CN103779869B - Consider the energy-accumulating power station capacity optimized calculation method of state-of-charge dynamic conditioning - Google Patents

Consider the energy-accumulating power station capacity optimized calculation method of state-of-charge dynamic conditioning Download PDF

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CN103779869B
CN103779869B CN201410063041.5A CN201410063041A CN103779869B CN 103779869 B CN103779869 B CN 103779869B CN 201410063041 A CN201410063041 A CN 201410063041A CN 103779869 B CN103779869 B CN 103779869B
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energy
storage
charge
wind
power
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CN103779869A (en
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刘海波
李建祥
袁弘
张秉良
高玉明
张华栋
郭亮
任杰
郭玉泉
赵金龙
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国家电网公司
国网山东省电力公司电力科学研究院
山东鲁能智能技术有限公司
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    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/76Power conversion electric or electronic aspects
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning, comprising: set up wind energy turbine set energy-storage system battery charge state partition model, and the control of over-charging of battery Cross prevention is carried out to model; Optimum for target structure wind energy turbine set energy storage system capacity optimization object function with the comprehensive benefit of energy storage, and set up energy-accumulating power station charge-discharge electric power constraints and output power fluctuation of wind farm horizontal restraint condition; Select PSO algorithm to solve calculating to energy storage system capacity optimization object function, determine the optimum capacitance values of wind energy turbine set energy-storage system.Beneficial effect of the present invention is: capacity optimization computation model of the present invention has considered the overall economics in energy-accumulating power station configuration and running, and be conducive to the effective combination with scene, the optimization for stored energy capacitance provides theoretical premise and guarantee.

Description

Consider the energy-accumulating power station capacity optimized calculation method of state-of-charge dynamic conditioning
Technical field
The present invention relates to power fluctuation and stabilize field, particularly relate to a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning.
Background technology
Wind energy is just worldwide utilized widely as a kind of regenerative resource.Due to the randomness of wind, intermittence and uncontrollability, making it exert oneself can have an impact to aspects such as the stability of line voltage and the qualities of power supply.And in the face of the sustainable growth of this kind of regenerative resource scale of wind energy, how solving the impact of its output-power fluctuation on electrical network becomes the major issue that current electric grid faces.At wind energy turbine set configuration certain capacity and the energy-storage system of power, can smooth wind power power fluctuation effectively, improve stability of power system.But the cost of energy-storage system configuration but restricts mutually with the effect stabilizing wind power fluctuation, how to be optimized stored energy capacitance for this reason, the validity and the economy that realize stabilizing wind power fluctuation need the problem of solution at present badly.
Calculate in the optimization of stored energy capacitance, current existence is not enough as follows: 1) at present with the research research being embodied in energy storage control plane of the state-of-charge of the energy-storage system energy-storage system that is parameter, and rarely have research based on state-of-charge and the energy-storage system optimum capacity planning of economy more; 2) stored energy capacitance is optimized in computational process, or only consider to ensure that wind power be stationary value is that standard carrys out configuration capacity in the long period, or with Wind turbines and energy storage device output-power fluctuation standard deviation for index is optimized, or consider that operating cost and cost of investment minimize as optimization aim, the index during all not enough using energy-storage system charge-discharge electric power or super-charge super-discharge state calculates as stored energy capacitance optimization the impact of stabilizing grid-connected power fluctuation.
State-of-charge (SOC) refers to the capacity ratio of its residual capacity and its fully charged state.As SOC=1, its span 0 to 1, represents that battery is full of completely, represent that battery discharge is complete as SOC=0.In energy-accumulating power station, under normal circumstances, the SOC of the state-of-charge maximum in each battery pack as whole energy-storage system is got during charging; The SOC of the state-of-charge minimum value in each battery pack as whole energy-storage system is got during electric discharge.Effectively can prevent the super-charge super-discharge phenomenon of single battery like this.
Traditional about in stored energy capacitance optimizing process, do not consider the state-of-charge of energy-storage system, such deficiency is: first, because super-charge super-discharge phenomenon frequently appears in energy-storage system, or be in abnormal work state-of-charge for a long time, cause greatly reduce its useful life, significantly add the cost of energy-storage system, be unfavorable for the consideration of economy; The second, the super-charge super-discharge of energy-storage system makes charge-discharge electric power be difficult to control, and the power injecting electrical network can be caused to occur big ups and downs, affect grid stability.
