CN110492506A - A kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system multiple target capacity optimization method - Google Patents
A kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system multiple target capacity optimization method Download PDFInfo
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- 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
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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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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- 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
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- 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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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- 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
Abstract
The invention discloses a kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating systems, including wind power plant, photovoltaic plant, electric heater, heat reservoir and electricity generation module, rectified rear and the sent out direct current general DC busbar of photovoltaic plant of wind-powered electricity generation place power generation energy, then pass through inverter, it is connected to the grid after primary substation, the form that the electric energy for exceeding channel capacity in wind power plant and photovoltaic plant is converted to thermal energy is stored in heat reservoir by electric heater, when wind power plant and the output electric energy deficiency channel capacity of photovoltaic plant, heat reservoir release thermal energy pushes electricity generation module to generate power and convey to power grid.Invention additionally discloses the system multiple target capacity optimization methods.A kind of wind-powered electricity generation-photovoltaic of the invention-heat accumulation combined generating system multiple target capacity optimization method, the cost of investment of combined generating system can be efficiently reduced and improve the channel utilization index of system, while the Pareto forward position figure that is obtained of Multipurpose Optimal Method is more conducive to policymaker and determines according to economy and reliability preference the optimum capacity proportion of combined generating system.
Description
Technical field
The present invention is related to more particularly to a kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system multiple target capacity optimization method, is belonged to
Optimized utilizing energy technical field.
Background technique
Wind-power electricity generation and photovoltaic power generation are current most widely used renewable energy power generation technologies, but since wind provides
The schedulability and flexibility ratio of the unstability and randomness of source and solar energy resources, individual wind power plant or photovoltaic plant
Not upper traditional thermal power generation, and itself and network process or even the safe and stable operation that will affect power grid.However honourable resource it
Between there is naturally negatively correlated characteristic, therefore often there is certain complementarity between wind power output and photovoltaic power output, therefore
Wind-powered electricity generation-photovoltaic combined generating system has good development prospect.
Conventional wind-electricity complementary system is usually using battery as energy-storage units, but battery has stringent charge and discharge
The disadvantages of limitation, cycle life is short, expensive, and the relative low price of heat reservoir, and heat reservoir is not only easy to greatly
Size expansion and heat accumulation efficiency are up to 95%-97%, therefore using heat reservoir as the energy storage device of wind-light complementary system
Higher economic benefit may be implemented.
About the capacity optimization problem of wind-light complementary system, there are many relevant researchs both at home and abroad.Wu Hongbin, Chen Bin,
The capacity of hybrid energy-storing unit optimizes [J] Journal of Agricultural Engineering in Guo Caiyun wind and solar hybrid generating system, 2011,27 (04):
241-245 improves the power supply of wind-powered electricity generation-photovoltaic combined generating system using battery-supercapacitor hybrid energy-storing unit can
By property, and establish the capacity Optimized model of hybrid energy-storing unit.But use battery-supercapacitor hybrid energy-storing list
Member is expensive, is unfavorable for Large scale construction.Wang Le is scared, Wei Zhiyong, Song Jie, Liu Haijun wind-powered electricity generation-water-storage joint system
Optimization operation study [J] the power grid and clean energy resource of system, 2014,30 (02): 70-75 has studied wind-powered electricity generation-water-storage mixing hair
The behavioral characteristics of electric system, and optimize wind-powered electricity generation-water-storage hybrid power system capacity with genetic algorithm, but draw water
Storage station is serious to be limited by geographical conditions.Yang Yong, Guo Su, Liu Qunming, Li Rong wind-powered electricity generation-CSP combined generating system are excellent
Change operation study [J] Proceedings of the CSEE, 2018,38 (S1): 151-157 have studied wind-powered electricity generation-photo-thermal combined generating system
Scheduling strategy, and propose to reduce abandonment loss using electric heater, improve the reliability of power supply.But this article is mainly ground
Study carefully the scheduling strategy of power station combined operating, the economic evaluation before not considering each power plant construction.Generally speaking, photo-thermal power station cost
Valuableness, and heat collecting field part occupies 50% construction cost, needs a kind of mode that can reduce electricity generation system cost.
