Wind power energy storage optimization method and system based on life cycle cost

Abstract

This disclosure pertains to the field of wind power energy storage technology, providing a wind power energy storage optimization method and system based on total life cycle cost. The method includes the following steps: acquiring wind farm operation data; constructing an energy storage control model for wind power fluctuation mitigation based on the acquired data; calculating various costs of the wind farm based on the constructed energy storage control model for wind power fluctuation mitigation to obtain the total life cycle cost of the energy storage system; and optimizing wind power energy storage by minimizing the obtained total life cycle cost as the objective function. This disclosure comprehensively considers fluctuation mitigation effects, energy storage operating status, and configuration costs, providing guidance and practical value for wind power consumption and energy storage integration under the new power system context.

Description

Technical Field

[0001] This disclosure belongs to the field of wind power energy storage technology, specifically relating to a wind power energy storage optimization method and system based on full life cycle cost. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Wind power, as a relatively mature new energy source, has advantages such as low cost, clean operation, and inexhaustibility. In recent years, under the strategic background of building a new power system, the installed capacity of wind power has continued to rise steadily.

[0004] In the construction of the new power system, the proportion of wind power on the generation side continues to rise. Based on the inherent characteristics of wind power, this brings two major challenges: first, increased intermittency and volatility on the generation side, significantly increasing the probability of power generation-consumption mismatch; second, a significant decrease in the inertia and adjustable capacity of the power system, weakening its regulatory effect. Therefore, allocating a certain proportion of energy storage to wind power to mitigate its volatility is an inevitable choice to ensure the safe and stable operation of the new power system.

[0005] Currently, there are some research results in the field of energy storage configuration technology applied to wind power fluctuation mitigation, mainly including: theoretical analysis method, simulation analysis method, and optimization model method. Theoretical analysis method derives energy storage configuration schemes based on time-frequency domain analysis, but does not consider the economics of the schemes; simulation analysis method selects the final scheme based on the performance of each energy storage configuration scheme under the same wind power fluctuation mitigation strategy, but the quality of the final scheme is difficult to guarantee due to the limited number of alternative schemes; similarly, it does not consider economics; optimization model method comprehensively considers reliability and economics, constructs and solves optimization models to obtain energy storage configuration schemes, but due to incomplete consideration of factors, it is difficult to guarantee its optimality. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a wind power energy storage optimization method and system based on full life cycle cost. It comprehensively considers the fluctuation mitigation effect, the energy storage operating status, and takes into account various costs incurred during the execution of energy storage control strategies, thereby constructing a full life cycle cost model for the energy storage system. This model has guiding significance and practical value for wind power consumption and energy storage access under the background of new power systems.

[0007] According to some embodiments, the first solution of this disclosure provides a wind power energy storage optimization method based on full life cycle cost, which adopts the following technical solution:

[0008] A wind power energy storage optimization method based on total life cycle cost includes the following steps:

[0009] Obtain operational data from wind farms;

[0010] Based on the acquired data, an energy storage control model for smoothing wind power fluctuations was constructed.

[0011] Based on the constructed energy storage control model for wind power fluctuation smoothing, the various costs of the wind farm are calculated, and the total life cycle cost of the energy storage system is obtained.

[0012] The goal is to optimize the energy storage of wind power by minimizing the total life cycle cost.

[0013] As a further technical limitation, the acquired wind farm operation data must include at least historical power data, charging and discharging power, and self-discharge rate.

[0014] As a further technical limitation, in the process of constructing the energy storage control model for wind power fluctuation mitigation, the wind power fluctuation mitigation effect is considered, and an hourly wind power fluctuation mitigation target value is set; the output of wind power energy storage is controlled according to the wind power fluctuation mitigation target value, so that the output power of wind power energy storage is equal to the wind power fluctuation mitigation target value, and the working state of wind power energy storage system is improved by restoring the state of charge (SOC) of the energy storage system, switching the energy storage charging and discharging state, and limiting the output of the energy storage system.

