CN110188915A - Energy-storage system Optimal Configuration Method and system in virtual plant based on scene collection - Google Patents
Energy-storage system Optimal Configuration Method and system in virtual plant based on scene collection Download PDFInfo
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Abstract
The invention discloses energy-storage system Optimal Configuration Method and systems in a kind of virtual plant based on scene collection.Energy-storage system Optimal Configuration Method of the invention carries out as follows: virtual plant model of the building containing blower, photovoltaic, electric car and energy-storage system；By uncertain factor in the sampled analog virtual plant of Monte Carlo, generation is combined into field of force Jing Ji；It establishes and considers that the energy storage system capacity of cost distributes objective function rationally；Energy-storage system Optimal Allocation Model by differential evolution algorithm, in conjunction with constraint conditions various in actual motion, in solving virtual power plant.The present invention considers the uncertainty of each distributed energy power output in virtual plant, optimizes configuration to energy-storage system, to achieve the purpose that the construction cost for reducing virtual plant.
Description
Technical field
The present invention relates to Operation of Electric Systems and planning field, in specifically a kind of virtual plant based on scene collection
Energy-storage system Optimal Configuration Method and system.
Background technique
With the rapid development of the new energy such as photovoltaic plant, wind power plant, electric car, virtual plant technology is in electric system
The application run with planning field is also more and more.What it is different from microgrid is that virtual plant technology more actively assists in electricity
Force system operation.But due to the uncertainty of distributed energy power output, the daily operation of virtual plant and power distribution network are pacified
It will cause certain influence entirely.Energy-storage system can not only adjust blower, light as component part important in virtual plant
The fluctuation of power output is lied prostrate, virtual plant competitive bidding power output situation can be also adjusted according to the fluctuation of market clearing price；But energy storage system
The higher cost of system, reasonably being distributed rationally to it is particularly important.Due to the use of energy-storage system in virtual plant
It is more frequently again related with practical application situation, therefore establish and consider energy-storage system initial investment cost, operation and maintenance cost
With the objective function of daily operating cost.
Related distributing rationally for virtual plant can be divided into two classes at present: the first is to the distributed energy inside virtual plant
Source optimizes configuration, mainly according to the type and capacity of the power output demand reasonable disposition distributed energy of virtual plant.But
Its defect is not account for the uncertainty of distributed energy power output, will cause serious abandonment, abandons the unnecessary wastes such as light.
Second is that configuration is integrally optimized to virtual plant, mainly optimizes virtual plant internal structure according to workload demand, though
Right global optimization configuration enables to virtual plant to be in optimal operating status, but it is only suitable for a kind of load, when
It also needs to optimize configuration again when load type changes or virtual plant structure changes.
Summary of the invention
In place of solving above-mentioned the shortcomings of the prior art, the present invention is provided in a kind of virtual plant based on scene collection
Energy-storage system Optimal Configuration Method makes it to fully consider the uncertainty of distributed energy power output in virtual plant
Energy-storage system construction cost minimum, daily operation are using maximum, to achieve the purpose that virtual plant is distributed rationally.
In order to achieve the above objectives, a kind of the technical solution adopted by the present invention are as follows: energy storage in the virtual plant based on scene collection
System optimization configuration method comprising step:
Step 1) constructs the virtual plant model containing blower, photovoltaic, electric car and energy-storage system；
Step 2), by uncertain factor in the sampled analog virtual plant of Monte Carlo, generation is combined into field of force Jing Ji；
Step 3) establishes the objective function for considering the energy storage system capacity Optimal Allocation Model of cost；
Step 4), the storage by differential evolution algorithm, in conjunction with constraint conditions various in actual motion, in solving virtual power plant
It can system optimization allocation models.
Further, the virtual plant model in step 1) includes wind generator system, photovoltaic generating system, electric car
System, miniature gas turbine and energy-storage system.
Further, in step 2), consider the uncertain factor of distributed energy power output in virtual plant, contribute to blower
When being simulated, the form parameter and scale parameter of Weibull distribution are first calculated according to the sampled value of day part mean wind speed, then
Air speed data is randomly generated by monte carlo method, contributes at random scene to obtain blower；Photovoltaic power output is simulated
When, according to the sampled value of day part average light intensity calculate Beta distribution form parameter, then using monte carlo method with
Machine generates photovoltaic power output scene；Finally different scene collection is generated in conjunction with the charge and discharge hobby of electric car.
Further, in step 2), it is in the cards that a scene ξ represents distributed energy one kind inside virtual plant
24 hours stochastic variables；It will lead to since scene collection scale is excessive and solve calculation amount increase, cutting down technology by scene will be virtual
Power plant's internal combination power output situation is reduced to limited NSA scene set.
Further, in step 3), the objective function of the virtual plant energy storage system capacity Optimal Allocation Model includes
Three parts, be respectively as follows: the daily operation of the initial investment cost of energy-storage system, operation and maintenance cost and virtual plant at
This.
