CN112290596A - Wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelet - Google Patents

Wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelet Download PDF

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CN112290596A
CN112290596A CN202011262750.8A CN202011262750A CN112290596A CN 112290596 A CN112290596 A CN 112290596A CN 202011262750 A CN202011262750 A CN 202011262750A CN 112290596 A CN112290596 A CN 112290596A
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storage battery
super capacitor
power
energy storage
frequency
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CN112290596B (en
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齐先军
陈庆会
吴红斌
王晓蓉
李庆
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China Electric Power Research Institute Co Ltd CEPRI
Hefei University of Technology
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China Electric Power Research Institute Co Ltd CEPRI
Hefei University of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelets, which comprises the following steps of: 1. establishing a double-layer optimization model of the hybrid energy storage configuration of the wind power plant; 2. respectively solving an upper layer optimization model and a lower layer optimization model of the hybrid energy storage configuration by utilizing a particle swarm algorithm and a heuristic algorithm based on Haar wavelet; 3. and outputting the optimized configuration capacity and charge-discharge power of the storage battery and the super capacitor. The method can give full play to the combined advantages of the storage battery and the super capacitor to carry out self-adaptive distribution of the power instruction sequence of the storage battery and the super capacitor, and more effectively and reasonably utilize the charging and discharging characteristics of the mixed energy storage power instruction sequence to carry out the distribution of the mixed energy storage power instruction sequence, thereby obtaining the mixed energy storage configuration scheme with lower cost and longer service life of the storage battery, reducing the influence of wind power fluctuation on a power grid and improving the wind power receiving capacity of the power grid.

Description

Wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelet
Technical Field
The invention relates to a wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelets, and belongs to the technical field of electrical engineering.
Background
In recent decades, renewable energy such as wind power has been widely used due to problems such as energy crisis and environmental pollution. However, wind power output has the characteristics of intermittency, volatility and the like, and serious influence is caused on the safe and stable operation of a power system. The energy storage is configured on the side of the wind power plant, which is an effective means for stabilizing wind power fluctuation and improving the wind power receiving capacity of a power grid, and can ensure the safe and stable operation of a power system.
In a wind power plant, common electric energy storage devices include a storage battery, a super capacitor and the like. The storage battery has the advantages of large energy density, strong storage capacity and the like, but also has the problems of short cycle service life, small power density, high cost and the like, and generally does not carry out frequent charge-discharge conversion in practical application; the super capacitor energy storage has the advantages of long cycle service life, high power density and the like, but the super capacitor energy storage has low energy density and weak storage capacity. Therefore, the storage battery and the super capacitor can complement each other in advantages to form hybrid energy storage, and the hybrid energy storage device is widely applied to stabilization of wind power fluctuation of a wind power plant.
The reasonable allocation of the hybrid energy storage power instruction sequence for stabilizing the wind power fluctuation to the storage battery and the super capacitor is an important task of the wind power plant and is the key for hybrid energy storage configuration. When a power instruction sequence borne by a storage battery and a super capacitor is distributed, the traditional method firstly decomposes a hybrid energy storage power instruction sequence by using methods such as wavelet transformation, Fourier transformation and the like; and then, dividing frequency dividing points according to different response frequencies of various energy storage devices, or distributing a mixed energy storage power instruction according to the charging and discharging limit times of the storage battery.
This method has the following disadvantages: firstly, when the hybrid energy storage power instruction sequence is decomposed, different basis functions are selected to decompose the signal, and the obtained decomposition results are different. In the traditional method, functions such as db6 wavelet function, sine wave function and the like are used as basis functions, and the basis function characteristics can not be matched with the charge and discharge characteristics of stored energy. Secondly, the formulation of frequency demarcation point and the charging and discharging limit times of the storage battery is still lack of enough theoretical basis. Finally, the traditional method does not have the capability of self-adaptively distributing power instruction sequences of the storage battery and the super capacitor, cannot give full play to the combined advantages of the storage battery and the super capacitor, and is not beneficial to prolonging the service life of stored energy. In summary, the conventional energy storage configuration method has the disadvantages of high energy storage configuration cost, reduced service life of the storage battery and insufficient utilization of the advantages of the super capacitor due to the reasons.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelets, so that the combined advantages of a storage battery and a super capacitor can be fully exerted to carry out self-adaptive distribution on power instruction sequences of the storage battery and the super capacitor, and the charge-discharge characteristics of the hybrid energy storage power instruction sequences are more effectively and reasonably utilized to carry out hybrid energy storage power instruction sequence distribution, so that a hybrid energy storage configuration scheme with lower cost and longer service life of the storage battery is obtained, the influence of wind power fluctuation on a power grid is reduced, and the wind power receiving capacity of the power grid is improved.
