CN102664423B  Wind power station energy storage capacity control method based on particle swarm optimization  Google Patents
Wind power station energy storage capacity control method based on particle swarm optimization Download PDFInfo
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 CN102664423B CN102664423B CN201210172086.7A CN201210172086A CN102664423B CN 102664423 B CN102664423 B CN 102664423B CN 201210172086 A CN201210172086 A CN 201210172086A CN 102664423 B CN102664423 B CN 102664423B
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 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention relates to a wind power station energy storage capacity control method based on particle swarm optimization. The wind power station energy storage capacity control method includes the steps of taking the interval reference value of the wind power station output power which is adapted to the dispatching cycle of a power grid as a foundation, taking the influence of the windabandoning energy of a wind power station and the lost energy of an energy storage system into consideration, taking the lowest costs of the energy storage investment and a wind and power operation system as target functions, establishing a policy model for energy storage capacity optimizing based on a storage battery energy storage system, and then applying the improved particle swarm optimization to solve the functions. By the aid of the wind power station energy storage capacity control method based on the particle swarm optimization, the wind power which is output under effect of the energy storage system can be output smoothly at intervals, so that effective connection between the energy storage system and the existing dispatching operation manner can be realized, and the best economic benefit can be achieved simultaneously.
Description
Technical field
The present invention relates to a kind of wind energy turbine set stored energy capacitance control method based on particle cluster algorithm.
Background technology
Follow the continuous expansion of windpowered electricity generation scale, when it carries a large amount of clean electric energy for electrical network, the impact that power system dispatching is moved is also in continuous intensification.Trace it to its cause, be mainly that the intermittence of wind energy and randomness cause wind power random fluctuation, and be difficult to Accurate Prediction.Therefore, research grid connected wind power power distribution characteristics, utilizes energy storage method to stabilize wind power fluctuation, realizes stable, reliable wind power dispatching and has urgent current demand.
The distribution character of wind energy has significant time cycle property, and its typical period of waves is as season, year.Corresponding with it, wind power has abovementioned cyclophysis equally.Therefore, utilize wind speed or wind power historical data, analyze the wind power regularity of distribution under the cycle in year, and determine the required stored energy capacitance size of wind energy turbine set with one or more criterions, with this, make poor the diminishing of peak, paddy of Power Output for Wind Power Field, and then it is smoothly exported.
In recent years, for the energystorage system of smooth wind power field power output, can realize power adjustments in a big way, can effectively suppress the fluctuation of wind power, thereby be applied widely.At present, the research of energy storage technology focuses mostly in the qualitative analysis of coordinating control and smooth effect, and obtains many achievements in research.Yet wind power proposes requirements at the higher level to dispatching of power netwoks because of its stochastic volatility, but power storage for wind power adapt to decision of power system dispatching provide may, and can this improve the receiving ability of electrical network to wind power.Thus, the utmost point is necessary to further investigate for the wind energy turbine set stored energy capacitance optimization problem of considering adaptation decision of power system dispatching.
Literary composition [Han Tao, Lu Jiping, Qiao Liang, Deng. Largescale Wind Power field stored energy capacitance prioritization scheme [J]. electric power network technique, 2010,34 (1): 169173.] regularity of distribution based on wind power output power, take wind energy turbine set average power level as expectation power output, consider that wind energy turbine set continues the impact of output hourage, determines the stored energy capacitance of wind energy turbine set, literary composition [TomokiAsao, RionTakahashi, Toshiaki Murata, et al.Evaluation method of power rating and energy capacity of superconducting magnetic energy storage system for output smoothing control of wind farm[C] .Proceedings of the 2008International Conference on Electrical Machines.2008, page:16] apply superconductive energy storage system (SMES) in conjunction with lowpass filtering theory smooth wind power power output, and provide energy storage control strategy, literary composition [Cheng Miaomiao, Kang Longyun, Xu great Ming, Deng. the Optimal Capacity of EnergyStoring Section in PV/Wind Hybrid System [J]. electrical applications, 2006,25 (6): 8790, Sun Yaojie, Kang Longyun, Shi Weixiang, etc. the chance constrained programming of best battery capacity [J] in distributed power source. Journal of System Simulation, 2005,17 (1): 4144] using storage battery and flywheel simultaneously as energystorage units, take the equilibrium of supply and demand as constraint, take system cost as target, by genetic algorithm, ask for the stored energy capacitance of honourable compound stand alone generating system, literary composition [Li Bihui, Shen Hong, soup gushes, etc. windsolarstorage joint electricity generation system stored energy capacitance is on the impact of active power and evaluation index [J]. electric power network technique, 2011,35 (4): 123128, Liang Liang, Li Jianlin, Huidong, Deng. distribute rationally [J] of stored energy capacitance for Large Scale Wind Farm Integration. high voltage technique, 2011,37 (4): 930936] consider that different stored energy capacitances are on stabilizing the impact of wind power output power, and provide the index of weighing total meritorious outputpower fluctuation, literary composition [Xu Daming, Kang Longyun, Chang Liuchen, et al.Optimal sizing of standalone hybrid wind/pv power systems using genetic algorithms[C] .18
^{th}annual Canadian Conference on Electrical and Computer Engineering.Saskatoon, Saskatchewan, Canada:IEEE, 2005, page:17221725] take into account the impact at winddriven generator type, capacity (number of units) and photovoltaic cell inclination angle, adopt the capacity configuration of genetic algorithm optimization Standalone Hybrid Wind/pv Power Systems.
