CN109888803B - Optimization method for capacity configuration of hybrid energy storage power supply in wind and solar power generation system - Google Patents

Optimization method for capacity configuration of hybrid energy storage power supply in wind and solar power generation system Download PDF

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CN109888803B
CN109888803B CN201910077473.4A CN201910077473A CN109888803B CN 109888803 B CN109888803 B CN 109888803B CN 201910077473 A CN201910077473 A CN 201910077473A CN 109888803 B CN109888803 B CN 109888803B
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王琪
韩晓新
诸一琦
罗印升
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Fanyaweide New Energy Technology Yinchuan Co ltd
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Jiangsu University of Technology
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Abstract

The invention discloses an optimization method for capacity allocation of a hybrid energy storage power supply in a wind and light power generation system, which comprises the following steps: respectively establishing energy models of a wind generating set, a photovoltaic solar generating set, a storage battery and a super capacitor; establishing an optimization model of capacity configuration and corresponding constraint conditions according to the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply; based on a tolerant hierarchical sequence optimization method, establishing an optimization objective function and corresponding optimization constraint conditions respectively aiming at the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply according to a preset importance degree ranking; according to the establishment sequence of the optimization objective function and the optimization constraint condition, the optimal parameters are obtained by solving in sequence, the optimization of the capacity allocation of the hybrid energy storage power supply is realized, and on the premise of meeting the load requirement, the wind energy and photovoltaic solar energy resources are utilized to the maximum extent, and meanwhile, the one-time investment cost and the operation cost of the whole life cycle are greatly reduced.

Description

Optimization method for capacity configuration of hybrid energy storage power supply in wind and solar power generation system
Technical Field
The invention relates to the technical field of batteries, in particular to an optimization method for capacity allocation of a hybrid energy storage power supply in a wind and light power generation system.
Background
With the rapid depletion of traditional petroleum resources and the increasing global warming phenomenon, researchers and scientists all over the world are seeking green alternative energy to the utmost extent, and a wind-solar power generation system consisting of wind energy and photovoltaic solar energy becomes a research hotspot. However, wind energy and photovoltaic solar energy are intermittent, and in order to ensure the stability and reliability of energy supply, a proper energy storage system is generally required to be arranged in a wind-solar power generation system. In general, in a medium-and small-sized power generation system composed of wind energy and photovoltaic solar energy, a storage battery is used for storing energy. However, the energy storage power supply composed of the storage battery has the problems of low life cycle, low power density, limited charge/discharge current, environmental influence and the like.
In recent years, super capacitors have been paid attention to by virtue of advantages such as high power density, long service life, and high charge-discharge efficiency, however, energy storage power supplies composed of super capacitors only have very limited energy storage capacity, and the cost is much higher than that of energy storage power supplies composed of storage batteries. Obviously, the single use of the storage battery or the super capacitor as the energy storage device of the wind-solar power generation system has certain problems, and thus the hybrid energy storage power source (including the storage battery and the super capacitor) combining the advantages of the storage battery and the super capacitor becomes a research direction for the energy storage device of the wind-solar power generation system.
In a hybrid energy storage power supply of a wind-solar power generation system, capacity configuration optimization is a difficult problem and is very important for reliable operation of the wind-solar power generation system at reasonable cost. At present, scholars at home and abroad have relatively few researches on capacity allocation optimization, and the main optimization method focuses on genetic algorithm and particle swarm optimization, wherein the genetic algorithm is simple and easy to implement, and the search capability of global solution is strong, but the method belongs to random algorithm, needs multiple operations, and has large calculation amount, long calculation time and can not obtain stable solution; although the particle swarm algorithm has the advantages of high searching speed, few parameters needing to be adjusted and high efficiency, the particle swarm algorithm has the defects of low convergence precision and easy trapping in local optimal solution.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an optimization method for capacity allocation of a hybrid energy storage power supply in a wind and light power generation system, which effectively solves the technical problem that the capacity allocation of the hybrid energy storage power supply of the prior wind and light power generation system cannot be effectively optimized.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the optimization method of the capacity configuration of the hybrid energy storage power supply in the wind and light power generation system is characterized by being applied to the wind and light power generation system, wherein the wind and light power generation system comprises a wind generating set, a photovoltaic solar generating set and the hybrid energy storage power supply, and the hybrid energy storage power supply comprises a storage battery for providing energy and a super capacitor for providing power; the optimization method for capacity configuration of the hybrid energy storage power supply in the wind and light power generation system comprises the following steps:
s10, respectively establishing energy models of the wind generating set, the photovoltaic solar generating set, the storage battery and the super capacitor;
s20, establishing an optimization model of capacity configuration and corresponding constraint conditions according to the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply, wherein the constraint conditions comprise charge and discharge current constraint and maximum residual energy constraint of a super capacitor, and the maximum residual energy constraint is established according to energy models of a wind generating set, a photovoltaic solar generating set, a storage battery and the super capacitor;
s30, based on a tolerant hierarchical sequence optimization method, establishing an optimization objective function and a corresponding optimization constraint condition respectively aiming at the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply according to the preset importance degree ranking and the optimization model and the constraint condition established in the step S20;
and S40, solving sequentially according to the establishment sequence of the optimization objective function and the optimization constraint condition to obtain optimal parameters, and realizing the optimization of the capacity configuration of the hybrid energy storage power supply.