Summary of the invention
It is an object of the invention in view of the shortcomings of the prior art, proposing that one kind can efficiently reduce combined generating system
Cost of investment simultaneously improves channel utilization index, while wind-powered electricity generation-photovoltaic-heat accumulation joint hair of capacity optimization is carried out to combined generating system
Electric system multiple target capacity optimization method.
In order to solve the above technical problems, the technical solution adopted by the present invention are as follows:
A kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system multiple target capacity optimization method, for wind-powered electricity generation-photovoltaic-heat accumulation connection
Electricity generation system is closed, the combined generating system includes wind power plant, photovoltaic plant, electric heater, heat reservoir and electricity generation module, institute
Rectified rear and the sent out direct current general DC busbar of the photovoltaic plant of wind-powered electricity generation place power generation energy is stated, inverter is then passed through,
It is connected to the grid after primary substation, the electric heater will exceed channel capacity in the wind power plant and the photovoltaic plant
The form that electric energy is converted to thermal energy is stored in the heat reservoir, when the output electricity of the wind power plant and the photovoltaic plant
When channel capacity that energy is insufficient, the heat reservoir release thermal energy pushes the electricity generation module to generate power and convey to the power grid, including
Following steps: step a establishes combined generating system with levelized cost minimum and channel utilization index maximum and turns to optimization aim；
Step b constructs the fitness function of more optimization aims；
Step c writes the program generation for solving fitness function in application software based on multi-objective particle
Code；
Step d is run multiple times the program code for solving fitness function, obtains the Pareto forward position of multi-objective optimization question
Figure, and capacity ratio optimal solution is determined according to Pareto forward position figure and corresponding decision-making technique.
In step a, the levelized cost calculation formula is as follows:
IGsystem=ICw+ICpv+ICTES+ICPB+ICEH (2)
ACsystem=ACw+ACpv+ACTES+ACPB+ACEH (3)
Wherein, LCOE indicates levelized cost, ICw, ICtes, ICPB, ICpvAnd ICEHRespectively indicate wind power plant, heat accumulation system
System, electricity generation module, the initial outlay cost of photovoltaic plant and electric heater, ACw, ACtes, ACEH, ACpvAnd ACPBRespectively indicate wind
The annual cost of investment of electric field, heat reservoir, electric heater, photovoltaic plant and electricity generation module, Ew, EPBAnd EpvRespectively indicate wind
Electric field, the first annual electricity generating capacity of electricity generation module and photovoltaic plant, dw, dcspAnd dpvRespectively indicate wind power plant, electricity generation module and photovoltaic electric
It stands annual attenuation rate, i indicates discount rate, and N indicates life expectancy.
In step a, channel utilization index refers to the ratio of the total electricity volume of combined generating system and annual channel capacity, meter
It is as follows to calculate formula:
Wherein, RchIndicate channel utilization index, Ew.i, Epv.iAnd EPB.iRespectively indicate i hours moment wind power plants, photovoltaic plant
With the electricity volume of electricity generation module, EchIndicate channel capacity.
In step c, application software uses MATLAB software, writes objective function according to the scheduling strategy of combined generating system
F1And F2, algorithm MULTIPSO is write according to the process of multi-objective particle and principle, is then run
[Xm, Fv]=MULTIPSO (@F1,@F2, S, c1, c2, w, D, M)
Wherein, XmIndicate optimal capacity ratio, FvIndicate adaptive optimal control angle value, F1, F2For objective function, S indicates initial kind
Group's number, c1, c2For Studying factors, D is solution vector dimension, and w is inertia weight, and M is iterative steps.
In step b, the fitness function of multi-objective optimization question are as follows: minimize:
Wherein, F1And F2Indicate two objective functions, Cw, CpvAnd CtesRespectively indicate wind power plant, photovoltaic plant and storage
The capacity of hot systems.
