Hybrid energy storage capacity optimization configuration method considering wind power fluctuation stabilization and auxiliary frequency modulation

Abstract

The invention relates to the technical field of energy storage capacity configuration, and particularly discloses a hybrid energy storage capacity optimal configuration method considering wind power fluctuation stabilization and auxiliary frequency modulation. Completely adaptive noise set empirical mode decomposition is utilized to decompose the wind power to obtain a wind power direct grid-connected component and a hybrid energy storage power task. Secondly, multi-type constraints of an energy storage system are comprehensively considered, the maximum annual average overall net income of the wind storage system is used as a target function, and a flywheel-electrochemical hybrid energy storage capacity optimization configuration model considering wind power stabilization-frequency modulation is established; and finally, carrying out iterative solution through an adaptive particle swarm algorithm to obtain a hybrid energy storage optimal capacity configuration scheme. Compared with hybrid energy storage only stabilizing, the method can generate higher frequency modulation income, effectively exerts the advantages of energy storage stabilizing-frequency modulation, and enables the annual average overall net income of the system to be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage capacity configuration, and in particular to a hybrid energy storage capacity optimization configuration method taking into account wind power fluctuation smoothing and auxiliary frequency modulation. Background Art

[0002] For new power systems, source-side regulation performance is an important indicator of the system's own regulation capabilities. For wind farms, it is necessary to strengthen their dispatching and operation capabilities and smooth the grid-connected power generation. At the same time, they need to have frequency regulation capabilities to participate in frequency regulation. In summary, how to improve the source-side regulation performance has become an urgent problem to be solved in current research. At the same time, the current single energy storage method can no longer meet the needs of smoothing and frequency regulation. The hybrid energy storage system integrates the advantages of different types of energy storage and can effectively solve the above problems, but how to economically and effectively configure the hybrid energy storage capacity is imminent.

[0003] In order to smooth out wind power fluctuations, the literature "Zhao Jingying, Qiao Hengpu, Yao Shuailiang, et al. Hybrid energy storage capacity configuration strategy for wind power fluctuation smoothing considering energy storage SOC self-recovery [J / OL]. Transactions of China Electrotechnical Society: 1-14 [2024-05-28]. " proposes a hybrid energy storage capacity optimization strategy taking into account the self-recovery of energy storage charge state; the literature "Hu Leiyong, Lin Xiaodong. Application of superconducting magnetic energy storage-battery hybrid energy storage system in smoothing wind farm power fluctuations [J]. High Voltage Technology, 2019, 45(3): 983-992. " proposes a hybrid energy storage system control scheme based on superconducting magnetic energy storage and electrochemical energy storage to avoid overcharging and over-discharging of the hybrid energy storage system; the literature "Hong Feng, Jia Xinyi, Liang Lu, et al. Hybrid energy storage hierarchical capacity optimization configuration for wind farm frequency support [J / OL]. Proceedings of the CSEE, 1-12[2024-03-29].》A strategy based on hybrid energy storage of flywheel energy storage and lithium battery to participate in grid-connected smooth output power and auxiliary frequency regulation is proposed; the literature "Wang Li, Hu Jiacheng, Zeng Xiangjun, et al. Power hierarchical coordination control strategy of AC / DC hybrid microgrid based on hybrid energy storage [J]. Transactions of the Chinese Society of Electrical Engineering, 2024, 39(08): 2311-2324." proposes to use flywheel energy storage and batteries to realize power coordination control of AC / DC hybrid microgrid; the literature "Tian Bowen, Zhang Zhiyu, Yang Mengfei. Research on power allocation and capacity setting of hybrid energy storage based on multiple sliding mean filtering [J]. Transactions of the Chinese Society of Electrical Engineering, 2024, 39(05): 1548-1564." proposes a research on energy storage power allocation strategy and capacity configuration based on multi-smoothing mean filtering.

[0004] In order to assist in participating in the grid frequency regulation, the literature "Song Jie, Geng Linxiao, Sang Yongfu, et al. Hybrid energy storage assisted thermal power unit primary frequency regulation capacity planning based on EMD decomposition [J]. Energy Storage Science and Technology, 2023, 12(2): 496-503." adopts a hybrid energy storage assisted thermal power unit frequency regulation capacity planning strategy based on EMD decomposition to optimize the configuration of the energy storage system; the literature "

[13] Xu Lei. Research on control strategy of hybrid energy storage assisted wind power smoothing power and primary frequency regulation [D]. North China Electric Power University (Beijing), 2024." proposes a frequency regulation segmented adaptive control strategy considering wind power penetration and a virtual droop coefficient fuzzy control strategy considering the charge state of hybrid energy storage; the literature "

[14] Yuan Man. Research on primary frequency regulation control method of "flywheel + lithium battery" hybrid energy storage system assisted new energy power station [D]. Shandong University, 202 4.》Designed a control strategy for a "flywheel + lithium battery" hybrid energy storage system to assist the frequency regulation of new energy power stations; Literature "Cai Zhenhua, Li Canbing, Yang Tongguang, et al. Energy storage batteries considering dynamic frequency inertia characteristics participate in the primary frequency regulation control of the power grid [J / OL]. Journal of Shanghai Jiaotong University: 1-21 [2024-06-18].》Proposed a energy storage control strategy considering dynamic frequency inertia characteristics to participate in frequency regulation; Literature "Cai Tingting, Xue Wendong. Research on capacity optimization of hybrid energy storage system for primary frequency regulation of power system based on VMD [J]. Journal of Northeast Electric Power University, 2024, 44(01): 61-71.》Utilizing the advantages of hybrid energy storage participating in wind farm frequency regulation and considering economic factors, a hybrid energy storage capacity optimization configuration method based on variational mode is proposed, but the power system's demand for smooth wind power output is ignored.

[0005] The technologies disclosed in the above documents only consider the smoothing of wind power fluctuations or only consider the auxiliary participation of wind farms in the frequency regulation of the power grid, but do not fully take both into account, and do not study the battery replacement cost, recovery cost and service life over a long period of time. With the current demand for accelerating the improvement of safe and efficient new energy systems, it is urgent to construct a hybrid energy storage optimization configuration method that takes into account wind power fluctuation smoothing and auxiliary participation in frequency regulation, so as to improve the ability to absorb new energy and the economic operation of the power system. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a hybrid energy storage capacity optimization configuration method taking into account wind power fluctuation smoothing and auxiliary frequency modulation, so as to improve the capacity of new energy consumption and economic operation of the power system and meet the needs of accelerating the improvement of a safe and efficient new energy system.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency regulation, the method comprising:

[0009] Obtain wind power data, and decompose the wind power data using fully adaptive noise set empirical mode decomposition to obtain a wind power direct grid-connected component and a hybrid energy storage power task;

[0010] Taking into account the various constraints of the energy storage system, and taking the maximum annual average overall net benefit of the wind storage system as the objective function, a capacity optimization configuration model of flywheel-electrochemical hybrid energy storage taking into account wind power leveling and frequency modulation is established.