The invention adopts the following technical scheme for solving the technical problems:
the invention relates to a hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelets, which is characterized by comprising the following steps of:
step 1, acquiring a hybrid energy storage power instruction sequence { P) for stabilizing wind power of a certain wind power plante(T) | T ═ 1,2, …, T }, where: pe(t) isA hybrid energy storage power instruction at the moment T, wherein T is the total number of wind power sampling points;
step 2, establishing an upper-layer optimization model of the hybrid energy storage configuration;
step 2.1, constructing an objective function f of a hybrid energy storage configuration upper-layer optimization model by using the formula (1)1And configuring an objective function f of an upper-layer optimization model by minimizing the hybrid energy storage1To optimize the objective:
f1(PB,EB,PC,EC)＝CB+CC (1)
in the formula (1), f1Represents the annual combined cost of the hybrid energy storage configuration; cBThe total cost of investment, operation and maintenance of the storage battery is obtained; cCThe investment and operation and maintenance total cost of the super capacitor are achieved; pB、EBRespectively representing the power and capacity of the battery configuration; pC、ECRespectively representing the power and capacity of the super capacitor configuration; power P in accumulator configurationBAnd capacity EBAnd power P of super capacitor configurationCAnd capacity ECAs decision variables of the upper optimization model;
obtaining the total cost C of investment, operation and maintenance of the storage battery by using the formula (2)B：
Figure BDA0002775154980000021
In the formula (2), CBT、CBYRespectively representing the initial investment cost and the annual operation and maintenance cost of the storage battery; y isBThe service life of the storage battery; k is a radical ofBpFor the power cost coefficient, k, of the accumulatorBeIs the capacity cost coefficient, k, of the storage batteryByMaintaining a cost factor for the operation of the battery; r is0The current rate is the current rate;
obtaining the service life Y of the storage battery by using the formula (3)B：
Figure BDA0002775154980000022
In the formula (3), DiM is the discharge depth of the ith discharge of the storage battery and the charge and discharge times of the storage battery in one day respectively, and is represented by a storage battery power instruction sequence { Pba(T) | T ═ 1,2, …, T } is calculated by a rain flow counting method; dbIs the reference depth of discharge of the battery; n is a radical ofC(Db) For the accumulator at a reference depth of discharge DbThe number of times of recycling;
obtaining the total cost C of investment, operation and maintenance of the super capacitor by using the formula (4)C：
Figure BDA0002775154980000031
In the formula (4), CCT、CCYRespectively representing the initial investment cost and the annual operation and maintenance cost of the super capacitor; k is a radical ofCpPower cost coefficient, k, for a super capacitorCeCapacity cost coefficient k for super capacitorCyMaintaining a cost coefficient for the operation of the super capacitor; y isCThe service life of the super capacitor is prolonged;
and 2.2, constructing the inequality constraint of the residual capacities of the storage battery and the super capacitor at the time t in the upper-layer optimization model by using the formula (5):
Figure BDA0002775154980000032
in the formula (5), αba、βbaThe upper and lower bound coefficients are the residual electric quantity of the storage battery; alpha is alphasc、βscThe coefficients are upper and lower bound coefficients of the residual electric quantity of the super capacitor; eba(t)、Esc(t) respectively representing the residual capacities of the storage battery and the super capacitor at the end of time t;
and 2.3, constructing the charge and discharge power constraints of the storage battery and the super capacitor at the time t in the upper layer optimization model by using the formula (6):
Figure BDA0002775154980000033
in the formula (6), etaBc、ηBdThe charging and discharging efficiency of the storage battery; etaCc、ηCdThe charging and discharging efficiency of the super capacitor is improved; pba(t)、Psc(t) energy storage power instructions of the storage battery and the super capacitor at the moment t respectively; eba(t-1)、Esc(t-1) respectively representing the residual capacities of the storage battery and the super capacitor at the end of the t-1 moment; delta t is the sampling period of wind power data;
step 3, establishing a lower-layer optimization model of the hybrid energy storage configuration;
step 3.1, constructing an objective function f of a hybrid energy storage configuration lower-layer optimization model by using the formula (7)2And configuring an objective function f of a lower-layer optimization model by maximizing the hybrid energy storage2To optimize the objective:
Figure BDA0002775154980000041
in the formula (7), f2Indicating the service life of the storage battery; { Pba(t)|t＝1,2,…,T}、{Psc(T) | T ═ 1,2, …, T } represents storage battery, super capacitor power command sequence respectively, and is used as decision variable in the lower optimization model;
step 3.2, the battery power command P at the time t is expressed by the formula (8)ba(t) and a supercapacitor power command PscThe equation for (t) constrains:
Pba(t)+Psc(t)＝Pe(t) (8)
in the formula (8), Pe(t) is a hybrid energy storage power command at time t;
and 3.3, respectively constructing the equation constraints of the residual electric quantity of the storage battery and the super capacitor between adjacent moments by using the formulas (9) and (10):
Figure BDA0002775154980000042
Figure BDA0002775154980000043
in the formulae (9) and (10), Eba(t)、Esc(t) respectively representing the residual capacities of the energy storage battery and the super capacitor at the end of time t;
step 3.4, residual capacity inequality constraints of the storage battery and the super capacitor at the time t in the lower-layer optimization model are constructed by using the formula (5); constructing charging and discharging power constraints of the storage battery and the super capacitor at the time t in the lower-layer optimization model by using the formula (6);
step 4, aiming at the mixed energy storage power instruction sequence { Pe(T) | T ═ 1,2, …, T } is subjected to one-layer Haar wavelet decomposition to obtain an initial value of the low-frequency storage battery power command sequence
Figure BDA0002775154980000044
And the initial value of the high-frequency super capacitor power instruction sequence
Figure BDA0002775154980000045
Step 5, the initial value of the power instruction sequence of the low-frequency storage battery is obtained
Figure BDA0002775154980000046
And the initial value of the high-frequency super capacitor power instruction sequence
Figure BDA0002775154980000047
An upper-layer optimization model with hybrid energy storage configuration is brought in, and a particle swarm algorithm is utilized to obtain a minimum system year comprehensive cost initial value f1 (0)Initial values of power and capacity of battery configuration
Figure BDA0002775154980000048
And power and capacity initialization values of the super capacitor configuration
Figure BDA0002775154980000049
Enabling k to be iteration times of the hybrid energy storage configuration upper and lower layer optimization models, and initializing k to be 1;
step 6, allocating the storage battery of the k-1 iterationPower and capacity of the device
Figure BDA0002775154980000051
And power and capacity of super capacitor configuration
Figure BDA0002775154980000052
The lower-layer optimization model of the hybrid energy storage configuration is brought in, and the heuristic algorithm based on Haar wavelet is utilized to obtain the low-frequency storage battery power instruction sequence of the kth iteration
Figure BDA0002775154980000053