In the optimization method for the capacity allocation of the hybrid energy storage power supply in the wind and light power generation system, after energy models of a wind generating set, a photovoltaic solar generating set, a storage battery and a super capacitor in the wind and light power generation system are respectively established, further establishing an optimization objective function and a corresponding optimization constraint condition according to a preset importance ranking and tolerant hierarchical sequence optimization method aiming at the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply respectively, and the differential evolution algorithm is adopted to solve and optimize in turn according to the establishment sequence of the optimization objective function and the optimization constraint condition, so as to respectively realize the minimization of the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply, the wind energy and photovoltaic solar energy resource utilization system can maximally utilize wind energy and photovoltaic solar energy resources on the premise of meeting load requirements, and greatly reduces one-time investment cost and operation cost of a full life cycle.
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A more complete understanding of the present invention, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:
FIG. 1 is a schematic flow chart of a method for optimizing capacity allocation of a hybrid energy storage power source in a wind and light power generation system according to the present invention.
Detailed Description
In order to make the contents of the present invention more comprehensible, the present invention is further described below with reference to the accompanying drawings. The invention is of course not limited to this particular embodiment, and general alternatives known to those skilled in the art are also covered by the scope of the invention.
The invention provides an optimization method for capacity allocation of a hybrid energy storage power supply in a wind-solar power generation system, aiming at the technical problem that the capacity allocation of the hybrid energy storage power supply of the existing wind-solar power generation system cannot be effectively optimized. In the working process, if the generated energy of the wind generating set and the photovoltaic solar generating set is large, the residual electric energy after the load consumption can be stored in the hybrid energy storage power supply on the premise of meeting the electric energy demand of the load; if the generated energy of the wind generating set and the photovoltaic solar generating set is less and is not enough to meet the electric energy requirement of the load, the hybrid energy storage power supply can provide the electric energy stored by the hybrid energy storage power supply for the load.
As shown in fig. 1, the method for optimizing the capacity configuration of the hybrid energy storage power supply in the wind-solar power generation system includes:
s10, respectively establishing energy models of the wind generating set, the photovoltaic solar generating set, the storage battery and the super capacitor;
s20, establishing an optimization model of capacity configuration and corresponding constraint conditions according to the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply, wherein the constraint conditions comprise charge and discharge current constraint and maximum residual energy constraint of the super capacitor, and the maximum residual energy constraint is established according to energy models of a wind generating set, a photovoltaic solar generating set, a storage battery and the super capacitor;
s30, based on a tolerant hierarchical sequence optimization method, establishing an optimization objective function and a corresponding optimization constraint condition respectively aiming at the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply according to the preset importance degree ranking and the optimization model and the constraint condition established in the step S20;
and S40, solving sequentially according to the establishment sequence of the optimization objective function and the optimization constraint condition to obtain optimal parameters, and realizing the optimization of the capacity configuration of the hybrid energy storage power supply.
In a wind power plant, wind speed is the most important factor in determining its power generation, and wind speed depends on many factors, such as weather, season, etc. Taking a wind generating set including a small turbine generator as an example for modeling, the output power is as follows:
Figure GDA0002583211460000041
wherein, PrRated power of small turbine generator, v is wind speed, v iscFor cutting into the wind speed, vrRated wind speed, vfFor cut-out wind speed, k' is the shape parameter of the Weibull distribution.
Thus, the annual energy production E of the small-sized turbine generatorwAs shown in formula (2):
Figure GDA0002583211460000042
wherein, tw1At wind speed v for small turbine generators each yearc≤v<vrOperating time of interval, tw2At wind speed v for small turbine generators each yearr≤v≤vfThe operating time of the interval.