[0011] Based on the power data obtained from the direct grid-connected component of wind power and the hybrid energy storage power task, the capacity optimization configuration model is iteratively solved using an adaptive particle swarm algorithm to obtain an optimal capacity configuration solution for hybrid energy storage.

[0012] Further, wind power data is obtained, and the wind power data is decomposed using fully adaptive noise set empirical mode decomposition to obtain a wind power direct grid-connected component and a hybrid energy storage power task, including:

[0013] Taking wind power data as the signal to be decomposed, adding Gaussian white noise to the signal to be decomposed to obtain a first signal, and performing empirical mode decomposition on the first signal to obtain a first-order component;

[0014] The N-order intrinsic mode functions obtained by EMD decomposition are accumulated and averaged to obtain the intrinsic IMF1 component;

[0015] Subtract the first signal from the intrinsic IMF1 component to obtain the first-order residual component r 1 (t);

[0016] In r 1 (t) by adding a positive and negative paired Gaussian white noise to obtain a second signal, and performing EMD decomposition on the second signal to obtain a new N-order intrinsic mode function;

[0017] The new N-order intrinsic mode functions obtained by EMD decomposition are accumulated and averaged to obtain the intrinsic IMF2 component;

[0018] r 1 (t) is subtracted from the intrinsic IMF2 component to obtain the second-order residual component r 2 (t) as the current margin signal;

[0019] Determine whether the current residual signal is a monotonic function. If not, decompose it again based on the current residual signal to obtain the nth order residual component. If yes, output the nth order IMF component and the nth order residual component.

[0020] Wind power allocation is performed based on the n-order IMF component to obtain the direct grid-connected wind power component and the hybrid energy storage power task.

[0021] Furthermore, the wind power distribution is performed based on the n-order IMF component to obtain the wind power direct grid-connected component and the hybrid energy storage power task, including:

[0022] Perform low-frequency reconstruction on the n-order IMF component to obtain the low-order reconstructed component L (k) ; Where k represents the order of the low-order reconstruction component;

[0023] Initialize k=1;

[0024] Calculate the low-order reconstruction component L (k) The amount of fluctuation;

[0025] In the low-order reconstruction component L (k) When the fluctuation of meets the grid connection conditions, it is determined whether k is equal to 1. If k = 1, it is determined that there is no direct grid-connected power, and the hybrid energy storage power task assists in frequency regulation and then connects to the grid. If k ≠ 1, then set k = k + 1 and recalculate the low-order reconstruction component L (k) The amount of fluctuation;

[0026] In the low-order reconstruction component L (k) If the fluctuation of the power does not meet the grid connection conditions, the HESS power task is executed: HESS =Hrf(n+1-k), where Hrf(n+1-k) represents the high-order reconstruction component, that is, the hybrid energy storage power task.

[0027] Furthermore, the n-order IMF component is reconstructed at low frequency by the following formula to obtain the low-order reconstructed component:

[0028]

[0029] Where, L (n+1) represents the n+1th low-order reconstruction component, r represents the residual component, and F n and F (n-1) denote the n-th and n-1-th order intrinsic mode functions respectively.

[0030] Furthermore, the low-order reconstruction component L (k) The fluctuation amount is the active power change limit required for wind farm grid connection. The constraint of the fluctuation amount is as follows:

[0031]

[0032] Where P Lfr(n),m represents the power of the reconstructed nth-order component in time period m; Δt represents the time interval, t represents the time; ΔP limit Indicates the wind power grid connection limit.

[0033] Furthermore, the hybrid energy storage power task is expressed as:

[0034]

[0035] In the formula, ΔP n represents the primary frequency modulation power demand; γ% represents the modulation difference rate; f sq Indicates frequency dead zone; P N represents the rated installed capacity of the wind farm; f represents the actual frequency; f 0 Indicates the rated frequency.

[0036] Furthermore, the objective function is constructed by the following method:

[0037] The full life cycle cost of the hybrid energy storage system is calculated using the following formula:

[0038] C=C in +C οp +C dis +C sd +C aw -C re (4)

[0039] In the formula, C represents the life cycle cost of the hybrid energy storage system, C in represents the investment cost, C op represents the operation and maintenance cost, C dis represents the update replacement cost, C sd represents the scrapping cost, C re Indicates the recycling value, C aw represents the penalty cost for wind curtailment;

[0040] The calculation formula for investment cost is:

[0041]

[0042] In the formula, C BESS_pi and C FES_pi Respectively represent the initial investment and replacement unit power cost of electrochemical energy storage and flywheel energy storage; C BESS_ei and C FES_ei Represent the unit capacity cost coefficients of the initial investment of electrochemical energy storage and flywheel energy storage respectively; Indicates the rated power of electrochemical energy storage; Indicates the rated power of flywheel energy storage; Indicates the rated capacity of electrochemical energy storage; Indicates the rated capacity of flywheel energy storage.

[0043] The calculation formula for operation and maintenance cost is:

[0044]

[0045] In the formula, a and b represent the proportion of operation and maintenance costs of electrochemical energy storage and flywheel energy storage systems to investment costs, respectively. represents the operation and maintenance cost of electrochemical energy storage; represents the investment cost of electrochemical energy storage; represents the operation and maintenance cost of flywheel energy storage; Represents the investment cost of flywheel energy storage.

[0046] The calculation formula for the replacement cost is:

[0047]

[0048] In the formula, j represents the number of years in a certain use cycle; N represents the life of the electrochemical energy storage cycle; T represents the full life cycle, which is 20 years.

[0049] The calculation formula for scrap disposal cost is:

[0050]

[0051] In the formula, C BESS_ps and C BESS_es They represent the scrapping cost coefficient of BESS unit power and capacity respectively;

[0052] The calculation formula for recycling value is:

[0053] C re =αC i (1+i) -T (11)

[0054] In the formula, C re represents the recovery value; α represents the residual value rate; C i represents the recovery value coefficient;

[0055] The calculation formula for the penalty cost of wind curtailment is:

[0056]

[0057] In the formula, c aw E represents the wind abandonment penalty coefficient; aw represents the amount of wind power abandoned in year t;

[0058] The wind power fluctuation compensation cost is calculated by the following formula:

[0059]

[0060] Where: represents the wind power fluctuation compensation cost; c com Indicates the compensation coefficient; P zheng_com,t and P fu_com,t Respectively represent the compensation required in the positive and negative directions at time t;

[0061] The wind power grid connection income, carbon trading income and frequency regulation income are calculated by the following formula:

[0062]

[0063] In the formula, R es represents the wind power grid-connected income; r es represents the unit price of electricity sold by the wind farm, and E(t) represents the electricity sold by the wind farm directly connected to the grid in year t;

[0064]

[0065] In the formula, R cs represents the carbon trading income; r cs represents the unit price of carbon trading, λ ESS It indicates the emission per unit of electricity;

[0066]

[0067] In the formula, I tp represents the frequency modulation gain; i tp represents the frequency modulation benefit coefficient, E BESS (t) and E FES (t) represents the amount of electricity used in frequency modulation by the electrochemical energy storage system and the flywheel energy storage system respectively;

[0068] The net benefit of the hybrid energy storage system is calculated by the following formula:

[0069] R=R es +R cs +I tp -C (17)

[0070] Where R is the net benefit of the hybrid energy storage system;

[0071] The average annual overall net benefit of the wind-storage system is calculated based on the net benefit of the hybrid energy storage system, and the objective function is constructed with the maximum annual average overall net benefit of the wind-storage system as the goal.