Abovementioned research or ensure that in a long time wind power is certain value, or there is larger fluctuation under single time window, all do not take into full account the adaptability between energy storage and decision of power system dispatching, so just, making wind energy turbine set when high wind power, abandon wind energy increases, wind energy utilization reduces, compensation power increases during low wind power simultaneously, thus make the cost of investment of energystorage system and operating cost economical not, stored energy capacitance is controlled also nonoptimum state.
Summary of the invention
Object of the present invention is exactly for addressing the above problem, a kind of wind energy turbine set stored energy capacitance control method based on particle cluster algorithm is provided, it be take and adapts to the Power Output for Wind Power Field period reference value in dispatching of power netwoks cycle and be basis, take into account wind energy turbine set simultaneously and abandon the impact of wind energy and energystorage system offenergy, take energy storage cost of investment and wind power system operating cost minimum is target function, foundation is based on energystorage system of accumulator (battery energy storage system, BESS) stored energy capacitance Optimization Decision Models, and the particle cluster algorithm of application enhancements solves.This research makes to realize at times smoothly output through the wind power of energystorage system effect output, realizes energystorage system be connected with the effective of existing dispatching running way with this, reaches optimum economic benefit simultaneously.
For achieving the above object, the present invention adopts following technical scheme:
A wind energy turbine set stored energy capacitance control method based on particle cluster algorithm, its step is:
1) set up target function
The equivalent power output variance minimum of take is target, and chooses different time window according to dispatching requirement, with this, asks for the Power Output for Wind Power Field period reference value that adapts to existing dispatching running way; Because changing the system operation cost variation causing, stored energy capacitance have wind energy turbine set to abandon the cost F of wind energy
_{lOWE}cost F with energystorage system anergy
_{loss}; With the regularity of distribution of a certain year wind power of wind energy turbine set, as the distribution character of wind power in this wind energy turbine set operation time limit, its wind energy turbine set is abandoned wind energy and energystorage system offenergy suc as formula shown in (1), (2);
The target function of wind storage system stored energy capacitance optimization comprises cost of investment and operating cost, as the formula (3);
min?f＝min（K
_{W}F
_{LOWE}+K
_{P}F
_{LOSS}+K
_{I}(ρ
_{I}C
_{bat.N}+r
_{s})）(3)
In above formula, ρ
_{p}and ρ
_{q}it is respectively the corresponding unit price that wind energy turbine set is abandoned wind energy and energystorage system anergy; N
_{year}it is the unit operation time limit; T investigates the period, is 1 year; C
_{bat.N}it is the rated value that wind energy turbine set is optimized stored energy capacitance; S
_{lOWE1}(t), S
_{lOWE2}(t), S
_{lOSS1}and S (t)
_{lOWE2}(t) be that wind energy turbine set is abandoned wind energy and energystorage system loses the Boolean quantity of energy situation definition in order to describe, shown in (4), formula (5); ρ
_{i}for stored energy capacitance unit capacity price; r
_{s}for energy storage device installation cost; K
_{w}, K
_{p}and K
_{i}it is the compromise coefficient of operating cost (wind energy turbine set is abandoned wind energy and energystorage system offenergy) and cost of investment;
In formula (1), wind energy turbine set is abandoned wind energy and is comprised two parts, and a part produces because of battery capacity restriction, when accumulator electricquantity is filled to after rated capacity, storage battery stops charging, and unnecessary windpowered electricity generation unloads by unloader, and this part is abandoned wind energy suc as formula shown in (1) first; A part is that, when the difference of Power Output for Wind Power Field and reference output power is greater than the maximum charge power of storage battery, storage battery can not be filled with battery by this power completely, can only be according to P because the maximum charge Power Limitation of storage battery produces
_{batc} ^{max}storage battery is charged, thus unnecessary P
_{batc} ^{max}the power of part can only discard, and this part is abandoned wind energy suc as formula shown in (1) second portion;