[0015] As a further technical limitation, the total life cycle cost of the energy storage system is related to the initial construction cost of the energy storage system, the operating cost of the energy storage system, the cost of replacing the energy storage battery, the recycling revenue of the energy storage system, and the operating subsidies of the energy storage system.

[0016] Furthermore, the operating cost of the energy storage system includes the penalty cost and the maintenance cost of the energy storage system; the maintenance cost of the energy storage system fully considers the configured power of the energy storage system, as well as the maintenance cost coefficient related to the configured power and the charging and discharging capacity of the energy storage system.

[0017] Furthermore, the cost of replacing the energy storage battery is related to the entire life cycle of the energy storage system, the annual interest rate, the cost of replacing the energy storage battery once, the lifespan of the energy storage battery, and the depth of cycle discharge of the energy storage battery.

[0018] As a further technical limitation, in the process of optimizing wind power energy storage, the objective function is to minimize the total life cycle cost, and the initial power and capacity of the energy storage system are used as constraints. The particle swarm optimization algorithm is used to solve for the total life cycle cost, thereby obtaining the optimal power and capacity of the energy storage system and realizing the optimization of wind power energy storage.

[0019] According to some embodiments, the second solution of this disclosure provides a wind power energy storage optimization system based on full life cycle cost, which adopts the following technical solution:

[0020] A wind power energy storage optimization system based on total life cycle cost includes:

[0021] The acquisition module is configured to acquire historical power data of wind farms;

[0022] The modeling module is configured to construct an energy storage control model for smoothing wind power fluctuations based on the acquired data; and to calculate various costs of the wind farm based on the constructed energy storage control model for smoothing wind power fluctuations, thereby obtaining the total life cycle cost of the energy storage system.

[0023] The optimization module is configured to optimize wind power energy storage by minimizing the obtained total life cycle cost as the objective function.

[0024] According to some embodiments, a third aspect of this disclosure provides a computer-readable storage medium, employing the following technical solution:

[0025] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps in the wind power energy storage optimization method based on life-cycle cost as described in the first aspect of this disclosure.

[0026] According to some embodiments, the fourth solution of this disclosure provides an electronic device that adopts the following technical solution:

[0027] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the wind power energy storage optimization method based on life cycle cost as described in the first aspect of this disclosure.

[0028] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0029] This disclosure comprehensively considers the fluctuation mitigation effect, the working status of energy storage, and takes into account the various costs incurred in the process of implementing energy storage control strategies, and constructs a full life cycle cost model for energy storage systems. It has guiding significance and practical value for wind power consumption and energy storage access under the background of new power systems. Attached Figure Description

[0030] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0031] Figure 1 This is a flowchart of the wind power energy storage optimization method based on full life cycle cost in Embodiment 1 of this disclosure;

[0032] Figure 2This is a flowchart of the steps for solving the energy storage life cycle cost model based on the particle swarm optimization algorithm in Embodiment 1 of this disclosure;

[0033] Figure 3(a) shows the effect of smoothing out typical daily wind power fluctuations in January in Embodiment 1 of this disclosure;

[0034] Figure 3(b) shows the effect of smoothing typical daily wind power fluctuations in April in Embodiment 1 of this disclosure;

[0035] Figure 3(c) shows the effect of smoothing typical daily wind power fluctuations in July in Embodiment 1 of this disclosure;

[0036] Figure 3(d) shows the effect of smoothing typical daily wind power fluctuations in October in Embodiment 1 of this disclosure;

[0037] Figure 4 This is a structural block diagram of the wind power energy storage optimization system based on full life cycle cost in Embodiment 2 of this disclosure. Detailed Implementation

[0038] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0040] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0041] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0042] Example 1

[0043] Embodiment 1 of this disclosure introduces a wind power energy storage optimization method based on total life cycle cost.