In photovoltaic solar power generation units, the output power of the photovoltaic array depends on many factors, the most important of which are the radiation intensity and the ambient temperature, in particular, at the radiation intensity SSTC＝1000W/m2And the ambient temperature TSTCOutput current of photovoltaic array under reference condition of 25 ℃I is of formula (3):
Figure GDA0002583211460000043
wherein V is the intensity of radiation SSTC＝1000W/m2And the ambient temperature TSTCOutput voltage of photovoltaic array at 25 deg.C reference, ISCShort circuit current, V, for photovoltaic arraysocIs the open circuit voltage, V, of the photovoltaic arraymFor maximum power P of photovoltaic arraymaxVoltage at output, ImFor maximum power P of photovoltaic arraymaxCurrent at the time of output.
Because the radiation intensity and the ambient temperature are constantly changed, a mathematical model is carried out on the photovoltaic array under the condition of any radiation intensity S and ambient temperature T, and the formula is (4):
Figure GDA0002583211460000051
wherein I (S, T) is the output current of the photovoltaic array, Delta I (S, T) is the correction factor of the output current of the photovoltaic array, V (S, T) is the output voltage of the photovoltaic array, Delta V (S, T) is the correction factor of the output voltage of the photovoltaic array, RSThe resistance is the series resistance of the photovoltaic array, alpha is the short-circuit temperature coefficient of the photovoltaic array, and beta is the open-circuit temperature coefficient of the photovoltaic array; t isNORThe normal operating temperature of the photovoltaic array is typically 40 ℃.
In cloudy weather, the solar radiation received by the ground is different from the normal condition, and after the solar radiation received by the ground is corrected by using a quadratic function, the actual output current I of the photovoltaic arrayrealAs shown in formula (5):
Figure GDA0002583211460000052
wherein, TCIs a weakening coefficient; n is a cloud cover coefficient, and is generally selected from 0 to 8, wherein 0 represents no cloud, and 8 represents full cloud; a. b and c are empirical coefficients, respectively, and are usually taken as a-0.0124, b-0.2784 and c＝1.04。
Thus, the annual energy production E of the photovoltaic arraysAs shown in formula (6):
Es＝η×Ireal×V(S,T)×NP×NS×ts(6)
where η is the efficiency of the photovoltaic array, NSNumber of series of photovoltaic arrays, NPNumber of parallel connections of the voltage array, tsRepresenting the annual operating time of the photovoltaic array.
Generally, the capacity level and voltage level of the battery need to be increased by series-parallel connection of battery cells to meet load demand. When the charging current and the discharging current of the storage battery monomer are both rated charging current IcWhen it is storing charging energy Qb1And discharge energy Qb2As shown in formulas (7) and (8):
Qb1＝U×Ic×tc(7)
Qb2＝U×Ic×td(8)
wherein, tcFor charging the accumulator cell, tdThe discharge time of the storage battery monomer is shown; u is the reference voltage of the accumulator cell, and is generally 12V.
Thereby, the energy Q of the storage batteryBATAs shown in formula (9):
QBAT＝(Qb1+Qb2)×h×l (9)
wherein h is the serial number of the storage battery monomers in the storage battery, and l is the parallel number of the storage battery monomers in the storage battery.
Since the supercapacitor cells can only store limited energy and cannot withstand high voltages, the capacity and voltage of the supercapacitor also need to be increased by connecting the supercapacitor cells in series-parallel. Assuming that the number of the series super capacitor monomers in the super capacitor is m and the number of the parallel super capacitor monomers is n, the equivalent capacity is as follows:
Figure GDA0002583211460000061
wherein, CfIs the capacity of the supercapacitor monomer.
Thus, the energy Q stored by the super capacitorSCAs shown in formula (11):
Figure GDA0002583211460000062
wherein, UmaxIs the maximum voltage of the supercapacitor, UminIs the minimum voltage of the supercapacitor, UsmaxIs the maximum voltage of the supercapacitor cell, UsminIs the minimum voltage of the supercapacitor cell.
