Wind power plant cluster shared energy storage optimization configuration method and system

Abstract

The invention discloses a wind power plant cluster shared energy storage optimization configuration method and system, and the method comprises the steps: obtaining a multiple uncertainty set according to the output of a wind power plant cluster, the electricity price of an electric energy market, and the compensation price of a frequency modulation auxiliary service market through constructing a shared energy storage double-layer planning model; performing joint clustering on the multiple uncertainty sets by using an improved K-means clustering method to obtain a typical scene, calculating the probability of the typical scene according to the annual occurrence frequency of the typical scene, and modeling the multiple uncertainty sets in a short time scale by using a distributed robust optimization method to obtain an uncertainty extreme scene. And updating the shared energy storage bilevel programming model by utilizing the typical scene probability and the uncertainty extreme scene to obtain an updated shared energy storage bilevel programming model, and solving by adopting a particle swarm algorithm to obtain a configuration result. According to the method, the shared energy storage optimization configuration is carried out by considering various uncertain factors, and the accuracy of the shared energy storage optimization configuration is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid energy storage optimization configuration, and in particular to a wind farm cluster shared energy storage optimization configuration method and system. Background Art

[0002] The optimization scheduling problem of shared energy storage power stations is of great significance to actual industrial users. By installing shared power stations on the industrial user side, the energy storage capacity needs of different industrial users can be met while improving energy utilization efficiency. The shared energy storage model breaks the information sharing barriers and energy sharing barriers brought about by the binding of ownership and use rights of energy storage resources. In this operating mechanism, the energy storage devices of each new energy power plant provide energy storage services to other entities or power grids and charge corresponding service fees by time-sharing and reusing surplus energy storage resources on the premise of meeting their own main needs. The energy storage devices can not only meet the needs of their own new energy power plants, but also realize energy mutual assistance among multiple new energy power plants.

[0003] However, current methods for optimizing the configuration of shared energy storage are mostly based on the needs of the entire power system, with the goals of smoothing output fluctuations and participating in frequency and peak regulation to prevent wind and solar power abandonment. They do not consider the impact of multiple uncertainties such as wind farm output on the configuration of shared energy storage capacity at multiple time scales, resulting in relatively low rationality of the configuration effect. Summary of the invention

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a method and system for optimizing configuration of shared energy storage in a wind farm cluster, which improves the accuracy of optimized configuration of shared energy storage by considering multiple uncertain factors.

[0005] A first aspect of an embodiment of the present invention provides a method for optimizing configuration of shared energy storage in a wind farm cluster, the method comprising:

[0006] Construct a two-level planning model for shared energy storage, where the upper model of the two-level planning model takes the highest economic benefit of shared energy storage as its objective function and the lower model takes the lowest operating cost of wind farm cluster as its objective function;

[0007] Based on the wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price, multiple uncertainty sets are obtained. The improved K-means clustering method is used to jointly cluster the multiple uncertainty sets to obtain typical scenarios. The probability of typical scenarios is calculated based on the annual occurrence frequency of typical scenarios.

[0008] The distributed robust optimization method is used to model multiple uncertainty sets on a short time scale to obtain uncertainty extreme scenarios. The shared energy storage two-level programming model is updated using the typical scenario probabilities and uncertainty extreme scenarios to obtain the updated shared energy storage two-level programming model. The particle swarm algorithm is used to solve the model and obtain the configuration result.

[0009] In a possible implementation of the first aspect, a shared energy storage two-layer planning model is constructed, including:

[0010] The upper model is constructed with the highest economic benefit of shared energy storage as the objective function, where the upper model is:

[0011]

[0012] In the formula, I R,d is the daily income from leasing the capacity of the wind farm cluster; I DNL,d is the daily income of the electric energy market; TP,d is the daily revenue of the frequency regulation auxiliary service market; C in Investment and construction costs for shared energy storage; C op Annual operation and maintenance costs for shared energy storage;

[0013]

[0014] In the formula, is the wind farm cluster rental power of shared energy storage at the tth moment on the dth day, p r,d is the unit power rental price of shared energy storage on day d, P DNL,d (t) is the power clearing power of the shared energy storage at the tth moment on the dth day, p DNL,d (t) is the clearing price of the electricity market at the tth moment on the dth day, P TP,d (t) is the bid capacity of shared energy storage in the frequency regulation ancillary service market at the tth moment on the dth day, p TP,d (t) is the frequency regulation compensation price of the frequency regulation auxiliary service market at the tth moment on the dth day, C p is the unit power investment and construction cost of shared energy storage, C s is the investment and construction cost per unit capacity of shared energy storage, N y For the full life cycle of shared energy storage, P max is the maximum charging and discharging power of shared energy storage, E SES is the optimal configuration capacity of shared energy storage, g is the discount rate, h is the number of replacements of shared energy storage devices, n is the total number of replacements of shared energy storage devices in their entire life cycle, C pom is the unit power operation and maintenance cost of shared energy storage, C som is the operation and maintenance cost per unit capacity of shared energy storage, W(t) is the charge and discharge volume of shared energy storage over its entire life cycle;

[0015] The lower model takes the lowest wind farm cluster operation cost as the objective function, where the lower model is:

[0016] f d =min(C p +C r -I s )

[0017]

[0018] In the formula, C p is the daily deviation assessment cost of the wind farm cluster, C r The daily rental cost of leasing shared energy storage for a wind farm cluster, I s is the daily electricity sales revenue of the wind farm cluster, is the actual wind power value of the wind farm cluster at the tth moment, is the day-ahead wind power forecast value of the wind farm cluster at the tth moment, p wind is the unit wind power deviation assessment price, p DNL (t) is the clearing price of the electricity market at the tth moment.

[0019] In a possible implementation of the first aspect, multiple uncertainty sets are jointly clustered using an improved K-means clustering method to obtain a typical scenario, including:

[0020] Extend the multiple uncertainty sets into long time scale sets, perform quantitative processing on the data in the long time scale sets, and obtain processed data;

[0021] Randomly select multiple data as cluster centers, calculate the distance difference and trend difference from all processed data to each cluster center, and obtain the comprehensive difference based on the distance difference and trend difference;

[0022] The processed data with comprehensive differences less than the preset value are assigned to the corresponding cluster centers to obtain multiple initial typical scenes. The means of the multiple uncertainty sets in each initial typical scene are recalculated, and the means are used as the new cluster centers. The comprehensive differences of all processed data to the new cluster centers are continued to be calculated and assigned until the cluster centers no longer change, and multiple typical scenes are obtained.