Equally, in formula (2), energystorage system offenergy comprises two parts, and a part produces because of the restriction of storage battery minimum capacity, shown in (9) first; A part is that the maximum discharge power restriction due to storage battery produces, shown in (2) second portion;
2) set up constraints
Constraints comprises storage battery constraint and wind energy turbine set power constraint:
Batteries to store energy capacityconstrained
The rate of change constraint of accumulator cell charging and discharging power:
Output power fluctuation of wind farm horizontal restraint:
P{ΔP
_{d}(i)≤ΔP
^{max}}≥β(10)
Abovementioned various in, C
_{batmin}minimum capacity for storage battery permission; C
_{bat.N}rated capacity for batteries to store energy; P
_{batc} ^{max}and P
_{batd} ^{max}maximum charge/discharge power for storage battery; DOD(depth of battery discharging) be the depth of discharge of storage battery; Δ P
_{d}(i), Δ P
^{max}for the undulating value of wind energy turbine set power output after energy storage leveling and the upper limit in allowed band thereof; β is corresponding confidence level;
3) particle cluster algorithm
By the abovementioned equation of PSO Algorithm, obtain required stored energy capacitance, and control thermal energy storage process with this.
In described step 1), in investigating period T, defining M sampling interval Δ t is a time window (1 ~ 2h), take that to investigate in the period equivalent power output variance and minimum in each time window be target function, suc as formula (11);
Wherein, t
_{1}the time of a time window, and t
_{1}Δ tM; t
_{2}the initial time of i time window, and t
_{2}=((i1) M+1) Δ t; P
_{ref}(i) be the wind energy turbine set reference output power of i time window; M is according to changing different dispatching cycles.
In step 3), concrete process of solution is as follows:
(1) input windpowered electricity generation unit power output and Power Output for Wind Power Field period reference value;
(2) put population dimension K
_{pSO}, maximum iteration time N
_{psomax}, computational accuracy σ
_{pso};
(3) position of initialization population and speed, give the C under calculating for settled time
_{bat.N}value;
(4) by formula (3), calculate required particle fitness value;
(5) each particle fitness value extreme value individual with it compared, as more excellent, upgrade current individual extreme value P
_{besti};
(6) each particle adaptive value and global value are compared, as more excellent, upgrade current global extremum G
_{best};
(7) according to formula (12) and (13), upgrade position and the speed of each particle, and after upgrading according to formula (15) ~ (17) check, whether particle meets constraints requirement, if do not met, regenerate particle rapidity, upgrade position, until meet constraints, if update times surpasses the number of times of regulation, with former feasible particle, replace;
In formula: n is current cycle time; c
_{1}, c
_{2}for particle weight coefficient; W is inertia weight; r
_{1}, r
_{2}for (0,1) interior uniform random number; x
_{i}, v
_{i}it is the Position And Velocity of i dimension particle; G is constraint factor;
(8) repeating step (4) is to step (6);
(9) judge whether current iteration number of times and error amount meet the demands, do not meet and upgrade C
_{bat.N}value, returns to step (7), otherwise stops particle optimizing, and exports result of calculation.
The invention has the beneficial effects as follows:
(1) cost of investment and the operating cost with energy storage is minimised as optimization aim, take and compromises thought as guidance, sets up the stored energy capacitance Optimization Decision Models with optimum economic benefit;
(2) by the selection of best stored energy capacitance, provide the wind power output power of level and smooth output at times simultaneously, be effectively connected existing dispatching of power netwoks operational mode, improve the receiving ability of electrical network to windpowered electricity generation;
(3) application enhancements particle cluster algorithm solves built Optimized model, and this algorithm has higher computational accuracy and speed, and can effectively overcome dynamic boundary condition problem, and conclusion has also been verified the validity of theory analysis;
(4) BESS is applied to containing in the gridconnected system of windpowered electricity generation unit, while analyzing gridconnected system different faults type, and site place has or not the stability of a system in BESS situation, as access point voltage, wind power generator rotor rotating speed etc., and formulate rational BESS control strategy.
