CN110503255A - A kind of wind fire storage Force system joint optimal operation method - Google Patents
A kind of wind fire storage Force system joint optimal operation method Download PDFInfo
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Abstract
The present invention relates to a kind of wind fire storage Force system joint optimal operation methods, on the basis of conventional thermal power unit, establish output of wind electric field model, the wind power output size of wind power plant future for 24 hours is predicted, ideal wind storage system is added, with total power production cost minimum and the minimum target of carbon emission amount, and it joined the aging cost of abandonment cost and energy storage, wind fire storage system joint optimal operation model finally is established, specifically includes the following steps: a, foundation consider that the wind fire of energy storage aging cost stores up joint optimal operation model；B, multi-objective problem is handled；C, wind fire storage system power output is coordinated.The present invention is by establishing wind fire storage system joint optimal operation model, and single goal is translated into using multi-objective means, it is solved using genetic algorithm, finally obtain whole system optimal solution, on the basis of improving wind power utilization, it realizes and reduces carbon emission amount, reduce the purpose of system total operating cost.
Description
Technical field
The present invention relates to power system optimal dispatch technical field, especially a kind of wind fire storage Force system combined optimization
Dispatching method.
Background technique
With the sustainable development of national economy, the region for the electricity consumption that generates electricity and time difference, load peak-valley difference, uncertainty
Factor etc. is all increasing, and Operation of Electric Systems scheduling decision becomes increasingly complex.Meanwhile energy crisis caused by power generation and
Problem of environmental pollution also becomes increasingly conspicuous.In order to realize the equilibrium of supply and demand of electric power, rationally utilize resource, reduction power generation energy consumption, reduction
The discharge of polluted gas especially greenhouse gases, being scheduled arrangement to the start and stop of generating set and power output in advance seems especially
Important, reasonable power scheduling will generate huge economy, environment and social benefit.
Summary of the invention
The technical problem to be solved by the present invention is the present invention provides a kind of combination in order to overcome the deficiencies in the existing technology
The specific feature of China's power supply, makes full use of clean reproducible energy to generate electricity, and improves power supply architecture, realizes that wind fire stores up power train
The dispatching method of system combined optimization.
The technical solution adopted by the present invention to solve the technical problems is: a kind of wind fire storage Force system combined optimization tune
Ideal wind storage system is added on the basis of conventional thermal power unit in degree method, with system cost of electricity-generating minimum and carbon emission
Amount is at least target, and considers abandonment cost and energy storage aging cost on this basis, so that it is excellent to establish wind fire storage system joint
Change scheduling model, specifically includes the following steps:
A, wind fire storage joint optimal operation model is established: with cost of electricity-generating minimum and carbon emission amount at least for target, herein
On the basis of consider the abandonment cost of wind fire storage system and the aging cost of energy storage simultaneously, building wind fire stores up joint optimal operation mould
Type；
B, multi-objective problem is handled: solving cost of electricity-generating minimum using the method for weighting and the least multiple target of carbon emission amount is asked
Topic, is converted to single-objective problem for multi-objective problem by adjusting the corresponding weight size of each target；
C, wind fire storage system power output is coordinated: it is a non-linear, non-convex multiple constraint that wind fire, which stores up joint optimal operation system,
Optimization problem, the Optimal Scheduling includes start and stop scheduling and the static economy scheduling sub-problem of unit, so that it is determined that unit
The start and stop and power output situation of next scheduling day, and heuristic genetic algorithms are used, obtain the optimization solution of problem.
Objective function:
1) with the minimum energy conservation object of system fired power generating unit cost of electricity-generating, function is as follows:
Ci=ai+biPi(t)+ciPi 2(t) (2)
Wherein, fgenFor fired power generating unit cost of electricity-generating；T is the period sum in dispatching cycle；N is fired power generating unit number；ui
It (t) is operating status of the fired power generating unit i in period t, ui(t)=1 unit booting, u are indicatedi(t)=0 compressor emergency shutdown is indicated；Ci
For fired power generating unit i period t fuel cost；PiIt (t) is active power output of the fired power generating unit i in period t；SiIt (t) is thermal motor
Booting expense of the group i in period t；ai、bi、ciFor fired power generating unit i fuel cost coefficient；cci、hciRespectively fired power generating unit i's
Hot and cold booting expense；It is limited for the minimum downtime of fired power generating unit i；Ti coldFor the cold start-up time of fired power generating unit i；
Ti off(t) time persistently shut down for fired power generating unit i to period t.
2) with the minimum emission reduction targets of system fired power generating unit carbon emission amount, function is as follows:
Wherein, EcFor the CO of fired power generating unit i2Discharge amount；αci、βci、γciFor the CO of fired power generating unit i2Emission factor.