[0023] In a possible implementation manner of the first aspect, calculating the distance difference and trend difference from all processed data to each cluster center, and obtaining a comprehensive difference according to the distance difference and trend difference, includes:

[0024] The Euclidean distance is used to calculate the distance difference between all processed data and each cluster center, and the trend difference calculation formula is used to calculate the trend difference between all processed data and each cluster center. The distance difference calculation formula is:

[0025]

[0026] In the formula, is the distance difference between the processed data on the dth day and the kth cluster center, is the wind power sequence of the kth cluster center of the wind farm cluster with multiple uncertainties, p DNL,k is the clearing price sequence of the kth cluster center in the multi-uncertainty aggregate electricity energy market, p TP,k is the frequency regulation compensation price sequence of the kth cluster center of the frequency regulation ancillary service market with multiple uncertainties;

[0027] The trend difference calculation formula is:

[0028]

[0029] In the formula, is the trend difference from the dth processed data to the kth cluster center;

[0030] The distance difference and trend difference from each processed data to the cluster center are normalized using the index to obtain the distance difference and trend difference. The calculation formula of the distance difference is:

[0031]

[0032] In the formula, γ 1 (d,k) is the distance difference between the dth processed data and the kth cluster center;

[0033] The calculation formula for trend difference is:

[0034]

[0035] In the formula, γ 2 (d,k) is the distance difference between the dth processed data and the kth cluster center;

[0036] The proportional coefficient method is used to fuse the distance difference and the trend difference to obtain the comprehensive difference from the processed data to each cluster center. The expression of the comprehensive difference is:

[0037] γ(d,k)=αγ 1 (d,k)+βγ 2 (d,k)

[0038] Where γ(d, k) is the comprehensive difference from the processed data to each cluster center, α and β are proportional coefficients, which represent the relative importance of the distance difference and the trend difference, α+β=1.

[0039] In a possible implementation of the first aspect, a distributed robust optimization method is used to model multiple uncertainty sets on a short time scale to obtain an uncertainty extreme scenario, including:

[0040] The robust parameters are used to construct the ensemble of multiple uncertainty sets on a short time scale, where the ensemble is:

[0041]

[0042]

[0043] In the formula, is the actual value of wind power of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the day-ahead wind power forecast value of the wind farm cluster at the t-th moment of the wind power sequence on the d-th day, is the maximum value of the wind power fluctuation range of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the distributed robustness parameter of the wind power sequence of the wind farm cluster on day d, p DNL,d (t) is the real value of the electricity price at the tth moment in the clearing price sequence of the electricity market on the dth day, is the forecast value of the day-ahead wind power price at the tth moment in the clearing price sequence of the electric energy market on the dth day, is the maximum fluctuation range of wind power at the tth moment in the clearing price sequence of the electric energy market on the dth day, χ DNL,d is the distributed robustness parameter of the clearing price sequence of the electric energy market on day d, p TP,d (t) is the actual value of the compensation price of the ancillary service market at the tth moment in the frequency regulation compensation price sequence on the dth day, is the day-ahead forecast value of the compensation price of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, is the maximum compensation price fluctuation range of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, χ TP,d is the distribution parameter of the frequency regulation compensation price series of the ancillary service market on day d;

[0044] The calculation formula of the distribution rod parameter is:

[0045]

[0046] In the formula, μDNL,d and μ TP,d Sequence Sequence Y DNL,d and sequence Y TP,d expectations; and Sequence Sequence Y DNL,d and sequence Y TP,d The standard deviation of α DNL,d and α TP,d are the confidence levels of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price in the distributed optimization process, is the probability distribution function of the standard normal distribution.

[0047] In a possible implementation of the first aspect, the updated shared energy storage two-layer planning model is:

[0048] f u =max(π k [I R,d (ω k )+I DNL,d (ω k )+I TP,d (ω k )]-C in -C op )

[0049]

[0050] In the formula, I R,d (ω k ), I DNL,d (ω k ) and I TP,d (ω k ) are the daily revenue of wind farm cluster capacity leasing, daily revenue of electric energy market and daily revenue of frequency regulation auxiliary service market under the kth typical scenario, respectively. p is the daily deviation assessment cost of the wind farm cluster under the kth typical scenario, C r is the daily rental cost of the wind farm cluster leasing shared energy storage in the kth typical scenario, I s is the daily electricity sales revenue of the wind farm cluster under the kth typical scenario, ψ is the multiple uncertainties of the wind farm cluster output and the clearing price of the electricity market, The worst scenario for multiple uncertainties.

[0051] A second aspect of an embodiment of the present invention provides a wind farm cluster shared energy storage optimization configuration system, the system comprising a construction module, a typical scenario calculation module and a configuration module.

[0052] The construction module is used to construct a shared energy storage two-layer planning model, wherein the two-layer planning model includes an upper model with the highest economic benefit of shared energy storage as the objective function and a lower model with the lowest operating cost of wind farm cluster as the objective function;

[0053] The typical scenario calculation module is used to obtain multiple uncertainty sets based on the wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price, and use the improved K-means clustering method to jointly cluster the multiple uncertainty sets to obtain typical scenarios, and calculate the probability of typical scenarios based on the annual occurrence frequency of typical scenarios;

[0054] The configuration module is used to model multiple uncertainty sets on a short time scale using the distributed robust optimization method to obtain uncertainty extreme scenarios, and to update the shared energy storage two-level programming model using typical scenario probabilities and uncertainty extreme scenarios to obtain an updated shared energy storage two-level programming model, which is then solved using a particle swarm algorithm to obtain the configuration result.

