Method and device for identifying aggregation flexibility of distributed energy storage resources

Abstract

The embodiment of the application relates to a kind of distributed energy storage resource aggregation flexibility identification method and device, the method comprises: the wind speed of wind farm area is predicted, and wind speed prediction data is obtained;Determine the unit output data of the wind farm based on the wind speed prediction data;Based on the unit output data, the safe operation constraint model of distribution network containing wind power and energy storage is constructed;Based on the safe operation constraint model of distribution network containing wind power and energy storage, the aggregation flexibility of distributed energy storage resource is identified by Benders decomposition method, by this method, the flexibility provided by distributed energy storage resource for power system operation is maximized, to provide reference for aggregated distributed resource to participate in power system optimization scheduling, it helps to improve the security and economy of distribution network operation.

Description

Technical Field

[0001] The embodiments of the present invention relate to the fields of power system and automation technology, and in particular to a method and apparatus for identifying the aggregation flexibility of distributed energy storage resources. Background Technology

[0002] Distributed resources can proactively respond to grid demands, providing reliable clean energy during peak periods of grid congestion and alleviating transmission congestion. This is of great significance for power system power planning and expansion. However, distributed resources are characterized by low access voltage levels, small individual installed capacity, and highly random and time-varying operating parameters, making it difficult for them to directly participate in grid optimization. To improve the controllability of distributed resources, aggregation is an effective means. By aggregating different types of distributed resources, such as distributed generation, energy storage systems, and controllable loads, through advanced control, metering, and communication technologies, it is more conducive to the rational optimization and utilization of resources, while improving power supply reliability.

[0003] Wind power output exhibits significant uncertainty and volatility, as do active and reactive loads in the distribution network. If the feasible region of distributed resources is calculated solely based on point forecasting results, the scheduling results may be unenforceable due to fluctuations in distributed resource output and errors in load forecasting. This could lead to safety issues such as power deficits, voltage exceedances, and power flow exceedances in the distribution network. Summary of the Invention

[0004] In view of this, in order to solve the above-mentioned technical problems or some of the technical problems, the present invention provides a method and apparatus for identifying the aggregation flexibility of distributed energy storage resources.

[0005] In a first aspect, embodiments of the present invention provide a method for identifying the aggregation flexibility of distributed energy storage resources, including:

[0006] Wind speed prediction data is obtained by predicting the wind speed in the area where the wind farm is located.

[0007] The turbine output data of the wind farm is determined based on the wind speed prediction data;

[0008] A power distribution network safety operation constraint model incorporating wind power and energy storage is constructed based on the unit output data.

[0009] Based on the aforementioned power distribution network safety operation constraint model including wind power and energy storage, the aggregation flexibility of distributed energy storage resources is identified using the Benders decomposition method.

[0010] In one possible implementation, the method further includes:

[0011] Obtain multiple historical wind speed data for the area where the wind farm is located;

[0012] Based on the aforementioned historical wind speed data, the future wind speed in the area where the wind farm is located is predicted, resulting in wind speed prediction data.

[0013] In one possible implementation, the method further includes:

[0014] Arrange the multiple historical wind speed data in ascending order to form a wind speed sample dataset;

[0015] The wind speed sample dataset is divided into multiple intervals, and the frequency of observed wind speeds in each interval is counted.

[0016] The scale and shape parameters of the probability density function are determined based on the observed wind speed frequency.

[0017] Based on the probability density function, the future wind speed in the area where the wind farm is located is predicted to obtain wind speed prediction data.

[0018] In one possible implementation, the method further includes:

[0019] Based on the wind speed prediction data, the average wind speed for each interval is determined using a preset time period as the interval.

[0020] Calculate the first average wind speed within each interval that is greater than the corresponding average wind speed, and the second average wind speed within each interval that is less than the corresponding average wind speed;

[0021] Based on the first average wind speed and the second average wind speed, the wind speed variation range within each interval is determined.

[0022] In one possible implementation, the method further includes:

[0023] Based on the wind speed variation range within each interval, the power output variation range of the wind farm units is determined.

[0024] In one possible implementation, the method further includes:

[0025] Obtain the basic operating parameters of the power distribution network;

[0026] Based on the basic operating parameters of the distribution network, the power output data of the generating units, and the pre-set constraints, a safety operation constraint model for the distribution network, including wind power and energy storage, is constructed.