The objective function of wind fire storage system energy-saving and emission-reduction totle drilling cost:
Wc+We=1 (6)
Wherein, TC is the totle drilling cost target of electric system energy-saving and emission-reduction, WcAnd WeRespectively energy conservation object and emission reduction targets
Corresponding weight；φiPenalty coefficient, P are discharged for the polluted gas of fired power generating unit ii maxIt (t) is fired power generating unit i in period t
Maximum output；
Abandonment cost and energy storage aging cost are added on this basis, it is hereby achieved that final wind fire storage system is excellent
Change scheduling model:
Wherein, F is system total operating cost；WcAnd WeThe respectively corresponding weight of energy conservation object and emission reduction targets；φiFor
The polluted gas of fired power generating unit i discharges penalty coefficient；ρpFor the corresponding unit price of wind power plant abandonment energy；For for 24 hours
Abandonment cost；For averagely daily battery aging cost；Y is energy-storage system service life；SLOWE1(t),SLOWE2(t) divide
It Wei not be for describing the Boolean quantity of wind power plant abandonment energy situation；T is total scheduling slot, herein for for 24 hours；Pbat(t) it is
The charge-discharge electric power of battery；Δ P (t) is that wind-powered electricity generation predicts error；PcmaxFor the maximum charge power of battery；CbatmaxTo store
The maximum capacity of battery；CbatIt (t-1) is actual capacity of the battery at the t-1 moment；meFor battery cell's capacity price；
DoDeFor the depth of discharge of battery；Le(DoDe) it is DoDeThe cycle life of lower battery；CbatmaxFor the maximum capacity of battery.
Corresponding constraint condition are as follows:
1. power-balance constraint
In formula, N is fired power generating unit number；PrefIt (t) is wind-powered electricity generation field prediction output power；PessIt (t) is energy storage period t's
Charge-discharge electric power；PLIt (t) is the burden with power of period t system.
2. conventional thermal power unit units limits
Pi min≤Pi(t)≤Pi max, i=1,2 ..., N (10)
In formula, Pi max、Pi minThe respectively active power output bound of fired power generating unit i.
3. conventional thermal power unit ramping rate constraints
Wherein,The respectively up and down creep speed of fired power generating unit i, Δ t are run the period.
4. system spinning reserve constrains
Wherein, PRIt (t) is the spare capacity of period t system.
5. fired power generating unit start-off time constraints
Wherein, Ti off(t)、Ti on(t) it is respectively fired power generating unit i has persistently been shut down in period t time and be persistently switched on
Time；The continuous downtime limitation of the minimum of respectively fired power generating unit i and minimum continuous available machine time limitation.
6. energy storage battery charge-discharge electric power constrains
-Pessdmax≤Pess(t)≤Pesscmax (15)
Wherein, Pesscmax、PessdmaxThe respectively maximum charge and discharge power of energy storage system storage battery.
7. accumulator capacity constrains
Chmin≤Ch(t)≤Chmax (16)
Wherein, ChIt (t) is the capacity of t period energy storage system storage battery；Chmin、ChmaxRespectively energy storage system storage battery
Minimum and maximum capacity.
The beneficial effects of the present invention are: the present invention on the basis of conventional thermal power unit, joined ideal wind storage system
System, with total power production cost minimum and carbon emission amount at least for objective function, establishes wind fire storage system joint optimal operation model.
Wind fire storage combined dispatching system and traditional thermoelectricity are dispatched system and are compared, wind electricity digestion rate, in terms of it is advantageous,
On the basis of improving wind power utilization, carbon emission amount can be reduced, reduces system total operating cost, to realize social benefit most
Bigization.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is wind fire storage joint optimal operation schematic diagram of the present invention.
Fig. 2 is wind fire storage system basic structure schematic diagram of the present invention.
Fig. 3 is practical wind power output of the present invention and prediction wind power output contrast curve chart.
Fig. 4 is original loads of the present invention and Optimized Operation afterload contrast curve chart.
Fig. 5 is energy-storage system relevant parameter table in embodiment of the present invention.
Fig. 6 is fired power generating unit relevant parameter table in embodiment of the present invention.
Fig. 7 is fired power generating unit power output table for 24 hours under pure thermoelectricity Optimized Operation in embodiment of the present invention.
Fig. 8 is fired power generating unit power output table for 24 hours under embodiment apoplexy fire of the present invention storage joint optimal operation.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with
Illustration illustrates basic structure of the invention, therefore it only shows the composition relevant to the invention.