Summary of the invention
Object of the present invention is exactly to solve the problem, and proposes a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning, the method achieves the stored energy capacitance optimization taking dispatching requirement, storage energy operation life-span and economy into account.
To achieve these goals, the present invention adopts following technical scheme:
Consider an energy-accumulating power station capacity optimized calculation method for state-of-charge dynamic conditioning, comprise the following steps:
(1) set up wind energy turbine set energy-storage system battery charge state partition model, and the control of over-charging of battery Cross prevention is carried out to model.
(2) under the prerequisite ensureing level and smooth power output, optimum for target structure wind energy turbine set energy storage system capacity optimization object function with the comprehensive benefit of energy storage, and set up energy-accumulating power station charge-discharge electric power constraints and output power fluctuation of wind farm horizontal restraint condition.
(3) under the condition meeting the protection of wind energy turbine set energy-storage system battery charging and discharging, select PSO algorithm to solve calculating to energy storage system capacity optimization object function, determine the optimum capacitance values of wind energy turbine set energy-storage system.
The concrete grammar of described step (1) is:
The restriction classification of battery charge state: Q when setting energy-storage system runs sOCmaxand Q sOCminbe respectively the upper and lower bound of energy-storage system state-of-charge, [Q sOCmin, Q sOClow-L2] be put region, [Q sOClow-L2, Q sOClow-L1] put region, [Q for pre-mistake sOClow-L1, Q sOChigh-L1] be normal region, [Q sOChigh-L1, Q sOChigh-L2] for overcharge region in advance, [Q sOChigh-L2, Q sOCmax] for overcharging region, Q sOChigh-L2and Q sOClow-L2be respectively super-charge super-discharge warning line.
When state-of-charge is positioned at and overcharges region in advance, if revise make it reduce; If then maintain initial value.
When state-of-charge was positioned in advance and put region, if revise make it reduce; If then maintain initial value.
When state-of-charge is positioned at normal region, maintain initial value.
Wherein, for t energy-storage system charge-discharge electric power, time, energy-storage system is in charged state, time, energy-storage system is in discharge condition.
When time, revise the correction factor making it reduce is:
δ i ( t ) = 1 - lg ( Q SOC max - Q SOCi ( t ) Q SOC max - Q SOChigh - L 1 )
Revised energy-storage system charge power is:
P ESS(t)=δ i(t)ΔP(t)η C
Wherein, Q sOCit () is the state-of-charge of t energy-storage system, Q sOCmaxfor the upper limit of energy-storage system state-of-charge, Q sOChigh-L1for overcharging the lower limit of region state-of-charge in advance, η cfor the charge efficiency of energy-storage system, Δ P (t) is t Power Output for Wind Power Field P w(t) and grid-connected target power P refthe difference of (t): Δ P (t)=P w(t)-P ref(t).
When time, revise the correction factor making it reduce is:
δ i ( t ) = 1 - lg ( Q SOCi ( t ) - Q SOClow - L 2 Q SOClow - L 1 - Q SOClow - L 2 )
Revised energy storage system discharges power is: P eSS(t)=δ i(t) Δ P (t)/η d.
Wherein, Q sOCit () is the state-of-charge of t energy-storage system, Q sOClow-L1and Q sOClow-L2be respectively lower limit and the upper limit crossing in advance and put region, η dfor the discharging efficiency of energy-storage system, Δ P (t) is t Power Output for Wind Power Field P w(t) and grid-connected target power P refthe difference of (t): Δ P (t)=P w(t)-P ref(t).
In described step (2), wind energy turbine set energy storage system capacity optimization object function is:
minC=K Lρ LL LOST+K Sρ SL SHORT+K Eρ EL ESS+C C
Wherein, ρ l, ρ s, ρ ebe respectively wind energy turbine set and abandon the corresponding unit price that wind off-energy, smooth power shortage off-energy and energy-storage system get over the conversion energy that line runs; L lOST, L sHORT, L eSSbe respectively wind energy turbine set and abandon the conversion energy that wind off-energy, smooth power shortage off-energy and energy-storage system get over line operation; ρ ll lOSTfor wind energy turbine set abandons wind cost of energy; ρ sl sHORTfor wind energy turbine set smooth power shortage off-energy cost; ρ el eSSfor energy-storage system gets over the conversion off-energy cost of line operation; K l, K sand K efor the penalty coefficient of operating cost; C cthe input cost of energy-storage system.