And high frequency super capacitor power instruction sequence
Figure BDA0002775154980000054
Step 7, the low-frequency storage battery power instruction sequence of the kth iteration is processed
Figure BDA0002775154980000055
And the high-frequency super capacitor power instruction sequence of the kth iteration
Figure BDA0002775154980000056
An upper-layer optimization model of the hybrid energy storage configuration is brought in, and the particle swarm algorithm is utilized to obtain the minimum hybrid energy storage configuration year comprehensive cost f of the kth iteration1 (k)Power and capacity of battery configuration
Figure BDA0002775154980000057
Figure BDA0002775154980000058
And power and capacity of super capacitor configuration
Figure BDA0002775154980000059
If f1 (0)(f1 (k)-f1 (k-1))/f1 (k-1)If the value is less than epsilon, the final result of the hybrid energy storage configuration double-layer optimization model is obtained, and the step 8 is executed, otherwise, k +1 is assigned to the valueAfter k, performing step 6; wherein epsilon is a double-layer optimization model iterative convergence judgment constant,
Figure BDA00027751549800000510
configuring annual comprehensive cost for the minimum hybrid energy storage of the k-1 iteration;
and 8, realizing self-adaptive double-layer optimization of the hybrid energy storage configuration according to a final result of the hybrid energy storage configuration double-layer optimization model, wherein the final result of the hybrid energy storage configuration double-layer optimization model comprises the following steps: minimum annual combined cost f1 (k)Longest service life f of accumulator2 (k)Power and capacity of battery configuration
Figure BDA00027751549800000511
And power and capacity of super capacitor configuration
Figure BDA00027751549800000512
Low frequency battery power command sequence
Figure BDA00027751549800000513
And high frequency super capacitor power instruction sequence
Figure BDA00027751549800000514
The hybrid energy storage configuration self-adaptive double-layer optimization method based on the Haar wavelet is also characterized in that the heuristic algorithm based on the Haar wavelet in the step 6 is carried out according to the following steps:
step 6.1, the low-frequency storage battery power instruction sequence of the k-1 iteration
Figure BDA00027751549800000515
The service life f of the storage battery of the lower layer optimization model in the k-1 iteration is obtained by taking the formula (3)2 (k-1,0)(ii) a Low-frequency storage battery power instruction sequence for k-1 iteration
Figure BDA00027751549800000516
Carrying out another Haar small layerWave decomposition is carried out to obtain the k-1 th iteration storage battery resoluble low-frequency power instruction sequence
Figure BDA00027751549800000517
And the storage battery re-decomposition high-frequency power instruction sequence of the k-1 iteration
Figure BDA00027751549800000518
Step 6.2, obtaining the storage battery resoluting high-frequency power instruction sequence of the (k-1) th iteration by using the formula (11)
Figure BDA0002775154980000061
Total number of components N in medium Haar wavelet form:
Figure BDA0002775154980000062
in the formula (11), the Floor symbol represents rounding down;
step 6.3, obtaining the storage battery resoluting high-frequency power instruction sequence of the (k-1) th iteration by using the formula (12)
Figure BDA0002775154980000063
Middle nth Haar wavelet form component sequence
Figure BDA0002775154980000064
Figure BDA0002775154980000065
In the formula (12), the value set of N is {1,2, …, N };
step 6.4. let n equal 1, f2 (k-1,0)Service life f of accumulator assigned to kth iteration2 (k)；
Step 6.5. component sequence of nth Haar wavelet form
Figure BDA0002775154980000066
High-frequency super capacitor power instruction sequence added to k-1 iteration under same time period
Figure BDA0002775154980000067
Obtaining a high-frequency super capacitor power instruction sequence { P 'for constraint judgment'sc(T) | T ═ 1,2, …, T }; low-frequency storage battery power instruction sequence of (k-1) th iteration in same time period
Figure BDA0002775154980000068
Subtracting the component sequence in the form of the nth Haar wavelet
Figure BDA0002775154980000069
Obtaining a low-frequency storage battery power command sequence { P 'for constraint judgment'ba(t)|t＝1,2,…,T}；
Step 6.6, a power instruction sequence { P 'of the low-frequency storage battery and the high-frequency super capacitor for constraint judgment'sc(t)|t＝1,2,…,T}、{P′ba(T) | T ═ 1,2, …, T } is substituted into the inequality constraint equation (6) of the charge and discharge power of the storage battery and the super capacitor, whether the constraint is established is judged, if the constraint is established, step 6.7 is executed, and if the constraint is not established, step 6.9 is executed;
step 6.7, a power instruction sequence { P 'of the low-frequency storage battery and the high-frequency super capacitor for constraint judgment'sc(t)|t＝1,2,…,T}、{P′ba(T) | T ═ 1,2, …, T } is substituted into equations (9) and (10) for constraint determination of charge/discharge power equations of the battery and the supercapacitor, respectively, to obtain a low-frequency battery and high-frequency supercapacitor residual capacity command sequence { E'sc(t)|t＝1,2,…,T}、{E′ba(T) | T ═ 1,2, …, T }; using the low-frequency storage battery and high-frequency super capacitor residual capacity instruction sequence { E 'for constraint judgment'sc(t)|t＝1,2,…,T}、{E′ba(T) | T ═ 1,2, …, T } is substituted into inequality constraint formula (5) of the residual capacity of the storage battery and the super capacitor, whether inequality constraint is established or not is judged, if so, step 6.8 is executed, and if not, step 6.9 is executed;
step 6.8, the low-frequency storage battery power instruction used for constraint judgmentSequence { P'ba(T) | T ═ 1,2, …, T } belt formula (3), obtain life f of battery of the nth iteration of lower layer optimization model in the k-1 iteration2 (k-1,n)(ii) a If f2 (k -1,n)-f2 (k-1,n-1)> 0, t is 2kTime t 2 to (n-1) +1kAt time n, P 'is sequentially added'ba(t) assigning a value to
Figure BDA0002775154980000071
P′sc(t) assigning a value to
Figure BDA0002775154980000072
And f2 (k-1,n)Is assigned to f2 (k)Then, step 6.10 is executed; otherwise, executing step 6.9; wherein f is2 (k-1,n-1)The service life of the storage battery of the (n-1) th iteration of the lower-layer optimization model in the (k-1) th iteration is represented;
step 6.9, from t to 2kTime t 2 to (n-1) +1kN time, will be
Figure BDA0002775154980000073
Is assigned to
Figure BDA0002775154980000074
Is assigned to
Figure BDA0002775154980000075
Step 6.10.N +1 is assigned to N, if N is less than or equal to N, step 6.5 is executed; otherwise, the longest service life f of the storage battery for obtaining the kth iteration is shown2 (k)And the kth iterative low-frequency storage battery power instruction sequence
Figure BDA0002775154980000076
And the high-frequency super capacitor power instruction sequence of the kth iteration
Figure BDA0002775154980000077
Compared with the prior art, the invention has the beneficial effects that:
1. the invention adopts Haar wavelet to carry out frequency band decomposition on the mixed energy storage power instruction sequence, the characteristic of the Harr wavelet basis function is matched with the charging and discharging characteristic of the energy storage, and the invention has obvious advantages compared with the basis functions such as db6 wavelet function, sine wave function and the like. The Haar wavelet basis function is a symmetrical rectangular wave, the linear combination of the Haar wavelet basis function is the same as the shape of the step wave of the energy storage power instruction sequence, and the Haar wavelet basis function can be used for simulating the complete energy storage charge-discharge process, so that the purpose of prolonging the service life of the storage battery is achieved.