[0055] In a possible implementation of the second aspect, a shared energy storage two-layer planning model is constructed, including:

[0056] The upper model is constructed with the highest economic benefit of shared energy storage as the objective function, where the upper model is:

[0057]

[0058] In the formula, I R,d is the daily income from leasing the capacity of the wind farm cluster; I DNL,d is the daily income of the electric energy market; TP,d is the daily revenue of the frequency regulation auxiliary service market; C in Investment and construction costs for shared energy storage; C op Annual operation and maintenance costs for shared energy storage;

[0059]

[0060] In the formula, is the wind farm cluster rental power of shared energy storage at the tth moment on the dth day, p r,d is the unit power rental price of shared energy storage on day d, P DNL,d (t) is the power clearing power of the shared energy storage at the tth moment on the dth day, p DNL,d (t) is the clearing price of the electricity market at the tth moment on the dth day, P TP,d (t) is the bid capacity of shared energy storage in the frequency regulation ancillary service market at the tth moment on the dth day, p TP,d (t) is the frequency regulation compensation price of the frequency regulation auxiliary service market at the tth moment on the dth day, Cp is the unit power investment and construction cost of shared energy storage, C s is the investment and construction cost per unit capacity of shared energy storage, N y For the full life cycle of shared energy storage, P max is the maximum charging and discharging power of shared energy storage, E SES is the optimal configuration capacity of shared energy storage, g is the discount rate, h is the number of replacements of shared energy storage devices, n is the total number of replacements of shared energy storage devices in their entire life cycle, C pom is the unit power operation and maintenance cost of shared energy storage, C som is the operation and maintenance cost per unit capacity of shared energy storage, W(t) is the charge and discharge volume of shared energy storage over its entire life cycle;

[0061] The lower model takes the lowest wind farm cluster operation cost as the objective function, where the lower model is:

[0062] f d =min(C p +C r -I s )

[0063]

[0064] In the formula, C p is the daily deviation assessment cost of the wind farm cluster, C r The daily rental cost of leasing shared energy storage for a wind farm cluster, I s is the daily electricity sales revenue of the wind farm cluster, is the actual wind power value of the wind farm cluster at the tth moment, is the day-ahead wind power forecast value of the wind farm cluster at the tth moment, p wind is the unit wind power deviation assessment price, p DNL (t) is the clearing price of the electricity market at the tth moment.

[0065] A third aspect of an embodiment of the present invention provides a computer device, including:

[0066] Memory for storing computer programs;

[0067] The processor is used to implement the wind farm cluster shared energy storage optimization configuration method as described in the first aspect when executing the computer program.

[0068] A fourth aspect of the embodiments of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing configuration of shared energy storage of a wind farm cluster as described in the first aspect are implemented.

[0069] The method for optimizing the configuration of shared energy storage for a wind farm cluster provided in an embodiment of the present invention constructs a shared energy storage two-layer programming model, an upper model of the two-layer programming model with the highest shared energy storage economic benefit as the objective function and a lower model with the lowest wind farm cluster operating cost as the objective function, obtains multiple uncertainty sets according to the wind farm cluster output, the electricity market price of the electric energy, and the compensation price of the frequency regulation auxiliary service market, and uses an improved K-means clustering method to jointly cluster the multiple uncertainty sets to obtain typical scenarios, calculates the probability of typical scenarios according to the annual occurrence frequency of typical scenarios, uses a distributed robust optimization method to model the multiple uncertainty sets on a short time scale, obtains uncertainty extreme scenarios, uses the typical scenario probability and uncertainty extreme scenarios to update the shared energy storage two-layer programming model, obtains an updated shared energy storage two-layer programming model, and uses a particle swarm algorithm to solve and obtain the configuration result. Through the above method, the shared energy storage is optimized by considering multiple uncertainty factors, thereby improving the accuracy of the shared energy storage optimization configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 : A schematic diagram of a configuration process of an embodiment of a method for optimizing configuration of shared energy storage in a wind farm cluster provided by the present invention;

[0071] Figure 2 : A flow chart of a shared energy storage optimization configuration method for a wind farm cluster shared energy storage optimization configuration method according to an embodiment of the present invention;

[0072] Figure 3 : A system block diagram of an embodiment of the wind farm cluster shared energy storage optimization configuration method provided by the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Please refer to Figure 1 , which is a flow chart of an embodiment of a method for generating a power grid equipment fault scenario and an extreme operation scenario provided by an embodiment of the present invention, including steps S101 to S103, each of which is specifically as follows:

[0075] S101. Construct a two-layer planning model for shared energy storage, wherein the two-layer planning model comprises an upper model with the highest economic benefit of shared energy storage as its objective function and a lower model with the lowest wind farm cluster operation cost as its objective function.

[0076] In this embodiment, if Figure 2 As shown in the figure, a two-level planning model for shared energy storage is constructed. The upper model solves the long-term optimal capacity planning of shared energy storage with the highest economic benefit of shared energy storage as the objective function, and the lower model solves the problem of coordinated optimization operation of wind farm clusters and shared energy storage with the lowest operation cost of wind farm clusters as the objective function.

[0077] In some embodiments, the step S101 of “building a shared energy storage two-layer planning model” includes but is not limited to the following steps:

[0078] The upper model is constructed with the highest economic benefit of shared energy storage as the objective function, wherein the upper model is:

[0079]

[0080] In the formula, I R,d is the daily income from leasing the capacity of the wind farm cluster; I DNL,d is the daily income of the electric energy market; TP,d is the daily revenue of the frequency regulation auxiliary service market; C in Investment and construction costs for shared energy storage; C op Annual operation and maintenance costs for shared energy storage;

[0081]

[0082] In the formula, is the wind farm cluster rental power of shared energy storage at the tth moment on the dth day, p r,d is the unit power rental price of shared energy storage on day d, P DNL,d (t) is the power clearing power of the shared energy storage at the tth moment on the dth day, p DNL,d (t) is the clearing price of the electricity market at the tth moment on the dth day, P TP,d (t) is the bid capacity of shared energy storage in the frequency regulation ancillary service market at the tth moment on the dth day, p TP,d (t) is the frequency regulation compensation price of the frequency regulation auxiliary service market at the tth moment on the dth day, C p is the unit power investment and construction cost of shared energy storage, C s is the investment and construction cost per unit capacity of shared energy storage, N y For the full life cycle of shared energy storage, P max is the maximum charging and discharging power of shared energy storage, E SES is the optimal configuration capacity of shared energy storage, g is the discount rate, h is the number of replacements of shared energy storage devices, n is the total number of replacements of shared energy storage devices in their entire life cycle, C pom is the unit power operation and maintenance cost of shared energy storage, C somis the operation and maintenance cost per unit capacity of shared energy storage, W(t) is the charge and discharge volume of shared energy storage over its entire life cycle;

[0083] The lower model takes the lowest wind farm cluster operation cost as the objective function, wherein the lower model is:

[0084] f d =min(C p +C r -I s )

[0085]

[0086] In the formula, C p is the daily deviation assessment cost of the wind farm cluster, C r The daily rental cost of leasing shared energy storage for a wind farm cluster, I s is the daily electricity sales revenue of the wind farm cluster, is the actual wind power value of the wind farm cluster at the tth moment, is the day-ahead wind power forecast value of the wind farm cluster at the tth moment, p wind is the unit wind power deviation assessment price, p DNL (t) is the clearing price of the electricity market at the tth moment.