In step c, the specific implementation process of multi-objective particle is as follows:
S01 initializes population: position and the speed of each particle is randomly generated in given population scale；
S02 utilizes objective function F1And F2Calculate separately the fitness value of each particle；
S03, in two objective function F1And F2It is lower find out each particle respectively individual extreme value ((pbest [i, 1],
Pbest [i, 2])；
S04, to two objective function F1And F2Global extremum (gbest [1], gbest [2]) is found out respectively；
S05 calculates the mean value gbest and Euclidean distance dgbest of two global extremums；
S06 calculates the Euclidean distance dpbest [i] between each particle individual extreme value.
S07 calculates the pbest [i] when each particle updates position and speed:
If dpbest [i] < dgbest
Pbest [i]=rand select (pbest [i, 1], pbest [i, 2]) # random selection
Else
Pbest [i]=average (pbest [i, 1], pbest [i, 2]) # selects mean value；
S08 updates the position and speed of each particle using S05 and the S07 gbest being calculated and pbest [i], more
New formula is as follows:
V [i+1]=ω v [i]+c1·rand·(pbest[i]-x[i])+c2·rand·(gbest-x[i]) (6)
X [i+1]=x [i]+v [i] (7)
Wherein, ω indicates inertia weight, c1And c2Indicate that Studying factors, rand indicate the random number on [0,1]；
S09, iteration terminate after reaching default the number of iterations to termination condition is met.
In step d, each point is accordingly to be regarded as two-dimentional variable [LCOE (i), R in the figure of Pareto forward positionch(i)], then in institute
In some Pareto forward position points, nearest point is chosen for optimal capacity ratio with the sum of the distance of other points, calculation formula
It is as follows:
Wherein, [LCOE (i), RchAnd [LCOE (j), R (i)]chIt (j)] is any two Pareto forward position point, n refers to pa
The number of all the points, Final indicate optimal capacity ratio in tired support forward position figure.
Beneficial effects of the present invention: wind-powered electricity generation-photovoltaic provided by the invention-heat accumulation combined generating system multiple target capacity optimization
Method, combined generating system can effectively adjust wind-powered electricity generation photovoltaic power output, improve channel utilization index and economic sex expression；The present invention
Combined generating system can effectively reduce the abandonment loss of wind power plant and the abandoning light loss of photovoltaic plant；More mesh of the invention
Mark optimization method can be effectively seen the trade-off relationship between the reliability and economy of system, and aid decision making person is preferably
Determine the optimal capacity ratio of combined generating system；Multipurpose Optimal Method of the invention weights multiple target relative to traditional
The method that summation is converted to single object optimization is more reasonable, and optimal capacity ratio can be determined according to decision-making technique.
Detailed description of the invention:
Fig. 1 is wind-powered electricity generation-photovoltaic of the invention-heat accumulation combined generating system structural block diagram；
Fig. 2 is wind-powered electricity generation-photovoltaic-heat accumulation combined generating system scheduling strategy of the invention；
Fig. 3 is the wind power output curve of unit MW；
Fig. 4 is the photovoltaic power curve of unit MW；
Fig. 5 is the calculated Pareto forward position figure of multi-objective particle；
Fig. 6 is four seasons typical case's day breeze electrical-optical volt-heat accumulation combined generating system and no energy-storage system spring in the embodiment of the present invention
Ji represents the power output comparison of day；
Fig. 7 is four seasons typical case's day breeze electrical-optical volt-heat accumulation combined generating system and no energy-storage system summer in the embodiment of the present invention
Ji represents the power output comparison of day
Fig. 8 is four seasons typical case's day breeze electrical-optical volt-heat accumulation combined generating system and no energy-storage system autumn in the embodiment of the present invention
Ji represents the power output comparison of day
Fig. 9 is four seasons typical case's day breeze electrical-optical volt-heat accumulation combined generating system and no energy-storage system winter in the embodiment of the present invention
Ji represents the power output comparison of day.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings, and following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, the present invention provides a kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system, the combined generating system is by wind
Electric field, photovoltaic plant, electric heater, heat reservoir and electricity generation module are formed.Wind power plant and photovoltaic plant are cogenerations
The main generator unit of system, wind-powered electricity generation place power generation can generate electricity after over commutation with photovoltaic plant energy general DC busbar, so
Alternating current is converted to by inverter afterwards, is connected to the grid after boosting finally by primary substation.