2. The invention does not depend on frequency division and the division of the charging and discharging limit times of the storage battery, and can carry out power instruction distribution in a self-adaptive manner according to the hybrid energy storage power instruction. And (3) solving the model by adopting the algorithm from the step 4 to the step 7 according to the hybrid energy storage configuration double-layer optimization model established in the step 2 and the step 3 to obtain an optimized storage battery and super capacitor power instruction sequence, so that the purpose of prolonging the service life of the storage battery is achieved while the energy storage configuration cost is optimized.
3. According to the invention, frequent charging and discharging operations are carried out by the super capacitor to the maximum extent through the Harr wavelet-based adaptive decomposition algorithm, so that the charging and discharging times of the storage battery are reduced, the service life of the storage battery can be effectively prolonged, and the advantages of the super capacitor (high power density and almost no influence of the charging and discharging times on the service life) are exerted to the maximum extent.
In conclusion, the invention can effectively prolong the service life of the storage battery, fully exert the combined advantages of the storage battery and the super capacitor, and finally is beneficial to stabilizing the wind power fluctuation and absorbing the wind power.
Drawings
FIG. 1 is a schematic flow diagram of a wind power plant hybrid energy storage configuration adaptive double-layer optimization method based on Haar wavelets.
FIG. 2 is a flow chart of a heuristic algorithm based on Haar wavelets in the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, a Haar wavelet-based wind farm hybrid energy storage configuration self-adaptive double-layer optimization method is to obtain a hybrid energy storage configuration scheme with lower cost and longer battery life by establishing a double-layer optimization model of hybrid energy storage configuration and performing iterative solution by using a particle swarm algorithm and a heuristic algorithm based on Haar wavelets, and specifically, the method is performed according to the following steps:
step 1, acquiring a hybrid energy storage power instruction sequence { P) for stabilizing wind power of a certain wind power plante(T) | T ═ 1,2, …, T }, where: pe(T) is a hybrid energy storage power instruction at the moment T, and T is the total number of wind power sampling points;
step 2, establishing an upper-layer optimization model of the hybrid energy storage configuration;
step 2.1, constructing an objective function f of a hybrid energy storage configuration upper-layer optimization model by using the formula (1)1And configuring an objective function f of an upper-layer optimization model by minimizing the hybrid energy storage1To optimize the objective:
f1(PB,EB,PC,EC)＝CB+CC (1)
in the formula (1), f1Represents the annual combined cost of the hybrid energy storage configuration; cBThe total cost of investment, operation and maintenance of the storage battery is obtained; cCThe investment and operation and maintenance total cost of the super capacitor are achieved; pB、EBRespectively representing the power and capacity of the battery configuration; pC、ECRespectively representing the power and capacity of the super capacitor configuration; power P in accumulator configurationBAnd capacity EBAnd power P of super capacitor configurationCAnd capacity ECAs decision variables of the upper optimization model;
obtaining the total cost C of investment, operation and maintenance of the storage battery by using the formula (2)B：
Figure BDA0002775154980000081
In the formula (2), CBT、CBYRespectively representing the initial investment cost and the annual operation and maintenance cost of the storage battery; y isBThe service life of the storage battery; k is a radical ofBpBeing accumulatorsCoefficient of power cost, kBeIs the capacity cost coefficient, k, of the storage batteryByMaintaining a cost factor for the operation of the battery; r is0The current rate is the current rate;
obtaining the service life Y of the storage battery by using the formula (3)B：
Figure BDA0002775154980000082
In the formula (3), DiM is the discharge depth of the ith discharge of the storage battery and the charge and discharge times of the storage battery in one day respectively, and is represented by a storage battery power instruction sequence { Pba(T) | T ═ 1,2, …, T } is calculated by a rain flow counting method; dbIs the reference depth of discharge of the battery; n is a radical ofC(Db) For the accumulator at a reference depth of discharge DbThe number of times of recycling;
the rain flow counting method can be called as a tower top method, is mainly used in the engineering field, and is particularly widely applied to fatigue life calculation; specific methods are described in the literature: the capacity optimization model of the hybrid energy storage system considering the service life of the battery, the report of Chinese Motor engineering, 2013,33(34) is 91-97.