Further, in step 3), the initial investment cost C of the energy-storage systeminv:
In formula: the initial investment cost coefficient of the years value such as A expression；CESSIndicate the cost of unit sulfuric acid lithium iron battery；
CbatmaxIndicate the rated capacity of energy-storage system；D indicates true rate of interest；NrIndicate the service life of energy-storage system.
Further, in step 3), the operation and maintenance cost C of the energy-storage systemopm:
Copm=∑ Cbat,maxCO
In formula: COIndicate the day operation and maintenance cost of unit sulfuric acid lithium iron battery；CbatmaxIndicate the specified of energy-storage system
Capacity.
Further, in step 3), the daily operating cost of virtual plant is made of six parts in total: system power damage
Consumption；Light quantity loss is abandoned in abandonment；Gas turbine power generation cost；From power grid purchases strategies；Energy storage cost depletions；Virtual plant operation
Income, as follows with the expression formula of superior function:
System power dissipation cost
In formula: ηlossIndicate unit power cost depletions；Indicate the power loss of t moment in the case where scene ξ；T
Indicate time constant；
Light quantity loss cost is abandoned in abandonment
In formula:WithIt is illustrated respectively in the abandonment amount of t moment in the case where scene ξ and abandons light quantity；Cbuy,tIndicate t
The purchases strategies at moment；
Miniature gas turbine cost of electricity-generating
In formula: aiAnd biIndicate the cost coefficient of i-th miniature gas turbine；Indicate that i-th miniature gas turbine exists
The generated output of t moment in the case where scene ξ；The number of units of M expression miniature gas turbine；
From power grid purchases strategies
In formula:Indicate output power of the t moment power grid to virtual plant in the case where scene ξ；
Energy storage cost depletions
In formula: α indicates energy storage charge and discharge cost coefficient；Pes,tIndicate t moment energy-storage system charge-discharge electric power；
Virtual plant running income Rξ:
In formula:Indicate that t moment virtual plant is to power grid electricity sales amount in the case where scene ξ；WithTable respectively
Show the charge-discharge electric power of t moment vehicle electric system in the case where scene ξ；Csell,tIndicate the sale of electricity valence of t moment virtual plant
Lattice；ksellIndicate electric car to virtual plant sale of electricity price in former electricity price on the basis of multiplied by proportionality coefficient；
It is as follows then only to consider that the energy storage system capacity of cost distributes objective function expression formula rationally:
Min.{Cinv+Copm+Cdop}。
Further, step 4) includes:
Constraint condition in step 4.1, setting virtual plant actual motion；
Step 4.2, setting initial parameter, comprising: mutagenic factor F, Population Size M, crossover probability CR and greatest iteration time
Number Gmax, generation solve number of parameters C；
Step 4.3, the initial population matrix X for generating M row C columnM×C 0, initial population matrix X is generated using formula (1)M×C 0In
S-th of individual Xs 0The ρ parameter Xsρ 0, to generate initial population matrix XM×C 0M individual C parameter；
Xsρ 0=kρ L+(kρ U-kρ L)×rand(0,1) (1)
In formula, kρ LAnd kρ UThe lower and upper limit of respectively the ρ parameter value；Rand (0,1) is generated between [0,1]
Random number；
Step 4.4, initialization the number of iterations G=1, in the case where existing initial population, the allocation plan of energy-storage system
Become determining situation, and then establish virtual plant power generation dispatching problem, keeps the day operation expense under all scenes minimum；
Step 4.5 plants mass matrix X to G generation using formula (2)M×C GS-th of individual Xs GMutation operation is carried out to be made a variation
S-th of individual H afterwardss G, thus to G generation kind mass matrix XM×C GM individual carry out mutation operation, M after make a variation is a
Individual, and G is constituted for Variation Matrix HM×C G=[H1 G,H2 G,...,Hs G,...,HM G]T
Hs G=Xp1 G+(Xp2 G-Xp3 G)×F (2)
In formula, Xp1 G、Xp2 G、Xp3 GIndicate G generation kind mass matrix XM×C GIn random three individuals；
Wherein 1≤p1≤M, 1≤p2≤M, 1≤p3≤M, and p1 ≠ p2 ≠ p3 ≠ s；
Step 4.6, the Variation Matrix H to G generationM×C GJth column element in more bound component be modified, it is described to cross the border
Element refers to less than kj LOr it is greater than kj UElement, 1≤j≤C, to be less than kj LElement be modified to kj L, to greater than kj UElement
It is modified to kj U, thus to the Variation Matrix H in G generationM×C GC column element in more bound component be modified；
Step 4.7, by G for Variation Matrix HM×C GWith G generation kind mass matrix XM×C GG is generated for cross matrix VM×C G；
G is sought for cross matrix V using formula (3)M×C GS-th of individual Vs GThe μ parameter Vsμ G, and then seek G generation intersection square
Battle array VM×C GS-th of individual Vs GC parameter, and then seek G for cross matrix VM×C GM individual C parameter；1≤μ
≤C；
In formula, Xsμ GFor G generation kind mass matrix XM×C GS-th of individual Xs GThe μ parameter；Hsμ GFor G generation variation square
Battle array HM×C GS-th of individual Hs GThe μ parameter；
Step 4.8, by G for cross matrix VM×C GWith G generation kind mass matrix XM×C GGenerate G+1 generation kind mass matrix
XM×C G+1；G+1 generation kind mass matrix X is sought using formula (4)M×C G+1S-th of individual Xs G+1, to seek G+1 for population square
Battle array XM×C G+1M individual；
In formula, f indicates the operation result current energy storage allocation plan substituted into the objective function for only considering cost；
Step 4.9 judges G+1=GmaxIt is whether true, show that kind of a mass matrix has evolved to highest generation if setting up, goes to step
Rapid 4.10 execute, and G+1 is otherwise assigned to G return step 4.4 and is executed；
Step 4.10 obtains the final allocation plan of energy-storage system.