[0072] Furthermore, the life loss of the electrochemical energy storage system is calculated by the following formula:

[0073]

[0074] Where N ctf (D 1 ) represents the cycle life corresponding to a discharge depth of 1; N ctf (D i ) indicates that when the discharge depth is D i Cycle life of N(D i) represents the electrochemical energy storage life loss.

[0075]

[0076] Where, N represents the equivalent cycle life of BESS; D oD Indicates the depth of charge and discharge.

[0077]

[0078] Where T represents the life loss of the electrochemical energy storage system.

[0079] Furthermore, the constraints of the capacity optimization configuration model include power balance constraints, charge and discharge power constraints, and hybrid energy storage state constraints;

[0080] Wherein, the power balance constraint is expressed as:

[0081]

[0082] Where P HESS,t represents the hybrid energy storage power task; They represent the charging power and discharging power of the electrochemical energy storage system at time t respectively; They represent the charging power and discharging power of the flywheel energy storage system at time t respectively; P zheng_com,t and P fu_com,t They represent the positive undercompensation and negative undercompensation at time t respectively;

[0083] The charge and discharge power constraint is expressed as:

[0084]

[0085] In the formula, Indicates the rated power of the electrochemical energy storage system; Indicates the rated power of the flywheel energy storage system;

[0086] The hybrid energy storage state constraint is expressed as:

[0087]

[0088]

[0089] SOC FESmin ≤SOC FES,t ≤SOC FESmax (29)

[0090] In the formula, SOC BESSmax and SOC BESSmin Respectively represent the maximum and minimum state of charge of the electrochemical energy storage system; SOC FESmaxand SOC FESmin They represent the maximum and minimum state of charge of the flywheel energy storage system respectively; SOC BESS,t and SOC BESS,t-1 Respectively represent the state of charge of the electrochemical energy storage system at time t and time t-1; SOC FES,t represents the charge state of the flywheel energy storage system at time t; Δt represents the time interval; η BESS Indicates the charge and discharge power of electrochemical energy storage; η FES Indicates the charge and discharge power of the flywheel energy storage; Indicates the rated capacity of electrochemical energy storage; Indicates the rated capacity of the flywheel energy storage.

[0091] Furthermore, the capacity optimization configuration model is iteratively solved using an adaptive particle swarm algorithm to obtain an optimal capacity configuration scheme for hybrid energy storage, including:

[0092] Get power data;

[0093] Initialize energy storage power and capacity;

[0094] Initialize the particle swarm and set the maximum number of iterations;

[0095] Set the number of iterations to 1 and start the iteration: In one iteration, calculate the fitness value and annual comprehensive cost of the particle, calculate the optimal value of the individual and the group, update the particle position and speed, and increase the number of iterations;

[0096] When the maximum number of iterations is reached here, the optimal capacity configuration plan of hybrid energy storage is output;

[0097] The update formula for particle position and velocity is as follows:

[0098]

[0099] Where: represents the velocity of the i-th particle in the j-th dimension at the s+1-th iteration, represents the position of the i-th particle in the j-th dimension at the s-th iteration, and Represents two random numbers, ω s is the inertia weight, φ p and φ g represents the learning factor; and They represent the position of the i-th particle in the optimal solution of the s-th iteration and the j-th dimension of the global optimal solution, respectively.

[0100] The inertia weight is dynamically adjusted according to the number of iterations s, and the calculation formula is as follows:

[0101]

[0102] Where: w max and w min They represent the upper and lower limits of the inertia weight respectively, and S represents the maximum number of iterations.

[0103] The present invention has at least the following beneficial effects:

[0104] The present invention uses complete noise-assisted aggregate empirical mode decomposition (CEEMDAN) to decompose and reconstruct typical daily wind power data and frequency modulation power to obtain hybrid energy storage power tasks. Based on the hybrid energy storage power tasks and in combination with the different characteristics of the energy storage system, the power tasks of electrochemical energy storage and flywheel energy storage are obtained by using secondary CEEMDAN decomposition. The power capacity and charge-discharge power constraints of various energy storage systems are comprehensively considered. The annual average overall net income of the wind storage system is used as the objective function to establish a capacity optimization configuration model of flywheel-electrochemical hybrid energy storage taking into account wind power leveling-frequency modulation, and then the adaptive particle swarm algorithm (APSO) is used to iteratively solve and obtain the optimal capacity configuration scheme of hybrid energy storage. The results show that compared with hybrid energy storage only leveling, the hybrid energy storage capacity configuration scheme considering leveling-frequency modulation is more economical, which improves the annual average overall net income of the wind storage joint system and can effectively give play to the advantages of hybrid energy storage in leveling and frequency modulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 A diagram of the FES-BESS hybrid energy storage operation architecture according to an embodiment of the present invention is shown.

[0106] Figure 2 A flow chart of a hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency regulation according to an embodiment of the present invention is shown.

[0107] Figure 3 A specific flow chart of the CEEMDAN algorithm according to an embodiment of the present invention is shown.

[0108] Figure 4 A flow chart of wind power allocation according to an embodiment of the present invention is shown.

[0109] Figure 5 A flow chart of iteratively solving a capacity optimization configuration model using an adaptive particle swarm algorithm according to an embodiment of the present invention is shown.

[0110] Figure 6 A schematic diagram of typical daily wind power data according to an embodiment of the present invention is shown.

[0111] Figure 7 A schematic diagram of direct grid-connected power of a wind farm according to an embodiment of the present invention is shown.

[0112] Figure 8 A schematic diagram of a hybrid energy storage power task according to an embodiment of the present invention is shown.

[0113] Fig. 9 A frequency deviation curve diagram before and after the hybrid energy storage participates in frequency modulation according to an embodiment of the present invention is shown.

[0114] Fig.10 A comparison diagram of electrochemical energy storage charge and discharge power in different scenarios according to an embodiment of the present invention is shown.