Heat reservoir includes cold tank, hot tank, heat-conducting work medium (fuse salt), heat accumulation working medium (fuse salt) and conveyance conduit institute
Composition, the main function of heat reservoir are the power output periods for adjusting combined generating system, and gentle system goes out fluctuation.
Fuse salt is mainly by 60%NaNO3And 40%KNO3It is formed；The cold tank of heat reservoir is used to store 288 DEG C cold
Salt, hot tank are used to store 565 DEG C of hot salt；The fuse salt stored in cold tank is heated to 565 into electric heater through pipeline
It is transported to after DEG C in hot tank；It is transported in cold tank after device of working medium heat exchange in the fuse salt and electricity generation module stored in hot tank.
Heat reservoir, can be by wind power plant and the electric energy beyond channel capacity of photovoltaic plant as a storage element
It is stored in the form of thermal energy in heat accumulation working medium, when wind power plant and the output electric energy deficiency channel capacity of photovoltaic plant, storage
The thermal energy that hot systems can discharge in heat accumulation working medium generates electricity into electricity generation module, therefore heat reservoir can play when adjusting power output
Section, gentle fluctuation out, to improve the effect of channel utilization index.
Electricity generation module is by preheater, evaporator, superheater, reheater, high pressure cylinder HP, low pressure (LP) cylinder LP, oxygen-eliminating device, condensation
Device, the composition such as heater (H1-H4) and generating set, the main function of electricity generation module are when system power output is less than burden requirement
When, the thermal energy in heat reservoir is converted to power output to power grid by electricity generation module.
Fuse salt in the hot tank of heat reservoir successively passes through superheater, evaporator, preheater, by thermal energy with convective heat transfer
Mode passes to device of working medium, is then return in the cold tank of heat reservoir.Device of working medium successively passes through preheater, evaporator, superheater
Steam with high temperature and pressure is converted into after fuse salt heat exchange, pushes the wheel rotation in high pressure cylinder HP and low pressure (LP) cylinder LP, thus
Generating set power generation is driven, the process that the thermal energy in heat reservoir is converted to electric energy is realized.
The effect of preheater is that device of working medium is preheating to certain temperature, and the effect of evaporator is to be heated to being saturated by device of working medium
Steam, the effect of superheater are that saturated vapor is heated to superheated steam, and the effect of reheater is to improve turbine low pressure cylinder
Vapor (steam) temperature, the effect of oxygen-eliminating device remove the oxygen in preheater entrance device of working medium, and the effect of condenser is low pressure (LP) cylinder
Mouthful place steam condensation Cheng Shui, high pressure cylinder HP and low pressure (LP) cylinder LP under the promotion of steam wheel rotation so that generating set be driven to send out
Electricity, the effect of heater H1-H4 are that the device of working medium of high pressure cylinder and low pressure (LP) cylinder exit is heated to certain temperature.
Electric heater be using the Joule effect of electric current by the fluctuation electric energy of wind-powered electricity generation subsystem and photovoltaic subsystem and
Extra electric energy is used to heat the cold salt in heat reservoir, is delivered in hot tank after being heated to 565 DEG C, to realize electric energy to heat
The process that can be converted.Heat source of the electric heater as heat reservoir can not only efficiently reduce abandonment and abandon light loss, Er Qieke
So that system has higher channel utilization index.
Also a kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system capacity optimization method of the present invention, comprising the following steps:
Step 1 establishes combined generating system with levelized cost minimum and channel utilization index maximum and turns to optimization aim.
The multiple target capacity optimization of combined generating system of the invention is to minimize levelized cost LOCE and maximize channel benefit
With rate RchFor target.
The calculating of levelized cost is as follows:
IGsystem=ICw+ICpv+ICTES+ICPB+ICEH (2)
ACsystem=ACw+ACpv+ACTES+ACPB+ACEH (3)
Wherein, LCOE indicates levelized cost, ICw, ICtes, ICPB, ICpvAnd ICEHRespectively indicate wind power plant, heat accumulation system
System, electricity generation module, the initial outlay cost of photovoltaic plant and electric heater, ACw, ACtes, ACEH, ACpvAnd ACPBRespectively indicate wind
The annual cost of investment of electric field, heat reservoir, electric heater, photovoltaic plant and electricity generation module, Ew, EPBAnd EpvRespectively indicate wind
Electric field, the first annual electricity generating capacity of electricity generation module and photovoltaic plant, dw, dcspAnd dpvRespectively indicate wind power plant, electricity generation module and photovoltaic electric
It stands annual attenuation rate, i indicates discount rate, and N indicates life expectancy.