The input cost C of energy-storage system ccomputational methods be:
C C=C M+C R+C B
C B=N bessρ 1W O+N bessρ 2W Om;
m = r ( 1 + r ) L m ( 1 + r ) L m - 1
Wherein, C mfor the maintenance cost of energy-storage system, C rfor the displacement cost of each energy-storage units of energy-storage system, C bfor the capital investment cost of energy-storage system, N bessfor the quantity of storage battery in energy-storage system; ρ 1for stored energy capacitance unit capacity installs price; W ofor the rated value of the optimum stored energy capacitance of wind energy turbine set; ρ 2for stored energy capacitance unit capacity price; M is coefficient of depreciation; R is allowance for depreciation; L mfor age of project.
Described wind energy turbine set is abandoned wind off-energy, smooth power shortage off-energy and energy-storage system and is got over the computational methods of conversion energy that line runs and be respectively:
L LOST = N y Σ i = 1 g Σ t = p q 1 - δ i ( t ) η C P ref ( t ) Δt
L SHORT = N y Σ i = 1 h Σ t = u v ( 1 - δ i ( t ) ) η D P ref ( t ) Δt
L LESS = N y Σ i = 1 k Σ t = x y ( Q SOCi ( t ) - Q SOChigh - L 2 ) W O + N y Σ i = 1 l Σ t = z a ( Q SOCi ( t ) - Q SOClow - L 2 ) W O
Wherein, N yfor year time of research object; G, h are N yin year, charge and discharge process continues δ ithe total degree in < 1 adjust operation interval; P, q are respectively the initial of g interval and end time; U, v are respectively the initial of h interval and end time; K is N yin year, energy-storage system running status is positioned at the total degree exceeding maximum state-of-charge; L is N yin year, energy-storage system running status is positioned at the total degree lower than minimum state-of-charge; X, y are respectively the initial of k interval and end time; Z, a are respectively the initial of l interval and end time, P reft () is grid-connected target power, δ it () is correction factor, Q sOCit () is the state-of-charge of t energy-storage system, Q sOChigh-L2and Q sOClow-L2be respectively super-charge super-discharge warning line, W ofor the rated value of the optimum stored energy capacitance of wind energy turbine set, η cfor the charge efficiency of energy-storage system, η dfor the discharging efficiency of energy-storage system, Δ t is sampling time step-length.
In described step (2), energy-accumulating power station charge-discharge electric power constraints is:
-P Dη D≤P W(t)-P ref(t)≤P C
In formula: P cand P dbe respectively the limit charge-discharge electric power of energy-storage system, regard electric discharge as negative charging process, its size is as the criterion with its absolute value; P wt () is t Power Output for Wind Power Field, P reft () is grid-connected target power.
Described output power fluctuation of wind farm horizontal restraint is:
P{|ΔP d(t)|≤ΔP dmax}≥Λ
In formula: Δ P dt () is the undulating value of Power Output for Wind Power Field after energy-storage system is stabilized; Δ P dmaxfor the permitted maximum range upper limit of undulating value; Λ is corresponding confidence level.
The step that described energy storage system capacity optimization object function solves calculating is:
A. wind power plant operation data time window length T and service data P(t thereof is extracted).
B. determine to expect power stage desired value P g, and given initial SOC value.
C., population dimension D is set, maximum iteration time M max, convergence precision C σ, initialization population position x and speed v simultaneously.
D. the fitness value of each particle is calculated and by himself particle extreme value p iand overall example extreme value p grelatively, if fitness value is less, then p is upgraded iand p g, otherwise, upgrade particle rapidity V and position X.
E. Δ σ is calculated 2judge whether to meet the condition of convergence, the described condition of convergence is:
lim t &RightArrow; &infin; &Delta;&sigma; 2 = C &sigma;
Δ σ in formula 2for the colony of population or the variable quantity of overall fitness variance, C σfor convergence precision, this convergence precision is the permanent number close to zero; If meet the condition of convergence, then obtain best stored energy capacitance W o; If do not meet the condition of convergence, again discharge example and set up new group, and repeat steps d.
The invention has the beneficial effects as follows:
Capacity optimization computation model of the present invention has considered the overall economics in energy-accumulating power station configuration and running, and be conducive to the effective combination with scene, the optimization for stored energy capacitance provides theoretical premise and guarantee.