[0115] Fig.11 A comparison chart of flywheel energy storage charging and discharging power under different scenarios according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0116] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0117] The specific implementation of the present invention is further described in detail below in conjunction with the drawings and examples.

[0118] The cycle life of electrochemical energy storage (Battery Energy System Storage, BESS) is low. To solve this problem, it is proposed to complement the advantages of flywheel energy storage (FES) with long cycle life and fast charge and discharge rate. Figure 1 The FES-BESS hybrid energy storage operation architecture shown in the figure is used to elaborate on the optimal configuration of the hybrid energy storage system for smoothing wind power fluctuations.

[0119] The embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency regulation. Figure 2 The method includes steps S1 to S3, which are described in detail as follows.

[0120] S1: Obtain wind power data, and use fully adaptive noise ensemble empirical mode decomposition to decompose the wind power data to obtain the direct grid-connected wind power component and the hybrid energy storage power task.

[0121] In step S1, this embodiment first applies CEEMDAN decomposition to realize the flattening-frequency modulation power allocation. Among them, CEEMDAN is a complementary set empirical mode decomposition method based on adaptive noise control, which is improved from CEEMD (complementary set empirical mode decomposition). Different from CEEMD, CEEMDAN adopts an adaptive control strategy to add random noise to better manage the intensity and distribution of noise, thereby improving the accuracy and stability of signal decomposition and effectively solving the problem of transferring white noise from high frequency to low frequency.

[0122] The specific process of the CEEMDAN algorithm is described as follows Figure 3 As shown, taking wind power data as the signal to be decomposed, step S1 specifically includes:

[0123] S11: Add Gaussian white noise to the signal to be decomposed to obtain a first signal, and perform empirical mode decomposition on the first signal to obtain a first-order component.

[0124] S12: Accumulate and average the N-order intrinsic mode functions obtained by EMD decomposition to obtain the intrinsic IMF1 component.

[0125] S13: Subtract the first signal from the intrinsic IMF1 component to obtain the first-order residual component r 1 (t).

[0126] S14: In r 1 Positive and negative paired Gaussian white noises are added to (t) to obtain a second signal, and the second signal is decomposed by EMD to obtain a new N-order intrinsic mode function.

[0127] S15: Accumulate and average the new N-order intrinsic mode functions obtained by EMD decomposition to obtain intrinsic IMF2 components.

[0128] S16: r 1 (t) is subtracted from the intrinsic IMF2 component to obtain the second-order residual component r 2 (t) is used as the current margin signal.

[0129] S17: Determine whether the current residual signal is a monotonic function. If not, decompose again based on the current residual signal to obtain the nth order residual component. If yes, the algorithm terminates and outputs the result, that is, outputs the nth order IMF component and the nth order residual component.

[0130] S18: Allocate wind power based on the n-order IMF component to obtain the wind power direct grid-connected component and the hybrid energy storage power task.

[0131] In order to avoid the adverse effects of wind power fluctuations on grid connection, grid connection guidelines have been established to provide detailed regulations on wind farm grid connection requirements. The grid connection guidelines are shown in Table 1. iwpc Installed capacity of wind farm.

[0132] Table 1 Wind power grid connection guidelines

[0133]

[0134] Figure 4 A wind power allocation flow chart provided in an embodiment of the present invention. High frequency reconstruction (Hfr) and low frequency reconstruction (Lfr) are two reconstruction methods. Figure 4 As shown, Figure 4 The process of obtaining the n-order IMF component through CEEMDAN is detailed in Figure 3 , which has been described in detail in the previous article, so I will not elaborate on it here. Figure 4 , step S18 specifically includes:

[0135] S181: Perform low-frequency reconstruction on the n-order IMF component to obtain a low-order reconstructed component L (k) ; Where k represents the order of the low-order reconstruction component.

[0136] S182: Initialize k=1.

[0137] S183: Calculate the low-order reconstruction component L (k) The amount of fluctuation.

[0138] S184: Determine the low-order reconstruction component L (k) Whether the fluctuation amount meets the grid connection conditions, if so, execute step S185, if not, execute step S186.

[0139] S185: Determine whether k is equal to 1. If k=1, execute step S187. If k≠1, set k=k+1 and return to step S183 to recalculate the low-order reconstruction component L. (k) The amount of fluctuation.

[0140] S186: HESS power mission: P HESS =Hrf(n+1-k), where Hrf(n+1-k) represents the high-frequency reconstruction component, that is, the hybrid energy storage power task.

[0141] S187, determines that there is no direct grid-connected power, and the hybrid energy storage power task assists in frequency regulation and then connects to the grid.

[0142] In this embodiment, reconstruction is performed using low-frequency components, and the reconstruction method is shown in formula (1).

[0143]

[0144] Where, L (n+1) represents the n+1th order low-order reconstruction component, r represents the residual component, F n and F (n-1) denote the n-th and n-1-th order intrinsic mode functions respectively.

[0145] Figure 4 The fluctuation mentioned in this paper is defined as: the active power variation limit required for wind farm grid connection, and the constraint of wind power fluctuation is shown in formula (2).

[0146]

[0147] Where P Lfr(n),m represents the power of the reconstructed nth-order component in time period m; Δt represents the time interval, t represents the time, which is 1 minute; ΔP limit Indicates the wind power grid connection limit.

[0148] In some embodiments, in the face of wind power grid-connected frequency safety issues, wind farms use hybrid energy storage to achieve frequency regulation. In order to ensure the maximum benefit of the wind farm, the wind power frequency regulation power task is all completed by the hybrid energy storage. The hybrid energy storage undertakes the wind farm frequency regulation power task as shown in formula (3).

[0149]

[0150] In the formula, ΔP n represents the rated installed capacity of the wind farm; γ% represents the regulation rate; f sq Indicates frequency dead zone; P N represents the rated installed capacity of the wind farm; f represents the actual frequency; f 0 Indicates the rated frequency.

[0151] S2: Taking into account the various types of constraints on the energy storage system, and taking the maximum annual overall net benefit of the wind storage system as the objective function, a capacity optimization configuration model for the flywheel-electrochemical hybrid energy storage taking into account wind power leveling and frequency modulation is established.

[0152] In order to avoid uneven power distribution in the hybrid energy storage system during the frequency regulation process, this paper uses a FES and BESS charge and discharge command allocation strategy based on filter order j. Taking the annual average overall net profit of the wind storage system as the maximum, considering the system power, capacity and charge and discharge power constraints, the rated capacity of BESS and FES is finally determined by iterative solution through adaptive particle swarm algorithm (APSO). With rated power

[0153] By combining power-type energy storage and energy-type energy storage, the hybrid energy storage system's regulation capabilities in both power and energy dimensions are significantly enhanced, providing a more efficient solution. This embodiment takes the maximum annual average net income of the wind storage system as the objective function, and iterates through the APSO algorithm to obtain the optimal configuration of the energy storage capacity.