[0044] like Figure 1 The wind power energy storage optimization method shown includes the following steps:

[0045] Step S01: Obtain historical power data of the wind farm, with a length of at least one year; obtain energy storage technology parameters, including charge and discharge efficiency and self-discharge rate;

[0046] Step S02: Taking into account the fluctuation smoothing effect and the energy storage working status, an energy storage control strategy for hourly wind power fluctuation smoothing is proposed to control the energy storage output under specific configurations;

[0047] Step S03: Taking into account all costs incurred during the execution of energy storage control strategies, construct an energy storage lifecycle cost model;

[0048] Step S04: Solve the energy storage life cycle cost model. The optimization objective is to minimize the annual cost, and the constraint is the energy storage configuration ratio limit stipulated by the current policy. The optimal configuration power and capacity of energy storage are obtained.

[0049] As one or more implementation methods, the specific process of step S02 is as follows:

[0050] Step S201: Considering the fluctuation mitigation effect, set the target value for hourly wind power fluctuation mitigation: expressed by the following formula:

[0051]

[0052] Among them, T a The annual operating duration is typically one year; ΔT is a fixed-length time window, which is one hour in this embodiment; M is the number of sampling intervals within the time window; Δt is the sampling interval length, which is the same as the sampling interval for the historical power data of the wind farm; P wind Historical power data for wind farms; P exp To maintain a constant target value for smoothing out the data within each time window;

[0053] Step S202: Control the energy storage output based on the mitigation target value, so that the total output power of the wind farm-energy storage system is as close as possible to the mitigation target value, expressed by the following formula:

[0054] P tar (t)=P exp (t)-P wind (t);

[0055]

[0056]

[0057]

[0058]

[0059] Among them, P tar (t) represents the energy storage output without considering energy storage power and energy constraints; a value greater than 0 indicates energy storage discharge. C,n (t), P D,n(t) represents the energy storage power and the energy storage charging and discharging power under energy constraints, respectively; P ESS.Max S ESS.Max The power and capacity configured for energy storage; σ is the self-discharge rate of energy storage; η DC-DC The efficiency of the energy storage DC-DC converter; η C ,η D For energy storage charging and discharging efficiency; E n (t) represents the remaining stored energy; P ESS (t) represents the actual output power of the energy storage; x j It is a variable of integers from 0 to 1;

[0060] Step S203: Improve the working conditions of energy storage by using SOC recovery, energy storage charge and discharge state switching restrictions, and energy storage output restriction strategies.

[0061] As one or more implementation methods, the time scale for both the historical power data of the wind farm and the energy storage control strategy is 5 minutes.

[0062] As one or more implementation methods, the specific process of step S203 is as follows:

[0063] Step S20301: When the SOC exceeds or falls below the specified value, initiate the SOC recovery strategy, iteratively raising or lowering the target value to the maximum or minimum value that satisfies the constraints. Taking the SOC exceeding the specified value as an example, the formula is as follows:

[0064] P exp.mod (t)=τP limit +P exp (t);

[0065] P limit =P ESS.Max +P MaxWP (t)-P exp (t);

[0066] Among them, P exp.mod (t) represents the target value for smoothing after the elevation; τ is the correction coefficient iterating from 0 to 1; P MaxWP (t) represents the maximum wind power within the time window; P limit To mitigate the natural limit of the target value increment.

[0067] The iteration process of τ will terminate if any of the following three conditions occur:

[0068] (1) The power of the energy storage configuration is difficult to meet the corrected expected energy storage power, which can be expressed by the following formula:

[0069]

[0070] Among them, P r{·} represents the probability of an event occurring, and μ is the corresponding confidence level.

[0071] (2) If the difference in expected power after correction between two adjacent time windows is too large, it can easily cause an impact on the power grid. This can be expressed by the following formula:

[0072] |P exp (t i )-P exp (t i-1 )|>ε;

[0073] Where ε is the corresponding threshold.

[0074] (3) If the net discharge of the energy storage in each time window is too large, the SOC will exceed the normal operating range, enter the lower half buffer zone or even the warning zone, and switch to a power shortage state, which can be expressed by the following formula:

[0075]

[0076] Where K is the overcharge / discharge limit coefficient for energy storage.