Channel utilization index refers to the ratio of the total electricity volume of combined generating system and annual channel capacity, and calculation formula is such as
Under:
Wherein, RchIndicate channel utilization index, Ew.i, Epv.iAnd EPB.iRespectively indicate i moment wind power plant, photovoltaic plant and hair
The electricity volume of electric module, EchIndicate channel capacity.
Step 2 constructs the fitness function of more optimization aims, the fitness function of multi-objective optimization question are as follows:
Minimize:
Wherein, F1And F2Indicate two objective functions, Cw, CpvAnd CtesRespectively indicate wind power plant, photovoltaic plant and storage
The capacity of hot systems.
Step 3 writes the program generation for solving fitness function in application software based on multi-objective particle
Code.Application software uses MATLAB software, writes objective function F according to the scheduling strategy of combined generating system1And F2, according to more
The process and principle of intended particle colony optimization algorithm write algorithm MULTIPSO, then run
[Xm, Fv]=MULTIPSO (@F1,@F2, S, c1, c2, w, D, M)
Wherein, Xm indicates that optimal capacity ratio, Fv indicate adaptive optimal control angle value, F1, F2For objective function, S indicates initial kind
Group's number, usually takes 100-1000；C1, c2 are Studying factors, usually take 0.5-2.5；D is solution vector dimension, according to solution vector
Depending on dimension, the solution vector of this paper is (wind-powered electricity generation capacity, photovoltaic capacity, heat accumulation duration), therefore search space dimension D=3；W is
Inertia weight usually takes 0.1-0.9；M is iterative steps, usually takes 100-500.
The set as composed by multiple three-dimensional vectors being randomly generated is the initial population of the optimization problem, in set with
The number of machine vector is initial population number S.
The specific implementation process of multi-objective particle is as follows:
S01 initializes population: position and the speed of each particle is randomly generated in given population scale；
S02 utilizes objective function F1And F2Calculate separately the fitness value of each particle；
S03, in two objective function F1And F2It is lower find out each particle respectively individual extreme value ((pbest [i, 1],
Pbest [i, 2])；
S04, to two objective function F1And F2Global extremum (gbest [1], gbest [2]) is found out respectively；
S05 calculates the mean value gbest and Euclidean distance dgbest of two global extremums；
S06 calculates the Euclidean distance dpbest [i] between each particle individual extreme value.
S07 calculates the pbest [i] when each particle updates position and speed:
If dpbest [i] < dgbest
Pbest [i]=rand select (pbest [i, 1], pbest [i, 2]) # random selection
Else
Pbest [i]=average (pbest [i, 1], pbest [i, 2]) # selects mean value；
S08 updates the position and speed of each particle using S05 and the S07 gbest being calculated and pbest [i], more
New formula is as follows:
V [i+1]=ω v [i]+c1·rand·(pbest[i]-x[i])+c2·rand·(gbest-x[i]) (6)
X [i+1]=x [i]+v [i] (7)
Wherein, ω indicates inertia weight, c1And c2Indicate that Studying factors, rand indicate the random number on [0,1]；
S09, iteration terminate after reaching default the number of iterations to termination condition is met.
The program code for solving fitness function is run multiple times, before obtaining the Pareto of multi-objective optimization question in step 4
Along figure, and capacity ratio optimal solution is determined according to Pareto forward position figure and corresponding decision-making technique.It is every in the figure of Pareto forward position
One point is accordingly to be regarded as two-dimentional variable [LCOE (i), Rch(i)], then in all Pareto forward position points, at a distance from other points
The sum of nearest point be chosen for optimal capacity ratio, calculation formula is as follows:
Wherein, [LCOE (i), RchAnd [LCOE (j), R (i)]chIt (j)] is any two Pareto forward position point, n refers to pa
The number of all the points, Final indicate optimal capacity ratio in tired support forward position figure.