The present invention also provides another technical solutions: energy-storage system is distributed rationally in a kind of virtual plant based on scene collection
System comprising:
Virtual plant model construction unit: virtual plant mould of the building containing blower, photovoltaic, electric car and energy-storage system
Type；
It is combined into field of force scape collection generation unit: by uncertain factor in the sampled analog virtual plant of Monte Carlo, producing
Life is combined into field of force Jing Ji；
Objective function establishes unit: establishing the objective function for considering the energy storage system capacity Optimal Allocation Model of cost；
Optimal Allocation Model solves unit: being solved by differential evolution algorithm in conjunction with constraint conditions various in actual motion
Energy-storage system Optimal Allocation Model in virtual plant.
Compared with prior art, the device have the advantages that being embodied in:
1, the present invention considers the uncertainty of distributed energy power output according to local historical data, is taken out using Monte Carlo
Its scene of contributing of sample comprehensive simulation, provides accurate predictive information for distributing rationally for energy-storage system.
2, the objective function constructed by the present invention is by energy-storage system initial investment cost, operation and maintenance cost and daily fortune
Cost composition is sought, the allocation plan of energy-storage system more can be all-sidedly and accurately designed, to the rule of energy-storage system in virtual plant
It draws and is of great significance with operation.
3, the present invention is directed to the virtual plant containing blower, photovoltaic and electric car, utilizes mixed integer nonlinear programming
Its internal distributed energy power output situation is solved, virtual plant practical operation situation is more effectively simulated.
4, the present invention applies punishment electricity price for blower, the abandonment of photovoltaic, abandoning light situation using the measure of punishment electricity price,
And then improve the utilization rate of distributed energy.
5, the present invention application convergence rate with higher differential evolution algorithm, have stronger ability of searching optimum with
And local mining ability, greatly improve solution efficiency and solving precision.
Detailed description of the invention
Fig. 1 is energy-storage system Optimal Configuration Method flow chart in the virtual plant according to the present invention based on scene collection.
Specific embodiment
The invention will be further described with specific embodiment with reference to the accompanying drawing.In following implementations of the invention
Specific embodiment described in mode is only used as the exemplary illustration of a specific embodiment of the invention, without constituting to this hair
The limitation of bright range.
Embodiment 1
As shown in Figure 1, energy-storage system Optimal Configuration Method is as follows in a kind of virtual plant based on scene collection
It carries out:
Step 1, the virtual plant model of analysis and building containing blower, photovoltaic, electric car and energy-storage system.
Step 1.1, wind generator system: the power output of blower fan power generation system is not only related to own operating characteristics, more with work as
The environmental factors such as wind speed, the meteorological condition on ground are related, it is considered that wind speed Follow Weibull Distribution.
The parameter that Weibull distribution is obtained by collecting local history meteorological data utilizes Monte Carlo sampling blower
Operating condition can be indicated by following formula:
In formula: k and c is respectively the form parameter and scale parameter of Weibull distribution；PWAnd Pr WRespectively wind-driven generator
Actual power and rated power；V, vci, vr, vcoThe respectively actual wind speed of same type wind-driven generator, incision wind speed, specified
Wind speed and cut-out wind speed.
Step 1.2, photovoltaic generating system: blower fan power generation system, the generated output and intensity of illumination, temperature of photovoltaic are similar to
Equal weather conditions are closely related, and Intensity of the sunlight obeys Beta distribution.
The probability density function profiles of photovoltaic can be indicated with power output situation by following formula:
In formula: PPFor photovoltaic generating system actual power；For the installed capacity of photovoltaic generating system；A and b are respectively
The form parameter of Beat distribution.
Step 1.3, vehicle electric system: New-energy electric vehicle accesses power distribution network in large quantities in recent years, due to its charge and discharge
The uncertainty of electricity exerts a certain influence to the safe operation of power grid, and the charge and discharge of electric car can be indicated by cloth Shandong variable
Electricity condition.