[0087] In this embodiment, the upper layer determines the shared energy storage configuration capacity and the maximum charge and discharge power with the maximum annual economic benefit of shared energy storage as the objective function. Among them, the annual economic benefit includes the annual income from the leasing of wind farm cluster capacity, the annual income from the electric energy market, the annual income from the frequency regulation auxiliary service market, the investment and construction cost of shared energy storage (averaged to one year over the entire life cycle), and the annual operation and maintenance cost of shared energy storage. Specifically:

[0088]

[0089] In the formula, I R,d is the daily income from leasing the capacity of the wind farm cluster; I DNL,d is the daily income of the electric energy market; TP,d is the daily revenue of the frequency regulation auxiliary service market; C in Investment and construction costs for shared energy storage; C op Annual operation and maintenance costs for shared energy storage;

[0090]

[0091]

[0092] In the formula, is the wind farm cluster rental power of shared energy storage at the tth moment on the dth day, p r,dis the unit power rental price of shared energy storage on day d, P DNL,d (t) is the power clearing power of the shared energy storage at the tth moment on the dth day, p DNL,d (t) is the clearing price of the electricity market at the tth moment on the dth day, P TP,d (t) is the bid capacity of shared energy storage in the frequency regulation ancillary service market at the tth moment on the dth day, p TP,d (t) is the frequency regulation compensation price of the frequency regulation auxiliary service market at the tth moment on the dth day, C p is the unit power investment and construction cost of shared energy storage, C s is the investment and construction cost per unit capacity of shared energy storage, N y For the full life cycle of shared energy storage, P max is the maximum charging and discharging power of shared energy storage, E SES is the optimal configuration capacity of shared energy storage, g is the discount rate, h is the number of replacements of shared energy storage devices, n is the total number of replacements of shared energy storage devices in their entire life cycle, C pom is the unit power operation and maintenance cost of shared energy storage, C som is the operation and maintenance cost per unit capacity of shared energy storage, W(t) is the charge and discharge volume of shared energy storage over its entire life cycle;

[0093] The lower layer takes the lowest daily operating cost of the wind farm cluster as the objective function to determine the optimal operation of the wind farm cluster and shared energy storage. Specifically:

[0094]

[0095]

[0096] In the formula, C p is the daily deviation assessment cost of the wind farm cluster, C r The daily rental cost of leasing shared energy storage for a wind farm cluster, I s is the daily electricity sales revenue of the wind farm cluster, is the actual wind power value of the wind farm cluster at the tth moment, is the day-ahead wind power forecast value of the wind farm cluster at the tth moment, p wind is the unit wind power deviation assessment price, p DNL (t) is the clearing price of the electricity market at the tth moment.

[0097] S102. Multiple uncertainty sets are obtained based on the wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price. The multiple uncertainty sets are jointly clustered using the improved K-means clustering method to obtain typical scenarios. The probability of typical scenarios is calculated based on the annual occurrence frequency of typical scenarios.

[0098] In this embodiment, by considering the impact of the uncertainty of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price on the shared energy storage upper optimization configuration model, the concept of scenario reduction is introduced, and the improved K-means clustering method is used to jointly cluster multiple uncertainties on a long time scale, and the multi-type scenarios on a long time scale are reduced to typical scenarios. The probabilities of different typical scenarios are calculated based on the annual frequency of occurrence of typical scenarios. The number of scenarios corresponding to the multiple typical scenarios determined by the improved K-means clustering method (i.e., annual frequency of occurrence) is statistically calculated, and the probabilities of different typical scenarios are calculated according to the normalization method.

[0099]

[0100] In the formula, π k is the probability corresponding to the kth typical scene; N k is the number of clusters corresponding to the kth typical scene.

[0101] In some embodiments, the step S102 of "using an improved K-means clustering method to jointly cluster the multiple uncertainty sets to obtain a typical scenario" includes but is not limited to the following steps:

[0102] Extend the multiple uncertainty sets into long time scale sets, perform quantitative processing on the data in the long time scale sets, and obtain processed data;

[0103] Randomly select multiple data as cluster centers, calculate the distance difference and trend difference from all processed data to each cluster center, and obtain the comprehensive difference based on the distance difference and trend difference;

[0104] The processed data with comprehensive differences less than the preset value are assigned to the corresponding cluster centers to obtain multiple initial typical scenes. The means of the multiple uncertainty sets in each initial typical scene are recalculated, and the means are used as the new cluster centers. The comprehensive differences of all processed data to the new cluster centers are continued to be calculated and assigned until the cluster centers no longer change, and multiple typical scenes are obtained.

[0105] In this embodiment, the uncertainty of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price in the shared energy storage two-layer planning model is described as a multiple uncertainty set, where the multiple uncertainty set is:

[0106]

[0107] Among them, s is the set of uncertain factors in the shared energy storage two-level planning model, is the wind power of the wind farm cluster at time t, p DNL (t) is the clearing price of the electricity market at time t, pTP (t) is the frequency regulation compensation price in the frequency regulation ancillary service market at time t.

[0108] Considering the impact of multiple uncertainties on the upper-level shared energy storage capacity planning in the long time scale, the improved K-means clustering method is used to jointly cluster multiple uncertainties, reducing the multi-type configuration scenarios in the long time scale to typical scenarios. Extending the multiple uncertainty set to the long time scale, the long time scale set is obtained, which is specifically expressed as follows:

[0109]

[0110] p DNL,d ={p DNL,d (1),p DNL,d (2),...,p DNL,d (t),...,p DNL,d (T)}

[0111] p TP,d ={p TP,d (1),p TP,d (2),...,p TP,d (t),...,p TP,d (T)}

[0112] In the formula, s d is the basic period of the uncertainty set on a long time scale; T is the basic period of each element of the uncertainty set on a long time scale, T = 24; is the wind power sequence of the wind farm cluster on day d; p DNL,d is the clearing price sequence of the electric energy market on day d; p TP,d is the frequency regulation compensation price series of the frequency regulation ancillary service market on day d.