The embodiment of the present invention sets wind-light complementary system for Pakistani somewhere (25 ° of 04 ' N, 67 ° of 56 ' E) is proposed to fill
Point using 100MW channel capacity, according to local sights resource by Mathematical Modeling Methods acquire unit MW wind power plant and
Then the power curve of photovoltaic plant obtains Pareto forward position figure by Multipurpose Optimal Method and is obtained most by decision-making technique
Excellent capacity ratio.Finally by the wind-powered electricity generation-photovoltaic-heat accumulation combined generating system and wind-powered electricity generation photovoltaic of optimal capacity ratio without energy-storage system
And wind-powered electricity generation photovoltaic storage battery system is compared, and as a result show that wind-powered electricity generation-photovoltaic-heat accumulation combined generating system can be mentioned effectively
High reliability and economic sex expression.
(1) data preparation
The power output of unit MW wind power plant and photovoltaic plant is obtained according to local wind-resources and solar energy resources data
Curve is as shown in Figure 3 and Figure 4.
(2) optimum results
(21) economy parameter for calculating levelized cost is as shown in table 1.
Table 1
| Wind-powered electricity generation | Photovoltaic | Heat reservoir | Electricity generation module | Electric heating | |
| Initial cost | 1695$/kW | 1040$/kW | 30$/kW | 102$/kW | 528655$ |
| O&M cost | 51$/kW | 9$/kW | 0.15$/kW | 25$/kW | 12550$ |
| Attenuation rate | 0 | 0.8% | 0.2% | 0 | 0 |
(22) the initial population S=1000 of multi-objective particle, Studying factors c1=c2=1.496 are determined,
Inertia weight w=0.7298, search space dimension D=3, iterative steps M=100, it is excellent to obtain multiple target for operation in MATLAB
The Pareto forward position figure of change problem is as shown in figure 4, the parameter of each index point is as shown in table 2 in figure.
Table 2
According to optimum results it is found that when changing from point A to point B, levelized cost from 82.7 $/kWh be changed to 90.93 $/
KWh, however channel utilization index has biggish promotion；When changing from point C to point D, channel utilization index has certain increase, but flat
Quasi- chemical conversion originally increases to 143.8 $/kWh from 119.9 $/kWh；However when changing from point B to point C, levelized cost and channel
The variation of utilization rate is relatively gentle.
(23) in multi-objective particle Pareto forward position obtained, each point is accordingly to be regarded as two-dimentional variable
[LCOE (i), Rch(i)].Then in all Pareto forward position points, nearest point is chosen for the sum of the distance of other points
Optimal capacity ratio, calculation formula are as follows:
Wherein, [LCOE (i), RchAnd [LCOE (j), R (i)]chIt (j)] is any two Pareto forward position point, n refers to pa
The number of all the points in tired support forward position figure, Final indicate optimal capacity ratio, the optimal solution of the multi-objective optimization question [LCOE,
Rch] it is [107.75 $/kWh, 0.75], optimal capacity ratio [Cw, Cpv, Ctes] it is [150MW, 250MW, 12h], in Fig. 5
As shown in point P.
(24) by the optimal capacity ratio of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system with capacity wind-powered electricity generation-photovoltaic without storage
Energy system is compared, and design parameter comparison is as shown in table 3.
Table 3
According to table 3, although wind-powered electricity generation-photovoltaic-energy storage combined generating system increases electric heater, heat reservoir etc. is set
It is standby, but due to the promotion of combined generating system electricity volume, levelized cost has instead definitely to be declined；And due to storage
The addition of hot systems, the channel utilization index of combined generating system, which has, to be obviously improved；Therefore compared to wind-powered electricity generation-photovoltaic without energy storage
System, wind-powered electricity generation-photovoltaic-heat accumulation combined generating system have preferably economy and reliable sex expression.