Progress load transfer foundation can be guided to determine relatively by electricity price in order to solve the stochastic problems of electric car
System model:
In formula: μi,tThe charged state of electric car, μ are indicated for cloth Shandong variablei,t=1 indicates i-th electricity to subscribe to the agreement
Electrical automobile is kept in t moment into net state, μi,t=0 indicates to be in off-network state；αi,tIndicate what electric car was influenced by electricity price
Charge and discharge preference, αi,t=1 indicates that i-th electric car to subscribe to the agreement is in power transmission state, α in t momenti,t=0 table
Show no power conveying.
Step 1.4, miniature gas turbine: the distributed energy controllable as a kind of stabilization, power output, miniature gas turbine are normal
The emergency situations such as rapid drawdown of contributing are influenced by weather as backup power source reply blower, photovoltaic, to guarantee the safety fortune of virtual plant
Row.
Step 1.5, energy-storage system: large-scale to store up as the cost for developing energy-storage system of energy storage technology is lower and lower
Energy system is gradually applied among electric system, and energy-storage system can not only adjust blower among virtual plant, photovoltaic is contributed
Fluctuation can also adjust virtual plant competitive bidding power output situation according to the fluctuation of market clearing price；In view of the daily fortune of virtual plant
Charge and discharge are frequent during row and have certain requirement to charge/discharge speed, therefore using circulation in the process of construction of energy-storage system
The good sulfuric acid lithium iron battery of performance.
Step 2 passes through uncertain factor in Monte Carlo sampled analog virtual plant, and generation is combined into field of force Jing Ji.
Blower, photovoltaic power output are simulated by Monte-carlo Simulation Method using formula (1)-(4), distribution is randomly generated
Formula energy power output scene generates different scene collection in conjunction with the charge and discharge hobby of electric car.One scene ξ represents virtual electricity
A kind of 24 hours stochastic variables in the cards of distributed energy inside factory；It will lead to solution since scene collection scale is excessive to calculate
Amount increases, and technology can be cut down by scene by virtual plant internal combination power output situation and is reduced to limited NSA scene set.
It is to replace scene in large scale with a small amount of scene with classical Space-time Model characteristic to subtract that scene, which cuts down technology,
The calculation amount of few stochastic programming.It samples the scene based on synchronous back substitution null method and cuts down technology, by each scene ξiAccording to formula (6)
It calculates and it is apart from shortest scene ξj。
In formula: ρjIndicate scene ξjProbability of happening；d(ξi,ξj) indicate scene ξiWith ξjEuclidean distance.According to formula (7)
The determination scene ξ to be deletedi。
Modification residue scene number N=N-1 tires out the probability of deleted scene to ensure that all the sum of scene probability are 1
It is added to it in nearest scene.It repeats the above process until remaining scene number reaches NS。
Step 3, the energy storage system capacity for establishing consideration cost distribute objective function rationally.
Step 3.1, the initial investment cost of energy-storage system:
In formula: the initial investment cost coefficient of the years value such as A expression；CESSIndicate the cost of unit sulfuric acid lithium iron battery；
CbatmaxIndicate the rated capacity of energy-storage system；D indicates true rate of interest；NrIndicate the service life of energy-storage system.
Step 3.2, the operation and maintenance cost of energy-storage system:
Copm=∑ Cbat,maxCO (10)
In formula: COIndicate the day operation and maintenance cost of unit sulfuric acid lithium iron battery.
Step 3.3, the daily operating cost of virtual plant:
The daily operating cost of virtual plant is made of six parts in total: system power dissipation；Light quantity damage is abandoned in abandonment
Consumption；Gas turbine power generation cost；From power grid purchases strategies；Energy storage cost depletions；Virtual plant running income, with the table of superior function
It is as follows up to formula:
System power dissipation cost
In formula: ηlossIndicate unit power cost depletions；Indicate the power loss of t moment in the case where scene ξ.
Light quantity loss cost is abandoned in abandonment
In formula:WithIt is illustrated respectively in the abandonment amount of t moment in the case where scene ξ and abandons light quantity；Cbuy,tIndicate t
The purchases strategies at moment.
Miniature gas turbine cost of electricity-generating
In formula: aiAnd biIndicate the cost coefficient of i-th miniature gas turbine；Indicate that i-th miniature gas turbine exists
The generated output of t moment in the case where scene ξ.
From power grid purchases strategies
In formula:Indicate output power of the t moment power grid to virtual plant in the case where scene ξ.
Energy storage cost depletions
In formula: α indicates energy storage charge and discharge cost coefficient；Pes,tIndicate t moment energy-storage system charge-discharge electric power.
Virtual plant running income Rξ:
In formula:Indicate that t moment virtual plant is to power grid electricity sales amount in the case where scene ξ；WithTable respectively
Show the charge-discharge electric power of t moment vehicle electric system in the case where scene ξ；Csell,tIndicate the sale of electricity valence of t moment virtual plant
Lattice；ksellIndicate electric car to virtual plant sale of electricity price in former electricity price on the basis of multiplied by proportionality coefficient；
It is as follows then only to consider that the energy storage system capacity of cost distributes objective function expression formula rationally:
Min.{Cinv+Copm+Cdop} (18)
Step 4, the storage by differential evolution algorithm, in conjunction with constraint conditions various in actual motion, in solving virtual power plant
It can system optimization allocation models.