[0027] In one possible implementation, the method further includes:

[0028] Based on the aforementioned power distribution network safety operation constraint model including wind power and energy storage, the optimal range of power limits and corresponding power range for distributed energy storage resource aggregation are determined by the Benders decomposition method.

[0029] The aggregation flexibility of the distributed energy storage resources is identified based on the optimal value range.

[0030] Secondly, embodiments of the present invention provide a device for identifying the aggregation flexibility of distributed energy storage resources, comprising:

[0031] The prediction module is used to predict the wind speed in the area where the wind farm is located, and obtain wind speed prediction data.

[0032] The determination module is used to determine the turbine output data of the wind farm based on the wind speed prediction data;

[0033] The module is used to construct a power distribution network safety operation constraint model that includes wind power and energy storage based on the unit output data.

[0034] The identification module is used to identify the aggregation flexibility of distributed energy storage resources based on the safety operation constraint model of the distribution network including wind power and energy storage using the Benders decomposition method.

[0035] Thirdly, embodiments of the present invention provide a computer device, including: a processor and a memory, wherein the processor is configured to execute a distributed energy storage resource aggregation flexibility identification program stored in the memory, so as to implement the distributed energy storage resource aggregation flexibility identification method described in the first aspect above.

[0036] Fourthly, embodiments of the present invention provide a storage medium, comprising: the storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the aggregation flexibility identification method for distributed energy storage resources described in the first aspect above.

[0037] The distributed energy storage resource aggregation flexibility identification scheme provided in this invention predicts wind speed in the area where the wind farm is located to obtain wind speed prediction data; determines the unit output data of the wind farm based on the wind speed prediction data; constructs a distribution network safety operation constraint model including wind power and energy storage based on the unit output data; and identifies the aggregation flexibility of distributed energy storage resources using the Benders decomposition method based on the distribution network safety operation constraint model including wind power and energy storage. Compared with the prior art, which only calculates the feasible region of distributed resources based on point prediction results, the scheduling results may not be executable due to fluctuations in the output of distributed resources and errors in load prediction, causing safety problems such as power deficit, voltage exceedance, and power flow exceedance in the distribution network. This scheme maximizes the flexibility provided by distributed energy storage resources for power system operation, provides a reference for aggregating distributed resources to participate in power system optimal scheduling, and helps improve the safety and economy of distribution network operation. Attached Figure Description

[0038] Figure 1 A flowchart illustrating a method for identifying the aggregation flexibility of distributed energy storage resources according to an embodiment of the present invention;

[0039] Figure 2 A flowchart illustrating another method for identifying the aggregation flexibility of distributed energy storage resources provided in an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of the structure of a distributed energy storage resource aggregation flexibility identification device provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0044] Figure 1 A flowchart illustrating a method for identifying the aggregation flexibility of distributed energy storage resources provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method specifically includes:

[0045] S11. Predict the wind speed in the area where the wind farm is located and obtain wind speed prediction data.

[0046] In this embodiment of the invention, the wind speed of the wind farm is assumed to follow a two-parameter Weibull distribution, wherein the probability density function of the two-parameter Weibull distribution is shown in Equation 1:

[0047]

[0048] In the formula, α is a scale parameter that characterizes the wind speed; the greater the wind speed, the larger the value of α. β is a shape parameter that affects the shape of the curve. v represents the wind speed.

[0049] Furthermore, the scale parameter and shape parameter of the probability density function are determined by the least squares-maximum likelihood estimation method, and the future wind speed in the area where the wind farm is located is predicted based on the probability density function to obtain wind speed prediction data.

[0050] S12. Determine the unit output data of the wind farm based on the wind speed prediction data.

[0051] Based on the relationship between wind speed and turbine output, the turbine output data of the wind farm can be determined. The relationship between wind speed and turbine output is shown in Formula 2:

[0052]

[0053] In the formula, ρ is the air density, A is the swept area of ​​the wind turbine, and C... p It is the wind energy utilization coefficient of a wind turbine, which is related to the structure of the wind turbine. It represents the proportion of useful wind energy that the wind turbine obtains from the wind. The output of the wind turbine in a wind farm is proportional to the cube of the wind speed.

[0054] S13. Construct a power distribution network safety operation constraint model including wind power and energy storage based on the unit output data.

[0055] To construct a safety operation constraint model for a distribution network that includes wind power and energy storage, the basic operating parameters of the distribution system are first obtained, including: distribution network parameters: distribution network topology and line parameters, transformer conductance and susceptance parameters, transmission capacity limits of distribution lines, and upper and lower limits of node voltage; wind power operating parameters: installed capacity of distributed power sources, minimum active power output, and maximum power factor angle; and prediction parameters: active load prediction and reactive load prediction.