[0113] The specific steps of using the improved K-means clustering method to jointly cluster long-time scale sets are as follows:

[0114] S11: Performing a non-quantum processing on the data in the long time scale set;

[0115] S12: Randomly select K uncertainty set sample data as cluster centers, k∈{1,2,...,K};

[0116] S13: Calculate the difference between all sample data in the uncertainty set and different cluster centers. The difference calculation includes two aspects: distance difference and trend difference, and is characterized by comprehensive difference.

[0117] S14: Allocate all sample data of the uncertainty set to different cluster centers according to the principle of minimum comprehensive difference, recalculate the uncertainty set means of different typical scenarios, and use the uncertainty set mean of each typical scenario as a new cluster center;

[0118] S15: Repeat S13 and S14 until each cluster center no longer changes, and obtain K typical scenarios ω k .

[0119] In some embodiments, in step S102, "calculating the distance difference and trend difference from all processed data to each cluster center, and obtaining a comprehensive difference according to the distance difference and trend difference" includes but is not limited to the following steps:

[0120] The Euclidean distance is used to calculate the distance difference between all processed data and each cluster center, and the trend difference calculation formula is used to calculate the trend difference between all processed data and each cluster center. The distance difference calculation formula is:

[0121]

[0122] In the formula, is the distance difference between the processed data on the dth day and the kth cluster center, is the wind power sequence of the kth cluster center of the wind farm cluster with multiple uncertainties, p DNL,k is the clearing price sequence of the kth cluster center in the multi-uncertainty aggregate electricity energy market, p TP,k is the frequency regulation compensation price sequence of the kth cluster center of the frequency regulation ancillary service market with multiple uncertainties;

[0123] The trend difference calculation formula is:

[0124]

[0125] In the formula, is the trend difference from the dth processed data to the kth cluster center;

[0126] The distance difference and trend difference from each processed data to the cluster center are normalized using the index to obtain the distance difference and trend difference. The calculation formula of the distance difference is:

[0127]

[0128] In the formula, γ 1 (d,k) is the distance difference between the dth processed data and the kth cluster center;

[0129] The calculation formula for trend difference is:

[0130]

[0131] In the formula, γ 2 (d,k) is the distance difference between the dth processed data and the kth cluster center;

[0132] The proportional coefficient method is used to fuse the distance difference and the trend difference to obtain the comprehensive difference from the processed data to each cluster center. The expression of the comprehensive difference is:

[0133] γ(d,k)=αγ 1 (d,k)+βγ 2 (d,k)

[0134] Where γ(d, k) is the comprehensive difference from the processed data to each cluster center, α and β are proportional coefficients, which represent the relative importance of the distance difference and the trend difference, α+β=1.

[0135] In this embodiment, the Euclidean distance is used to calculate the distance difference between the data in the uncertainty set after dimensionless processing and different cluster centers, specifically:

[0136]

[0137] In the formula, is the distance difference between the processed data on the dth day and the kth cluster center, is the wind power sequence of the kth cluster center of the wind farm cluster with multiple uncertainties, p DNL,k is the clearing price sequence of the kth cluster center in the multi-uncertainty aggregate electricity energy market, p TP,k is the frequency regulation compensation price sequence of the kth cluster center of the frequency regulation ancillary service market with multiple uncertainties;

[0138] The index is used to normalize the distance difference between the data in the uncertainty set and the different cluster centers to obtain the distance difference, which is:

[0139]

[0140] In the formula, γ 1 (d,k) is the distance difference from the data in the dth uncertainty set to the kth cluster center.

[0141] The trend difference of uncertainty set sample data to different cluster centers is calculated using the following formula:

[0142]

[0143] In the formula, is the trend difference from the dth processed data to the kth cluster center;

[0144] The trend difference of the data in the uncertainty set to different cluster centers is normalized using the index to obtain the trend difference, which is:

[0145]

[0146] In the formula, γ 2 (d,k) is the trend difference from the data in the dth uncertainty set to the kth cluster center.

[0147] The proportional coefficient method is used to fuse the distance difference and the trend difference to obtain the comprehensive difference from the processed data to each cluster center. The expression of the comprehensive difference is:

[0148] γ(d,k)=αγ 1 (d,k)+βγ 2 (d,k)

[0149] Where γ(d, k) is the comprehensive difference from the processed data to each cluster center, α and β are proportional coefficients, which represent the relative importance of the distance difference and the trend difference, α+β=1.

[0150] S103: The distributed robust optimization method is used to model multiple uncertainty sets on a short time scale to obtain uncertainty extreme scenarios. The shared energy storage two-level programming model is updated using typical scenario probabilities and uncertainty extreme scenarios to obtain an updated shared energy storage two-level programming model. The particle swarm algorithm is used to solve the model to obtain the configuration result.

[0151] In this embodiment, by considering the impact of the uncertainty of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price on the shared energy storage lower optimization operation model, the distributed robust optimization method is used to model multiple uncertainty sets in a short time scale, and multiple uncertainty extremes are characterized by the worst scenario. Then, based on the processing results of multiple uncertainty sets, i.e., the typical scenario probability and uncertainty extreme scenario, the shared energy storage two-level programming model is updated and corrected, and the particle swarm algorithm supporting commercial solution software is used for joint solution.

[0152] In some embodiments, step S103 of "using the distributed robust optimization method to model the multiple uncertainty sets on a short time scale to obtain an uncertainty extreme scenario" includes but is not limited to the following steps:

[0153] The set of the multiple uncertainty sets on a short time scale is constructed using robust parameters, wherein the set is:

[0154]

[0155] In the formula, is the actual value of wind power of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the day-ahead wind power forecast value of the wind farm cluster at the t-th moment of the wind power sequence on the d-th day, is the maximum value of the wind power fluctuation range of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the distributed robustness parameter of the wind power sequence of the wind farm cluster on day d, p DNL,d (t) is the real value of the electricity price at the tth moment in the clearing price sequence of the electricity market on the dth day, is the forecast value of the day-ahead wind power price at the tth moment in the clearing price sequence of the electric energy market on the dth day, is the maximum fluctuation range of wind power at the tth moment in the clearing price sequence of the electric energy market on the dth day, χ DNL,d is the distributed robustness parameter of the clearing price sequence of the electric energy market on day d, p TP,d (t) is the actual value of the compensation price of the ancillary service market at the tth moment in the frequency regulation compensation price sequence on the dth day, is the day-ahead forecast value of the compensation price of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, is the maximum compensation price fluctuation range of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, χ TP,d is the distribution parameter of the frequency regulation compensation price series of the ancillary service market on day d;

[0156] The calculation formula of the distributed rod parameter is:

[0157]

[0158] In the formula, μ DNL,d and μ TP,d Sequence Sequence Y DNL,d and sequence Y TP,d expectations; and Sequence Sequence Y DNL,d and sequence Y TP,d The standard deviation of α DNL,d and α TP,d are the confidence levels of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price in the distributed optimization process, is the probability distribution function of the standard normal distribution.

