Energy storage capacity configuration method considering output coincidence rate of wind power plant

Abstract

The invention discloses an energy storage capacity configuration method considering the output coincidence rate of a wind power plant, and the method comprises the following steps: carrying out the output monitoring and data collection of a wind power plant cluster, collecting the generated power data of the wind power plant cluster in different time periods, and building an output database; based on the output database, applying a Pearson's correlation coefficient to calculate the output coincidence rate between different wind power plants, and measuring the similarity degree of the output change of every two wind power plants at the same moment; constructing an energy storage capacity optimization configuration model by taking energy storage cost reduction as a target function according to the overall output fluctuation stabilization demand of the wind power plant cluster and the obtained output coincidence rate; and solving the energy storage capacity optimal configuration model through a particle swarm optimization algorithm to obtain a centralized energy storage optimal configuration scheme sent by the wind power plant cluster. According to the invention, the problems of unreasonable energy storage configuration and over-high cost when the offshore wind power plant cluster is sent out in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy electric power, and in particular relates to a method for configuring energy storage capacity taking into account the output simultaneity rate of a wind farm. Background Art

[0002] As the global demand for clean energy grows, the proportion of new energy such as wind power and photovoltaics in the power system continues to increase. However, new energy power generation has natural volatility and intermittency, which brings many challenges to the stable operation of the power system. In order to smooth out the volatility of new energy power generation, according to national policy requirements, new energy such as wind power and photovoltaics need to be equipped with energy storage devices. The common storage ratio is, for example, 10% of installed capacity and a discharge time of 2 hours.

[0003] In the scenario of large-scale offshore wind power development, multiple offshore wind farms are often bundled together to transmit electricity. Since these wind farms are widely distributed in space, their output characteristics are not consistent. After aggregation, the volatility of their output will be weakened to a certain extent. If the conventional fixed capacity storage allocation method for a single wind farm is used, energy storage is configured for each wind farm separately, and then the energy storage of all wind farms is accumulated, it will undoubtedly cause over-allocation of energy storage resources and significantly increase the cost of engineering construction.

[0004] The Chinese patent with publication number discloses a method for optimizing energy storage configuration of a wind farm, the method comprising: step S1: using parameter historical data and a traditional wind power prediction model to obtain a wind power historical model value; step S2: obtaining a wind power historical error based on the wind power historical model value and the wind power historical measured value; step S3: minimizing the average absolute value of the wind power historical error at different prediction points in the same wind farm to obtain the optimal correction amount for the wind power historical error; step S4: combining the wind power historical error with the optimal correction amount to obtain a wind power corrected historical error; step S5: obtaining the energy storage configuration capacity and energy storage configuration power based on the wind power corrected historical error. The method described in the invention relies on the environmental historical data of the wind farm to predict and correct the energy storage configuration, ignoring the impact of the wind farm's simultaneous output rate and the overall output fluctuation smoothing demand of the wind farm cluster on the energy storage configuration. Summary of the invention

[0005] The purpose of the present invention is to provide a method for configuring energy storage capacity taking into account the simultaneous output rate of wind farms, so as to achieve, through precise calculation, the energy storage effect of centralized configuration equivalent to the energy storage effect of each wind farm individually configured, effectively reduce the cost of engineering construction, and solve the problems of unreasonable energy storage configuration and excessive cost when offshore wind farm clusters are transmitted in the prior art.

[0006] The technical solution of the present invention is as follows:

[0007] In one aspect, the present invention provides a method for configuring energy storage capacity taking into account the simultaneous output rate of a wind farm, comprising the following steps:

[0008] Monitor the output and collect data of wind farm clusters, collect the power generation data of wind farm clusters in different time periods, and establish an output database.

[0009] Based on the output database, the Pearson correlation coefficient is used to calculate the output simultaneity rate between different wind farms to measure the similarity of output changes between two wind farms at the same time.

[0010] According to the demand for smoothing the overall output fluctuation of the wind farm cluster and the obtained output simultaneity rate, a model for optimizing the configuration of energy storage capacity is constructed with the objective function of reducing the energy storage cost.

[0011] The energy storage capacity optimization configuration model is solved by the particle swarm optimization algorithm to obtain the optimal configuration scheme of centralized energy storage delivered by the wind farm cluster.