Compare the wind-powered electricity generation-photovoltaic-heat accumulation combined generating system and wind electrical-optical that each season in 1 year represents day by analyzing
Power output situation of the volt without energy-storage system is shown in Fig. 6 to Fig. 9, for showing the power regulation effect of electric heater and heat reservoir,
To effectively improve the channel utilization index of combined generating system.
(25) by wind-powered electricity generation-photovoltaic-heat accumulation combined generating system and with wind-powered electricity generation-photovoltaic-battery cogeneration system of capacity
System is compared, and specific parameter comparison is as shown in table 4.
Table 4
According to table 4, when wind-powered electricity generation-photovoltaic-battery combined generating system is wanted to reach and wind-powered electricity generation-photovoltaic-heat accumulation connection
When closing the identical channel utilization index of electricity generation system, levelized cost needs to increase to 121.53 $/MWh；Therefore it can be concluded that,
Compared to wind-powered electricity generation-photovoltaic-battery combined generating system, wind-powered electricity generation-photovoltaic-heat accumulation combined generating system has better economy
Performance.
The above is only a preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (7)
1. a kind of wind-powered electricity generation-photovoltaic-heat accumulation combined generating system multiple target capacity optimization method, it is characterised in that: be used for wind electrical-optical
Volt-heat accumulation combined generating system, the combined generating system include wind power plant, photovoltaic plant, electric heater, heat reservoir and hair
Then electric module is passed through with the sent out direct current general DC busbar of the photovoltaic plant after the wind-powered electricity generation place power generation energy is rectified
Inverter is crossed, is connected to the grid after primary substation, the electric heater will exceed in the wind power plant and the photovoltaic plant
The form that the electric energy of channel capacity is converted to thermal energy is stored in the heat reservoir, when the wind power plant and the photovoltaic electric
When the output electric energy deficiency channel capacity stood, it is described that the heat reservoir release thermal energy pushes the electricity generation module to generate power and convey to
Power grid, comprising the following steps:
Step a establishes combined generating system with levelized cost minimum and channel utilization index maximum and turns to optimization aim；
Step b constructs the fitness function of more optimization aims；
Step c writes the program code for solving fitness function in application software based on multi-objective particle；
Step d is run multiple times the program code for solving fitness function, obtains the Pareto forward position figure of multi-objective optimization question,
And capacity ratio optimal solution is determined according to Pareto forward position figure and corresponding decision-making technique.
2. a kind of wind-powered electricity generation-photovoltaic according to claim 1-heat accumulation combined generating system multiple target capacity optimization method,
Be characterized in that: in step a, the levelized cost calculation formula is as follows:
ICsystem=ICw+ICpv+ICTES+ICPB+ICEH (2)
ACsystem=ACw+ACpv+ACTES+ACPB+ACEH (3)
Wherein, LCOE indicates levelized cost, ICw, ICtes, ICPB, ICpvAnd ICEHRespectively indicate wind power plant, heat reservoir, power generation
The initial outlay cost of module, photovoltaic plant and electric heater, ACw, ACtes, ACEH, ACpvAnd ACPBWind power plant is respectively indicated, is stored up
The annual cost of investment of hot systems, electric heater, photovoltaic plant and electricity generation module, Ew, EPBAnd EpvWind power plant is respectively indicated, is sent out
The first annual electricity generating capacity of electric module and photovoltaic plant, dw, dcspAnd dpvWind power plant is respectively indicated, electricity generation module and photovoltaic plant are annual
Attenuation rate, i indicate discount rate, N indicate life expectancy.
3. a kind of wind-powered electricity generation-photovoltaic according to claim 2-heat accumulation combined generating system multiple target capacity optimization method,
Be characterized in that: in step a, channel utilization index refers to the ratio of the total electricity volume of combined generating system and annual channel capacity,
Calculation formula is as follows:
Wherein, RchIndicate channel utilization index, Ew.i, Epv.iAnd EPB.iRespectively indicate i hours moment wind power plants, photovoltaic plant and power generation
The electricity volume of module, EchIndicate channel capacity.