[0056] The constraint model for safe operation of the power distribution network includes the following formulas:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] In the formula, k is the scheduling period, and Δk is the time interval. Formula 3 is the charging equation for distributed energy storage. It is the amount of electricity stored in energy storage n during time period k. α is the charging power of energy storage n during time period k. nIt is the self-discharge coefficient of energy storage n; Formula 4 is the upper and lower limit constraint of energy storage charging power. These are the maximum and minimum charging power of energy storage n, respectively; Formula 5 represents the upper and lower limits of the energy storage n's capacity. This represents the upper limit of the energy storage capacity; Formulas 6 and 7 respectively configure the active and reactive power balance constraints of the grid node storing energy n in time period k. and These are the active and reactive power of the distribution line ending at node n during time period k, respectively. Let n be the wind power output of node n in the interval representation. and These represent the active and reactive loads of node n during time period k. It is the reactive power absorbed by energy storage n during time period k; Equation 8 is the power flow equation of the distribution network, V n (k) is the voltage of node n in time period k, r n and x n These are the resistance and reactance of the distribution line ending at node n, respectively; Formula 9 represents the transmission capacity constraint of the distribution line. and These are the upper and lower limits of active power transmission for the distribution line ending at node n, respectively; Formula 10 represents the upper and lower limits of node voltage constraints. and V n These are the upper and lower voltage limits for node n, respectively.

[0066] S14. Based on the safety operation constraint model of the distribution network including wind power and energy storage, the aggregation flexibility of distributed energy storage resources is identified by the Benders decomposition method.

[0067] The aggregation flexibility of distributed energy storage resources is identified using the Benders decomposition method. To improve the flexibility of distributed resources, an optimization model is constructed to optimize the aggregation flexibility of distributed energy storage resources within the feasible region, that is, to maximize the upper limit of energy storage power and the range of energy capacity. The specific formula is as follows:

[0068] maxη T Formula 11

[0069] In the formula, θ is the upper limit of the power of distributed energy storage resource aggregation. Battery upper and lower limits E B and The column vector formed by these two parameters are the upper and lower limits of the energy after the energy storage resources are aggregated. These are the parameters for optimization. η is a constant vector with the same number of elements as the number of rows of θ. When there is no preference for each variable, all elements of η can be set to 1. When there is a preference, it can be flexibly adjusted, such as η = [1; 10; 10].

[0070] Furthermore, the best and worst optimal value models are constructed and solved separately, and the optimal solution interval of θ is obtained by using the Benders method through the solver CPLEX.

[0071] It is important to note that this model includes interval equality constraints, requiring the optimal objective function to replace the interval objective function, the maximum range inequality to replace the inequality constraints, and the two boundary inequalities to replace the equality constraints. The best optimal value should be found by solving for these constraints. θ Replace the interval objective function with the worst-case objective function, replace inequality constraints with minimum range inequalities, and replace equality constraints with two boundary inequalities to solve for the worst-case optimal value. The range of values ​​for the optimal value is then: Obtaining the optimal power range for distributed energy storage means obtaining the optimal model parameters for distributed energy storage resource aggregation, which can characterize the aggregation flexibility of distributed energy storage.

[0072] The method for identifying the aggregation flexibility of distributed energy storage resources provided in this invention involves predicting wind speed in the area where the wind farm is located to obtain wind speed prediction data; determining the turbine output data of the wind farm based on the wind speed prediction data; constructing a distribution network safety operation constraint model including wind power and energy storage based on the turbine output data; and identifying the aggregation flexibility of distributed energy storage resources using the Benders decomposition method based on the distribution network safety operation constraint model including wind power and energy storage. Compared with the prior art, which only calculates the feasible region of distributed resources based on point prediction results, the method may fail to execute the scheduling results due to fluctuations in the output of distributed resources and errors in load prediction, leading to safety problems such as power deficit, voltage exceedance, and power flow exceedance in the distribution network. This method maximizes the flexibility provided by distributed energy storage resources for power system operation, provides a reference for aggregating distributed resources to participate in power system optimal scheduling, and helps improve the safety and economy of distribution network operation.

[0073] Figure 2 A flowchart illustrating another method for identifying the aggregation flexibility of distributed energy storage resources provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method specifically includes:

[0074] S21. Obtain multiple historical wind speed data for the area where the wind farm is located.