Constraint condition in step 4.1, setting virtual plant actual motion:
Virtual plant needs certain constraint condition to guarantee the safe operation of power grid and load in actual moving process, wraps
Include blower, photovoltaic units limits, miniature gas turbine units limits, electric car charge and discharge constraint, power-balance constraint；Simultaneously
Energy-storage system also has constraint condition due to the limitation of itself capacity and charge-discharge velocity；
Blower, photovoltaic units limits:
In formula:WithIt is illustrated respectively in the output power of t moment blower and photovoltaic in the case where scene ξ；WithIt is illustrated respectively in t moment blower minimum and maximum output power in the case where scene ξ；WithIt respectively indicates on the scene
T moment photovoltaic minimum and maximum output power in the case where scape ξ；
Miniature gas turbine units limits:
In formula: PGi,minAnd PGi,maxRespectively indicate the minimum and maximum output power of miniature gas turbine；
Energy-storage system electricity and charge and discharge constraint:
SSOCmin< SSOC(t) < SSOCmax (23)
In formula:WithRespectively indicate the maximum charge power of energy-storage system and discharge power；SSOCminWith
SSOCmaxRespectively indicate energy-storage system SOC lower and upper limit；WithRespectively indicate the energy-storage system initial moment and it is final when
The SOC at quarter；
Electric car charge and discharge constraint:
In formula:WithRespectively indicate i-th electric car t moment maximum charge power and maximum electric discharge function
Rate；Indicate the charge capacity of i-th electric car t moment；WithRespectively indicate i-th electric car minimum and most
Big charge capacity；
Power-balance constraint:
Step 4.2, setting initial parameter, comprising: mutagenic factor F, Population Size M, crossover probability CR and greatest iteration time
Number Gmax, generation solve number of parameters C；
Step 4.3, the initial population matrix X for generating M row C columnM×C 0.Initial population matrix X is generated using formula (28)M×C 0In
S-th of individual Xs 0The ρ parameter Xsρ 0, to generate initial population matrix XM×C 0M individual C parameter；
Xsρ 0=kρ L+(kρ U-kρ L)×rand(0,1) (28)
In formula (28), kρ LAnd kρ UThe lower and upper limit of respectively the ρ parameter value；Rand (0,1) is between [0,1]
The random number of generation；
Step 4.4, initialization the number of iterations G=1, in the case where existing initial population, the allocation plan of energy-storage system
Become determining situation, and then establish virtual plant power generation dispatching problem, keeps the day operation expense under all scenes minimum.
The power trade mathematical model of substantially one multicycle can be solved by CPLEX solver.To storage
Can the charge-discharge electric power of system, miniature gas turbine output power, blower photovoltaic abandon electricity, electric car charge-discharge electric power, from
The decision variables such as power grid purchase of electricity carry out by when optimize.
Step 4.5 plants mass matrix X to G generation using formula (29)M×C GS-th of individual Xs GMutation operation is carried out to be become
S-th of individual H after differents G, thus to G generation kind mass matrix XM×C GM individual carry out mutation operation, the M after being made a variation
Individual, and G is constituted for Variation Matrix HM×C G=[H1 G,H2 G,...,Hs G,...,HM G]T
Hs G=Xp1 G+(Xp2 G-Xp3 G)×F (29)
Step 4.6, the Variation Matrix H to G generationM×C GJth column element in more bound component be modified, it is described to cross the border
Element refers to less than kj LOr it is greater than kj UElement, 1≤j≤C, to be less than kj LElement be modified to kj L, to greater than kj UElement
It is modified to kj U, thus to the Variation Matrix H in G generationM×C GC column element in more bound component be modified；
Step 4.7, by G for Variation Matrix HM×C GWith G generation kind mass matrix XM×C GG is generated for cross matrix VM×C G。
G is sought for cross matrix V using formula (30)M×C GS-th of individual Vs GThe μ parameter Vsμ G, and then seek G generation intersection
Matrix VM×C GS-th of individual Vs GC parameter, and then seek G for cross matrix VM×C GM individual C parameter；1
≤μ≤C；
In formula, Xsμ GFor G generation kind mass matrix XM×C GS-th of individual Xs GThe μ parameter；Hsμ GFor G generation variation square
Battle array HM×C GS-th of individual Hs GThe μ parameter；
Step 4.8, by G for cross matrix VM×C GWith G generation kind mass matrix XM×C GGenerate G+1 generation kind mass matrix
XM×C G+1.G+1 generation kind mass matrix X is sought using formula (31)M×C G+1S-th of individual Xs G+1, to seek G+1 for population square
Battle array XM×C G+1M individual；
In formula (31), f indicates the operation result current energy storage allocation plan substituted into the objective function for only considering cost.