[0154] In some embodiments, in step S2, the objective function is constructed by the following steps:

[0155] S201: Calculate the full life cycle cost C of the hybrid energy storage system. The calculation formula is as follows:

[0156] C=C in +C οp +C dis +C sd +C aw -C re (4)

[0157] In the formula, C represents the life cycle cost of the hybrid energy storage system, C in represents the investment cost, C op represents the operation and maintenance cost, C dis represents the update replacement cost, C sd represents the scrapping cost, C re Indicates the recycling value, C aw Represents the penalty cost for wind curtailment.

[0158] S202: Calculate investment cost C in , the calculation formula is as follows:

[0159]

[0160] In the formula, C BESS_pi and C FES_pi Respectively represent the initial investment and replacement unit power cost of electrochemical energy storage and flywheel energy storage; C BESS_ei and C FES_ei Represent the unit capacity cost coefficients of the initial investment of electrochemical energy storage and flywheel energy storage respectively; Indicates the rated power of electrochemical energy storage; Indicates the rated power of flywheel energy storage; Indicates the rated capacity of electrochemical energy storage; Indicates the rated capacity of flywheel energy storage.

[0161] Where: C BESS_pi and C FES_pi ——The initial investment and replacement unit power cost of BESS and FES; C BESS_ei and C FES_ei ——Unit capacity cost coefficient of initial investment of BESS and FES.

[0162] S203: Calculate operation and maintenance cost C op .

[0163] The operation and maintenance costs of hybrid energy storage can be estimated according to the corresponding proportion of its investment cost.

[20] , as shown in equations (6) and (7) respectively.

[0164]

[0165] In the formula, a and b represent the proportion of operation and maintenance costs of electrochemical energy storage and flywheel energy storage systems to investment costs, respectively. represents the operation and maintenance cost of electrochemical energy storage; represents the investment cost of electrochemical energy storage; represents the operation and maintenance cost of flywheel energy storage; Represents the investment cost of flywheel energy storage.

[0166] S204: Calculate the update replacement cost C dis , the calculation formula is as follows:

[0167]

[0168] In the formula, j represents the number of years in a certain use cycle; N represents the life of the electrochemical energy storage cycle; T represents the full life cycle, which is 20 years.

[0169] S205: Calculate the scrapping cost C sd , the calculation formula is as follows:

[0170]

[0171] In the formula, C BESS_ps and C BESS_es They represent the scrapping cost coefficient of BESS unit power and capacity respectively.

[0172] S206: Calculate the recycling value C re , the calculation formula is as follows:

[0173]

[0174] In the formula, C re represents the recovery value; α represents the residual value rate; C i Represents the recovery value coefficient.

[0175] S207: Calculate the penalty cost C for wind curtailment aw , the calculation formula is as follows:

[0176]

[0177] In the formula, caw E represents the wind abandonment penalty coefficient; aw represents the amount of wind power abandoned in year t.

[0178] S208: Calculate wind power fluctuation compensation cost C com , the calculation formula is as follows:

[0179]

[0180] Where: represents the wind power fluctuation compensation cost; c com Indicates the compensation coefficient; P zheng_com,t and P fu_com,t They represent the positive and negative compensation required at time t respectively.

[0181] S209: Calculate the wind power grid connection income R es .

[0182] Wind power grid-connected revenue refers to the revenue obtained by directly connecting the wind power of a wind farm to the grid through decomposition and reconstruction.

[0183]

[0184] In the formula, R es represents the wind power grid-connected income; r es represents the unit price of electricity sold by the wind farm, and E(t) represents the electricity sold by the wind farm directly connected to the grid in year t.

[0185] S210: Calculate carbon trading income R cs .

[0186] Wind power is a clean energy source. The source of this type of income is the subsidy from thermal power plants for carbon emission reduction in wind farms. The calculation formula is as follows:

[0187]

[0188] In the formula, R cs represents the carbon trading income; r cs represents the unit price of carbon trading, λ ESS Indicates emissions per unit of electricity.

[0189] S211: Calculate frequency modulation benefit I tp .

[0190] Frequency regulation benefits refer to the benefits gained from frequency regulation using hybrid energy storage. The participation of hybrid energy storage in frequency regulation is beneficial for wind farms to take the initiative in system frequency regulation. The calculation formula for frequency regulation benefits is as follows:

[0191]

[0192] In the formula, I tp represents the frequency modulation gain; itp represents the frequency modulation benefit coefficient, E BESS (t) and E FES (t) represents the amount of electricity involved in frequency regulation by the electrochemical energy storage system and the flywheel energy storage system respectively.

[0193] S212: Calculate net income.

[0194] The net benefit of the hybrid energy storage system is the difference between the total system benefit and the total system cost, as shown in formula (16).

[0195] R=R es +R cs +I tp -C (16)

[0196] Where R is the net benefit of the hybrid energy storage system.

[0197] In some embodiments, the constraints of the capacity optimization configuration model include power balance constraints, charge and discharge power constraints, and hybrid energy storage state constraints.

[0198] For the power balance constraint, when the power task is positive fluctuation, the system absorbs the fluctuating power by charging the BESS or FES. When the power task is negative fluctuation, the system relies on the discharge of the BESS or FES to compensate for the fluctuating power. This strategy provides an effective mechanism for balancing power demand and supply. Therefore, the power balance constraint can be expressed as:

[0199]

[0200] Where P HESS,t represents the hybrid energy storage power task; They represent the charging power and discharging power of the electrochemical energy storage system at time t respectively; They represent the charging power and discharging power of the flywheel energy storage system at time t respectively; P zheng_com,t and P fu_com,t They represent the positive undercompensation and negative undercompensation at time t respectively.

[0201] For the charge and discharge power constraints, it is expressed as:

[0202] ①Charge and discharge power constraints of electrochemical energy storage

[0203]

[0204] ②Charge and discharge power constraints of flywheel energy storage

[0205]

[0206] Where: Indicates the rated power of the electrochemical energy storage system; Indicates the rated power of the flywheel energy storage system.

[0207] The hybrid energy storage state constraint is mainly constrained by the energy storage charge state limit, which is shown in equations (22) to (25) respectively.