The important indicator of the present invention using state-of-charge as energy-storage system running status, setting up by changing charge-discharge electric power correction factor the charging and recharging model suppressing excessive charge and discharge, achieving the adjustable strategies of SOC in stored energy capacitance layoutprocedure.This control strategy is after stored energy capacitance configuration simultaneously, can use for reference in corresponding actual wind energy turbine set-energy storage combined operation system, form actual wind energy turbine set energy storage control strategy; On this basis, introduce operating cost corresponding to state-of-charge, constructing with economic index is that target function sets up energy storage system capacity Optimized model, and this model realization takes the stored energy capacitance optimization of dispatching requirement, storage energy operation life-span and economy into account.
Accompanying drawing explanation
Fig. 1 is that power stage curve is expected in the present embodiment cross section seclected time;
Fig. 2 is that effect schematic diagram is stabilized in the present embodiment cross section seclected time;
Fig. 3 is the present embodiment SOC curve synoptic diagram.
Embodiment:
Below in conjunction with accompanying drawing and embodiment, the present invention will be further described:
1 state-of-charge partition model
The energy storage strategy of wind energy turbine set energy-storage system is: when Wind turbines power output is greater than grid-connected value and power reference, and energy-storage system charging is to stabilize output-power fluctuation; When Wind turbines power output is less than grid-connected value and power reference, energy storage system discharges, to make up the deficiency of power output, with the power output of this smooth wind power unit, realizes the stability of wind-electricity integration power.
T Power Output for Wind Power Field P w(t) and grid-connected target power P reft the difference DELTA P (t) of () is:
ΔP(t)=P W(t)-P ref(t)(1)
Then the charge-discharge electric power of energy-storage system is such as formula shown in (2,3).
When energy-storage system is in charged state:
P ESS ref ( t ) = &Delta;P ( t ) &eta; C - - - ( 2 )
When energy-storage system is in discharge condition:
P ESS ref ( t ) = &Delta;P ( t ) / &eta; D - - - ( 3 )
In formula: for t energy-storage system charge-discharge electric power; When time, energy-storage system charges, time, energy storage system discharges; η cfor the charge efficiency of energy-storage system, generally get 0.65 ~ 0.85.
The instruction of energy-accumulating power station charge-discharge electric power should consider current SOC level and the power instruction size of current time, and namely when SOC is positioned at normal range of operation, the charge-discharge electric power of energy-accumulating power station remains unchanged; When SOC gets over line to non-normal working scope, need to adjust charge-discharge electric power in time, prevent super-charge super-discharge phenomenon.
The restriction classification of SOC when setting energy-storage system runs.Wherein, Q sOCmaxand Q sOCminbe respectively the upper and lower bound of energy-storage system state-of-charge, [Q sOCmin, Q sOClow-L2] be put region, [Q sOClow-L2, Q sOClow-L1] put region, [Q for pre-mistake sOClow-L1, Q sOChigh-L1] be normal region, [Q sOChigh-L1, Q sOChigh-L2] for overcharge region in advance, [Q sOChigh-L2, Q sOCmax] for overcharging region, be more than the traffic coverage of the different state-of-charge of energy-storage system, wherein Q sOChigh-L2and Q sOClow-L2super-charge super-discharge warning line respectively.
2 super-charge super-discharge protecting control
The change of energy-storage system state-of-charge traffic coverage will cause the correspondence adjustment of corrected coefficient of power, be changed the charge-discharge electric power of energy-storage system, to reach the operation controlling energy-storage system in advance, avoid it to reach the state of super-charge super-discharge by corrected coefficient of power.Concrete control strategy is as shown in table 1.
Table 1 corrected coefficient of power control law
Analyze known: when energy-storage system state-of-charge is higher, be namely positioned at when overcharging region in advance, represent that energy storage is tending towards saturated.If place in the charge state it is right to need control in advance, through type (4) Modulating Power correction factor, revise make it reduce, to alleviate the speed that its state-of-charge raises, prevent energy-storage system from occurring the state overcharged; If place in the discharged condition then maintain initial value.Vice versa, when energy-storage system state-of-charge is on the low side, was namely positioned at and crosses when putting region in advance, if be in discharge condition through type (5) Modulating Power correction factor, revises make it reduce, to slow down the speed that its state-of-charge reduces, prevent energy-storage system from occurring the state of deep discharge.If place in the charge state then maintain initial value.When energy-storage system state-of-charge is positioned at normal region, maintains correction factor constant, make its normal discharge and recharge.