4. a kind of wind-powered electricity generation-photovoltaic according to claim 3-heat accumulation combined generating system multiple target capacity optimization method,
Be characterized in that: in step c, application software uses MATLAB software, writes target letter according to the scheduling strategy of combined generating system
Number F1And F2, algorithm MULTIPSO is write according to the process of multi-objective particle and principle, is then run
[Xm,Fv]=MULTIPSO (@F1,@F2,S,c1,c2,w,D,M)
Wherein, XmIndicate optimal capacity ratio, FvIndicate adaptive optimal control angle value, F1, F2For objective function, S indicates initial population number,
c1,c2For Studying factors, D is solution vector dimension, and w is inertia weight, and M is iterative steps.
5. a kind of wind-powered electricity generation-photovoltaic according to claim 4-heat accumulation combined generating system multiple target capacity optimization method,
It is characterized in that: in step b, the fitness function of multi-objective optimization question are as follows:
Wherein, F1And F2Indicate two objective functions, Cw, CpvAnd CtesRespectively indicate wind power plant, photovoltaic plant and heat accumulation system
The capacity of system.
6. a kind of wind-powered electricity generation-photovoltaic according to claim 5-heat accumulation combined generating system multiple target capacity optimization method,
Be characterized in that: in step c, the specific implementation process of multi-objective particle is as follows:
S01 initializes population: position and the speed of each particle is randomly generated in given population scale；
S02 utilizes objective function F1And F2Calculate separately the fitness value of each particle；
S03, in two objective function F1And F2Lower individual extreme value ((pbest [i, 1], pbest for finding out each particle respectively
[i,2])；
S04, to two objective function F1And F2Global extremum (gbest [1], gbest [2]) is found out respectively；
S05 calculates the mean value gbest and Euclidean distance dgbest of two global extremums；
S06 calculates the Euclidean distance dpbest [i] between each particle individual extreme value.
S07 calculates the pbest [i] when each particle updates position and speed:
If dpbest [i] < dgbest
Pbest [i]=rand select (pbest [i, 1], pbest [i, 2]) # random selection
Else
Pbest [i]=average (pbest [i, 1], pbest [i, 2]) # selects mean value；
S08 updates the position and speed of each particle using S05 and the S07 gbest being calculated and pbest [i], updates public
Formula is as follows:
V [i+1]=ω v [i]+c1·rand·(pbest[i]-x[i])+c2·rand·(gbest-x[i]) (6)
X [i+1]=x [i]+v [i] (7)
Wherein, ω indicates inertia weight, c1And c2Indicate that Studying factors, rand indicate the random number on [0,1]；
S09, iteration terminate after reaching default the number of iterations to termination condition is met.
7. a kind of wind-powered electricity generation-photovoltaic according to claim 6-heat accumulation combined generating system multiple target capacity optimization method,
Be characterized in that: in step d, each point is accordingly to be regarded as two-dimentional variable [LCOE (i), R in the figure of Pareto forward positionch(i)], then in institute
In some Pareto forward position points, nearest point is chosen for optimal capacity ratio with the sum of the distance of other points, calculation formula
It is as follows:
Wherein, [LCOE (i), RchAnd [LCOE (j), R (i)]chIt (j)] is any two Pareto forward position point, before n refers to Pareto
The number of all the points along figure, Final indicate optimal capacity ratio.
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| CN112381269A (en) * | 2020-10-30 | 2021-02-19 | 上海电气集团股份有限公司 | Independent micro-grid capacity optimal configuration method considering load importance and electricity price excitation |
| CN112949177A (en) * | 2021-02-26 | 2021-06-11 | 国网北京市电力公司 | Multi-optimization target weighting method and system in construction of comprehensive energy system |
| CN112949177B (en) * | 2021-02-26 | 2024-03-08 | 国网北京市电力公司 | Multi-optimization target weighting method and system in comprehensive energy system construction |
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| CN114336602B (en) * | 2021-12-27 | 2023-10-24 | 山东电力建设第三工程有限公司 | Configuration optimization method for photo-thermal-wind power-photovoltaic combined power generation system |
| CN115238505A (en) * | 2022-07-27 | 2022-10-25 | 国网山东省电力公司枣庄供电公司 | Optimal configuration method and system for combined heat, power and hydrogen supply system based on wind and light station |
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