[0208] ① Hybrid energy storage state constraints of electrochemical energy storage

[0209]

[0210] SOC BESSmin ≤SOC BESS,t ≤SOC BESSmax (twenty three)

[0211] ② Hybrid energy storage state constraints of flywheel energy storage

[0212]

[0213] SOC FESmin ≤SOC FES,t ≤SOC FESmax (25)

[0214] Where: SOC BESSmax and SOC BESSmin Respectively represent the maximum and minimum state of charge of the electrochemical energy storage system; SOC FESmax and SOC FESmin They represent the maximum and minimum state of charge of the flywheel energy storage system respectively; SOC BESS,t and SOC BESS,t-1 Respectively represent the state of charge of the electrochemical energy storage system at time t and time t-1; SOC FES,t represents the charge state of the flywheel energy storage system at time t; Δt represents the time interval; η BESS Indicates the charge and discharge power of electrochemical energy storage; η FES Indicates the charge and discharge power of the flywheel energy storage; Indicates the rated capacity of electrochemical energy storage; Indicates the rated capacity of the flywheel energy storage.

[0215] In some embodiments, the cycle life of FES is affected by factors such as temperature and discharge rate. At the same time, its service life is more than 15 years, and the cycle life is as high as 500,000 to 1 million times. Therefore, this embodiment does not consider the life of FES, and mainly considers the equivalent cycle life of BESS through the rain flow counting method. The life of BESS is related to factors such as discharge depth and charge and discharge times. Overcharging and over-discharging caused by frequent fluctuations in wind power will shorten the service life of BESS.

[0216] The discharge depth of the i-th cycle of BESS is Di, and the equivalent cycle life is shown in formula (26).

[0217]

[0218] Where N ctf (D 1 ) represents the cycle life corresponding to a discharge depth of 1; N ctf (D i ) indicates that when the discharge depth is D i Cycle life of N(D i ) indicates that when the discharge depth is D i time life loss.

[0219] The equivalent cycle life of BESS within the working cycle is shown in formula (27).

[0220]

[0221] Where, N represents the equivalent cycle life of BESS; D oD Indicates the depth of charge and discharge.

[0222] The BESS life loss is defined as shown in formula (28).

[0223]

[0224] Where T represents the life loss of electrochemical energy storage.

[0225] When N=N ctf (D 1 ) or T = 1, it means that the service life of the BESS has expired and needs to be replaced.

[0226] S3: Based on the power data obtained from the direct grid-connected wind power component and the hybrid energy storage power task, the adaptive particle swarm algorithm is used to iteratively solve the capacity optimization configuration model to obtain the optimal capacity configuration plan for hybrid energy storage.

[0227] Adaptive Particle Swarm Optimization is an optimization algorithm, which is a variant of Particle Swarm Optimization (PSO). Unlike traditional PSO, APSO uses an adaptive strategy to adjust the parameters of the algorithm to improve the performance and convergence speed of the algorithm.

[0228] The main idea of ​​APSO is to dynamically adjust the algorithm parameters according to the convergence of the group. The core algorithm of APSO is similar to PSO, which consists of the speed and position update rules of particles. Each particle updates its position and speed by comparing with the local optimal solution and the global optimal solution. In APSO, the speed and position update formulas are as follows:

[0229]

[0230] Where: represents the velocity of the i-th particle in the j-th dimension at the s+1-th iteration, represents the position of the i-th particle in the j-th dimension at the s-th iteration, and Represents two random numbers, ω s is the inertia weight, φ p and φ g represents the learning factor; and They represent the position of the i-th particle in the optimal solution of the s-th iteration and the j-th dimension of the global optimal solution, respectively.

[0231] Compared with traditional PSO, the inertia weight of APSO is dynamically adjusted according to the number of iterations s, as shown in formula (30).

[0232]

[0233] Where: w max and w min They represent the upper and lower limits of the inertia weight respectively, and S represents the maximum number of iterations.

[0234] For example, Figure 5 The flowchart of using the adaptive particle swarm algorithm to iteratively solve the capacity optimization configuration model provided by the embodiment of the present invention. Figure 5 As shown in the figure, the adaptive particle swarm algorithm is used to iteratively solve the capacity optimization configuration model to obtain the optimal capacity configuration scheme of hybrid energy storage, which specifically includes:

[0235] S31: Read parameters, that is, obtain power data, which can be obtained based on the wind power direct grid-connected component and the hybrid energy storage power task obtained in step S1.

[0236] S32: Initialize energy storage power and capacity.

[0237] S33: Initialize the particle swarm, set relevant parameters and perform iterative updates, wherein setting relevant parameters includes setting the maximum number of iterations and setting the current number of iterations to 1.

[0238] S34: Iteration starts.

[0239] S35: Calculate the fitness value and annual comprehensive cost of the particle.

[0240] S36: Calculate individual and group optimal values.

[0241] S37: Update particle position and velocity.

[0242] S38: Increase the number of iterations to determine whether it reaches the preset number of iterations (ie, the maximum number of iterations). If yes, execute step S38; if no, return to step S35.

[0243] S38: Output result. The output result is the optimal capacity configuration plan of hybrid energy storage.

[0244] The feasibility and advancement of the present invention will be further illustrated below with reference to a specific example.

[0245] This embodiment takes the typical daily wind power data of a wind farm as an example to carry out research on the configuration of FES-BESS hybrid energy storage capacity. The total installed capacity of the wind turbine is 100MW, and the typical daily wind power is Figure 6 The parameters of the hybrid energy storage system are shown in Table 2.

[0246] Table 2 Hybrid energy storage system parameters

[0247]

[0248]

[0249] The actual power data and frequency data of a 100MW wind farm in Northwest China are used as the research object, with a time scale of 1 minute. Based on the typical daily wind power, CEEMDAN is used for processing, the low-frequency component is connected to the grid, and the high-frequency component is used as the hybrid energy storage power task.

[0250] In this embodiment, the wind turbines do not participate in frequency regulation, and the hybrid energy storage system independently bears the frequency regulation demand, thereby obtaining the direct grid-connected power of the wind farm and the power tasks of the hybrid energy storage for smoothing and frequency regulation. The direct grid-connected power curve of the wind farm is as follows: Figure 7 shown.

[0251] Figure 8 A schematic diagram of the power task of hybrid energy storage smoothing and frequency regulation is given.

[0252] Fig. 9 The frequency deviation curves before and after the hybrid energy storage participates in frequency regulation are given. After configuring the hybrid energy storage, the frequency fluctuation of the power grid is effectively controlled, and the frequency regulation capability of the wind farm is improved.

[0253] In order to evaluate the economic feasibility and reliability of hybrid energy storage in different scenarios, the hybrid energy storage capacity configuration results in different scenarios are compared and analyzed. The hybrid energy storage capacity configuration is mainly divided into two scenarios.

[0254] Scenario 1: Optimal configuration of hybrid energy storage that only participates in wind power leveling;

[0255] Scenario 2: Optimal configuration of hybrid energy storage taking into account wind power leveling and auxiliary frequency regulation.

[0256] The charging and discharging power diagram of BESS and FES under different scenarios is shown in the figure. Fig.10 , Fig.11 Compared with scenario 1, scenario 2 energy storage adds auxiliary frequency regulation tasks, which leads to an increase in the charging and discharging power of BESS and FES.