[0159] In this embodiment, taking into account the smoothing effect of output forecast and electricity price forecast, the uncertainty set of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price in the short time scale is constructed by using robust parameters:

[0160]

[0161] In the formula, is the actual value of wind power of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the day-ahead wind power forecast value of the wind farm cluster at the t-th moment of the wind power sequence on the d-th day, is the maximum value of the wind power fluctuation range of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the distributed robustness parameter of the wind power sequence of the wind farm cluster on day d, p DNL,d (t) is the real value of the electricity price at the tth moment in the clearing price sequence of the electricity market on the dth day, is the forecast value of the day-ahead wind power price at the tth moment in the clearing price sequence of the electric energy market on the dth day, is the maximum fluctuation range of wind power at the tth moment in the clearing price sequence of the electric energy market on the dth day, χ DNL,d is the distributed robustness parameter of the clearing price sequence of the electric energy market on day d, p TP,d (t) is the actual value of the compensation price of the ancillary service market at the tth moment in the frequency regulation compensation price sequence on the dth day, is the day-ahead forecast value of the compensation price of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, is the maximum compensation price fluctuation range of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, χ TP,d is the distribution and reinforcement parameter of the frequency regulation compensation price series in the ancillary service market on day d.

[0162] The robust parameters of the uncertainty set of wind farm cluster output, electricity market price and frequency regulation ancillary service market compensation price in the short time scale can be determined by the following formula:

[0163]

[0164] In the formula, μ DNL,d and μ TP,d Sequence Sequence Y DNL,d and sequence Y TP,d expectations, and Sequence Sequence YDNL,d and sequence Y TP,d The standard deviation of α DNL,d and α TP,d are the confidence levels of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price in the distributed optimization process, is the probability distribution function of the standard normal distribution.

[0165] In some embodiments, the “updated shared energy storage two-layer planning model” in step S103 includes but is not limited to the following steps:

[0166]

[0167]

[0168] In the formula, I R,d (ω k ), I DNL,d (ω k ) and I TP,d (ω k ) are the daily revenue of wind farm cluster capacity leasing, daily revenue of electric energy market and daily revenue of frequency regulation auxiliary service market under the kth typical scenario, respectively. p is the daily deviation assessment cost of the wind farm cluster under the kth typical scenario, C r is the daily rental cost of the wind farm cluster leasing shared energy storage in the kth typical scenario, I s is the daily electricity sales revenue of the wind farm cluster under the kth typical scenario, ψ is the multiple uncertainties of the wind farm cluster output and the clearing price of the electricity market, The worst scenario for multiple uncertainties.

[0169] In this embodiment, the update and correction results of the shared energy storage two-layer planning model are:

[0170] f u =max(π k [I R,d (ω k )+I DNL,d (ω k )+I TP,d (ω k )]-C in -C op )

[0171]

[0172] In the formula, I R,d (ω k ), I DNL,d (ω k) and I TP,d (ω k ) are the daily revenue of wind farm cluster capacity leasing, daily revenue of electric energy market and daily revenue of frequency regulation auxiliary service market under the kth typical scenario, respectively. p is the daily deviation assessment cost of the wind farm cluster under the kth typical scenario, C r is the daily rental cost of the wind farm cluster leasing shared energy storage in the kth typical scenario, I s is the daily electricity sales revenue of the wind farm cluster under the kth typical scenario, ψ is the uncertainty set of the wind farm cluster output and the clearing price of the electricity energy market, The worst scenario for the uncertainty set.

[0173] A two-level planning model is constructed with the upper optimal shared energy storage capacity configuration and the lower wind farm cluster and shared energy storage collaborative optimization operation model. On a long time scale, the concept of scenario reduction is introduced, and the improved K-means clustering method is used to jointly cluster the wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price, reducing the multi-type scenarios on a long time scale to typical scenarios, reducing the impact of multiple uncertainties on the shared energy storage configuration level. On a short time scale, the distributed robust optimization method is used to model multiple uncertainties, characterize the extreme situations of multiple uncertainties through the worst scenario, and formulate a shared energy storage optimization operation strategy to reduce the impact of multiple uncertainties on the shared energy storage operation level. The multi-time scale uncertainty processing results are substituted back into the shared energy storage two-level planning model for updating and correction, and the particle swarm algorithm and commercial solution software are used to jointly solve the optimal configuration capacity of the wind farm cluster leasing shared energy storage.

[0174] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0175] In some embodiments, Figure 3 As shown, it shows a block diagram of a wind farm cluster shared energy storage optimization configuration system 300 provided in an embodiment of the present application, including: a construction module 301, a typical scenario calculation module 302 and a configuration module 303, wherein:

[0176] The construction module is used to construct a shared energy storage two-layer planning model, wherein the two-layer planning model includes an upper model with the highest economic benefit of shared energy storage as the objective function and a lower model with the lowest operating cost of wind farm cluster as the objective function;

[0177] The typical scenario calculation module is used to obtain multiple uncertainty sets based on the wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price, and use the improved K-means clustering method to jointly cluster the multiple uncertainty sets to obtain typical scenarios, and calculate the probability of typical scenarios based on the annual occurrence frequency of typical scenarios;

[0178] The configuration module is used to model multiple uncertainty sets on a short time scale using the distributed robust optimization method to obtain uncertainty extreme scenarios, and to update the shared energy storage two-level programming model using typical scenario probabilities and uncertainty extreme scenarios to obtain an updated shared energy storage two-level programming model, which is then solved using a particle swarm algorithm to obtain the configuration result.