For optimization of capacity allocation of a hybrid energy storage power supply, the objective is to minimize one-time investment cost and full-life-cycle operation cost of the hybrid energy storage power supply on the premise of satisfying all performance parameters of a wind and light power generation system and ensuring stable operation of the hybrid energy storage power supply, so that, assuming that the life cycle of the wind and light power generation system is k (usually 20) years, an optimization model L of the capacity allocation of the hybrid energy storage power supply is established as formula (12):
Figure GDA0002583211460000071
wherein L is1One-time investment cost for hybrid energy storage power supply, and L1＝pBAT×QBAT+pSC×QSC；L2The operating cost for the whole life cycle of the hybrid energy storage power supply, and L2＝k×λBAT×pBAT×QBAT+k×mBAT×QBAT+k×λSC×pSC×QSC+k×mSC×QSC；pBATPrice of accumulator per unit energy, pSCFor the price of a supercapacitor per unit energy, λBATIs the annual rate of conversion per unit energy, lambda, of the accumulatorSCIs the annual rate of conversion per unit energy, m, of the super capacitorBATFor the annual maintenance charge per unit energy of the accumulator, mSCThe annual maintenance cost per unit energy of the super capacitor.
In addition, in the process of optimizing the capacity configuration of the storage battery and super capacitor hybrid energy storage power supply, the charge-discharge current constraint and the maximum residual energy constraint of the super capacitor need to be considered, specifically:
since the super capacitor is mainly used for instantaneous maximum load fluctuation of the hybrid energy storage power supply when the energy density is low, the maximum fluctuation current of the load is assumed to be 8IcThe maximum charging current of the wind-solar power generation system is 3IcAnd then, the charging and discharging current of the super capacitor is restricted as (13):
Figure GDA0002583211460000072
wherein, Is1Charging current for the supercapacitor, Is2Is the discharge current of the supercapacitor, IsmaxThe maximum charge-discharge current of the super capacitor;
in order to fully absorb the redundant energy and improve the utilization rate of the wind-solar power generation system under the conditions of strong wind and sufficient illumination, the hybrid energy storage power supply must be operated at full load with the maximum residual energy, so that the maximum residual energy is constrained as (14):
Ew+Es-El≥QBAT+QSC(14)
wherein E islThe energy consumed for the load.
The optimization goal of the capacity configuration of the hybrid energy storage power supply is to minimize the total cost L in equation (12), i.e., the one-time investment cost L1And a full lifecycle operational cost L2Needs to be minimized, belongs to the multi-objective optimization problem, and aims to solve the problem of one-time investment cost L based on the method for optimizing the tolerant hierarchical sequence1And a full lifecycle operational cost L2The importance degree ranking is sequentially minimized, and the optimization target of the capacity configuration of the hybrid energy storage power supply is realized.
Since the one-time investment cost precedes the full lifecycle operating cost, the one-time investment cost L is set in order to minimize the total cost from the source1Is minimized as a first optimization objective, a full lifecycle operational cost L2Is minimized to a second optimization objective, namely the one-time investment cost L1Is ranked in the operating cost L of the whole life cycle2The method specifically comprises the following steps:
first optimization objective function f established aiming at one-time investment cost of hybrid energy storage power supply1As shown in formula (15):
f1＝min(L1)＝min(pBAT×QBAT+pSC×QSC) (15)
the first optimization constraint is as follows (16):
Figure GDA0002583211460000081
second optimization objective function f established for full life cycle operation cost of hybrid energy storage power supply2As in formula (17):
Figure GDA0002583211460000082
the second optimization constraint is as follows (18):
Figure GDA0002583211460000083
wherein, is a tolerance coefficient, and>0，
Figure GDA0002583211460000084
for one-off investment cost L1The minimum value of (a) is determined,
Figure GDA0002583211460000085
for the first optimization of the objective function f1Optimized super capacitor charging current Is1The optimum solution of (a) to (b),
Figure GDA0002583211460000086
for the first optimization of the objective function f1Optimized discharge current I of super capacitors2The optimal solution of (1).
In the optimization process, firstlyAccording to the first optimization constraint condition, solving the first optimization objective function by adopting a differential evolution algorithm to obtain the charging current I of the super capacitors1Of (2) an optimal solution
Figure GDA0002583211460000087
Discharge current I of super capacitors2Of (2) an optimal solution
Figure GDA0002583211460000091
And a one-time investment cost L1Minimum value of (2)
Figure GDA0002583211460000092
Then, according to the second optimization constraint condition and the first optimization result, the differential evolution algorithm is adopted to solve the second optimization objective function to obtain the full life cycle operation cost L2Minimum value of (2)
Figure GDA0002583211460000093
I.e. the minimum value of the total cost L is obtained:
Figure GDA0002583211460000094
therefore, the capacity configuration of the hybrid energy storage power supply is optimized.