[0075] S22. Arrange the multiple historical wind speed data in ascending order to form a wind speed sample dataset.

[0076] S23. Divide the wind speed sample dataset into multiple intervals and count the frequency of observed wind speeds in each interval.

[0077] S24. Determine the scale parameter and shape parameter of the probability density function based on the observed wind speed frequency.

[0078] S25. Based on the probability density function, predict the future wind speed in the area where the wind farm is located to obtain wind speed prediction data.

[0079] S26. Based on the wind speed prediction data, determine the average wind speed for each interval using a preset time period as the interval.

[0080] S27. Calculate the first average wind speed within each interval that is greater than the corresponding average wind speed, and the second average wind speed within each interval that is less than the corresponding average wind speed.

[0081] S28. Based on the first average wind speed and the second average wind speed, determine the wind speed variation range within each interval.

[0082] The following provides a unified explanation of S21 to S28:

[0083] Obtain multiple historical wind speed data points for the wind farm's location, and replace instantaneous wind speeds with the average wind speed over a 1-hour period to construct a sample dataset {v1, v2, v3, ..., v n Arrange the samples in ascending order of their numerical values; divide the sample set into m intervals. The frequency of observed wind speeds within each interval is recorded and denoted as f1, f2, ..., f n Then the cumulative frequencies are F1 = f1, F2 = F1 + f2, ..., F n =F n-1 +f n The variables xi and yi in the regression equation are calculated using the following formula:

[0084] x i =lnv i Formula 13

[0085] y i =ln[-ln(1-F)] i )] Formula 14

[0086] Furthermore, the initial values ​​for the scale and shape parameters are obtained through iteration, as shown in the following formula:

[0087]

[0088]

[0089] Furthermore, a simplified likelihood equation is constructed, and the Newton-Raphson method is used to iterate until the error tolerance is satisfied, thus solving for the scale parameter and shape parameter, as shown in the following formula:

[0090]

[0091]

[0092] Furthermore, using historical wind speed data and meteorological data, the minute-by-minute wind speed for future periods is predicted, and the average wind speed v is calculated over a 1-hour interval. i ; Calculate the wind speed greater than the average wind speed v in each interval. i The average wind speed is denoted as . Less than the average wind speed v i The average wind speed is denoted as . v i The wind speed variation range for each interval is represented as follows:

[0093] S29. Based on the wind speed variation range within each interval, determine the unit output variation range of the wind farm.

[0094] S210. Obtain the basic operating parameters of the distribution network.

[0095] S211. Based on the basic operating parameters of the distribution network, the power output data of the generating units, and the pre-set constraints, construct a distribution network safety operation constraint model including wind power and energy storage.

[0096] S212. Based on the safety operation constraint model of the distribution network including wind power and energy storage, the optimal range of power upper limit of distributed energy storage resource aggregation and the corresponding power range are determined by the Benders decomposition method.

[0097] S213. Identify the aggregation flexibility of the distributed energy storage resources based on the optimal value range.

[0098] The following provides a unified explanation of S29 to S213:

[0099] Based on the relationship between wind speed and turbine output, the turbine output data of the wind farm can be determined. The relationship between wind speed and turbine output is shown in Formula 2:

[0100]

[0101] In the formula, ρ is the air density, A is the swept area of ​​the wind turbine, and C... p It is the wind energy utilization coefficient of a wind turbine, which is related to the structure of the wind turbine. It represents the proportion of useful wind energy that the wind turbine obtains from the wind. The output of the wind turbine in a wind farm is proportional to the cube of the wind speed.

[0102] To construct a safety operation constraint model for a distribution network that includes wind power and energy storage, the basic operating parameters of the distribution system are first obtained, including: distribution network parameters: distribution network topology and line parameters, transformer conductance and susceptance parameters, transmission capacity limits of distribution lines, and upper and lower limits of node voltage; wind power operating parameters: installed capacity of distributed power sources, minimum active power output, and maximum power factor angle; and prediction parameters: active load prediction and reactive load prediction.