Accompanying drawing explanation
Fig. 1 is the flow chart of invention;
Fig. 2 is particle cluster algorithm block diagram;
Power Output for Wind Power Field when Fig. 3 is optimum energy storage;
Power Output for Wind Power Field when Fig. 4 is complete energy storage.
Embodiment
Below in conjunction with accompanying drawing and case study on implementation, the present invention will be further described.
In Fig. 1, the energy storage strategy of wind storage electricity generation system is: when wind power generation power is greater than the power output period during reference value, charge in batteries.If accumulator electricquantity arrives its heap(ed) capacity C
_{bat.N}, next moment storage battery will not recharge, and now by unloader, unnecessary windpowered electricity generation be unloaded, and this energy is referred to as wind energy turbine set and abandons wind energy (Loss of Wind Energy, LOWE).When wind power generation power is less than the power output period during reference value, battery discharging.If accumulator electricquantity arrives its minimum capacity C
_{batmin}, next moment storage battery will no longer discharge, and now have partial reference power output and be not being met, and the unappeasable electric energy of this part is referred to as energystorage system offenergy (Loss of Storage System, LOSS).
T is wind power output power P constantly
_{wG}(t) with period reference output power P
_{ref}(t) difference P
_{Δ}(t) be:
P
_{Δ}(t)＝P
_{WG}(t)P
_{ref}(t)??(14)
Disregarding under the restriction prerequisite of battery capacity and maximum charge/discharge power thereof, the charge/discharge power of storage battery is suc as formula shown in (14).If only consider the impact of the maximum charge/discharge power of storage battery, the charge/discharge power of storage battery is:
In formula, P '
_{bat}(t) be the charge/discharge power of storage battery while only considering maximum charge/discharge power influences; P
_{batc} ^{max}the maximum charge power of storage battery; P
_{batd} ^{max}it is the maximum discharge power of storage battery.
If P '
_{bat}(t) >0, t moment storage battery is in charged state; Otherwise, in discharge condition.
Based on above working foundation, consider the impact of battery capacity and maximum charge/discharge power thereof herein simultaneously, the t1 moment and t stored energy capacitance, charge status of battery constantly can be divided into following situation.
Charge in batteries
When Power Output for Wind Power Field is greater than the wind power output power period during reference value, storage battery stores unnecessary energy with charging form, until storage battery is full of.T constantly storage battery initial capacity is:
In formula, η
_{cha}for charge in batteries efficiency, be generally taken as 0.65 ~ 0.85; Δ t is wind power samples interval; P
_{bat}(t) be on the occasion of, be t charge in batteries power constantly while taking into account storage battery heap(ed) capacity and maximum charge Power Limitation; And meet in the Δ t period, theoretical chargeable electric weight is less than this chargeable electric weight of storage battery constantly, meets formula P
_{bat}' (t) * Δ t < C
_{bat.N}C
_{bat}(t1).
In charging process, if battery capacity is filled to C at t constantly
_{bat.N}, storage battery stops charging, and unnecessary windpowered electricity generation unloads by unloader.Have:
In formula, P
_{bat}' (t) * Δ t > C
_{bat.N}C
_{bat}(t1).
Battery discharging
When Power Output for Wind Power Field is less than the wind power output power period during reference value, battery discharging is to meet power demand.T constantly storage battery initial capacity is:
In formula, η
_{dech}be the discharging efficiency of storage battery, generally get 1; P
_{bat}(t) being negative value, is t battery discharging constantly power while taking into account storage battery minimum capacity and the restriction of maximum discharge power; And satisfied theory needs discharge electricity amount be less than this constantly storage battery can discharge electricity amount, meet formula  P
_{bat}' (t) * Δ t < C
_{bat}(t1)C
_{batmin}.
During battery discharging, if be discharged to C at t constantly
_{batmin}, stop electric discharge.Have:
In formula, meet  P
_{bat}' (t) * Δ t > C
_{bat}(t1)C
_{batmin}.