Claims (4)

1. The optimization method for the capacity configuration of the hybrid energy storage power supply in the wind and light power generation system is characterized by being applied to the wind and light power generation system, wherein the wind and light power generation system comprises a wind generating set, a photovoltaic solar generating set and the hybrid energy storage power supply, the hybrid energy storage power supply comprises a storage battery for providing energy and a super capacitor for providing power, and the wind generating set and the photovoltaic solar generating set are respectively connected with the hybrid energy storage power supply in parallel; the optimization method for capacity configuration of the hybrid energy storage power supply in the wind and light power generation system comprises the following steps:
s10, respectively establishing energy models of a wind generating set, a photovoltaic solar generating set, a storage battery and a super capacitor, wherein the wind generating set comprises a small turbine generator;
s20, establishing an optimization model of capacity configuration and corresponding constraint conditions according to the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply, wherein the constraint conditions comprise charge and discharge current constraint and maximum residual energy constraint of a super capacitor, and the maximum residual energy constraint is established according to energy models of a wind generating set, a photovoltaic solar generating set, a storage battery and the super capacitor;
s30, based on a tolerant hierarchical sequence optimization method, establishing an optimization objective function and a corresponding optimization constraint condition respectively aiming at the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply according to the preset importance degree ranking and the optimization model and the constraint condition established in the step S20;
s40, according to the establishment sequence of the optimization objective function and the optimization constraint condition, solving is sequentially carried out to obtain optimal parameters, and optimization of capacity allocation of the hybrid energy storage power supply is achieved;
in the step S20, in step S20,
assuming that the life cycle of the wind-solar power generation system is k years, establishing an optimization model L of capacity allocation of the hybrid energy storage power supply according to the one-time investment cost and the operation cost of the full life cycle of the hybrid energy storage power supply as follows:
Figure FDA0002583211450000011
wherein L is1One-time investment cost for hybrid energy storage power supply, and L1＝pBAT×QBAT+pSC×QSC；L2The operating cost for the whole life cycle of the hybrid energy storage power supply, and L2＝k×λBAT×pBAT×QBAT+k×mBAT×QBAT+k×λSC×pSC×QSC+k×mSC×QSC；pBATPrice of accumulator per unit energy, pSCFor the price of a supercapacitor per unit energy, λBATIs the annual rate of conversion per unit energy, lambda, of the accumulatorSCIs the annual rate of conversion per unit energy, m, of the super capacitorBATFor each unit of accumulatorAnnual maintenance charge of energy, mSCAnnual maintenance charge, Q, for a supercapacitor per unit of energyBATIs the energy of the accumulator, QSCEnergy stored for the super capacitor;
the charge and discharge current constraints of the super capacitor are as follows:
Figure FDA0002583211450000021
wherein, Is1Charging current for the supercapacitor, Is2Is the discharge current of the supercapacitor, IsmaxThe maximum charge-discharge current of the super capacitor; i iscThe rated charging current of the storage battery monomer is obtained;
the maximum remaining energy constraint is:
Ew+Es-El≥QBAT+QSC
wherein E islEnergy consumed for the load; ewAnnual energy production of small turbo-generators, EsThe annual energy production of a photovoltaic array in the photovoltaic solar generating set;
in step S30, the importance ranks set in advance are: firstly considering the one-time investment cost and secondly considering the operation cost of the whole life cycle;
first optimization objective function f established aiming at one-time investment cost of hybrid energy storage power supply1Comprises the following steps:
f1＝min(L1)＝min(pBAT×QBAT+pSC×QSC)
the first optimization constraint is:
Figure FDA0002583211450000022
second optimization objective function f established for full life cycle operation cost of hybrid energy storage power supply2Comprises the following steps:
Figure FDA0002583211450000023
the second optimization constraint condition is as follows:
Figure FDA0002583211450000024
wherein, is a tolerance coefficient, and>0；
Figure FDA0002583211450000031
for one-off investment cost L1The minimum value of (a) is determined,
Figure FDA0002583211450000032
for the first optimization of the objective function f1Optimized super capacitor charging current Is1The optimum solution of (a) to (b),
Figure FDA0002583211450000033
for the first optimization of the objective function f1Optimized discharge current I of super capacitors2The optimal solution of (2);
in the step S10, in step S10,
when the charging current and the discharging current of the storage battery monomer are both rated charging current IcWhen it is storing charging energy Qb1And discharge energy Qb2Respectively as follows:
Qb1＝U×Ic×tc
Qb2＝U×Ic×td
wherein, tcFor charging the accumulator cell, tdThe discharge time of the storage battery monomer is U, and the reference voltage of the storage battery monomer is U;
energy Q of accumulatorBATComprises the following steps:
QBAT＝(Qb1+Qb2)×h×l
h is the serial number of the storage battery monomers in the storage battery, and l is the parallel number of the storage battery monomers in the storage battery;
in the step S10, in step S10,
the equivalent capacitance C of the supercapacitor is:
Figure FDA0002583211450000034
wherein m is the serial number of the super capacitor monomers in the super capacitor, n is the parallel number of the super capacitor monomers in the super capacitor, CfThe capacity of the supercapacitor monomer;
energy Q stored by a supercapacitorSCComprises the following steps:
Figure FDA0002583211450000035
wherein, UmaxIs the maximum voltage of the supercapacitor, UminIs the minimum voltage of the supercapacitor, UsmaxIs the maximum voltage of the supercapacitor cell, UsminIs the minimum voltage of the supercapacitor cell.