[0012] Preferably, the output synchronicity rate between different wind farms is calculated using the Pearson correlation coefficient and is expressed as:

[0013]

[0014] In the formula, ρ ij is the output synchronization rate between wind farm i and wind farm j; P i (t) is the power generation of wind farm i at time t; T is the number of monitored moments, t = 1, 2, ..., T; and are the average power generation of wind farm i and wind farm j during the monitoring period; P j (t) is the power generation of wind farm j at time t.

[0015] Preferably, the objective function of the energy storage capacity optimization configuration model is expressed as:

[0016] minF=S+k·P

[0017] Where F is the objective function of the energy storage capacity optimization configuration model; S is the total capacity of the energy storage system; k is the coefficient reflecting the cost ratio between the energy storage system and the converter boost system; and P is the maximum charge and discharge power of the energy storage system.

[0018] The constraints of the energy storage capacity optimization configuration model include: power balance constraint, lower limit constraint of energy storage capacity and lower limit constraint of energy storage power.

[0019] Preferably, the power balance constraint is expressed as:

[0020]

[0021] Where P total (0), P total (t) are the change rates of the total power generation of the wind farm cluster at time 0 and time t respectively; P eSS (0), P ESS (t) are the charging and discharging power of the energy storage system at time 0 and time t respectively; i traverses all wind farms; ε is the allowable power fluctuation rate; X is the total installed capacity of the wind farm cluster.

[0022] At the same time, the energy storage system charging and discharging power P ESS (t) Must meet the following requirements:

[0023] -P≤P ESS (t)≤P

[0024] When P ESS When (t)>0, the energy storage system discharges; when P ESS When (t)<0, the energy storage system is charged.

[0025] Preferably, the lower limit constraint of the energy storage capacity is expressed as:

[0026]

[0027] In the formula, α is the capacity safety factor; is the total fluctuation variance; T is the fluctuation time range that the energy storage needs to cover.

[0028] The total output fluctuation variance of the wind farm is solved as follows:

[0029]

[0030] In the formula, σ i , σ j are the standard deviations of output fluctuations of wind farm i and wind farm j respectively; ρ ij is the output synchronization rate between wind farm i and wind farm j; n is the total number of wind farms.

[0031] Preferably, the lower limit constraint of the energy storage power is expressed as:

[0032]

[0033] In the formula, β is the power safety factor; is the total volatility variance.

[0034] Preferably, the energy storage capacity optimization configuration model is solved by a particle swarm optimization algorithm, and the optimal configuration scheme of the centralized energy storage delivered by the wind farm cluster is obtained as follows:

[0035] Initialize the particle swarm. Each particle in the particle swarm represents a set of possible values ​​of energy storage capacity S and charge / discharge power P. Suppose the position vector of particle k is The velocity vector is By iteratively updating the position and velocity of particles, the particles evolve towards the optimal value of the objective function while satisfying the constraints.

[0036] In each iteration, the particle velocity update formula is expressed as:

[0037]

[0038] Where n is the number of iterations; w is the inertia weight; is the velocity vector c of particle k in the nth iteration 1 and c 2 is the learning factor; r 1 and r 2 is a random number between [0,1]; is the optimal position experienced by particle k; is the position vector of particle k in the nth iteration; is the optimal position experienced by the group.

[0039] The particle position update formula is expressed as:

[0040]

[0041] In the formula, is the position vector of particle k in the n+1th iteration; is the velocity vector of particle k in the n+1th iteration.

[0042] When the set iteration termination condition is reached, the optimal configuration scheme of centralized energy storage delivered by the wind farm cluster is obtained, that is, the energy storage capacity S and the charging and discharging power setting value P required for the offshore wind farm cluster to deliver are determined.

[0043] On the other hand, the present invention provides an energy storage capacity configuration system that takes into account the wind farm output simultaneity rate, including an output database construction module, an output correlation analysis module, an energy storage capacity optimization configuration model construction module, and an energy storage capacity optimization configuration solution module.

[0044] The output database building module is used to monitor the output of wind farm clusters and collect data, collect the power generation data of wind farm clusters in different time periods, and establish an output database.

[0045] The output correlation analysis module is used to calculate the output simultaneity rate between different wind farms based on the output database using the Pearson correlation coefficient, and to measure the similarity of output changes between two wind farms at the same time.

[0046] The energy storage capacity optimization configuration model construction module is used to construct the energy storage capacity optimization configuration model based on the overall output fluctuation smoothing demand of the wind farm cluster, combined with the obtained output simultaneity rate, with the reduction of energy storage cost as the objective function.