[0103] The constraint model for safe operation of the power distribution network includes the following formulas:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] In the formula, k is the scheduling period, and Δk is the time interval. Formula 3 is the charging equation for distributed energy storage. It is the amount of electricity stored in energy storage n during time period k. α is the charging power of energy storage n during time period k. n It is the self-discharge coefficient of energy storage n; Formula 4 is the upper and lower limit constraint of energy storage charging power. These are the maximum and minimum charging power of energy storage n, respectively; Formula 5 represents the upper and lower limits of the energy storage n's capacity. This represents the upper limit of the energy storage capacity; Formulas 6 and 7 respectively configure the active and reactive power balance constraints of the grid node storing energy n in time period k. and These are the active and reactive power of the distribution line ending at node n during time period k, respectively. Let n be the wind power output of node n in the interval representation. and These represent the active and reactive loads of node n during time period k. It is the reactive power absorbed by energy storage n during time period k; Equation 8 is the power flow equation of the distribution network, V n (k) is the voltage of node n in time period k, rn and x n These are the resistance and reactance of the distribution line ending at node n, respectively; Formula 9 represents the transmission capacity constraint of the distribution line. and These are the upper and lower limits of active power transmission for the distribution line ending at node n, respectively; Formula 10 represents the upper and lower limits of node voltage constraints. and V n These are the upper and lower voltage limits for node n, respectively.

[0113] The aggregation flexibility of distributed energy storage resources is identified using the Benders decomposition method. To improve the flexibility of distributed resources, an optimization model is constructed to optimize the aggregation flexibility of distributed energy storage resources within the feasible region, that is, to maximize the upper limit of energy storage power and the range of energy capacity. The specific formula is as follows:

[0114] maxη T Formula 11

[0115] In the formula, θ is the upper limit of the power of distributed energy storage resource aggregation. Battery upper and lower limits E B and The column vector formed by these two parameters are the upper and lower limits of the energy after the energy storage resources are aggregated. These are the parameters for optimization. η is a constant vector with the same number of elements as the number of rows of θ. When there is no preference for each variable, all elements of η can be set to 1. When there is a preference, it can be flexibly adjusted, such as η = [1; 10; 10].

[0116] Furthermore, the best and worst optimal value models are constructed and solved separately, and the optimal solution interval of θ is obtained by using the Benders method through the solver CPLEX.

[0117] It is important to note that this model includes interval equality constraints, requiring the optimal objective function to replace the interval objective function, the maximum range inequality to replace the inequality constraints, and the two boundary inequalities to replace the equality constraints. The best optimal value should be found by solving for these constraints. θ Replace the interval objective function with the worst-case objective function, replace inequality constraints with minimum range inequalities, and replace equality constraints with two boundary inequalities to solve for the worst-case optimal value. The range of values ​​for the optimal value is then: Obtaining the optimal power range for distributed energy storage means obtaining the optimal model parameters for distributed energy storage resource aggregation, which can characterize the aggregation flexibility of distributed energy storage.

[0118] The method for identifying the aggregation flexibility of distributed energy storage resources provided in this invention involves predicting wind speed in the area where the wind farm is located to obtain wind speed prediction data; determining the turbine output data of the wind farm based on the wind speed prediction data; constructing a distribution network safety operation constraint model including wind power and energy storage based on the turbine output data; and identifying the aggregation flexibility of distributed energy storage resources using the Benders decomposition method based on the distribution network safety operation constraint model including wind power and energy storage. This method maximizes the flexibility provided by distributed energy storage resources for power system operation, provides a reference for aggregating distributed resources to participate in power system optimal scheduling, and helps improve the safety and economy of distribution network operation.

[0119] Figure 3 This is a schematic diagram of the structure of a distributed energy storage resource aggregation flexibility identification device provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the device includes:

[0120] The prediction module 301 is used to predict the wind speed in the area where the wind farm is located, and obtain wind speed prediction data. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0121] The determining module 302 is used to determine the turbine output data of the wind farm based on the wind speed prediction data. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0122] Module 303 is used to construct a distribution network safety operation constraint model including wind power and energy storage based on the unit output data. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0123] The identification module 304 is used to identify the aggregation flexibility of distributed energy storage resources based on the safety operation constraint model of the distribution network including wind power and energy storage using the Benders decomposition method. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0124] The distributed energy storage resource aggregation flexibility identification device provided in this embodiment of the invention is used to execute the distributed energy storage resource aggregation flexibility identification method provided in the above embodiment. Its implementation method and principle are the same. For details, please refer to the relevant description of the above method embodiment, which will not be repeated here.

[0125] Figure 4 A computer device according to an embodiment of the present invention is shown, such as Figure 4 As shown, the computer device may include a processor 901 and a memory 902, wherein the processor 901 and the memory 902 may be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0126] Processor 901 can be a Central Processing Unit (CPU). Processor 901 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0127] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods provided in the embodiments of the present invention. The processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 902, thereby implementing the methods in the above-described method embodiments.