Obtaining the total cost C of investment, operation and maintenance of the super capacitor by using the formula (4)C：
Figure BDA0002775154980000091
In the formula (4), CCT、CCYRespectively representing the initial investment cost and the annual operation and maintenance cost of the super capacitor; k is a radical ofCpPower cost coefficient, k, for a super capacitorCeCapacity cost coefficient k for super capacitorCyMaintaining a cost coefficient for the operation of the super capacitor; y isCThe service life of the super capacitor is prolonged;
and 2.2, constructing the inequality constraint of the residual capacities of the storage battery and the super capacitor at the time t in the upper-layer optimization model by using the formula (5):
Figure BDA0002775154980000092
in the formula (5), αba、βbaThe upper and lower bound coefficients are the residual electric quantity of the storage battery; alpha is alphasc、βscThe coefficients are upper and lower bound coefficients of the residual electric quantity of the super capacitor; eba(t)、Esc(t) respectively representing the residual capacities of the storage battery and the super capacitor at the end of time t;
in the inequality constraint formula (5) of the residual capacities of the storage battery and the super capacitor at the time of the upper optimization model t, the configured capacities E of the storage battery and the super capacitorB、ECIs a variable;
and 2.3, constructing the charge and discharge power constraints of the storage battery and the super capacitor at the time t in the upper layer optimization model by using the formula (6):
Figure BDA0002775154980000093
in the formula (6), etaBc、ηBdThe charging and discharging efficiency of the storage battery; etaCc、ηCdThe charging and discharging efficiency of the super capacitor is improved; pba(t)、Psc(t) energy storage power instructions of the storage battery and the super capacitor at the moment t respectively; eba(t-1)、Esc(t-1) respectively representing the residual capacities of the storage battery and the super capacitor at the end of the t-1 moment; delta t is the sampling period of wind power data;
in the inequality constraint formula (6) of charging and discharging power of the storage battery and the super capacitor at the time t of the upper optimization model, the power P configured by the storage batteryBAnd capacity EBAnd power P of super capacitor configurationCAnd capacity ECIs a variable;
step 3, establishing a lower-layer optimization model of the hybrid energy storage configuration;
step 3.1, constructing an objective function f of a hybrid energy storage configuration lower-layer optimization model by using the formula (7)2And configuring an objective function f of a lower-layer optimization model by maximizing the hybrid energy storage2To optimize the objective:
Figure BDA0002775154980000101
in the formula (7), f2Indicating the service life of the storage battery; { Pba(t)|t＝1,2,…,T}、{Psc(T) | T ═ 1,2, …, T } represents storage battery, super capacitor power command sequence respectively, and is used as decision variable in the lower optimization model;
step 3.2, the battery power command P at the time t is expressed by the formula (8)ba(t) and a supercapacitor power command PscThe equation for (t) constrains:
Pba(t)+Psc(t)＝Pe(t) (8)
in the formula (8), Pe(t) is a hybrid energy storage power command at time t;
and 3.3, respectively constructing the equation constraints of the residual electric quantity of the storage battery and the super capacitor between adjacent moments by using the formulas (9) and (10):
Figure BDA0002775154980000102
Figure BDA0002775154980000103
in the formulae (9) and (10), Eba(t)、Esc(t) respectively representing the residual capacities of the energy storage battery and the super capacitor at the end of time t;
step 3.4, residual capacity inequality constraints of the storage battery and the super capacitor at the time t in the lower-layer optimization model are constructed by using the formula (5); constructing charging and discharging power constraints of the storage battery and the super capacitor at the time t in the lower-layer optimization model by using the formula (6);
in the inequality constraint formula (5) of the residual capacities of the storage battery and the super capacitor at the time t of the lower optimization model, the residual capacities E of the energy storage battery and the super capacitor at the end of the time tba(t)、Esc(t) is a variable; in the inequality constraint formula (6) of charging and discharging power of the storage battery and the super capacitor at the time t of the lower optimization model, the storage battery is stored at the time tEnergy storage power instruction P of pool and super capacitorba(t)、Psc(t) is a variable;
step 4, aiming at the mixed energy storage power instruction sequence { Pe(T) | T ═ 1,2, …, T } is subjected to one-layer Haar wavelet decomposition to obtain an initial value of the low-frequency storage battery power command sequence
Figure BDA0002775154980000111
And the initial value of the high-frequency super capacitor power instruction sequence
Figure BDA0002775154980000112
Step 5, the initial value of the power instruction sequence of the low-frequency storage battery is obtained
Figure BDA0002775154980000113
And the initial value of the high-frequency super capacitor power instruction sequence
Figure BDA0002775154980000114
An upper-layer optimization model with hybrid energy storage configuration is brought in, and a particle swarm algorithm is utilized to obtain a minimum system year comprehensive cost initial value f1 (0)Initial values of power and capacity of battery configuration
Figure BDA0002775154980000115
And power and capacity initialization values of the super capacitor configuration
Figure BDA0002775154980000116
Enabling k to be iteration times of the hybrid energy storage configuration upper and lower layer optimization models, and initializing k to be 1;
the particle swarm algorithm is an optimization search algorithm based on a bird swarm motion model proposed by doctor Kennedy in 1995, and the specific algorithm can be referred to documents as follows: a non-invasive household load decomposition method based on a dynamic self-adaptive particle swarm algorithm is disclosed, wherein the power grid technology is 2018,42(6) 1819-1826.
Step 6, configuring the power and the capacity of the storage battery of the (k-1) th iteration
Figure BDA0002775154980000117
And power and capacity of super capacitor configuration
Figure BDA0002775154980000118
The lower-layer optimization model of the hybrid energy storage configuration is brought in, and the heuristic algorithm based on Haar wavelet is utilized to obtain the low-frequency storage battery power instruction sequence of the kth iteration
Figure BDA0002775154980000119
And high frequency super capacitor power instruction sequence
Figure BDA00027751549800001110
Note that: in the 1 st iteration, the power and capacity initial values of the storage battery are configured
Figure BDA00027751549800001111
Figure BDA00027751549800001112
And power and capacity initialization values of the super capacitor configuration
Figure BDA00027751549800001113
The lower-layer optimization model is brought into the hybrid energy storage configuration, as shown in fig. 2;
step 6.1, the low-frequency storage battery power instruction sequence of the k-1 iteration