Stored energy capacitance Optimized model
The optimization aim of wind energy turbine set stored energy capacitance is to guarantee, under the prerequisite of minimizing wind power output power fluctuation, to realize the onroad efficiency optimization of wind storage system with minimum energy storage cost of investment and operating cost.Due to according to Power Output for Wind Power Field period reference value, considered that wind energy turbine set abandons the impact of wind energy and energystorage system offenergy, stored energy capacitance Optimized model herein can adapted under existing dispatching of power netwoks operational mode prerequisite, reach optimum economic benefit, realize the steady output of wind power in the single period.
Target function
The basis of stored energy capacitance Optimized model is Power Output for Wind Power Field period reference value, and this value requires to adapt to the dispatching of power netwoks cycle, for energystorage system and effective linking of existing dispatching of power netwoks operational mode lay the foundation.For this reason, the equivalent power output variance minimum of first take is herein target, and chooses different time window according to dispatching requirement, with this, asks for the Power Output for Wind Power Field period reference value that adapts to existing dispatching running way.
Circular is: in investigating period T, defining M sampling interval Δ t is a time window (1 ~ 2h), to investigate
In period, in each time window, equivalent power output variance is target function with minimum, suc as formula (7).
Wherein, t
_{1}the time of a time window, and t
_{1}Δ tM; t
_{2}the initial time of i time window, and t
_{2}=((i1) M+1) Δ t; P
_{ref}(i) be the wind energy turbine set reference output power of i time window; M can change according to different dispatching cycles.
The wind energy turbine set that different stored energy capacitances obtains is stabilized effect difference, in assurance, meets under the prerequisite of output power fluctuation of wind farm requirement, by the compromise of stored energy capacitance cost of investment and operating cost, processes, and makes the comprehensive benefit of energy storage reach optimization.Because changing the system operation cost variation causing, stored energy capacitance have wind energy turbine set to abandon the cost F of wind energy
_{lOWE}cost F with energystorage system anergy
_{loss}.
Because wind power output power has annual cyclophysis, analyze the regularity of distribution of the wind energy turbine set wind power of a certain year, can be used as the distribution character of wind power in this wind energy turbine set operation time limit.Therefore herein the wind power regularity of distribution that can a certain year is as the research object of optimizing stored energy capacitance, and its wind energy turbine set is abandoned wind energy and energystorage system offenergy suc as formula shown in (8), (9).
The target function of wind storage system stored energy capacitance optimization comprises cost of investment and operating cost, as the formula (10).
min?f＝min（K
_{W}F
_{LOWE}+K
_{P}F
_{LOSS}+K
_{I}(ρ
_{I}C
_{bat.N}+r
_{s})）(10)
In above formula, ρ
_{p}and ρ
_{q}it is respectively the corresponding unit price that wind energy turbine set is abandoned wind energy and energystorage system anergy; N
_{year}it is the unit operation time limit; T investigates the period, is 1 year herein; C
_{bat.N}it is the rated value that wind energy turbine set is optimized stored energy capacitance; S
_{lOWE1}(t), S
_{lOWE2}(t), S
_{lOSS1}and S (t)
_{lOWE2}(t) be that wind energy turbine set is abandoned wind energy and energystorage system loses the Boolean quantity of energy situation definition in order to describe, shown in (11) ~ (14); ρ
_{i}for stored energy capacitance unit capacity price; r
_{s}for energy storage device installation cost; K
_{w}, K
_{p}and K
_{i}it is the compromise coefficient of operating cost (wind energy turbine set is abandoned wind energy and energystorage system offenergy) and cost of investment.
In formula (8), wind energy turbine set is abandoned wind energy and is comprised two parts, and a part produces because of battery capacity restriction, when accumulator electricquantity is filled to after rated capacity, storage battery stops charging, and unnecessary windpowered electricity generation unloads by unloader, and this part is abandoned wind energy suc as formula shown in (8) first; A part is that, when the difference of Power Output for Wind Power Field and reference output power is greater than the maximum charge power of storage battery, storage battery can not be filled with battery by this power completely, can only be according to P because the maximum charge Power Limitation of storage battery produces
_{batc} ^{max}storage battery is charged, thus unnecessary P
_{batc} ^{max}the power of part can only discard, and this part is abandoned wind energy suc as formula shown in (8) second portion.