[0047] The energy storage capacity optimization configuration solution module is used to solve the energy storage capacity optimization configuration model through the particle swarm optimization algorithm to obtain the optimal configuration plan of the centralized energy storage delivered by the wind farm cluster.

[0048] On the other hand, the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the energy storage capacity configuration method considering the wind farm output simultaneity rate as described in any embodiment of the present invention when executing the computer program.

[0049] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for configuring energy storage capacity taking into account the wind farm output simultaneity rate as described in any embodiment of the present invention is implemented.

[0050] Compared with the prior art, the present invention has the following technical effects:

[0051] The energy storage capacity configuration method considering the wind farm output simultaneity rate proposed in the present invention is solved by wind farm output correlation analysis and particle swarm optimization algorithm, which effectively reduces the cost of the energy storage system and improves the dispatch flexibility of the wind farm cluster and the operating efficiency of the energy storage system. By combining the demand for smoothing wind farm output fluctuations and the energy storage capacity optimization goal, the capacity configuration problem of the energy storage system is solved, and the output stability, economy and energy utilization efficiency of the wind farm cluster are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is an overall flow chart of the energy storage capacity configuration method considering the wind farm output simultaneity rate described in the present invention. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0054] Embodiment 1

[0055] This embodiment provides a method for configuring energy storage capacity taking into account the simultaneous output rate of wind farms. Figure 1 As shown, the following steps are included:

[0056] Conduct long-term output monitoring and data collection for each wind farm in the wind farm cluster, collect the power generation data of the wind farm cluster in different time periods (such as minutes and hours), and establish an output database. Specifically, the wind farm power generation data can be inferred by obtaining the historical wind speed data of the area where each wind farm is located, and data cleaning and data preprocessing can be performed.

[0057] Based on the output database, the Pearson correlation coefficient is used to calculate the output simultaneity rate between different wind farms to measure the similarity of output changes between two wind farms at the same time.

[0058] As a preferred implementation of this embodiment, the output simultaneity rate between different wind farms is calculated using the Pearson correlation coefficient and is expressed as:

[0059]

[0060] In the formula, ρ ij is the output synchronization rate between wind farm i and wind farm j; P i (t) is the power generation of wind farm i at time t; T is the number of monitored moments, t = 1, 2, ..., T; and are the average power generation of wind farm i and wind farm j during the monitoring period, P j (t) is the power generation of wind farm j at time t.

[0061] According to the overall output fluctuation smoothing demand of the wind farm cluster (such as the power fluctuation range that meets the grid access requirements), combined with the obtained output simultaneity rate, the energy storage capacity optimization configuration model is constructed with the objective function of reducing the energy storage cost. In the energy storage capacity optimization configuration model, the total capacity and charging and discharging power of the energy storage system are used as decision variables, and the objective function is to meet the overall output fluctuation smoothing demand of the wind farm cluster and reduce the energy storage cost, while considering the constraints such as the charging and discharging efficiency and life of the energy storage system.

[0062] As a preferred implementation of this embodiment, the total capacity of the energy storage system is S, and the maximum charge and discharge power is P as variables to be solved. The objective function is to minimize the product of S and P (i.e., the comprehensive cost index of the energy storage system) while meeting the fluctuation smoothing demand. The objective function of the energy storage capacity optimization configuration model is expressed as:

[0063] minF=S+k·P

[0064] Where F is the objective function of the energy storage capacity optimization configuration model; S is the total capacity of the energy storage system (unit: MWh); k is the coefficient reflecting the cost ratio between the energy storage system and the power conversion and boosting system; P is the maximum charging and discharging power of the energy storage system (unit: MW).

[0065] The constraints of the energy storage capacity optimization configuration model include: power balance constraint, lower limit constraint of energy storage capacity and lower limit constraint of energy storage power.

[0066] As a preferred implementation of this embodiment, the power balance constraint is that within the fluctuation time range T that the energy storage needs to cover, the change rate P of the total power generation power of the wind farm cluster total (t) and the energy storage system charging and discharging power P ESS The sum of (t) should meet the power fluctuation range required for grid access, expressed as:

[0067]

[0068] Where P total (0), P total (t) are the change rates of the total power generation of the wind farm cluster at time 0 and time t respectively; P ESS (0), P ESS (t) are the charging and discharging power of the energy storage system at time 0 and time t respectively; i traverses all wind farms; ε is the allowable power fluctuation rate, which is determined according to the grid company’s stability requirements for the wind farm cluster to be connected to the grid; X is the total installed capacity of the wind farm cluster.