&delta; i ( t ) = 1 - lg ( Q SOC max - Q SOCi ( t ) Q SOC max - Q SOChigh - L 1 ) - - - ( 4 )
&delta; i ( t ) = 1 - lg ( Q SOCi ( t ) - Q SOClow - L 2 Q SOClow - L 1 - Q SOClow - L 2 - - - ( 5 )
In formula, δ it () is t charge-discharge electric power correction factor, when energy-storage system is positioned at normal region, value is 1; Q sOCt () is the state-of-charge of t energy-storage system.Here logarithm barrier function is adopted, when state-of-charge is close to Q sOCmaxor Q sOClow-L2time, because logarithmic function convergence is strong, δ can be reduced faster it (), plays the effect controlling charge-discharge electric power in advance better, effectively avoid the state-of-charge of energy-storage system to reach and overcharge or excessively put state.
It should be noted that, the corrected coefficient of power control method that the present invention proposes reaches Q at energy-storage system state-of-charge sOChigh-L2time, δ it () minimum value is not 0, its object is to ensure making full use of of stored energy capacitance, still can continue charging; And state-of-charge reaches Q sOClow-L2time by δ it () is modified to zero, strictly can control the lowest capacity of energy-storage system like this, thoroughly avoids energy-storage system to operate in and puts region, reduces the life consumption of energy-storage system.
Thus, the energy-storage system charge-discharge electric power after can being adjusted.
When energy-storage system is in charged state:
P ESS(t)=δ i(t)ΔP(t)η C(6)
When energy-storage system is in discharge condition:
P ESS(t)=δ i(t)ΔP(t)/η D(7)
In formula (6), formula (7): P eSSt () is the energy-storage system charge-discharge electric power of t after corrected coefficient of power adjustment, work as P eSSt, during () > 0, energy-storage system charges, P eSSduring (t) < 0, energy storage system discharges.
Stored energy capacitance is planned
Under the target of wind farm energy storage capacity optimization is the prerequisite ensureing to reduce wind power output power fluctuation, regulate the mutual restricting relation between input cost and operating cost, under the prerequisite ensureing level and smooth power output, realize the on-road efficiency optimization of wind energy turbine set energy-storage system with the input cost of minimum energy storage and operating cost.
1 target function
Wind energy turbine set configures the wind power fluctuation that different stored energy capacitances obtains and stabilizes effect difference, under guarantee meets the prerequisite of output power fluctuation of wind farm requirement, for the restricting relation of stored energy capacitance input cost and operating cost, reach optimum for target with the comprehensive benefit of energy storage.Wherein, the input cost C of energy-storage system ccomprise the maintenance cost C of energy-storage system m, displacement cost (only considering when the useful life of energy-storage units the is less than age of project) C of each energy-storage units of energy-storage system rwith the capital investment cost C of energy-storage system b.
C C=C M+C R+C B(8)
C B=N bessρ 1W O+N bessρ 2W Om(9)
In formula: N bessfor the quantity of storage battery in energy-storage system; ρ 1for stored energy capacitance unit capacity installs price; W ofor the rated value of the optimum stored energy capacitance of wind energy turbine set; ρ 2for stored energy capacitance unit capacity price; M is coefficient of depreciation, and it is defined as:
m = r ( 1 + r ) L m ( 1 + r ) L m - 1 - - - ( 10 )
In formula: r is allowance for depreciation; L mfor age of project.
Operating cost comprises the wind energy turbine set caused because of corrected coefficient of power adjustment and abandons windage loss and lose cost, and smooth power shortage loss cost and energy-storage system get over the conversion loss cost that line runs, and three all changes because of the change of stored energy capacitance.