Equally, in formula (9), energystorage system offenergy comprises two parts, and a part produces because of the restriction of storage battery minimum capacity, shown in (9) first; A part is that the maximum discharge power restriction due to storage battery produces, shown in (9) second portion.
Constraints
Constraints comprises storage battery constraint and wind energy turbine set power constraint.
Batteries to store energy capacityconstrained:
The rate of change constraint of accumulator cell charging and discharging power:
Output power fluctuation of wind farm horizontal restraint:
P{ΔP
_{d}(i)≤ΔP
^{max}}≥β??(17)
Abovementioned various in, C
_{batmin}minimum capacity for storage battery permission; C
_{bat.N}rated capacity for batteries to store energy; P
_{batc} ^{max}and P
_{batd} ^{max}maximum charge/discharge power for storage battery; DOD(depth of battery discharging) be the depth of discharge of storage battery; Δ P
_{d}(i), Δ P
^{max}for the undulating value of wind energy turbine set power output after energy storage leveling and the upper limit in allowed band thereof; β is corresponding confidence level.
Method for solving
In Fig. 2, particle cluster algorithm (Particle Swarm Optimization, PSO) be applicable to solving the hybrid optimization problem of real variable, for equality constraint, can effectively transform, and the form of inequality constraints by penalty function be additional in target function, have advantages of that strong robustness, computational efficiency are high, but have local optimum problem simultaneously.Stored energy capacitance optimization is calculated and is related to equality constraint and inequality constraints, meets the application of particle cluster algorithm, for the pluses and minuses of PSO, by the moderate improvement to particle cluster algorithm, with this, effectively overcomes dynamic boundary condition problem herein.Concrete process of solution is as follows:
(1) input windpowered electricity generation unit power output and Power Output for Wind Power Field period reference value;
(2) put population dimension K
_{pSO}, maximum iteration time N
_{psomax}, computational accuracy σ
_{pso};
(3) position of initialization population and speed, give the C under calculating for settled time
_{bat.N}value;
(4) by formula (10), calculate required particle fitness value;
(5) each particle fitness value extreme value individual with it compared, as more excellent, upgrade current individual extreme value P
_{besti};
(6) each particle adaptive value and global value are compared, as more excellent, upgrade current global extremum G
_{best};
(7) according to formula (18) and (19), upgrade position and the speed of each particle, and after upgrading according to formula (15) ~ (17) check, whether particle meets constraints requirement, if do not met, regenerate particle rapidity, upgrade position, until meet constraints, if update times surpasses the number of times of regulation, with former feasible particle, replace;
In formula: n is current cycle time; c
_{1}, c
_{2}for particle weight coefficient; W is inertia weight; r
_{1}, r
_{2}for (0,1) interior uniform random number; x
_{i}, v
_{i}it is the Position And Velocity of i dimension particle; G is constraint factor;
(8) repeating step (4) ~ (6);
(9) judge whether current iteration number of times and error amount meet the demands, do not meet and upgrade C
_{bat.N}value, returns to step (7), otherwise stops particle optimizing, and exports result of calculation.
Sample calculation analysis
The historical wind power output power data in 5241# windpowered electricity generation base, Arkansas area, southern US area of take are herein basis
^{[17]}, because wind power output power has year periodically, with the data instance of 2006, to putting forward correctness and the validity of stored energy capacitance optimization method above, carry out computational analysis.
Time window is the sample calculation analysis of 1h
Because of the dispatching of power netwoks cycle different, optimum stored energy capacitance is difference to some extent, this section be take the validity of time window 1h as the example checking energy storage model of being put forward.
The installed capacity in 5241# windpowered electricity generation base is 100Mw, applies that equivalent power output variance is minimum asks for this Power Output for Wind Power Field period reference value for target.On this basis, ignore the impact of stored energy capacitance on installation cost, relevant parameter is chosen and is seen attached list 1.
According to formula (8) ~ (14), and be optimized calculating by process of solution, acquired results is as shown in table 1.
Example system optimization result when table 1 time window is 1h
Table.1Example?system?optimization?results?for?1h
In table 1,18.70MWh is corresponding optimal compensation capacity value, and now to abandon wind energy be 2.40MWh to wind energy turbine set, and energystorage system anergy is 3.33*10
^{3}mWh; 111.67MWh is the minimum stored energy capacitance meeting with reference to Power Output for Wind Power Field period reference value, is called complete energy storage.