[0069] At the same time, the energy storage system charging and discharging power P ESS (t) Must meet the following requirements:

[0070] -P≤P ESS (t)≤P

[0071] When P ESS When (t)>0, the energy storage system discharges; when P ESS When (t)<0, the energy storage system is charged.

[0072] As a preferred implementation of this embodiment, the lower limit constraint of the energy storage capacity is expressed as:

[0073]

[0074] In the formula, α is the capacity safety factor; is the total fluctuation variance; T is the fluctuation time range that the energy storage needs to cover (such as the set number of hours).

[0075] The total output fluctuation variance of the wind farm is solved as follows:

[0076]

[0077] In the formula, σ i , σ j are the standard deviations of output fluctuations of wind farm i and wind farm j respectively; ρ ijis the output synchronization rate between wind farm i and wind farm j; n is the total number of wind farms.

[0078] As a preferred implementation of this embodiment, the lower limit constraint of the energy storage power is expressed as:

[0079]

[0080] In the formula, β is the power safety factor; is the total volatility variance.

[0081] The energy storage capacity optimization configuration model is solved by the particle swarm optimization algorithm to obtain the optimal configuration scheme of centralized energy storage delivered by the wind farm cluster.

[0082] As a preferred implementation of this embodiment, the energy storage capacity optimization configuration model is solved by a particle swarm optimization algorithm, and the optimal configuration scheme of centralized energy storage delivered by the wind farm cluster is obtained as follows:

[0083] Initialize the particle swarm. Each particle in the particle swarm represents a set of possible values ​​of energy storage capacity S and charge / discharge power P. Suppose the position vector of particle k is The velocity vector is By iteratively updating the position and velocity of particles, the particles evolve towards the optimal value of the objective function while satisfying the constraints.

[0084] In each iteration, the particle velocity update formula is expressed as:

[0085]

[0086] Where n is the number of iterations; w is the inertia weight; is the velocity vector c of particle k in the nth iteration 1 and c 2 is the learning factor; r 1 and r 2 is a random number between [0,1]; is the optimal position experienced by particle k; is the position vector of particle k in the nth iteration; is the optimal position experienced by the group.

[0087] The particle position update formula is expressed as:

[0088]

[0089] In the formula, is the position vector of particle k in the n+1th iteration; is the velocity vector of particle k in the n+1th iteration.

[0090] When the set iteration termination conditions are reached (such as reaching the maximum number of iterations, etc.), the optimal configuration plan for the centralized energy storage delivered by the wind farm cluster is obtained, that is, the energy storage capacity S and the charging and discharging power setting value P required for the offshore wind farm cluster to be delivered are determined, and the energy storage equipment is selected, installed and debugged based on this to ensure the stable and efficient delivery of electricity by the wind farm cluster.

[0091] Compared with the traditional method of allocating energy storage to each wind farm in a fixed ratio and then accumulating it, the method described in this embodiment can reasonably allocate energy storage according to the actual output characteristics of the wind farm, significantly reduce the cost of energy storage construction, improve the economic benefits and market competitiveness of offshore wind farm clusters, and provide strong technical support for the large-scale development and utilization of new energy electricity.

[0092] In order to verify the effectiveness and superiority of the method provided in this embodiment, some specific cases are provided below:

[0093] An offshore wind farm cluster includes three wind farms, namely wind farm A, wind farm B and wind farm C, with a total installed capacity of X = 1000MW. The monitoring time T = 120 minutes (2 hours), and the power generation data of each wind farm is collected within the 120-minute period.

[0094] The total power generated by wind farm A in 120 minutes is 20000MW·minute, and the average power generation is The total power generation of wind farm B is 18000MW·min, and the average power generation The total power generation of wind farm C is 16000MW·min, and the average power generation

[0095] Calculate the output synchronization rate ρ of wind farm A and wind farm B AB : Calculated according to the Pearson correlation coefficient formula but

[0096] Similarly, the output synchronization rate ρ of wind farm A and wind farm C is calculated Ac =0.8, the output synchronization rate of wind farm B and wind farm C ρ BC =0.85.

[0097] The power grid company requires the power fluctuation rate of the wind farm cluster connected to the grid to be ε = 0.2. The coefficient k reflecting the cost ratio between the energy storage battery system and the converter and boost system is set to be 0.1, and the capacity safety factor α = 1.1, β = 1.2.