[0077] Step S20302: When the total output power of the wind farm-energy storage system is close to the target value for smoothing out the next moment, and the wind power does not change significantly, the energy storage charging and discharging state switching restriction strategy is activated to keep the energy storage output constant. This can be expressed by the following formula:

[0078] P ess (t)=P ess (t-1);

[0079] |P exp (t)-P gc (t-1)|≤σ1;

[0080] P wind (t)-P wind (t-1)|≤σ2;

[0081] Among them, P gc σ1 and σ2 are the total output power of the wind farm-energy storage system.

[0082] Step S20303: When the wind power is close to the mitigation target value, the energy storage output limiting strategy is activated, and the energy storage stops working to maintain the SOC, as expressed by the following formula:

[0083] P ess (t) = 0;

[0084] |P exp (t)-P wind (t)|≤σ3;

[0085] Where σ3 is the corresponding threshold.

[0086] As one or more implementation methods, the specific process of step S03 is as follows:

[0087] Step S301: Calculate the initial one-time construction cost of energy storage construction, expressed by the following formula:

[0088]

[0089] C P =k P P ESS.Max ;

[0090] C S =k S S ESS.Max ;

[0091] Among them, C inv C represents the one-time construction cost in the initial stage of energy storage construction. aff The cost of ancillary facilities for the energy storage system includes the cost of environmental control systems, battery management systems, circuit breakers, transformers, site costs, etc. LCC Let C be the lifespan of the energy storage system, r be the annual interest rate; P For the power configuration cost of energy storage systems, C S Cost of capacity configuration for energy storage systems; k P ,k S The configuration price per unit power and capacity of the energy storage system; P ESS.Max S ESS.Max The power and capacity configured for the energy storage system.

[0092] Step S302: Calculate the operating cost of energy storage.

[0093] This embodiment takes the discharge of an energy storage system as an example for detailed explanation, namely:

[0094] C ope =C pen +C main

[0095]

[0096] C pen =C penp +C pens

[0097]

[0098]

[0099]

[0100]

[0101] Among them, C ope For energy storage operating costs, C main For energy storage maintenance costs, k main.P k main.E These are the maintenance cost coefficients related to the energy storage configuration power and charge / discharge capacity, respectively; C pen To incur the cost of energy storage penalties, C penp Penalty cost for insufficient power configuration of energy storage, P penal k is the energy deficit penalty term coefficient determined by the configured power. penp The energy storage is configured with a power insufficiency penalty coefficient; γ is the insufficiency mitigation discrimination coefficient; C pens Penalty costs for insufficient energy storage capacity, S penal k is the energy deficit penalty term determined by the configured capacity. pens Configure a power deficiency penalty factor for energy storage.

[0102] Step S303: Calculate the cost of replacing the energy storage battery; the specific process of step S303 is as follows:

[0103] Step S30301: Analyze the annual SOC time-series data of energy storage using the four-point rainflow counting method, decomposing it into several charge-discharge cycles. The charge-discharge depth of these cycles is the DOD (Depth of Discharge). Taking cycle SOC1-SOC2-SOC1 as an example, the formula is as follows:

[0104] DOD = |SOC1 - SOC2|;

[0105] Step S30302: Calculate the lifespan of the energy storage battery using DOD, taking a lithium iron phosphate battery as an example:

[0106] N(DOD)=k1e k2·DOD +k3e k4·DOD ;

[0107]

[0108] Where N is the DOD-cycle count curve obtained by fitting measured DOD-cycle count data of lithium iron phosphate batteries; k1~k4 are the coefficients of the fitting curve; n represents the total charge-discharge cycles obtained by decomposing the annual SOC time-series data, and DOD i T represents the depth of discharge in each cycle. life This refers to the lifespan of the energy storage battery.

[0109] Step S30303: Use T life The cost of replacing batteries in energy storage systems can be calculated using the following formula:

[0110]

[0111] Among them, C rep The cost of replacing batteries for energy storage, n LCC The energy storage's entire lifecycle is represented by r; the annual interest rate is C. S The cost of replacing a battery.