Figure BDA00027751549800001114
The service life f of the storage battery of the lower layer optimization model in the k-1 iteration is obtained by taking the formula (3)2 (k-1,0)(ii) a Low-frequency storage battery power instruction sequence for k-1 iteration
Figure BDA00027751549800001115
Performing one more layer of Haar wavelet decomposition to obtain a k-1 iteration storage battery decomposed low-frequency power instruction sequence
Figure BDA00027751549800001116
And (k-1) th iteration of battery re-decompositionHigh frequency power command sequence
Figure BDA00027751549800001117
Step 6.2, obtaining the storage battery resoluting high-frequency power instruction sequence of the (k-1) th iteration by using the formula (11)
Figure BDA00027751549800001118
Total number of components N in medium Haar wavelet form:
Figure BDA0002775154980000121
in the formula (11), the Floor symbol represents rounding down;
step 6.3, obtaining the storage battery resoluting high-frequency power instruction sequence of the (k-1) th iteration by using the formula (12)
Figure BDA0002775154980000122
Middle nth Haar wavelet form component sequence
Figure BDA0002775154980000123
Figure BDA0002775154980000124
In the formula (12), the value set of N is {1,2, …, N };
step 6.4. let n equal 1, f2 (k-1,0)Service life f of accumulator assigned to kth iteration2 (k)；
Step 6.5. component sequence of nth Haar wavelet form
Figure BDA0002775154980000125
High-frequency super capacitor power instruction sequence added to k-1 iteration under same time period
Figure BDA0002775154980000126
In the obtaining of the constraint judgmentHigh-frequency super capacitor power instruction sequence { P'sc(T) | T ═ 1,2, …, T }; low-frequency storage battery power instruction sequence of (k-1) th iteration in same time period
Figure BDA0002775154980000127
Subtracting the component sequence in the form of the nth Haar wavelet
Figure BDA0002775154980000128
Obtaining a low-frequency storage battery power command sequence { P 'for constraint judgment'ba(t)|t＝1,2,…,T}；
Step 6.6, a power instruction sequence { P 'of the low-frequency storage battery and the high-frequency super capacitor for constraint judgment'sc(t)|t＝1,2,…,T}、{P′ba(T) | T ═ 1,2, …, T } is substituted into the inequality constraint equation (6) of the charge and discharge power of the storage battery and the super capacitor, whether the constraint is established is judged, if the constraint is established, step 6.7 is executed, and if the constraint is not established, step 6.9 is executed;
step 6.7, a power instruction sequence { P 'of the low-frequency storage battery and the high-frequency super capacitor for constraint judgment'sc(t)|t＝1,2,…,T}、{P′ba(T) | T ═ 1,2, …, T } is substituted into equations (9) and (10) for constraint determination of charge/discharge power equations of the battery and the supercapacitor, respectively, to obtain a low-frequency battery and high-frequency supercapacitor residual capacity command sequence { E'sc(t)|t＝1,2,…,T}、{E′ba(T) | T ═ 1,2, …, T }; using the low-frequency storage battery and high-frequency super capacitor residual capacity instruction sequence { E 'for constraint judgment'sc(t)|t＝1,2,…,T}、{E′ba(T) | T ═ 1,2, …, T } is substituted into inequality constraint formula (5) of the residual capacity of the storage battery and the super capacitor, whether inequality constraint is established or not is judged, if so, step 6.8 is executed, and if not, step 6.9 is executed;
step 6.8, using the low-frequency storage battery power instruction sequence { P 'for constraint judgment'ba(T) | T ═ 1,2, …, T } belt formula (3), obtain life f of battery of the nth iteration of lower layer optimization model in the k-1 iteration2 (k-1,n)(ii) a If f2 (k -1,n)-f2 (k-1,n-1)> 0, t is 2kTime t 2 to (n-1) +1kAt time n, P 'is sequentially added'ba(t) assigning a value to
Figure BDA0002775154980000131
P′sc(t) assigning a value to
Figure BDA0002775154980000132
And f2 (k-1,n)Is assigned to f2 (k)Then, step 6.10 is executed; otherwise, executing step 6.9; wherein f is2 (k-1,n-1)The service life of the storage battery of the (n-1) th iteration of the lower-layer optimization model in the (k-1) th iteration is represented;
step 6.9, from t to 2kTime t 2 to (n-1) +1kN time, will be
Figure BDA0002775154980000133
Is assigned to
Figure BDA0002775154980000134
Is assigned to
Figure BDA0002775154980000135
Step 6.10.N +1 is assigned to N, if N is less than or equal to N, step 6.5 is executed; otherwise, the longest service life f of the storage battery for obtaining the kth iteration is shown2 (k)And the kth iterative low-frequency storage battery power instruction sequence
Figure BDA0002775154980000136
And the high-frequency super capacitor power instruction sequence of the kth iteration
Figure BDA0002775154980000137
Step 7, the low-frequency storage battery power instruction sequence of the kth iteration is processed
Figure BDA0002775154980000138
And the high-frequency super capacitor power instruction sequence of the kth iteration
Figure BDA0002775154980000139
An upper-layer optimization model of the hybrid energy storage configuration is brought in, and the particle swarm algorithm is utilized to obtain the minimum hybrid energy storage configuration year comprehensive cost f of the kth iteration1 (k)Power and capacity of battery configuration
Figure BDA00027751549800001310
Figure BDA00027751549800001311
And power and capacity of super capacitor configuration
Figure BDA00027751549800001312
If f1 (0)(f1 (k)-f1 (k-1))/f1 (k-1)If the value is less than epsilon, the final result of the hybrid energy storage configuration double-layer optimization model is obtained, and step 8 is executed, otherwise, step 6 is executed after k +1 is assigned to k; wherein epsilon is a double-layer optimization model iterative convergence judgment constant,
Figure BDA00027751549800001313
configuring annual comprehensive cost for the minimum hybrid energy storage of the k-1 iteration; in this example, ε is taken to be 0.001;
and 8, realizing self-adaptive double-layer optimization of the hybrid energy storage configuration according to a final result of the hybrid energy storage configuration double-layer optimization model, wherein the final result of the hybrid energy storage configuration double-layer optimization model comprises the following steps: minimum annual combined cost f1 (k)Longest service life f of accumulator2 (k)Power and capacity of battery configuration
Figure BDA00027751549800001314
And power and capacity of super capacitor configuration
Figure BDA00027751549800001315
Low frequency battery power command sequence
Figure BDA00027751549800001316
And high frequency super capacitor power instruction sequence
Figure BDA00027751549800001317

Claims (2)

1. A hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelets is characterized by comprising the following steps:
step 1, acquiring a hybrid energy storage power instruction sequence { P) for stabilizing wind power of a certain wind power plante(T) | T ═ 1,2, …, T }, where: pe(T) is a hybrid energy storage power instruction at the moment T, and T is the total number of wind power sampling points;
step 2, establishing an upper-layer optimization model of the hybrid energy storage configuration;