[0179] The specific implementation of the wind farm cluster shared energy storage optimization configuration system is basically the same as the specific implementation of the wind farm cluster shared energy storage optimization configuration method described above, and will not be repeated here.

[0180] In one embodiment of the present application, a computer device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and the above steps are implemented when the processor executes the computer program; the computer device provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiments, and will not be repeated here.

[0181] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the above steps are implemented when the computer program is executed by a processor; the computer-readable storage medium provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be repeated here.

[0182] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0183] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0184] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of shared energy storage in a wind farm cluster, characterized in that: include: Constructing a shared energy storage two-layer planning model, wherein the two-layer planning model comprises an upper layer model with the highest economic benefit of shared energy storage as the objective function and a lower layer model with the lowest wind farm cluster operation cost as the objective function; A multiple uncertainty set is obtained based on the wind farm cluster output, the electricity market price and the frequency regulation auxiliary service market compensation price. The multiple uncertainty sets are jointly clustered using the improved K-means clustering method to obtain a typical scenario. The probability of the typical scenario is calculated based on the annual occurrence frequency of the typical scenario. The multiple uncertainty sets are modeled on a short time scale using a distributed robust optimization method to obtain uncertainty extreme scenarios. The shared energy storage two-level programming model is updated using the typical scenario probabilities and the uncertainty extreme scenarios to obtain an updated shared energy storage two-level programming model, which is then solved using a particle swarm algorithm to obtain the configuration result.

2. The method for optimizing configuration of wind farm cluster shared energy storage according to claim 1, characterized in that: The construction of a shared energy storage two-layer planning model includes: The upper model is constructed with the highest economic benefit of shared energy storage as the objective function, wherein the upper model is: In the formula, I R,d is the daily income from leasing the capacity of the wind farm cluster; I DNL,d is the daily income of the electric energy market; TP,d is the daily revenue of the frequency regulation auxiliary service market; C in Investment and construction costs for shared energy storage; C op Annual operation and maintenance costs for shared energy storage; In the formula, is the wind farm cluster rental power of shared energy storage at the tth moment on the dth day, p r,d is the unit power rental price of shared energy storage on day d, P DNL,d (t) is the power clearing power of the shared energy storage at the tth moment on the dth day, p DNL,d (t) is the clearing price of the electricity market at the tth moment on the dth day, P TP,d (t) is the bid capacity of shared energy storage in the frequency regulation ancillary service market at the tth moment on the dth day, p TP,d (t) is the frequency regulation compensation price of the frequency regulation auxiliary service market at the tth moment on the dth day, C p is the unit power investment and construction cost of shared energy storage, C s is the investment and construction cost per unit capacity of shared energy storage, N y For the full life cycle of shared energy storage, P max is the maximum charging and discharging power of shared energy storage, E SES is the optimal configuration capacity of shared energy storage, g is the discount rate, h is the number of replacements of shared energy storage devices, n is the total number of replacements of shared energy storage devices in their entire life cycle, C pom is the unit power operation and maintenance cost of shared energy storage, C som is the operation and maintenance cost per unit capacity of shared energy storage, W(t) is the charge and discharge volume of shared energy storage over its entire life cycle; The lower model takes the lowest wind farm cluster operation cost as the objective function, wherein the lower model is: f d =min(C p +C r -I s ) In the formula, C p is the daily deviation assessment cost of the wind farm cluster, C r The daily rental cost of leasing shared energy storage for a wind farm cluster, I s is the daily electricity sales revenue of the wind farm cluster, is the actual wind power value of the wind farm cluster at the tth moment, is the day-ahead wind power forecast value of the wind farm cluster at the tth moment, p wind is the unit wind power deviation assessment price, p DNL (t) is the clearing price of the electricity market at the tth moment.

3. The method for optimizing configuration of wind farm cluster shared energy storage according to claim 1, characterized in that: The improved K-means clustering method is used to jointly cluster the multiple uncertainty sets to obtain typical scenarios, including: Extending the multiple uncertainty set into a long time scale set, performing a non-quantum-scale process on the data in the long time scale set to obtain processed data; Randomly select multiple data as cluster centers, calculate the distance difference and trend difference from all processed data to each cluster center, and obtain a comprehensive difference based on the distance difference and trend difference; The processed data whose comprehensive difference is less than the preset value is assigned to the corresponding cluster center to obtain multiple initial typical scenes, and the mean of the multiple uncertainty sets in each of the initial typical scenes is recalculated. The mean is used as the new cluster center, and the comprehensive difference of all processed data to the new cluster center is continued to be calculated and assigned until the cluster center no longer changes, and multiple typical scenes are obtained.

4. The method for optimizing the configuration of wind farm cluster shared energy storage as claimed in claim 3, characterized in that: The calculating of the distance difference and trend difference from all processed data to each cluster center, and obtaining the comprehensive difference according to the distance difference and trend difference, includes: The distance difference from all processed data to each cluster center is calculated using the Euclidean distance, and the trend difference calculation formula is used to calculate the trend difference from all processed data to each cluster center, wherein the distance difference calculation formula is: In the formula, is the distance difference between the processed data on the dth day and the kth cluster center, is the wind power sequence of the kth cluster center of the wind farm cluster with multiple uncertainties, p DNL,k is the clearing price sequence of the kth cluster center in the multi-uncertainty aggregate electricity energy market, p TP,k is the frequency regulation compensation price sequence of the kth cluster center of the frequency regulation ancillary service market with multiple uncertainties; The trend difference calculation formula is: In the formula, is the trend difference from the dth processed data to the kth cluster center; The distance difference and trend difference from each processed data to the cluster center are normalized by using the index to obtain the distance difference value and the trend difference value, wherein the calculation formula of the distance difference value is: In the formula, γ1(d,k) is the distance difference between the dth processed data and the kth cluster center; The calculation formula of the trend difference is: In the formula, γ2(d,k) is the trend difference from the dth processed data to the kth cluster center; The distance difference and the trend difference are fused by using the proportional coefficient method to obtain the comprehensive difference from the processed data to each cluster center, where the expression of the comprehensive difference is: γ(d,k)=αγ1(d,k)+βγ2(d,k) Where γ(d, k) is the comprehensive difference from the processed data to each cluster center, α and β are proportional coefficients, which represent the relative importance of the distance difference and the trend difference, α+β=1.