[0098] Calculate the standard deviation of output fluctuation of each wind farm. The standard deviation of output fluctuation of wind farm A is σ A =30MW, the standard deviation of wind farm B’s output fluctuation σ B=25MW, the standard deviation of wind farm C’s output fluctuation σ C =20MW.

[0099] Calculate the total output fluctuation variance of the wind farm cluster:

[0100]

[0101] Energy storage capacity lower limit constraint:

[0102] Energy storage power lower limit constraint:

[0103] The objective function is: minF=S+k·P=S+0.1P.

[0104] Power balance constraint: Assume the total power generation of the wind farm cluster is P total (t) At a certain time t is 600MW, the charging and discharging power of the energy storage system P ESS (t) satisfies -P≤P ESS (t)≤P, and

[0105] The particle swarm optimization algorithm is used to solve the problem: 10 particles are initialized, where the initial position vector of particle 1 is (i.e. energy storage capacity S = 60MWh, charging and discharging power P = 45MW), speed vector

[0106] The maximum number of iterations is preset to 50. After 50 iterations, the optimal centralized energy storage configuration that meets the requirements is finally obtained, with energy storage capacity S = 95MWh and charging and discharging power P = 90MW. Based on this result, the energy storage equipment is selected, installed and debugged to ensure that the wind farm cluster can deliver electricity stably and efficiently.

[0107] Embodiment 2

[0108] Accordingly, this embodiment provides an energy storage capacity configuration system that takes into account the wind farm output concurrency rate. The system is used to implement the energy storage capacity configuration method that takes into account the wind farm output concurrency rate as described in any embodiment of the present invention, including an output database construction module, an output correlation analysis module, an energy storage capacity optimization configuration model construction module, and an energy storage capacity optimization configuration solution module.

[0109] The output database building module is used to monitor the output of wind farm clusters and collect data, collect the power generation data of wind farm clusters in different time periods, and establish an output database.

[0110] The output correlation analysis module is used to calculate the output simultaneity rate between different wind farms based on the output database using the Pearson correlation coefficient, and to measure the similarity of output changes between two wind farms at the same time.

[0111] The energy storage capacity optimization configuration model construction module is used to construct the energy storage capacity optimization configuration model based on the overall output fluctuation smoothing demand of the wind farm cluster, combined with the obtained output simultaneity rate, with the reduction of energy storage cost as the objective function.

[0112] The energy storage capacity optimization configuration solution module is used to solve the energy storage capacity optimization configuration model through the particle swarm optimization algorithm to obtain the optimal configuration plan of the centralized energy storage delivered by the wind farm cluster.

[0113] Embodiment 3

[0114] This embodiment provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the energy storage capacity configuration method considering the wind farm output simultaneity rate as described in any embodiment of the present invention when executing the computer program.

[0115] Embodiment 4

[0116] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for configuring energy storage capacity considering the wind farm output simultaneity rate as described in any embodiment of the present invention is implemented.

[0117] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0118] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0120] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0121] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for configuring energy storage capacity considering the simultaneous output rate of wind farms, characterized in that: The following steps are involved: Monitor the output of wind farm clusters and collect data, collect the power generation data of wind farm clusters in different time periods, and establish an output database; Based on the output database, the Pearson correlation coefficient is used to calculate the output simultaneity rate between different wind farms, which measures the similarity of output changes between two wind farms at the same time. According to the overall output fluctuation smoothing demand of the wind farm cluster, combined with the obtained output simultaneity rate, an energy storage capacity optimization configuration model is constructed with the reduction of energy storage cost as the objective function; The energy storage capacity optimization configuration model is solved by the particle swarm optimization algorithm to obtain the optimal configuration scheme of centralized energy storage delivered by the wind farm cluster.

2. The energy storage capacity configuration method considering the wind farm output simultaneity rate according to claim 1 is characterized in that: The Pearson correlation coefficient is used to calculate the output synchronization rate between different wind farms, which is expressed as: In the formula, ρ ij is the output synchronization rate between wind farm i and wind farm j; P i (t) is the power generation of wind farm i at time t; T is the number of monitored moments, t = 1, 2, ..., T; and are the average power generation of wind farm i and wind farm j during the monitoring period; P j (t) is the power generation of wind farm j at time t.