step 2.1, constructing an objective function f of a hybrid energy storage configuration upper-layer optimization model by using the formula (1)1And configuring an objective function f of an upper-layer optimization model by minimizing the hybrid energy storage1To optimize the objective:
f1(PB,EB,PC,EC)＝CB+CC (1)
in the formula (1), f1Represents the annual combined cost of the hybrid energy storage configuration; cBThe total cost of investment, operation and maintenance of the storage battery is obtained; cCThe investment and operation and maintenance total cost of the super capacitor are achieved; pB、EBRespectively representing the power and capacity of the battery configuration; pC、ECRespectively representing the power and capacity of the super capacitor configuration; power P in accumulator configurationBAnd capacity EBAnd power P of super capacitor configurationCAnd capacity ECAs decision variables of the upper optimization model;
obtaining the total cost C of investment, operation and maintenance of the storage battery by using the formula (2)B：
Figure FDA0002775154970000011
In the formula (2), CBT、CBYRespectively representing the initial investment cost and the annual operation and maintenance cost of the storage battery; y isBThe service life of the storage battery; k is a radical ofBpFor the power cost coefficient, k, of the accumulatorBeIs the capacity cost coefficient, k, of the storage batteryByMaintaining a cost factor for the operation of the battery; r is0The current rate is the current rate;
obtaining the service life Y of the storage battery by using the formula (3)B：
Figure FDA0002775154970000012
In the formula (3), DiM is the discharge depth of the ith discharge of the storage battery and the charge and discharge times of the storage battery in one day respectively, and is represented by a storage battery power instruction sequence { Pba(T) | T ═ 1,2, …, T } is calculated by a rain flow counting method; dbIs the reference depth of discharge of the battery; n is a radical ofC(Db) For the accumulator at a reference depth of discharge DbThe number of times of recycling;
obtaining the total cost C of investment, operation and maintenance of the super capacitor by using the formula (4)C：
Figure FDA0002775154970000021
In the formula (4), CCT、CCYRespectively representing the initial investment cost and the annual operation and maintenance cost of the super capacitor; k is a radical ofCpPower cost coefficient, k, for a super capacitorCeCapacity cost coefficient k for super capacitorCyMaintaining a cost coefficient for the operation of the super capacitor; y isCThe service life of the super capacitor is prolonged;
and 2.2, constructing the inequality constraint of the residual capacities of the storage battery and the super capacitor at the time t in the upper-layer optimization model by using the formula (5):
Figure FDA0002775154970000022
in the formula (5), αba、βbaThe upper and lower bound coefficients are the residual electric quantity of the storage battery; alpha is alphasc、βscThe coefficients are upper and lower bound coefficients of the residual electric quantity of the super capacitor; eba(t)、Esc(t) respectively representing the residual capacities of the storage battery and the super capacitor at the end of time t;
and 2.3, constructing the charge and discharge power constraints of the storage battery and the super capacitor at the time t in the upper layer optimization model by using the formula (6):
Figure FDA0002775154970000023
in the formula (6), etaBc、ηBdThe charging and discharging efficiency of the storage battery; etaCc、ηCdThe charging and discharging efficiency of the super capacitor is improved; pba(t)、Psc(t) energy storage power instructions of the storage battery and the super capacitor at the moment t respectively; eba(t-1)、Esc(t-1) respectively representing the residual capacities of the storage battery and the super capacitor at the end of the t-1 moment; delta t is the sampling period of wind power data;
step 3, establishing a lower-layer optimization model of the hybrid energy storage configuration;
step 3.1, constructing an objective function f of a hybrid energy storage configuration lower-layer optimization model by using the formula (7)2And configuring an objective function f of a lower-layer optimization model by maximizing the hybrid energy storage2To optimize the objective:
Figure FDA0002775154970000024
in the formula (7), f2Indicating the service life of the storage battery; { Pba(t)|t＝1,2,…,T}、{Psc(T) | T ═ 1,2, …, T } represents storage battery, super capacitor power command sequence respectively, and is used as decision variable in the lower optimization model;
step 3.2, the battery power command P at the time t is expressed by the formula (8)ba(t) and super capacitor PowerInstruction PscThe equation for (t) constrains:
Pba(t)+Psc(t)＝Pe(t) (8)
in the formula (8), Pe(t) is a hybrid energy storage power command at time t;
and 3.3, respectively constructing the equation constraints of the residual electric quantity of the storage battery and the super capacitor between adjacent moments by using the formulas (9) and (10):
Figure FDA0002775154970000031
Figure FDA0002775154970000032
in the formulae (9) and (10), Eba(t)、Esc(t) respectively representing the residual capacities of the energy storage battery and the super capacitor at the end of time t;
step 3.4, residual capacity inequality constraints of the storage battery and the super capacitor at the time t in the lower-layer optimization model are constructed by using the formula (5); constructing charging and discharging power constraints of the storage battery and the super capacitor at the time t in the lower-layer optimization model by using the formula (6);
step 4, aiming at the mixed energy storage power instruction sequence { Pe(T) | T ═ 1,2, …, T } is subjected to one-layer Haar wavelet decomposition to obtain an initial value of the low-frequency storage battery power command sequence
Figure FDA0002775154970000033
And the initial value of the high-frequency super capacitor power instruction sequence
Figure FDA0002775154970000034
Step 5, the initial value of the power instruction sequence of the low-frequency storage battery is obtained
Figure FDA0002775154970000035
And the initial value of the high-frequency super capacitor power instruction sequence
Figure FDA0002775154970000036
An upper-layer optimization model with hybrid energy storage configuration is brought in, and a particle swarm algorithm is utilized to obtain a minimum system year comprehensive cost initial value f1 (0)Initial values of power and capacity of battery configuration
Figure FDA0002775154970000037
And power and capacity initialization values of the super capacitor configuration
Figure FDA0002775154970000038
Enabling k to be iteration times of the hybrid energy storage configuration upper and lower layer optimization models, and initializing k to be 1;
step 6, configuring the power and the capacity of the storage battery of the (k-1) th iteration
Figure FDA0002775154970000039
And power and capacity of super capacitor configuration
Figure FDA00027751549700000310
The lower-layer optimization model of the hybrid energy storage configuration is brought in, and the heuristic algorithm based on Haar wavelet is utilized to obtain the low-frequency storage battery power instruction sequence of the kth iteration
Figure FDA00027751549700000311
And high frequency super capacitor power instruction sequence