Because the wind power fluctuating range on April 1st, 2006 is larger, using that this has more representativeness as impact of analyzing different stored energy capacitance smooth wind power power, as shown in Fig. 3 ~ 4.
By Fig. 3 ~ 4, can be found out, optimum energy storage is being weaker than complete energy storage aspect level and smooth active power fluctuation, there is sharp wave in power stage curve, because battery stores energy is released into minimum value, the power of windpowered electricity generation unit does not meet Power Output for Wind Power Field period reference value, now according to the reality of windpowered electricity generation unit, sends out power and remaining accumulator electricquantity power output.
While there is sharp wave in windpowered electricity generation unit power output, if when now the difference of wind power and reference output power is greater than maximum charge/discharge power, have Partial Power can not be filled with storage battery, or battery discharging power does not meet requirements.This kind of situation can cause wind energy turbine set to abandon wind or energystorage system energy loss.
Consider energystorage system cost of investment and output power fluctuation of wind farm situation, choosing 18.70MWh is stored energy capacitance.The sample calculation analysis of different time window
The time window of dispatching of power netwoks operation is different, makes wind power output power period reference value that stored energy capacitance optimizes difference to some extent, and then affects determining of optimum stored energy capacitance.As shown in table 2.
The optimum stored energy capacitance of table 2 different time window
Table.2Example?system?optimization?results?for?different?time?window
From table 2, time window is larger, and optimum stored energy capacitance is larger, and cost is thereupon larger.This is because after time window increase, and the equivalent power output variance of calculating Power Output for Wind Power Field period reference value becomes large, i.e. fluctuation change is large, and under a time window in office, it is large that the electric weight that need to charge and discharge becomes thus, and then stored energy capacitance is become greatly.
Table 3 Optimize parameter is chosen table
Table1?Example?system?parameter?value
Title  Parameter  Title  Parameter 
??ρ _{p}  ??1  ??ρ _{q}  ??1 
??ρ _{I}  ??4  ??K _{W}  ??0.33 
??K _{P}  ??0.33  ??K _{I}  ??0.33 
??DOD  ??0.5  ??η _{dech}  ??1 
??η _{cha}  ??0.7  ??β  ??0.98 
Note: fetching data in table is the relative value that stored energy capacitance and wind energy turbine set are abandoned wind energy, energystorage system anergy unit price, the correctness of not impact analysis conclusion.
To sum up analyze, according to the requirement of local management and running plan, choose suitable time window, the optimized calculation method of the stored energy capacitance of carrying is herein guaranteeing, under the prerequisite of smooth wind power field power output, can to make decision scheme have Optimum Economic.
Claims (3)
1. the wind energy turbine set stored energy capacitance control method based on particle cluster algorithm, is characterized in that, its step is:
1) set up target function
The equivalent power output variance minimum of take is target, and chooses different time window according to dispatching requirement, with this, asks for the Power Output for Wind Power Field period reference value that adapts to existing dispatching running way; Because changing the system operation cost variation causing, stored energy capacitance have wind energy turbine set to abandon the cost F of wind energy
_{lOWE}cost F with energystorage system anergy
_{loss}; With the regularity of distribution of a certain year wind power of wind energy turbine set, as the distribution character of wind power in this wind energy turbine set operation time limit, its wind energy turbine set is abandoned wind energy and energystorage system offenergy suc as formula shown in (8), (9);
The target function of wind storage system stored energy capacitance optimization comprises cost of investment and operating cost, shown in (10);
minf＝min(K
_{W}F
_{LOWE}+K
_{P}F
_{LOSS}+K
_{I}(ρ
_{I}C
_{bat.N}+r
_{s}))?????(10)
In above formula, ρ
_{p}and ρ
_{q}it is respectively the corresponding unit price that wind energy turbine set is abandoned wind energy and energystorage system anergy; N
_{year}it is the unit operation time limit; T investigates the period, is 1 year; C
_{bat.N}it is the rated capacity of batteries to store energy; S
_{lowE1 (t)}, S
_{lowE2 (t)}, S
_{lOSS1 (t)}and S
_{lOSS2 (t)}that wind energy turbine set is abandoned wind energy and energystorage system loses the Boolean quantity of energy situation definition in order to describe, shown in (11)～(14); ρ
_{i}for stored energy capacitance unit capacity price; r
_{s}for energy storage device installation cost; K
_{w}, K
_{p}and K