As shown in Figure 1 to 4, a kind of wind fire storage Force system joint optimal operation method, using the method for weighting and heuristic
Genetic algorithm realizes the joint optimal operation to the wind fire storage system for considering abandonment cost and energy storage aging cost.
In order to guarantee wind fire storage system joint optimal operation to the maximum extent reduce system cost of electricity-generating in the case where,
It is reduced as far as CO2Discharge amount, that is, coordinate energy conservation and the two targets of emission reduction, solve this complicated multiple target mixing
Integer programming problem obtains optimal solution, and the present invention uses the method for weighting, respectively assigns a weight, then cumulative conduct to two targets
Fresh target is solved under constraint identical with former problem.Weight distribution between two targets reflects system to difference
Target stresses degree, by constantly adjusting the corresponding weight size of each target, available different optimization solution, then passes through
Heuristic genetic algorithms can obtain the last solution for enabling policymaker satisfied.
The smallest objective function of energy saving of system emission reduction totle drilling cost:
Wc+We=1 (2)
In formula, TC is the general objective of electric system energy-saving and emission-reduction；WcAnd WeRespectively energy conservation object and emission reduction targets are corresponding
Weight；φiPenalty coefficient is discharged for the polluted gas of fired power generating unit i；Pi maxIt (t) is maximum of the fired power generating unit i in period t
Power output.
It is hereby achieved that the final the smallest model of wind fire storage system Optimized Operation total operating cost:
In formula, F is system total operating cost.
The present invention mainly passes through genetic algorithm and verifies to model.The fitness function of genetic algorithm are as follows: Fitness
=A/F
Being mainly characterized by for genetic algorithm be simple, general, strong robustness, is suitable for Serial Distribution Processing, application range
Extensively.Fired power generating unit is divided by waist lotus machine according to the switching cost of the specific consumption of unit and unit, minimum switching on and shutting down time first
Group, peak load unit, the group that must be switched on must shut down group.Heuristic thought is incorporated in genetic algorithm, the group that must be switched on has relatively large
Power output, be always maintained at open state within entire dispatching cycle.With priority method according to each unit minimum specific consumption by small
To big sequence, initial Unit Commitment sequence (meeting unit itself constraint) corresponding to day part is determined.As initial kind
2/3 part of group is evolved, and the 1/3 of initial population is random to be generated, and uses best individual preservation strategy, most by fitness
Strongest two individuals of two individual fitness of difference replace, and can guarantee that the evolution result in genetic algorithm per generation is better than in this way
The resulting combined result of priority method, to guarantee the validity and convergence of algorithm.
Intersection and mutation operator in genetic algorithm are the key that influence genetic algorithm behavior and performance, directly affect something lost
The convergence of propagation algorithm.When problem scale is larger or more complex, since search space is very big, so as to cause genetic algorithm
Convergence rate it is very slow or easy " precocity ".This disadvantage can effectively be overcome using adaptive intersection and mutation operator.Its
Intersect, mutation operation is carried out according to following formula respectively:
K is the number of iterations, P in formulac, PmRespectively intersect, mutation probability, Pcmax,PcminFor maximum, minimum crossing-over rate,
1.0,0.5 are taken respectively；For the value of kth time iteration；Respectively the initial value of mutation probability and kth for iteration value,
WhereinIt is taken as 0.001；ng,maxFor maximum allowable the number of iterations.In the early period of iteration, crossover probability is larger, mutation probability
It is smaller, to improve reproductive efficiency；In the iteration later period, the code chain in population has tended towards stability, and cross action is reduced at this time,
Crossover probability is reduced, and converges on locally optimal solution in order to prevent, increases mutation probability.By cross and variation, there may be not
Meet the individual of unit itself constraint, therefore correcting strategy is set simultaneously herein, repair operator is sentenced after intersecting with variation
Whether disconnected individual meets minimum startup-shutdown constraint and Climing constant, is unsatisfactory for, and first searching for forward and adjusting backward again keeps it full
Foot.
Embodiment
The population invariable number of GA is 100, and the number of iterations was 200 generations, and target function value is restrained when in 170 generation.Wind power plant is real
Border power output is shown in attached drawing 3 with prediction power output.Energy-storage system relevant parameter is shown in Fig. 5.Energy storage battery capacity is 224.4MWh.Thermoelectricity
Unit relevant parameter is shown in Fig. 6.Fired power generating unit is contributed for 24 hours under pure thermoelectricity Optimized Operation and wind fire stores up fired power generating unit under Optimized Operation
It contributes for 24 hours and sees Fig. 7 and Fig. 8 respectively.Take Wc=0.8, We=0.2, for 10 fired power generating units herein, analyzed fired power generating unit
1,2 it is set as the group that must be switched on, open state is always maintained in entire dispatching cycle, fired power generating unit 3~7 belongs to waist lotus unit, fire
Motor group 8~10 belongs to peak load unit.It is computed to obtain polluted gas discharge penalty coefficient φiAbout 3.5.