Because Power Output for Wind Power Field has annual cycles, the research object optimized using annual Power Output for Wind Power Field as stored energy capacitance, its wind energy turbine set is abandoned wind off-energy, smooth power shortage off-energy and energy-storage system and is got over the conversion energy of line operation respectively such as formula shown in (11), formula (12), formula (13):
L LOST = N y &Sigma; i = 1 g &Sigma; t = p q 1 - &delta; i ( t ) &eta; C P ref ( t ) &Delta;t - - - ( 11 )
L SHORT = N y &Sigma; i = 1 h &Sigma; t = u v ( 1 - &delta; i ( t ) ) &eta; D P ref ( t ) &Delta;t - - - ( 12 )
L LESS = N y &Sigma; i = 1 k &Sigma; t = x y ( Q SOCi ( t ) - Q SOChigh - L 2 ) W O + N y &Sigma; i = 1 l &Sigma; t = z a ( Q SOCi ( t ) - Q SOClow - L 2 ) - - - ( 13 )
In formula: N yfor year time of research object; G, h are N yin year, charge and discharge process continues δ ithe total degree in < 1 adjust operation interval; P, q are respectively the initial of g interval and end time; U, v are respectively the initial of h interval and end time; K is N yin year, energy-storage system running status is positioned at the total degree exceeding maximum state-of-charge; L is N yin year, energy-storage system running status is positioned at the total degree lower than minimum state-of-charge; X, y are respectively the initial of k interval and end time; Z, a are respectively the initial of l interval and end time.
The target of wind farm energy storage capacity optimization is
minC=K Lρ LL LOST+K Sρ SL SHORT+K Eρ EL ESS+C C(14)
In formula: ρ l, ρ s, ρ ebe respectively wind energy turbine set and abandon the corresponding unit price that wind off-energy, smooth power shortage off-energy and energy-storage system get over the conversion energy that line runs; ρ ll lOSTfor wind energy turbine set abandons wind cost of energy; ρ sl sHORTfor wind energy turbine set smooth power shortage off-energy cost; ρ el eSSfor energy-storage system gets over the conversion off-energy cost of line operation; K l, K sand K efor the penalty coefficient of operating cost; C cthe input cost of energy-storage system.
In formula (13), the conversion loss cost that energy-storage system gets over line operation comprises 2 parts: one is when energy-storage system operates in too high state-of-charge, and energy-storage system is not in the conversion cost that reasonable running status affects its shelf-life cycle; Two is when energy-storage system state-of-charge is too low, and energy-storage system is not in the conversion cost that reasonable running status affects its shelf-life cycle.
2 constraintss
Charge-discharge electric power retrains:
-P Dη D≤P W(t)-P ref(t)≤P C(14)
In formula: P dand P cbe respectively the limit charge-discharge electric power of energy-storage system, regard electric discharge as negative charging process, its size is as the criterion with its absolute value.
Constraints comprises output power fluctuation of wind farm horizontal restraint:
P{|ΔP d(t)|≤ΔP dmax}≥Λ(15)
In formula: Δ P d(t) Δ P dt () is the undulating value of Power Output for Wind Power Field after energy-storage system is stabilized; Δ P dmaxfor the permitted maximum range upper limit of undulating value; Λ is corresponding confidence level.
3 method for solving
Population (PSO) algorithm is that one has calculating simply, the intelligent group computational methods of the advantages such as good convergence, be widely used in solving all kinds of Numerical Optimization, but still there is search precision when solving some of complex optimization problem high and be easily absorbed in the defect of locally optimal solution.For this reason, the present invention considers that PSO algorithm is to solve the present invention and comprise dynamic boundary condition and stochastic optimization problems containing multiple stochastic variable.Concrete mode is as follows:
Step 1: selected research object time cross-section length of window y and service data P(t thereof);
Step 2: based on best power output model determination desired output desired value P g, and given initial SOC is equivalent;
Step 3: population dimension D is set, maximum iteration time M max, convergence precision C σ, initialization population position x and speed x simultaneously;
Step 4: according to discharge and recharge strategy of the present invention, c1, c2, ω, V are set min, V maxetc. parameter, convolution (11-15) calculates the fitness value of each particle and by himself particle extreme value p iand overall example extreme value p grelatively, if fitness value is less, then p is upgraded iand p g, upgrade particle rapidity V if not idand position X id;
Step 5: calculate Δ σ 2judge whether to meet the condition of convergence, the search condition of convergence is:
lim t &RightArrow; &infin; &Delta;&sigma; 2 = C &sigma; - - - ( 16 )
Δ σ in formula 2for the colony of population or the variable quantity of overall fitness variance, C σfor close to zero permanent number.If so, best stored energy capacitance W is then obtained o; If not, again discharge example and set up new group, and repeat step (4).
Certain wind energy turbine set installed capacity 90MW, choose wind power data in 2011, frequency acquisition is 5min, stabilizes desired value as shown in Figure 1.