3. The energy storage capacity configuration method considering the wind farm output simultaneity rate according to claim 1 is characterized in that: The objective function of the energy storage capacity optimization configuration model is expressed as: minF=S+k·P In the formula, F is the objective function of the energy storage capacity optimization configuration model; S is the total capacity of the energy storage system; k is the coefficient reflecting the cost ratio between the energy storage system and the variable current boost system; P is the maximum charge and discharge power of the energy storage system; The constraints of the energy storage capacity optimization configuration model include: power balance constraint, lower limit constraint of energy storage capacity and lower limit constraint of energy storage power.

4. The energy storage capacity configuration method considering the wind farm output simultaneity rate according to claim 3 is characterized in that: The power balance constraint is expressed as: Where P total (0), P total (t) are the change rates of the total power generation of the wind farm cluster at time 0 and time t respectively; P ESS (0), P ESS (t) is the charging and discharging power of the energy storage system at time 0 and time t respectively; i traverses all wind farms; ε is to determine the allowable power fluctuation rate; X is the total installed capacity of the wind farm cluster; At the same time, the energy storage system charging and discharging power P ESS (t) Must meet the following requirements: -P≤P ESS (t)≤P When P ESS (t)>0, the energy storage system discharges; when P ESS When (t)<0, the energy storage system is charged.

5. The energy storage capacity configuration method considering the wind farm output simultaneity rate according to claim 3 is characterized in that: The lower limit constraint of energy storage capacity is expressed as: In the formula, α is the capacity safety factor; is the total fluctuation variance; T is the fluctuation time range that the energy storage needs to cover; The total output fluctuation variance of the wind farm is solved as follows: In the formula, σ i , σ j are the standard deviations of output fluctuations of wind farm i and wind farm j respectively; ρ ij is the output synchronization rate between wind farm i and wind farm j; n is the total number of wind farms.

6. The energy storage capacity configuration method considering the wind farm output simultaneity rate according to claim 3 is characterized in that: The lower limit constraint of energy storage power is expressed as: In the formula, β is the power safety factor; is the total volatility variance.

7. The energy storage capacity configuration method considering the wind farm output simultaneity rate according to claim 1 is characterized in that: The particle swarm optimization algorithm is used to solve the energy storage capacity optimization configuration model, and the optimal configuration scheme of centralized energy storage delivered by the wind farm cluster is obtained as follows: Initialize the particle swarm. Each particle in the particle swarm represents a set of possible values ​​of energy storage capacity S and charge / discharge power P. Suppose the position vector of particle k is The velocity vector is By iteratively updating the position and velocity of particles, the particles evolve towards the optimal value of the objective function while satisfying the constraints; In each iteration, the particle velocity update formula is expressed as: Where n is the number of iterations; w is the inertia weight; is the velocity vector of particle k in the nth iteration. c1 and c2 are learning factors; r1 and r2 are random numbers between [0,1]; is the optimal position experienced by particle k; is the position vector of particle k in the nth iteration; The optimal position experienced by the group; The particle position update formula is expressed as: In the formula, is the position vector of particle k in the n+1th iteration; is the velocity vector of particle k in the n+1th iteration; When the set iteration termination condition is reached, the optimal configuration scheme of centralized energy storage delivered by the wind farm cluster is obtained, that is, the energy storage capacity S and the charging and discharging power setting value P required for the offshore wind farm cluster to deliver are determined.

8. An energy storage capacity configuration system considering the wind farm output simultaneity rate, characterized in that: The system is used to implement the energy storage capacity configuration method considering the wind farm output simultaneity rate as described in any one of claims 1 to 7, including an output database construction module, an output correlation analysis module, an energy storage capacity optimization configuration model construction module and an energy storage capacity optimization configuration solution module; The output database construction module is used to monitor the output of wind farm clusters and collect data, collect the power generation data of wind farm clusters in different time periods, and establish an output database; The output correlation analysis module is used to calculate the output simultaneity rate between different wind farms based on the output database using the Pearson correlation coefficient, and to measure the similarity of output changes between two wind farms at the same time; The energy storage capacity optimization configuration model building module is used to build an energy storage capacity optimization configuration model based on the overall output fluctuation smoothing demand of the wind farm cluster and the obtained output simultaneity rate with the objective function of reducing energy storage cost; The energy storage capacity optimization configuration solution module is used to solve the energy storage capacity optimization configuration model through the particle swarm optimization algorithm to obtain the optimal configuration plan of the centralized energy storage delivered by the wind farm cluster.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the energy storage capacity configuration method taking into account the wind farm output simultaneity rate as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for configuring energy storage capacity taking into account the wind farm output simultaneity rate as described in any one of claims 1 to 7 is implemented.

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