Total energy-saving and emission-reduction cost of conventional 10 unit fired power generating units is 564284 dollars, and wherein thermal power plant's cost of electricity-generating is about
It is 458811 dollars, about 105473 dollars of polluted gas discharge punishment, CO2Discharge amount is about 151 tons.
The system optimization for containing only wind fire dispatches to obtain system total operating cost to be 550745 dollars, and wherein thermal power plant generates electricity
Cost is about 447204 dollars, and about 101212.8 dollars of polluted gas discharge punishment, abandonment cost is about 1328.2 dollars,
Abandonment amount is 21.81MW, CO2Discharge amount is about 145 tons.
Wind fire store up association system Optimized Operation system total operating cost be 547062 dollars, wherein thermal power plant power generation at
This about 446073 dollars, about 100928.6 dollars of polluted gas discharge punishment, abandonment cost is about 49.7 dollars, aging
Cost is about 10.7 dollars, and abandonment amount is 0.71MW, CO2Discharge amount is about 144 tons.
It can be seen that the peak load shifting that the Optimized Operation of wind fire storage association system plays the role of load from attached drawing 4.With routine
Fired power generating unit is compared, and the total operating cost of wind fire storage combined dispatching system reduces 17222 dollars, i.e., cost reduces about
3%；CO2Discharge amount reduces 7 tons, that is, reduces about 5% carbon emission amount；Abandonment cost reduces 1477 dollars, and wind-powered electricity generation disappears
The rate of receiving improves 3%.
It can be seen that the validity and superiority of the wind fire storage joint optimal operation model that the present invention constructs.
Taking the above-mentioned ideal embodiment according to the present invention as inspiration, through the above description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property range is not limited to the contents of the specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.
Claims (3)
1. a kind of wind fire storage Force system joint optimal operation method, it is characterized in that: being added on the basis of conventional thermal power unit
Ideal wind storage system, with system cost of electricity-generating minimum and carbon emission amount at least for target, and consider on this basis abandonment at
Sheet and energy storage aging cost, so that wind fire storage system joint optimal operation model is established, specifically includes the following steps:
A, wind fire storage joint optimal operation model is established: basic herein with cost of electricity-generating minimum and carbon emission amount at least for target
Above while considering the abandonment cost of wind fire storage system and the aging cost of energy storage, constructs wind fire storage Force system joint optimal operation
Model；
B, multi-objective problem is handled: being solved cost of electricity-generating minimum and the least multi-objective problem of carbon emission amount using the method for weighting, is led to
It crosses and adjusts each target corresponding weight size multi-objective problem is converted into single-objective problem；
C, wind fire storage system power output is coordinated: it is a non-linear, non-convex multiconstraint optimization that wind fire, which stores up joint optimal operation system,
Problem, the Optimal Scheduling includes start and stop scheduling and the static economy scheduling sub-problem of unit, so that it is determined that one under unit
The start and stop and power output situation of a scheduling day, and heuristic genetic algorithms are used, obtain the optimization solution of problem.
2. wind fire storage Force system joint optimal operation method as described in claim 1, it is characterized in that: in step a, with system
The minimum energy conservation object of fired power generating unit cost of electricity-generating, function are as follows:
Ci=ai+biPi(t)+ciPi 2(t) (2)
Wherein, fgenFor fired power generating unit cost of electricity-generating；T is number of segment when dispatching total；N is fired power generating unit number；uiIt (t) is fired power generating unit i
In the operating status of period t, ui(t)=1 unit booting, u are indicatedi(t)=0 compressor emergency shutdown is indicated；CiFor fired power generating unit i when
The fuel cost of section t；PiIt (t) is active power output of the fired power generating unit i in period t；SiIt (t) is booting of the fired power generating unit i in period t
Expense；ai、bi、ciFor fired power generating unit i consumption characteristic coefficient；cci、hciThe hot and cold starting expense of respectively fired power generating unit i；
It is limited for the minimum downtime of fired power generating unit i；Ti coldFor the cold start-up time of fired power generating unit i；Ti offIt (t) is fired power generating unit i
In the time that period t has persistently been shut down；
With the minimum emission reduction targets of system fired power generating unit carbon emission amount, function is as follows:
Wherein, EcFor fired power generating unit CO in system2Discharge amount；αci、βci、γciFor the CO of fired power generating unit i2Emission factor.