Optimize computation model according to civilian invention charge-discharge electric power adjustable strategies and stored energy capacitance, obtain stabilizing fluctuation curve of output as shown in Figure 2, the inventive method result of calculation as shown in Table 2.
Form 2 result of calculation
Analyze above-mentioned example can obtain, capacity planning aspect, the inventive method effectively achieves the optimization of stored energy capacitance; Stabilize power offset aspect, the inventive method is close with conventional method, slightly increases, and its reason is that corrected coefficient of power adjustable strategies improves the probability abandoned wind or stabilize not enough energy; More limiting value operation aspect, the present invention significantly reduces the numerical value of N, and it decreases by 96.2%, successful.Investigate the changing condition of SOC in the optimum procurement of reserve capacity process of energy-accumulating power station, as shown in Figure 3.Can find out, in the inventive method SOC this section more limiting value to run, effective guarantee useful life of ESS.
To sum up can obtain, capacity optimization computation model of the present invention has considered the overall economics in energy-accumulating power station configuration and running, is conducive to the effective combination with scene.Above-mentioned theory research is that the optimization of stored energy capacitance provides theoretical premise and guarantee.Meanwhile, real data sample calculation analysis demonstrates above-mentioned conclusion.
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.

Claims (8)

1. consider an energy-accumulating power station capacity optimized calculation method for state-of-charge dynamic conditioning, it is characterized in that, comprise the following steps:
(1) set up wind energy turbine set energy-storage system battery charge state partition model, and the control of over-charging of battery Cross prevention is carried out to model;
(2) under the prerequisite ensureing level and smooth power output, optimum for target structure wind energy turbine set energy storage system capacity optimization object function with the comprehensive benefit of energy storage, and set up energy-accumulating power station charge-discharge electric power constraints and output power fluctuation of wind farm horizontal restraint condition;
(3) under the condition meeting the protection of wind energy turbine set energy-storage system battery charging and discharging, select PSO algorithm to solve calculating to energy storage system capacity optimization object function, determine the optimum capacitance values of wind energy turbine set energy-storage system;
The concrete grammar of described step (1) is:
The restriction classification of battery charge state: Q when setting energy-storage system runs sOCmaxand Q sOCminbe respectively the upper and lower bound of energy-storage system state-of-charge, [Q sOCmin, Q sOClow-L2] be put region, [Q sOClow-L2, Q sOClow-L1] put region, [Q for pre-mistake sOClow-L1, Q sOChigh-L1] be normal region, [Q sOChigh-L1, Q sOChigh-L2] for overcharge region in advance, [Q sOChigh-L2, Q sOCmax] for overcharging region, Q sOChigh-L2and Q sOClow-L2be respectively super-charge super-discharge warning line;
Wherein, Q sOClow-L1and Q sOChigh-L1be respectively the energy-storage system state-of-charge limit value of normal region;
When state-of-charge is positioned at and overcharges region in advance, if revise make it reduce; If then maintain initial value;
When state-of-charge was positioned in advance and put region, if revise make it reduce; If then maintain initial value;
When state-of-charge is positioned at normal region, maintain initial value;
Wherein, for t energy-storage system charge-discharge electric power, time, energy-storage system is in charged state, time, energy-storage system is in discharge condition.
2. a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning as claimed in claim 1, is characterized in that, time, revise the correction factor making it reduce is:
&delta; i 2 ( t ) = 1 - lg ( Q S O C max - Q S O C i ( t ) Q S O C max - Q S O C h i g h - L 1 ) ;
Revised energy-storage system charge power is:
P ESS(t)=δ i1(t)ΔP(t)η C
Wherein, Q sOCit () is the state-of-charge of t energy-storage system, Q sOCmaxfor the upper limit of energy-storage system state-of-charge, Q sOChigh-L1for overcharging the lower limit of region state-of-charge in advance, η cfor the charge efficiency of energy-storage system, Δ P (t) is t Power Output for Wind Power Field P w(t) and grid-connected target power P refthe difference of (t): Δ P (t)=P w(t)-P ref(t).
3. a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning as claimed in claim 1, is characterized in that, when time, revise the correction factor making it reduce is:
&delta; i 2 ( t ) = 1 - lg ( Q S O C i ( t ) - Q S O C l o w - L 2 Q S O C l o w - L 1 - Q S O C l o w - L 2 ) ;
Revised energy storage system discharges power is: P eSS(t)=δ i2(t) Δ P (t)/η d;
Wherein, Q sOCit () is the state-of-charge of t energy-storage system, Q sOClow-L1and Q sOClow-L2be respectively lower limit and the upper limit crossing in advance and put region, η dfor the discharging efficiency of energy-storage system, Δ P (t) is t Power Output for Wind Power Field P w(t) and grid-connected target power P refthe difference of (t): Δ P (t)=P w(t)-P ref(t).