[0257] contrast Fig.10 and Fig.11 It can be seen that according to the characteristics of different energy storage systems, in order to avoid the frequent operation of BESS having a significant impact on its service life, the low-frequency components in the power task are processed by BESS, while the high-frequency components in the power task are absorbed by FES, which can give full play to the complementary advantages of hybrid energy storage.

[0258] Table 3 Hybrid energy storage capacity and power in different scenarios

[0259]

[0260]

[0261] Table 3 summarizes the energy storage capacity configuration results under different scenarios. It can be seen from Table 3 that as the hybrid energy storage power task increases, the power configuration results are basically the same, and the configuration capacity also increases.

[0262] Table 4 Hybrid energy storage configuration results under different scenarios

[0263]

[0264] Table 4 gives the results of hybrid energy storage configuration under different scenarios. It can be seen from Table 4 that although the increase in capacity configuration leads to an increase in the total investment cost of energy storage, the total investment cost of energy storage increased from 104.5253 million yuan to 114.7587 million yuan, an increase of 9.8% year-on-year, while scenario 2 obtains frequency regulation benefits by participating in frequency regulation. While increasing investment costs, frequency regulation benefits are more profitable, so the final average annual overall net income increases, an increase of 0.7% compared to scenario 1. At the same time, the service life of BESS energy storage in scenario 2 increases from 7.89 years to 9.25 years compared to scenario 1, indicating that multi-scenario applications are more economical and feasible than single scenarios.

[0265] In summary, this embodiment comprehensively considers the multi-type constraints of FES and BESS, formulates a hybrid energy storage capacity optimization configuration strategy that comprehensively considers wind power fluctuation smoothing and auxiliary frequency regulation, and uses the rain flow counting method to obtain the equivalent cycle life of BESS. Finally, a FES-BESS hybrid energy storage capacity configuration model for smoothing and frequency regulation is established. Combined with the simulation analysis of specific examples, the following conclusions are drawn:

[0266] 1) After hybrid energy storage assists in frequency regulation, it effectively improves the frequency regulation capability of the power grid and reduces frequency fluctuations, playing a vital role in smoothing wind power fluctuations and stabilizing the power grid frequency.

[0267] 2) The scenario proposed in this embodiment is more economical than the hybrid energy storage of a single application scenario, with an average annual net income increased by 0.7%, and the service life of the BESS increased from 7.89 years to 9.25 years, which in turn leads to a reduction in the replacement cost of the BESS, which is beneficial to reducing environmental pollution. This shows that the scenario proposed in this embodiment is more economical and feasible than a single scenario.

[0268] The above implementation modes are only used to illustrate the present invention, but not to limit the present invention. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The patent protection scope of the present invention should be defined by the claims.

Claims

1. A hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation, characterized in that: The method comprises: Obtain wind power data, and decompose the wind power data using fully adaptive noise set empirical mode decomposition to obtain a wind power direct grid-connected component and a hybrid energy storage power task; Taking into account the various constraints of the energy storage system, and taking the maximum annual average overall net benefit of the wind storage system as the objective function, a capacity optimization configuration model of flywheel-electrochemical hybrid energy storage taking into account wind power leveling and frequency modulation is established; Based on the power data obtained from the direct grid-connected component of wind power and the hybrid energy storage power task, the capacity optimization configuration model is iteratively solved using an adaptive particle swarm algorithm to obtain an optimal capacity configuration solution for hybrid energy storage.

2. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation as claimed in claim 1 is characterized in that: Obtain wind power data, and use fully adaptive noise set empirical mode decomposition to decompose the wind power data to obtain wind power direct grid-connected components and hybrid energy storage power tasks, including: Taking wind power data as the signal to be decomposed, adding Gaussian white noise to the signal to be decomposed to obtain a first signal, and performing empirical mode decomposition on the first signal to obtain a first-order component; The N-order intrinsic mode functions obtained by EMD decomposition are accumulated and averaged to obtain the intrinsic IMF1 component; Subtract the first signal from the intrinsic IMF1 component to obtain a first-order residual component r1(t); Adding positive and negative paired Gaussian white noise to r1(t) to obtain a second signal, performing EMD decomposition on the second signal to obtain a new N-order intrinsic mode function; The new N-order intrinsic mode functions obtained by EMD decomposition are accumulated and averaged to obtain the intrinsic IMF2 component; Subtract r1(t) from the intrinsic IMF2 component to obtain the second-order residual component r2(t) as the current residual signal; Determine whether the current residual signal is a monotonic function. If not, decompose it again based on the current residual signal to obtain the nth order residual component. If yes, output the nth order IMF component and the nth order residual component. Wind power allocation is performed based on the n-order IMF component to obtain the direct grid-connected wind power component and the hybrid energy storage power task.

3. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation as claimed in claim 2 is characterized in that: Based on the n-order IMF component, wind power is allocated to obtain the wind power direct grid-connected component and the hybrid energy storage power task, including: Perform low-frequency reconstruction on the n-order IMF component to obtain the low-order reconstructed component L (k) ; Where k represents the order of the low-order reconstruction component; Initialize k=1; Calculate the low-order reconstruction component L (k) The amount of fluctuation; In the low-order reconstruction component L (k) When the fluctuation of meets the grid connection conditions, it is determined whether k is equal to 1. If k = 1, it is determined that there is no direct grid-connected power, and the hybrid energy storage power task assists in frequency regulation and then connects to the grid. If k ≠ 1, then set k = k + 1 and recalculate the low-order reconstruction component L (k) The amount of fluctuation; In the low-order reconstruction component L (k) If the fluctuation of the power does not meet the grid connection conditions, the HESS power task is executed: HESS =Hrf(n+1-k), where Hrf(n+1-k) represents the high-frequency reconstruction component, that is, the hybrid energy storage power task.

4. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation as claimed in claim 3 is characterized in that: The n-order IMF component is reconstructed at low frequency using the following formula to obtain the low-order reconstructed component: Where, L (n+1) represents the n+1th order low-order reconstruction component, r represents the residual component, F n and F (n-1) denote the n-th and n-1-th order intrinsic mode functions respectively.

5. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation as claimed in claim 3 is characterized in that: The low-order reconstruction component L (k) The fluctuation amount is the active power change limit required for wind farm grid connection. The fluctuation amount constraint is as follows: Where P Lfr(n),m represents the power of the reconstructed nth-order component in time period m; Δt represents the time interval, t represents the time, which is 1 minute; ΔP limit Indicates the wind power grid connection limit.

6. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency regulation according to any one of claims 1 to 5, characterized in that: The hybrid energy storage power task is expressed as: Where ΔP n represents the rated installed capacity of the wind farm; γ% represents the regulation rate; f sq Indicates frequency dead zone; P N represents the rated installed capacity of the wind farm; f represents the actual frequency; f0 represents the rated frequency.

7. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation as claimed in claim 1 is characterized in that: The objective function is constructed as follows: The full life cycle cost of the hybrid energy storage system is calculated using the following formula: C=C in +C οp +C dis +C sd +C aw -C re (4) In the formula, C represents the life cycle cost of the hybrid energy storage system, C in represents the investment cost, C op represents the operation and maintenance cost, C dis represents the replacement cost, C sd represents the scrapping cost, C re Indicates the recycling value, C aw represents the penalty cost for wind curtailment; The calculation formula for investment cost is: In the formula, C BESS_pi and C FES_pi Respectively represent the initial investment and replacement unit power cost of electrochemical energy storage and flywheel energy storage; C BESS_ei and C FES_ei Represent the unit capacity cost coefficients of the initial investment of electrochemical energy storage and flywheel energy storage respectively; Indicates the rated power of electrochemical energy storage; Indicates the rated power of flywheel energy storage; Indicates the rated capacity of electrochemical energy storage; Indicates the rated capacity of flywheel energy storage; The calculation formula for operation and maintenance cost is: In the formula, a and b represent the proportion of operation and maintenance costs of electrochemical energy storage and flywheel energy storage systems to investment costs, respectively. represents the operation and maintenance cost of electrochemical energy storage; represents the investment cost of electrochemical energy storage; represents the operation and maintenance cost of flywheel energy storage; represents the investment cost of flywheel energy storage; The calculation formula for the replacement cost is: In the formula, j represents the number of years in a certain use cycle; N represents the life of the electrochemical energy storage cycle; T represents the full life cycle; The calculation formula for scrap disposal cost is: In the formula, C BESS_ps and C BESS_es They represent the scrapping cost coefficient of BESS unit power and capacity respectively; The calculation formula for recycling value is: C re =αC i (1+i) -T (11) In the formula, C re represents the recovery value; α represents the residual value rate; C i represents the recovery value coefficient; The calculation formula for the penalty cost of wind curtailment is: In the formula, c aw E represents the wind abandonment penalty coefficient; aw represents the amount of wind power abandoned in year t; The wind power fluctuation compensation cost is calculated by the following formula: Where: represents the wind power fluctuation compensation cost; c com Indicates the compensation coefficient; P zheng_com,t and P fu_com,t Respectively represent the compensation required in the positive and negative directions at time t; The wind power grid connection income, carbon trading income and frequency regulation income are calculated by the following formula: In the formula, R es represents the wind power grid-connected income; r es represents the unit price of electricity sold by the wind farm, and E(t) represents the electricity sold by the wind farm directly connected to the grid in year t; In the formula, R cs represents the carbon trading income; r cs represents the unit price of carbon trading, λ ESS It indicates the emission per unit of electricity; In the formula, I tp represents the frequency modulation gain; i tp represents the frequency modulation benefit coefficient, E BESS (t) and E FES (t) represents the amount of electricity used in frequency modulation by the electrochemical energy storage system and the flywheel energy storage system respectively; The net benefit of the hybrid energy storage system is calculated by the following formula: R=R es +R cs +I tp -C (17) Where R is the net benefit of the hybrid energy storage system; The average annual overall net benefit of the wind-storage system is calculated based on the net benefit of the hybrid energy storage system, and the objective function is constructed with the maximum annual average overall net benefit of the wind-storage system as the goal.

8. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation as claimed in claim 7 is characterized in that: The life loss of the electrochemical energy storage system is calculated by the following formula: Where N ctf (D1) indicates the cycle life corresponding to the discharge depth of 1; N ctf (D i ) indicates that when the discharge depth is D i Cycle life of ; N(D i ) represents the life loss of electrochemical energy storage; Where, N represents the equivalent cycle life of BESS; D oD Indicates the depth of charge and discharge; Where T represents the life loss of the electrochemical energy storage system.

9. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency modulation as claimed in claim 1 is characterized in that: The constraints of the capacity optimization configuration model include power balance constraints, charge and discharge power constraints, and hybrid energy storage state constraints; Wherein, the power balance constraint is expressed as: Where P HESS,t represents the hybrid energy storage power task; They represent the charging power and discharging power of the electrochemical energy storage system at time t respectively; They represent the charging power and discharging power of the flywheel energy storage system at time t respectively; P zheng_com,t and P fu_com,t They represent the positive undercompensation and negative undercompensation at time t respectively; The charge and discharge power constraint is expressed as: Where: Indicates the rated power of the electrochemical energy storage system; Indicates the rated power of the flywheel energy storage system; The hybrid energy storage state constraint is expressed as: SOC BESSmin ≤SOC BESS,t ≤SOC BESSmax (27) SOC FESmin ≤SOC FES,t ≤SOC FESmax (29) Where: SOC BESSmax and SOC BESSmin Respectively represent the maximum and minimum state of charge of the electrochemical energy storage system; SOC FESmax and SOC FESmin They represent the maximum and minimum state of charge of the flywheel energy storage system respectively; SOC BESS,t and SOC BESS,t-1 Respectively represent the state of charge of the electrochemical energy storage system at time t and time t-1; SOC FES,t represents the charge state of the flywheel energy storage system at time t; Δt represents the time interval; η BESS Represents the charge and discharge power of electrochemical energy storage; η FES Indicates the charge and discharge power of the flywheel energy storage; Indicates the rated capacity of electrochemical energy storage; Indicates the rated capacity of the flywheel energy storage.

10. The hybrid energy storage capacity optimization configuration method considering wind power fluctuation smoothing and auxiliary frequency regulation according to claim 1, characterized in that: The capacity optimization configuration model is iteratively solved using an adaptive particle swarm algorithm to obtain the optimal capacity configuration scheme for hybrid energy storage, including: Get power data; Initialize energy storage power and capacity; Initialize the particle swarm and set the maximum number of iterations; Set the number of iterations to 1 and start the iteration: In one iteration, calculate the fitness value and annual comprehensive cost of the particle, calculate the optimal value of the individual and the group, update the particle position and speed, and increase the number of iterations; When the maximum number of iterations is reached here, the optimal capacity configuration plan of hybrid energy storage is output; The update formula for particle position and velocity is as follows: Where: represents the velocity of the i-th particle in the j-th dimension at the s+1-th iteration, represents the position of the i-th particle in the j-th dimension at the s-th iteration, and Represents two random numbers, ω s is the inertia weight, φ p and φ g represents the learning factor; and They represent the position of the i-th particle in the optimal solution of the s-th iteration and the j-th dimension of the global optimal solution respectively; The inertia weight is dynamically adjusted according to the number of iterations s, and the calculation formula is as follows: Where: w max and w min They represent the upper and lower limits of the inertia weight respectively, and S represents the maximum number of iterations.

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