Step 4.9 judges G+1=GmaxIt is whether true, show that kind of a mass matrix has evolved to highest generation if setting up, goes to step
Rapid 4.10 execute, and G+1 is otherwise assigned to G return step 4.4 and is executed；
Step 4.10 obtains the final allocation plan of energy-storage system.
Embodiment 2
Energy-storage system Optimizing Configuration System in a kind of virtual plant based on scene collection comprising:
Virtual plant model construction unit: virtual plant mould of the building containing blower, photovoltaic, electric car and energy-storage system
Type；
It is combined into field of force scape collection generation unit: by uncertain factor in the sampled analog virtual plant of Monte Carlo, producing
Life is combined into field of force Jing Ji；
Objective function establishes unit: establishing the objective function for considering the energy storage system capacity Optimal Allocation Model of cost；
Optimal Allocation Model solves unit: being solved by differential evolution algorithm in conjunction with constraint conditions various in actual motion
Energy-storage system Optimal Allocation Model in virtual plant.
Claims (10)
1. energy-storage system Optimal Configuration Method in the virtual plant based on scene collection, which is characterized in that comprising steps of
Step 1) constructs the virtual plant model containing blower, photovoltaic, electric car and energy-storage system；
Step 2), by uncertain factor in the sampled analog virtual plant of Monte Carlo, generation is combined into field of force Jing Ji；
Step 3) establishes the objective function for considering the energy storage system capacity Optimal Allocation Model of cost；
Step 4), the energy storage system by differential evolution algorithm, in conjunction with constraint conditions various in actual motion, in solving virtual power plant
System Optimal Allocation Model.
2. energy-storage system Optimal Configuration Method in the virtual plant according to claim 1 based on scene collection, feature exist
In the virtual plant model in step 1) includes wind generator system, photovoltaic generating system, vehicle electric system, miniature gas
Turbine and energy-storage system.
3. energy-storage system Optimal Configuration Method in the virtual plant according to claim 1 based on scene collection, feature exist
In, in step 2), consider the uncertain factor of distributed energy power output in virtual plant, it is first when being simulated to blower power output
The form parameter and scale parameter of Weibull distribution are calculated according to the sampled value of day part mean wind speed, then pass through Monte Carlo
Air speed data is randomly generated in method, contributes at random scene to obtain blower；When being simulated to photovoltaic power output, according to day part
The sampled value of average light intensity calculates the form parameter of Beta distribution, then photovoltaic power output is randomly generated using monte carlo method
Scene；Finally different scene collection is generated in conjunction with the charge and discharge hobby of electric car.
4. energy-storage system Optimal Configuration Method in the virtual plant according to claim 3 based on scene collection, feature exist
In in step 2), a scene ξ represents random change in distributed energy a kind of 24 hours in the cards inside virtual plant
Amount；It will lead to since scene collection scale is excessive and solve calculation amount increase, technology is cut down for virtual plant internal combination by scene
Power output situation is reduced to limited NSA scene set.
5. energy-storage system Optimal Configuration Method in the virtual plant according to claim 1 or 2 based on scene collection, feature
It is, in step 3), the objective function of the virtual plant energy storage system capacity Optimal Allocation Model includes three parts, respectively
Are as follows: the daily operating cost of the initial investment cost of energy-storage system, operation and maintenance cost and virtual plant.
6. energy-storage system Optimal Configuration Method in the virtual plant according to claim 5 based on scene collection, feature exist
In, in step 3), the initial investment cost C of the energy-storage systeminv:
In formula: the initial investment cost coefficient of the years value such as A expression；CESSIndicate the cost of unit sulfuric acid lithium iron battery；
CbatmaxIndicate the rated capacity of energy-storage system；D indicates true rate of interest；NrIndicate the service life of energy-storage system.
7. energy-storage system Optimal Configuration Method in the virtual plant according to claim 6 based on scene collection, feature exist
In, in step 3), the operation and maintenance cost C of the energy-storage systemopm:
Copm=∑ Cbat,maxCO
In formula: COIndicate the day operation and maintenance cost of unit sulfuric acid lithium iron battery；CbatmaxIndicate the rated capacity of energy-storage system.