4. a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning as claimed in claim 1, is characterized in that, in described step (2), wind energy turbine set energy storage system capacity optimization object function is:
minC=K Lρ LL LOST+K Sρ SL SHORT+K Eρ EL ESS+C C
Wherein, ρ l, ρ s, ρ ebe respectively wind energy turbine set and abandon the corresponding unit price that wind off-energy, smooth power shortage off-energy and energy-storage system get over the conversion energy that line runs; L lOST, L sHORT, L eSSbe respectively wind energy turbine set and abandon the conversion energy that wind off-energy, smooth power shortage off-energy and energy-storage system get over line operation; ρ ll lOSTfor wind energy turbine set abandons wind cost of energy; ρ sl sHORTfor wind energy turbine set smooth power shortage off-energy cost; ρ el eSSfor energy-storage system gets over the conversion off-energy cost of line operation; K l, K sand K efor the penalty coefficient of operating cost; C cthe input cost of energy-storage system.
5. a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning as claimed in claim 4, is characterized in that, the input cost C of energy-storage system ccomputational methods be:
C C=C M+C R+C B
C B=N bessρ 1W O+N bessρ 2W Om;
m = r ( 1 + r ) L m ( 1 + r ) L m - 1 ;
Wherein, C mfor the maintenance cost of energy-storage system, C rfor the displacement cost of each energy-storage units of energy-storage system, C bfor the capital investment cost of energy-storage system, N bessfor the quantity of storage battery in energy-storage system; ρ 1for stored energy capacitance unit capacity installs price; W ofor the rated value of the optimum stored energy capacitance of wind energy turbine set; ρ 2for stored energy capacitance unit capacity price; M is coefficient of depreciation; R is allowance for depreciation; L mfor age of project.
6. a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning as claimed in claim 1, is characterized in that, in described step (2), energy-accumulating power station charge-discharge electric power constraints is:
-P Dη D≤P W(t)-P ref(t)≤P C
In formula: P cand P dbe respectively the limit charge-discharge electric power of energy-storage system, regard electric discharge as negative charging process, its size is as the criterion with its absolute value; P wt () is t Power Output for Wind Power Field, P reft () is grid-connected target power, η dfor the discharging efficiency of energy-storage system.
7. a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning as claimed in claim 1, is characterized in that, in described step (2), output power fluctuation of wind farm horizontal restraint is:
P{|ΔP d(t)|≤ΔP dmax}≥Λ
In formula: Δ P dt () is the undulating value of Power Output for Wind Power Field after energy-storage system is stabilized; Δ P dmaxfor the permitted maximum range upper limit of undulating value; Λ is corresponding confidence level.
8. a kind of energy-accumulating power station capacity optimized calculation method considering state-of-charge dynamic conditioning as claimed in claim 1, it is characterized in that, the step that described energy storage system capacity optimization object function solves calculating is:
A. wind power plant operation data time window length T and service data P (t) thereof is extracted;
B. determine to expect power stage desired value P g, and given initial SOC value;
C., population dimension D is set, maximum iteration time M max, convergence precision C σ, initialization population position x and speed v simultaneously;
D. the fitness value px of each particle is calculated id, and by himself particle extreme value p iand overall example extreme value p grelatively, if fitness value is less, then p is upgraded iand p g, otherwise, upgrade particle rapidity V and position X;
E. Δ σ is calculated 2judge whether to meet the condition of convergence, the described condition of convergence is:
lim t &RightArrow; &infin; &Delta;&sigma; 2 = C &sigma;
Δ σ in formula 2for the colony of population or the variable quantity of overall fitness variance, C σfor convergence precision, this convergence precision is for connecing
Be bordering on the permanent number of zero; If meet the condition of convergence, then obtain best stored energy capacitance W o; If do not meet the condition of convergence, again discharge example and set up new group, and repeat steps d.
CN201410063041.5A 2014-02-24 2014-02-24 Consider the energy-accumulating power station capacity optimized calculation method of state-of-charge dynamic conditioning CN103779869B (en)

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