2. The method for optimizing the capacity allocation of the hybrid energy storage power source in the wind-solar power generation system according to claim 1, wherein the output power P of the small-sized turbine generator is provided at step S10w(v) Comprises the following steps:
Figure FDA0002583211450000041
wherein, PrRated power of small turbine generator, v is wind speed, v iscFor cutting into the wind speed, vrRated wind speed, vfIn order to cut out the wind speed, k' is the shape parameter of the Weibull distribution;
annual energy production E of small turbine generatorwComprises the following steps:
Figure FDA0002583211450000042
wherein, tw1At wind speed v for small turbine generators each yearc≤v<vrOf intervalsWorking time, tw2At wind speed v for small turbine generators each yearr≤v≤vfThe operating time of the interval.
3. The method for optimizing the capacity allocation of the hybrid energy storage power source in the wind-solar power generation system according to claim 1, wherein in step S10,
under the condition of any radiation intensity S and ambient temperature T, the mathematical model of the photovoltaic array in the photovoltaic solar generating set is as follows:
Figure FDA0002583211450000043
wherein I (S, T) is the output current of the photovoltaic array, Delta I (S, T) is the correction factor of the output current of the photovoltaic array, V (S, T) is the output voltage of the photovoltaic array, Delta V (S, T) is the correction factor of the output voltage of the photovoltaic array, RSIs the series resistance of the photovoltaic array, alpha is the short circuit temperature coefficient of the photovoltaic array, beta is the open circuit temperature coefficient of the photovoltaic array, and TNORIs the normal operating temperature of the photovoltaic array; i is at the radiation intensity SSTC＝1000W/m2And the ambient temperature TSTCOutput current of the photovoltaic array at 25 deg.C as a reference, and
Figure FDA0002583211450000051
wherein V is the intensity of radiation SSTC＝1000W/m2And the ambient temperature TSTCOutput voltage of photovoltaic array at 25 deg.C reference, ISCShort circuit current, V, for photovoltaic arraysocIs the open circuit voltage, V, of the photovoltaic arraymFor maximum power P of photovoltaic arraymaxVoltage at output, ImFor maximum power P of photovoltaic arraymaxCurrent at the time of output;
actual output current I of photovoltaic arrayrealComprises the following steps:
Figure FDA0002583211450000052
wherein, TCIs a weakening coefficient; n is a cloud cover coefficient, and is generally selected from 0 to 8, wherein 0 represents no cloud, and 8 represents full cloud; a. b and c are empirical coefficients respectively;
annual energy production E of photovoltaic arraysComprises the following steps:
Es＝η×Ireal×V(S,T)×NP×NS×ts
where η is the efficiency of the photovoltaic array, NSNumber of series of photovoltaic arrays, NPNumber of parallel connections of the photovoltaic array, tsRepresenting the annual operating time of the photovoltaic array.
4. The method for optimizing the capacity allocation of the hybrid energy storage power source in the wind-solar energy generation system according to claim 1, wherein in step S40, the method further comprises:
s41, solving the first optimization objective function by adopting a differential evolution algorithm according to the first optimization constraint condition to obtain the charging current I of the super capacitors1Of (2) an optimal solution
Figure FDA0002583211450000053
Discharge current I of super capacitors2Of (2) an optimal solution
Figure FDA0002583211450000054
And a one-time investment cost L1Minimum value of (2)
Figure FDA0002583211450000055
S42, solving the second optimization objective function by adopting a differential evolution algorithm according to the second optimization constraint condition to obtain the full life cycle operation cost L2Minimum value of (2)
Figure FDA0002583211450000061
And the optimization of the capacity configuration of the hybrid energy storage power supply is completed.
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