8. energy-storage system Optimal Configuration Method in the virtual plant according to claim 7 based on scene collection, feature exist
In in step 3), the daily operating cost of virtual plant is made of six parts in total: system power dissipation；Light quantity is abandoned in abandonment
Loss；Gas turbine power generation cost；From power grid purchases strategies；Energy storage cost depletions；Virtual plant running income, with superior function
Expression formula is as follows:
System power dissipation cost
In formula: ηlossIndicate unit power cost depletions；Indicate the power loss of t moment in the case where scene ξ；T is indicated
Time constant；
Light quantity loss cost is abandoned in abandonment
In formula:WithIt is illustrated respectively in the abandonment amount of t moment in the case where scene ξ and abandons light quantity；Cbuy,tIndicate t moment
Purchases strategies；
Miniature gas turbine cost of electricity-generating
In formula: aiAnd biIndicate the cost coefficient of i-th miniature gas turbine；Indicate i-th miniature gas turbine in scene ξ
In the case where t moment generated output；The number of units of M expression miniature gas turbine；
From power grid purchases strategies
In formula:Indicate output power of the t moment power grid to virtual plant in the case where scene ξ；
Energy storage cost depletions
In formula: α indicates energy storage charge and discharge cost coefficient；Pes,tIndicate t moment energy-storage system charge-discharge electric power；
Virtual plant running income Rξ:
In formula:Indicate that t moment virtual plant is to power grid electricity sales amount in the case where scene ξ；WithIt is illustrated respectively in
The charge-discharge electric power of t moment vehicle electric system in the case where scene ξ；Csell,tIndicate the sale of electricity price of t moment virtual plant；
ksellIndicate electric car to virtual plant sale of electricity price in former electricity price on the basis of multiplied by proportionality coefficient；
It is as follows then only to consider that the energy storage system capacity of cost distributes objective function expression formula rationally:
Min.{Cinv+Copm+Cdop}。
9. energy-storage system Optimal Configuration Method in the virtual plant according to claim 7 based on scene collection, feature exist
In step 4) includes:
Constraint condition in step 4.1, setting virtual plant actual motion；
Step 4.2, setting initial parameter, comprising: mutagenic factor F, Population Size M, crossover probability CR and maximum number of iterations
Gmax, generation solve number of parameters C；
Step 4.3, the initial population matrix X for generating M row C columnM×C 0, initial population matrix X is generated using formula (1)M×C 0In s-th
Individual Xs 0The ρ parameter Xsρ 0, to generate initial population matrix XM×C 0M individual C parameter；
Xsρ 0=kρ L+(kρ U-kρ L)×rand(0,1) (1)
In formula, kρ LAnd kρ UThe lower and upper limit of respectively the ρ parameter value；Rand (0,1) generates random between [0,1]
Number；
Step 4.4, initialization the number of iterations G=1, in the case where existing initial population, the allocation plan of energy-storage system becomes
Determining situation, and then virtual plant power generation dispatching problem is established, keep the day operation expense under all scenes minimum；Step 4.5,
Using formula (2) to G generation kind mass matrix XM×C GS-th of individual Xs GCarry out s-th of individual after mutation operation is made a variation
Hs G, thus to G generation kind mass matrix XM×C GM individual carry out mutation operation, M after make a variation is individual, and composition G
For Variation Matrix HM×C G=[H1 G,H2 G,...,Hs G,...,HM G]T
Hs G=Xp1 G+(Xp2 G-Xp3 G)×F (2)
In formula, Xp1 G、Xp2 G、Xp3 GIndicate G generation kind mass matrix XM×C GIn random three individuals；
Wherein 1≤p1≤M, 1≤p2≤M, 1≤p3≤M, and p1 ≠ p2 ≠ p3 ≠ s；
Step 4.6, the Variation Matrix H to G generationM×C GJth column element in more bound component be modified, it is described more bound component
Refer to and is less than kj LOr it is greater than kj UElement, 1≤j≤C, to be less than kj LElement be modified to kj L, to greater than kj UElement amendment
For kj U, thus to the Variation Matrix H in G generationM×C GC column element in more bound component be modified；
Step 4.7, by G for Variation Matrix HM×C GWith G generation kind mass matrix XM×C GG is generated for cross matrix VM×C G；It utilizes
Formula (3) seeks G for cross matrix VM×C GS-th of individual Vs GThe μ parameter Vsμ G, and then G is sought for cross matrix
VM×C GS-th of individual Vs GC parameter, and then seek G for cross matrix VM×C GM individual C parameter；1≤μ≤
C；
In formula, Xsμ GFor G generation kind mass matrix XM×C GS-th of individual Xs GThe μ parameter；Hsμ GIt is G for Variation Matrix
HM×C GS-th of individual Hs GThe μ parameter；
Step 4.8, by G for cross matrix VM×C GWith G generation kind mass matrix XM×C GGenerate G+1 generation kind mass matrix XM×C G+1；Benefit
G+1 generation kind mass matrix X is sought with formula (4)M×C G+1S-th of individual Xs G+1, to seek G+1 generation kind mass matrix XM×C G+1's
M individual；
In formula, f indicates the operation result current energy storage allocation plan substituted into the objective function for only considering cost；
Step 4.9 judges G+1=GmaxIt is whether true, show that kind of a mass matrix has evolved to highest generation if setting up, goes to step
4.10 execute, and G+1 is otherwise assigned to G return step 4.4 and is executed；
Step 4.10 obtains the final allocation plan of energy-storage system.