Figure FDA00027751549700000312
Step 7, the low-frequency storage battery power instruction sequence of the kth iteration is processed
Figure FDA0002775154970000041
And the high-frequency super capacitor power instruction sequence of the kth iteration
Figure FDA0002775154970000042
An upper-layer optimization model of the hybrid energy storage configuration is brought in, and the particle swarm algorithm is utilized to obtain the minimum hybrid energy storage configuration year comprehensive cost f of the kth iteration1 (k)Power and capacity of battery configuration
Figure FDA0002775154970000043
Figure FDA0002775154970000044
And power and capacity of super capacitor configuration
Figure FDA0002775154970000045
If f1 (0)(f1 (k)-f1 (k-1))/f1 (k-1)If the value is less than epsilon, the final result of the hybrid energy storage configuration double-layer optimization model is obtained, and step 8 is executed, otherwise, step 6 is executed after k +1 is assigned to k; wherein epsilon is a double-layer optimization model iterative convergence judgment constant,
Figure FDA0002775154970000046
configuring annual comprehensive cost for the minimum hybrid energy storage of the k-1 iteration;
and 8, realizing self-adaptive double-layer optimization of the hybrid energy storage configuration according to a final result of the hybrid energy storage configuration double-layer optimization model, wherein the final result of the hybrid energy storage configuration double-layer optimization model comprises the following steps: minimum annual combined cost f1 (k)Longest service life of accumulator
Figure FDA0002775154970000047
Power and capacity of battery configuration
Figure FDA0002775154970000048
And power and capacity of super capacitor configuration
Figure FDA0002775154970000049
Low frequency battery power command sequence
Figure FDA00027751549700000410
And high frequency super capacitor power instruction sequence
Figure FDA00027751549700000411
2. The method for self-adaptive double-layer optimization of hybrid energy storage configuration based on Haar wavelet as claimed in claim 1, wherein the heuristic algorithm based on Haar wavelet in step 6 is performed as follows:
step 6.1, the low-frequency storage battery power instruction sequence of the k-1 iteration
Figure FDA00027751549700000412
Carrying in formula (3) to obtain the service life of the storage battery of the lower layer optimization model in the k-1 iteration
Figure FDA00027751549700000413
Low-frequency storage battery power instruction sequence for k-1 iteration
Figure FDA00027751549700000414
Performing one more layer of Haar wavelet decomposition to obtain a k-1 iteration storage battery decomposed low-frequency power instruction sequence
Figure FDA00027751549700000415
And the storage battery re-decomposition high-frequency power instruction sequence of the k-1 iteration
Figure FDA00027751549700000416
Step 6.2, obtaining the storage battery resoluting high-frequency power instruction sequence of the (k-1) th iteration by using the formula (11)
Figure FDA00027751549700000417
Total number of components N in medium Haar wavelet form:
Figure FDA00027751549700000418
in the formula (11), the Floor symbol represents rounding down;
step 6.3, obtaining the storage battery resoluting high-frequency power instruction sequence of the (k-1) th iteration by using the formula (12)
Figure FDA00027751549700000419
Middle nth Haar wavelet form component sequence
Figure FDA0002775154970000051
Figure FDA0002775154970000052
In the formula (12), the value set of N is {1,2, …, N };
step 6.4, making n equal to 1,
Figure FDA0002775154970000053
service life of accumulator assigned to kth iteration
Figure FDA0002775154970000054
Step 6.5. component sequence of nth Haar wavelet form
Figure FDA0002775154970000055
High-frequency super capacitor power instruction sequence added to k-1 iteration under same time period
Figure FDA0002775154970000056
Obtaining a high-frequency super capacitor power instruction sequence { P 'for constraint judgment'sc(T) | T ═ 1,2, …, T }; low-frequency storage battery power instruction sequence of (k-1) th iteration in same time period
Figure FDA0002775154970000057
Subtracting the component sequence in the form of the nth Haar wavelet
Figure FDA0002775154970000058
Obtaining a low-frequency storage battery power command sequence { P 'for constraint judgment'ba(t)|t＝1,2,…,T}；
Step 6.6, a power instruction sequence { P 'of the low-frequency storage battery and the high-frequency super capacitor for constraint judgment'sc(t)|t＝1,2,…,T}、{P′ba(T) | T ═ 1,2, …, T } is substituted into the inequality constraint equation (6) of the charge and discharge power of the storage battery and the super capacitor, whether the constraint is established is judged, if the constraint is established, step 6.7 is executed, and if the constraint is not established, step 6.9 is executed;
step 6.7, a power instruction sequence { P 'of the low-frequency storage battery and the high-frequency super capacitor for constraint judgment'sc(t)|t＝1,2,…,T}、{P′ba(T) | T ═ 1,2, …, T } is substituted into equations (9) and (10) for constraint determination of charge/discharge power equations of the battery and the supercapacitor, respectively, to obtain a low-frequency battery and high-frequency supercapacitor residual capacity command sequence { E'sc(t)|t＝1,2,…,T}、{E′ba(T) | T ═ 1,2, …, T }; using the low-frequency storage battery and high-frequency super capacitor residual capacity instruction sequence { E 'for constraint judgment'sc(t)|t＝1,2,…,T}、{E′ba(T) | T ═ 1,2, …, T } is substituted into inequality constraint formula (5) of the residual capacity of the storage battery and the super capacitor, whether inequality constraint is established or not is judged, if so, step 6.8 is executed, and if not, step 6.9 is executed;
step 6.8, using the low-frequency storage battery power instruction sequence { P 'for constraint judgment'ba(T) | T ═ 1,2, …, T } belt formula (3), obtain in k-1 iteration under layer optimization model n time the life of battery of iteration
Figure FDA0002775154970000059
If it is
Figure FDA00027751549700000510
Then from t to 2kTime t 2 to (n-1) +1kAt time n, P 'is sequentially added'ba(t) assigning a value to
Figure FDA00027751549700000511
P′sc(t) assigning a value to
Figure FDA00027751549700000512
And
Figure FDA00027751549700000513
is assigned to
Figure FDA00027751549700000514
Then, step 6.10 is executed; otherwise, executing step 6.9; wherein the content of the first and second substances,
Figure FDA00027751549700000515
the service life of the storage battery of the (n-1) th iteration of the lower-layer optimization model in the (k-1) th iteration is represented;
step 6.9, from t to 2kTime t 2 to (n-1) +1kN time, will be
Figure FDA00027751549700000516
Is assigned to
Figure FDA00027751549700000517
Is assigned to
Figure FDA0002775154970000061
Step 6.10.N +1 is assigned to N, if N is less than or equal to N, step 6.5 is executed; otherwise, the longest service life of the storage battery for obtaining the kth iteration is shown
Figure FDA0002775154970000062
Low-frequency storage battery power instruction sequence of kth iteration
Figure FDA0002775154970000063
And the high-frequency super capacitor power instruction sequence of the kth iteration
Figure FDA0002775154970000064
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