[0112] Step S304: Calculate the energy storage recovery revenue, expressed by the following formula:

[0113]

[0114] in, This represents the residual value rate of the energy storage system.

[0115] Step S305: Calculate the operating subsidy for the energy storage system, expressed by the following formula:

[0116]

[0117] Where, k rev Operating subsidies for energy storage facilities per unit of electricity.

[0118] Step S306: Construct a full life-cycle cost model for energy storage, expressed by the following formula:

[0119] C AV =C inv (P ESS.Max S ESS.Max )+C ope (P ESS.Max P ESS P penal S penal )+C rep (T life S ESS.Max )-C rec (P ESS.Max S ESS.Max )-C rev (P ESS );

[0120] Among them, C AV This refers to the total lifecycle cost of energy storage.

[0121] As one or more implementation methods, such as Figure 2 As shown, the specific process of step S04 is as follows:

[0122] Step S401: Randomly generate configuration power and capacity based on the energy storage configuration ratio restrictions stipulated by the current policy, as the initial values ​​for the optimization problem.

[0123] Current policies stipulate that the energy storage configuration ratio is limited to 5% to 30%, and the time is 15 minutes to 2 hours.

[0124] Step S402: Solve the energy storage life cycle cost model using the particle swarm optimization algorithm. The optimization objective is to minimize the annual value of the energy storage life cycle cost, and the constraint is the energy storage configuration ratio limit stipulated by the current policy.

[0125] Step S403: When the global optimal values ​​of adjacent generations are small and the accuracy requirements are met, or when the number of iterations reaches the maximum, the iteration is terminated to obtain the optimal power and capacity configuration for energy storage.

[0126] The following specific examples further illustrate this embodiment:

[0127] Using the annual historical power data of a 16MW wind farm as the input sample, with a sampling interval of 5 minutes, the power output of this wind farm fluctuates greatly and is difficult to meet the grid connection requirements.

[0128] The wind farm is configured with energy storage using the wind power energy storage optimization method based on the whole life cycle cost in this embodiment.

[0129] The energy storage parameters are as follows: energy storage charge / discharge efficiency is 94%; DC-DC converter efficiency is 94%; energy storage self-discharge rate is 0.005% / 5min; unit power cost is 600,000 yuan / MW; unit capacity cost is 900,000 yuan / MW·h; life cycle is 20 years; annual interest rate is 8%; the initial one-time construction cost is 10% of the total energy storage power capacity cost; energy storage maintenance cost is 20,000 yuan / MW, 100 yuan / MW·h; the penalty cost for insufficient energy storage power configuration is 1,500 yuan / MW·h, and the penalty cost for insufficient energy storage capacity configuration is 90,000 yuan / MW·h; the energy storage recycling price is based on a residual value rate of 50% for construction cost and power cost, and a residual value rate of 2% for capacity cost; the energy storage operation subsidy is 0.1 yuan / kW·h.

[0130] Figures 3(a), 3(b), 3(c), and 3(d) show the effects of wind power fluctuation mitigation on typical days in January, April, July, and October, respectively, after the wind farm was equipped with energy storage according to the present invention.

[0131] Example 2

[0132] Embodiment 2 of this disclosure introduces a wind power energy storage optimization system based on total life cycle cost.

[0133] like Figure 4 The wind power energy storage optimization system shown includes:

[0134] The acquisition module is configured to acquire historical power data of wind farms;

[0135] The modeling module is configured to construct an energy storage control model for smoothing wind power fluctuations based on the acquired data; and to calculate various costs of the wind farm based on the constructed energy storage control model for smoothing wind power fluctuations, thereby obtaining the total life cycle cost of the energy storage system.

[0136] The optimization module is configured to optimize wind power energy storage by minimizing the obtained total life cycle cost as the objective function.