10. energy-storage system Optimizing Configuration System in a kind of virtual plant based on scene collection characterized by comprising
Virtual plant model construction unit: virtual plant model of the building containing blower, photovoltaic, electric car and energy-storage system；
It is combined into field of force scape collection generation unit: by uncertain factor in the sampled analog virtual plant of Monte Carlo, generation group
Close power output scene collection；
Objective function establishes unit: establishing the objective function for considering the energy storage system capacity Optimal Allocation Model of cost；
Optimal Allocation Model solves unit: by differential evolution algorithm, in conjunction with constraint conditions various in actual motion, solving virtual
Energy-storage system Optimal Allocation Model in power plant.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110912158A (en) * | 2019-12-15 | 2020-03-24 | 兰州交通大学 | Multi-terminal flexible direct-current power transmission system frequency stability control method with wind power participating in frequency modulation |
| CN111832217A (en) * | 2020-06-04 | 2020-10-27 | 华北电力大学 | Virtual power plant optimized operation method considering wind power consumption |
| CN112186756A (en) * | 2020-09-27 | 2021-01-05 | 国网辽宁省电力有限公司经济技术研究院 | Energy storage capacity configuration method for virtual power plant |
| CN112215641A (en) * | 2020-10-10 | 2021-01-12 | 国网上海市电力公司 | Control method and system for intelligent building type virtual power plant to participate in energy frequency modulation |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914734A (en) * | 2014-03-20 | 2014-07-09 | 浙江工业大学 | Micro-grid capacity address optimizing and distributing method based on improved ant colony algorithm |
| CN106300418A (en) * | 2016-08-30 | 2017-01-04 | 合肥工业大学 | Photovoltaic DC-to-AC converter based on adaptive differential evolution algorithm controls the discrimination method of parameter |
| CN107895971A (en) * | 2017-11-28 | 2018-04-10 | 国网山东省电力公司德州供电公司 | Regional Energy internet dispatching method based on stochastic programming and Model Predictive Control |
| CN109583136A (en) * | 2018-12-28 | 2019-04-05 | 上海电力学院 | Electric car based on schedulable potentiality, which fills, changes storage one station method for establishing model |
-
2019
- 2019-04-10 CN CN201910285028.7A patent/CN110188915A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914734A (en) * | 2014-03-20 | 2014-07-09 | 浙江工业大学 | Micro-grid capacity address optimizing and distributing method based on improved ant colony algorithm |
| CN106300418A (en) * | 2016-08-30 | 2017-01-04 | 合肥工业大学 | Photovoltaic DC-to-AC converter based on adaptive differential evolution algorithm controls the discrimination method of parameter |
| CN107895971A (en) * | 2017-11-28 | 2018-04-10 | 国网山东省电力公司德州供电公司 | Regional Energy internet dispatching method based on stochastic programming and Model Predictive Control |
| CN109583136A (en) * | 2018-12-28 | 2019-04-05 | 上海电力学院 | Electric car based on schedulable potentiality, which fills, changes storage one station method for establishing model |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN111832217A (en) * | 2020-06-04 | 2020-10-27 | 华北电力大学 | Virtual power plant optimized operation method considering wind power consumption |
| CN112186756A (en) * | 2020-09-27 | 2021-01-05 | 国网辽宁省电力有限公司经济技术研究院 | Energy storage capacity configuration method for virtual power plant |
| CN112186756B (en) * | 2020-09-27 | 2024-03-19 | 国网辽宁省电力有限公司经济技术研究院 | Energy storage capacity configuration method for virtual power plant |
| CN112215641A (en) * | 2020-10-10 | 2021-01-12 | 国网上海市电力公司 | Control method and system for intelligent building type virtual power plant to participate in energy frequency modulation |
| CN112215641B (en) * | 2020-10-10 | 2024-06-07 | 国网上海市电力公司 | Control method and system for participating in energy frequency modulation of intelligent building type virtual power plant |
| CN112350435A (en) * | 2020-10-16 | 2021-02-09 | 湖北华夏明源能源管理有限公司 | Virtual power plant management and control device based on micro-grid group and electric power controllable load |
| CN112215433A (en) * | 2020-10-21 | 2021-01-12 | 国网冀北电力有限公司 | Virtual power plant day-ahead optimized scheduling method based on uncertainty of market-derived electricity price |
| CN112215433B (en) * | 2020-10-21 | 2024-05-07 | 国网冀北电力有限公司 | Virtual power plant day-ahead optimal scheduling method based on uncertainty of market electricity price |
| CN113610380A (en) * | 2021-08-02 | 2021-11-05 | 上海电气集团股份有限公司 | Multi-energy complementary energy planning method |
| CN113937798A (en) * | 2021-10-12 | 2022-01-14 | 浙江华云电力工程设计咨询有限公司 | Energy storage system configuration method considering new energy consumption under multi-station fusion scene |
| CN113937798B (en) * | 2021-10-12 | 2024-04-30 | 浙江华云电力工程设计咨询有限公司 | Energy storage system configuration method considering new energy consumption in multi-station fusion scene |
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