3. wind fire storage Force system joint optimal operation method as described in claim 1, it is characterized in that: in step b, the right to use
Weight method solves multi-objective problem, i.e., a weight is respectively assigned to two targets, then cumulative as fresh target, identical as former problem
Constraint under use heuristic genetic algorithms to be solved；Weight distribution between two targets reflects system to different target
Stress degree, can obtain enabling the satisfied last solution of policymaker, wind fire by constantly adjusting the corresponding weight size of each target
The objective function of storage Force system energy-saving and emission-reduction totle drilling cost:
Wc+We=1 (6)
Wherein, TC is the totle drilling cost target of electric system energy-saving and emission-reduction, WcAnd WeRespectively energy conservation object and emission reduction targets is corresponding
Weight；φiPenalty coefficient, P are discharged for the polluted gas of fired power generating unit ii max(t) go out for maximum of the fired power generating unit i in period t
Power；
Abandonment cost and energy storage aging cost are added on this basis, it is hereby achieved that final wind fire storage system Optimized Operation
Model:
Wherein, F is system total operating cost, ρpUnit price is corresponded to for wind power plant abandonment energy,For abandonment for 24 hours at
This,For averagely daily battery aging cost, Y is energy-storage system service life, and T is total scheduling slot, in the present invention
For for 24 hours；
Wherein, SLOWE1(t),SLOWE2It (t) is respectively Boolean quantity for describing wind power plant abandonment energy situation, PbatIt (t) is electric power storage
The charge-discharge electric power in pond, Δ P (t) are that wind-powered electricity generation predicts error, PcmaxFor the maximum charge power of battery, CbatmaxFor battery
Maximum capacity, CbatIt (t-1) is actual capacity of the battery at the t-1 moment, meFor battery cell's capacity price, DoDeFor electricity
The depth of discharge in pond, Le(DoDe) it is DoDeThe cycle life of lower battery, CbatmaxFor the maximum capacity of battery.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110867857A (en) * | 2019-11-29 | 2020-03-06 | 沈阳工业大学 | Optimization method of peak shaver set combination based on wind-storage combined system |
| CN112421626A (en) * | 2020-11-16 | 2021-02-26 | 国网河南省电力公司 | Method, device and equipment for acquiring green scheduling optimization decision scheme |
| CN112531757A (en) * | 2020-12-01 | 2021-03-19 | 西安峰频能源科技有限公司 | Wind power plant advanced energy storage system based on artificial intelligence flexible control strategy |
| CN115864429A (en) * | 2022-08-31 | 2023-03-28 | 湖北工业大学 | Multi-objective optimization AGC method for wind and fire storage cooperation under double-carbon target |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767105A (en) * | 2018-12-29 | 2019-05-17 | 东北电力大学 | It is a kind of that power generation dispatching method is coordinated based on honourable extreme misery providing multiple forms of energy to complement each other for association system of storage |
-
2019
- 2019-08-13 CN CN201910743392.3A patent/CN110503255A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767105A (en) * | 2018-12-29 | 2019-05-17 | 东北电力大学 | It is a kind of that power generation dispatching method is coordinated based on honourable extreme misery providing multiple forms of energy to complement each other for association system of storage |
Non-Patent Citations (2)
| Title |
|---|
| 张晓花等: "考虑不确定性的智能电网多目标机组组合研究", 《太阳能学报》 * |
| 殷志敏等: "波动率优于国网标准的风电场储能容量经济优化配置", 《可再生能源》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110867857A (en) * | 2019-11-29 | 2020-03-06 | 沈阳工业大学 | Optimization method of peak shaver set combination based on wind-storage combined system |
| CN110867857B (en) * | 2019-11-29 | 2021-10-15 | 沈阳工业大学 | Optimization method of peak shaver set combination based on wind-storage combined system |
| CN112421626A (en) * | 2020-11-16 | 2021-02-26 | 国网河南省电力公司 | Method, device and equipment for acquiring green scheduling optimization decision scheme |
| CN112421626B (en) * | 2020-11-16 | 2023-07-14 | 国网河南省电力公司 | Method, device and equipment for acquiring green scheduling optimization decision scheme |
| CN112531757A (en) * | 2020-12-01 | 2021-03-19 | 西安峰频能源科技有限公司 | Wind power plant advanced energy storage system based on artificial intelligence flexible control strategy |
| CN115864429A (en) * | 2022-08-31 | 2023-03-28 | 湖北工业大学 | Multi-objective optimization AGC method for wind and fire storage cooperation under double-carbon target |
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