[0137] The detailed steps are the same as those of the wind power energy storage optimization method based on the whole life cycle cost provided in Example 1, and will not be repeated here.

[0138] Example 3

[0139] Embodiment 3 of this disclosure provides a computer-readable storage medium.

[0140] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps in the wind power energy storage optimization method based on life cycle cost as described in Embodiment 1 of this disclosure.

[0141] The detailed steps are the same as those of the wind power energy storage optimization method based on the whole life cycle cost provided in Example 1, and will not be repeated here.

[0142] Example 4

[0143] Embodiment 4 of this disclosure provides an electronic device.

[0144] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the wind power energy storage optimization method based on life cycle cost as described in Embodiment 1 of this disclosure.

[0145] The detailed steps are the same as those of the wind power energy storage optimization method based on the whole life cycle cost provided in Example 1, and will not be repeated here.

[0146] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A wind power energy storage optimization method based on total life cycle cost, characterized in that, Includes the following steps: Obtain operational data from wind farms; Based on the acquired data, an energy storage control model for smoothing wind power fluctuations was constructed. In the process of constructing the energy storage control model for wind power fluctuation mitigation, the wind power fluctuation mitigation effect is considered, and an hourly wind power fluctuation mitigation target value is set; the output of wind power energy storage is controlled according to the wind power fluctuation mitigation target value, so that the output power of wind power energy storage is equal to the wind power fluctuation mitigation target value. The working status of wind power energy storage systems can be improved by restoring the state of charge of the energy storage system, switching the energy storage charging and discharging state, and limiting the output of the energy storage system. Based on the constructed energy storage control model for wind power fluctuation smoothing, the various costs of the wind farm are calculated, and the total life cycle cost of the energy storage system is obtained. The goal is to optimize the energy storage of wind power by minimizing the total life cycle cost.

2. The wind power energy storage optimization method based on total life cycle cost as described in claim 1, characterized in that, The acquired wind farm operation data should include at least historical power data, charge and discharge power, and self-discharge rate.

3. The wind power energy storage optimization method based on full life cycle cost as described in claim 1, characterized in that, The total life cycle cost of the energy storage system is related to the initial construction cost, operating cost, cost of replacing batteries, recycling revenue, and operating subsidies.

4. The wind power energy storage optimization method based on total life cycle cost as described in claim 3, characterized in that, The operating cost of the energy storage system includes the penalty cost and the maintenance cost of the energy storage system; the maintenance cost of the energy storage system fully considers the configured power of the energy storage system, as well as the maintenance cost coefficient related to the configured power and the charging and discharging capacity of the energy storage system.

5. The wind power energy storage optimization method based on total life cycle cost as described in claim 3, characterized in that, The cost of replacing the energy storage battery is related to the entire life cycle of the energy storage system, the annual interest rate, the cost of replacing the energy storage battery once, the life of the energy storage battery, and the depth of cycle discharge of the energy storage battery.

6. The wind power energy storage optimization method based on total life cycle cost as described in claim 1, characterized in that, In the process of optimizing wind power energy storage, the objective function is to minimize the total life cycle cost, and the initial power and capacity of the energy storage system are used as constraints. The particle swarm optimization algorithm is used to solve for the total life cycle cost, so as to obtain the optimal power and capacity of the energy storage system and realize the optimization of wind power energy storage.

7. A wind power energy storage optimization system based on total life cycle cost, employing the wind power energy storage optimization method as described in any one of claims 1-6, characterized in that, include: The acquisition module is configured to acquire historical power data of wind farms; The modeling module is configured to construct an energy storage control model for smoothing wind power fluctuations based on the acquired data; and to calculate various costs of the wind farm based on the constructed energy storage control model for smoothing wind power fluctuations, thereby obtaining the total life cycle cost of the energy storage system. The optimization module is configured to optimize wind power energy storage by minimizing the obtained total life cycle cost as the objective function.

8. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the wind power energy storage optimization method based on the total life cycle cost as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the wind power energy storage optimization method based on full life cycle cost as described in any one of claims 1-6.

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