5. The method for optimizing configuration of wind farm cluster shared energy storage according to claim 1, characterized in that: The multiple uncertainty sets are modeled on a short time scale using the distributed robust optimization method to obtain uncertainty extreme scenarios, including: The set of the multiple uncertainty sets on a short time scale is constructed using robust parameters, wherein the set is: In the formula, is the actual value of wind power of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the day-ahead wind power forecast value of the wind farm cluster at the t-th moment of the wind power sequence on the d-th day, is the maximum value of the wind power fluctuation range of the wind farm cluster at the tth moment of the wind power sequence on the dth day, is the distributed robustness parameter of the wind power sequence of the wind farm cluster on day d, p DNL,d (t) is the real value of the electricity price at the tth moment in the clearing price sequence of the electricity market on the dth day, is the forecast value of the day-ahead wind power price at the tth moment in the clearing price sequence of the electric energy market on the dth day, is the maximum fluctuation range of wind power at the tth moment in the clearing price sequence of the electric energy market on the dth day, χ DNL,d is the distributed robustness parameter of the clearing price sequence of the electric energy market on day d, p TP,d (t) is the actual value of the compensation price of the ancillary service market at the tth moment in the frequency regulation compensation price sequence on the dth day, is the day-ahead forecast value of the compensation price of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, is the maximum compensation price fluctuation range of the frequency regulation compensation price sequence of the ancillary service market at the tth moment on the dth day, χ TP,d is the distribution parameter of the frequency regulation compensation price series of the ancillary service market on day d; The calculation formula of the distributed rod parameter is: In the formula, μ DNL,d and μ TP,d Sequence Sequence Y DNL,d and sequence Y TP,d expectations; and Sequence Sequence Y DNL,d and sequence Y TP,d The standard deviation of α DNL,d and α TP,d are the confidence levels of wind farm cluster output, electricity market price and frequency regulation auxiliary service market compensation price in the distributed optimization process, is the probability distribution function of the standard normal distribution.

6. The method for optimizing configuration of wind farm cluster shared energy storage according to claim 1, characterized in that: The updated shared energy storage two-level planning model is: f u =max(π k [I R,d (oh k )+I DNL,d (oh k )+I TP,d (oh k )]-C in -C op ) In the formula, I R,d (ω k ), I DNL,d (ω k ) and I TP,d (ω k ) are the daily revenue of wind farm cluster capacity leasing, daily revenue of electric energy market and daily revenue of frequency regulation auxiliary service market under the kth typical scenario, respectively. p is the daily deviation assessment cost of the wind farm cluster under the kth typical scenario, C r is the daily rental cost of the wind farm cluster leasing shared energy storage in the kth typical scenario, I s is the daily electricity sales revenue of the wind farm cluster under the kth typical scenario, ψ is the multiple uncertainty set of the wind farm cluster output and the clearing price of the electricity market, The worst scenario for multiple uncertainties.

7. A wind farm cluster shared energy storage optimization configuration system, characterized in that: Including building modules, typical scenario calculation modules and configuration modules, The construction module is used to construct a shared energy storage two-layer planning model, wherein the upper model of the two-layer planning model takes the highest economic benefit of shared energy storage as the objective function and the lower model takes the lowest wind farm cluster operation cost as the objective function; The typical scenario calculation module is used to obtain multiple uncertainty sets based on the wind farm cluster output, the electricity market price and the frequency regulation auxiliary service market compensation price, and jointly cluster the multiple uncertainty sets using the improved K-means clustering method to obtain typical scenarios, and calculate the probability of typical scenarios based on the annual occurrence frequency of the typical scenarios; The configuration module is used to model the multiple uncertainty sets on a short time scale using a distributed robust optimization method to obtain uncertainty extreme scenarios, and to update the shared energy storage two-layer programming model using the typical scenario probabilities and the uncertainty extreme scenarios to obtain an updated shared energy storage two-layer programming model, and to solve the model using a particle swarm algorithm to obtain the configuration result.

8. The wind farm cluster shared energy storage optimization configuration system according to claim 7, characterized in that: The construction of a shared energy storage two-layer planning model includes: The upper model is constructed with the highest economic benefit of shared energy storage as the objective function, wherein the upper model is: In the formula, I R,d is the daily income from leasing the capacity of the wind farm cluster; I DNL,d is the daily income of the electric energy market; TP,d is the daily revenue of the frequency regulation auxiliary service market; C in Investment and construction costs for shared energy storage; C op Annual operation and maintenance costs for shared energy storage; In the formula, is the wind farm cluster rental power of shared energy storage at the tth moment on the dth day, p r,d is the unit power rental price of shared energy storage on day d, P DNL,d (t) is the power clearing power of the shared energy storage at the tth moment on the dth day, p DNL,d (t) is the clearing price of the electricity market at the tth moment on the dth day, P TP,d (t) is the bid capacity of shared energy storage in the frequency regulation ancillary service market at the tth moment on the dth day, p TP,d (t) is the frequency regulation compensation price of the frequency regulation auxiliary service market at the tth moment on the dth day, C p is the unit power investment and construction cost of shared energy storage, C s is the investment and construction cost per unit capacity of shared energy storage, N y For the full life cycle of shared energy storage, P max is the maximum charging and discharging power of shared energy storage, E SES is the optimal configuration capacity of shared energy storage, g is the discount rate, h is the number of replacements of shared energy storage devices, n is the total number of replacements of shared energy storage devices in their entire life cycle, C pom is the unit power operation and maintenance cost of shared energy storage, C som is the operation and maintenance cost per unit capacity of shared energy storage, W(t) is the charge and discharge volume of shared energy storage over its entire life cycle; The lower model takes the lowest wind farm cluster operation cost as the objective function, wherein the lower model is: f d =min(C p +C r -I s ) In the formula, C p is the daily deviation assessment cost of the wind farm cluster, C r The daily rental cost of leasing shared energy storage for a wind farm cluster, I s is the daily electricity sales revenue of the wind farm cluster, is the actual wind power value of the wind farm cluster at the tth moment, is the day-ahead wind power forecast value of the wind farm cluster at the tth moment, p wind is the unit wind power deviation assessment price, p DNL (t) is the clearing price of the electricity market at the tth moment.

9. A computer device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the method for optimizing the configuration of shared energy storage in a wind farm cluster as described in any one of claims 1 to 6 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for optimizing configuration of shared energy storage of a wind farm cluster as claimed in any one of claims 1 to 6 are implemented.

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