[0128] The memory 902 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 901, etc. Furthermore, the memory 902 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 902 may optionally include memory remotely located relative to the processor 901, and these remote memories may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] One or more modules are stored in memory 902, and when executed by processor 901, they perform the methods described in the above method embodiments.

[0130] The specific details of the aforementioned computer equipment can be understood by referring to the relevant descriptions and effects in the above method embodiments, and will not be repeated here.

[0131] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0132] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for identifying the aggregation flexibility of distributed energy storage resources, characterized in that, include: Wind speed prediction data is obtained by predicting the wind speed in the area where the wind farm is located. The turbine output data of the wind farm is determined based on the wind speed prediction data; Based on the unit output data, a power distribution network safety operation constraint model including wind power and energy storage is constructed; Based on the safety operation constraint model of the distribution network including wind power and energy storage, the aggregation flexibility of distributed energy storage resources is identified by the Benders decomposition method, including: based on the safety operation constraint model of the distribution network including wind power and energy storage, constructing and solving the best optimal value model and the worst optimal value model respectively, and determining the optimal range of power upper limit of distributed energy storage resource aggregation and the corresponding power range by the Benders decomposition method; and identifying the aggregation flexibility of the distributed energy storage resources based on the optimal range of values.

2. The method according to claim 1, characterized in that, The prediction of wind speed in the area where the wind farm is located, to obtain wind speed prediction data, includes: Obtain multiple historical wind speed data for the area where the wind farm is located; Based on the aforementioned historical wind speed data, the future wind speed in the area where the wind farm is located is predicted, resulting in wind speed prediction data.

3. The method according to claim 2, characterized in that, The prediction of future wind speed in the area where the wind farm is located based on the multiple historical wind speed data, to obtain wind speed prediction data, includes: Arrange the multiple historical wind speed data in ascending order to form a wind speed sample dataset; The wind speed sample dataset is divided into multiple intervals, and the frequency of observed wind speeds in each interval is counted. The scale and shape parameters of the probability density function are determined based on the observed wind speed frequency. Based on the probability density function, the future wind speed in the area where the wind farm is located is predicted to obtain wind speed prediction data.

4. The method according to claim 3, characterized in that, The method further includes: Based on the wind speed prediction data, the average wind speed for each interval is determined using a preset time period as the interval. Calculate the first average wind speed within each interval that is greater than the corresponding average wind speed, and the second average wind speed within each interval that is less than the corresponding average wind speed; Based on the first average wind speed and the second average wind speed, the wind speed variation range within each interval is determined.

5. The method according to claim 4, characterized in that, The process of determining the turbine output data of the wind farm based on the wind speed prediction data includes: Based on the wind speed variation range within each interval, the power output variation range of the wind farm units is determined.

6. The method according to claim 5, characterized in that, The construction of a distribution network safety operation constraint model based on the unit output data, including wind power and energy storage, includes: Obtain the basic operating parameters of the power distribution network; Based on the basic operating parameters of the distribution network, the power output data of the generating units, and the pre-set constraints, a safety operation constraint model for the distribution network, including wind power and energy storage, is constructed.

7. A device for identifying the aggregation flexibility of distributed energy storage resources, characterized in that, include: The prediction module is used to predict the wind speed in the area where the wind farm is located, and obtain wind speed prediction data. The determination module is used to determine the turbine output data of the wind farm based on the wind speed prediction data; The module is used to construct a power distribution network safety operation constraint model that includes wind power and energy storage based on the unit output data. The identification module is used to identify the aggregation flexibility of distributed energy storage resources based on the safety operation constraint model of the distribution network including wind power and energy storage using the Benders decomposition method. The identification module includes: based on the distribution network safety operation constraint model containing wind power and energy storage, constructing and solving the best optimal value model and the worst optimal value model respectively; determining the optimal range of power upper limit of distributed energy storage resource aggregation and the corresponding power range through the Benders decomposition method; and identifying the aggregation flexibility of the distributed energy storage resources based on the optimal range.

8. A computer device, characterized in that, include: A processor and a memory, the processor being configured to execute a distributed energy storage resource aggregation flexibility identification program stored in the memory, to implement the distributed energy storage resource aggregation flexibility identification method according to any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the aggregation flexibility identification method for distributed energy storage resources as described in any one of claims 1 to 6.

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