_{i}it is the compromise coefficient of operating cost (wind energy turbine set is abandoned wind energy and energystorage system offenergy) and cost of investment;
In formula (8), wind energy turbine set is abandoned wind energy and is comprised two parts, a part produces because of battery capacity restriction, when accumulator electricquantity is filled to after rated capacity, storage battery stops charging, unnecessary windpowered electricity generation unloads by unloader, and this part is abandoned wind energy suc as formula shown in (8) first; A part is that, when the difference of Power Output for Wind Power Field and reference output power is greater than the maximum charge power of storage battery, storage battery can not be filled with battery by this power completely, can only be according to P because the maximum charge Power Limitation of storage battery produces
_{batc} ^{max}storage battery is charged, thus unnecessary P
_{batc} ^{max}the power of part can only discard, and this part is abandoned wind energy suc as formula shown in (8) second portion;
Equally, in formula (9), energystorage system offenergy comprises two parts, and a part produces because of the restriction of storage battery minimum capacity, shown in (9) first; A part is that the maximum discharge power restriction due to storage battery produces, shown in (9) second portion;
2) set up constraints
Constraints comprises storage battery constraint and wind energy turbine set power constraint:
Batteries to store energy capacityconstrained
The rate of change constraint of accumulator cell charging and discharging power:
Output power fluctuation of wind farm horizontal restraint:
P{ΔP
_{d}(i)≤ΔP
^{max}}≥β????(17)
Abovementioned various in, C
_{batmin}minimum capacity for storage battery permission; C
_{bat.N}rated capacity for batteries to store energy; P
_{batc} ^{max}and P
_{batd} ^{max}maximum charge/discharge power for storage battery; DOD (depth of battery discharging) is the depth of discharge of storage battery; Δ P
_{d}(i), Δ P
^{max}for the undulating value of wind energy turbine set power output after energy storage leveling and the upper limit in allowed band thereof; β is corresponding confidence level; P
_{bat}(t) be the power variation rate of storage battery; Δ P
_{batc} ^{max}withΔ P
_{batd} ^{max}be respectively the maximum charge and discharge power variation rate of storage battery;
3) particle cluster algorithm
By the abovementioned equation of PSO Algorithm, obtain required stored energy capacitance, and control thermal energy storage process with this.
2. the wind energy turbine set stored energy capacitance control method based on particle cluster algorithm as claimed in claim 1, it is characterized in that, described step 1) in, in investigating period T, defining M sampling interval Δ t is a time window (1～2h), take that to investigate in the period equivalent power output variance and minimum in each time window be target function, suc as formula (7);
Wherein, t
_{1}the time of a time window, and t
_{1}=Δ tM; t
_{2}the initial time of i time window, and t
_{2}=((i1) M+1) Δ t; P
_{ref}(i) be the wind energy turbine set reference output power of i time window; M can change according to different dispatching cycles.
3. the wind energy turbine set stored energy capacitance control method based on particle cluster algorithm as claimed in claim 1, is characterized in that step 3) in, concrete process of solution is as follows:
(1) input windpowered electricity generation unit power output and Power Output for Wind Power Field period reference value;
(2) put population dimension K
_{pSO}, maximum iteration time N
_{psomax}, computational accuracy σ
_{pso};
(3) position of initialization population and speed, give the C under calculating for settled time
_{bat.N}value;
(4) by formula (10), calculate required particle fitness value;
(5) each particle fitness value extreme value individual with it compared, as more excellent, upgrade current individual extreme value P
_{besti};
(6) each particle adaptive value and global value are compared, as more excellent, upgrade current global extremum G
_{best};
(7) according to formula (18) and (19), upgrade position and the speed of each particle, and after upgrading according to formula (15)～(17) check, whether particle meets constraints requirement, if do not met, regenerate particle rapidity, upgrade position, until meet constraints, if update times surpasses the number of times of regulation, with former feasible particle, replace;
In formula: n is current cycle time; c
_{1}, c
_{2}for particle weight coefficient; W is inertia weight; r
_{1}, r
_{2}for (0,1) interior uniform random number; x
_{i}, v
_{i}it is the Position And Velocity of i dimension particle; G is constraint factor;
(8) repeating step (4)～(6);
(9) judge whether current iteration number of times and error amount meet the demands, do not meet and upgrade C
_{bat.N}value, returns to step (7), otherwise stops particle optimizing, and exports result of calculation.
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