A method for calculating the frequency regulation capacity of a distributed wind storage system

Abstract

This application discloses a method for calculating the frequency regulation capacity of a distributed wind-storage system, comprising: calculating the power flow distribution based on the distribution network topology, the physical and mathematical models of the distributed wind-storage system, and the predicted output power of the wind turbines; using the reduction of power fluctuations in single-node wind-storage systems and the construction and operation costs of energy storage devices as the objective function, and solving for the optimal configuration of the single-node wind-storage system; based on the optimal configuration of the single-node wind-storage system, considering the reactive power balance of the distribution network and node voltage limitations, further optimizing the configuration of each single-node wind-storage system to obtain the optimal configuration of each single-node wind-storage system; and solving for the frequency regulation capacity of the distributed wind-storage system based on the output power of the wind turbines and the optimal configuration of each single-node wind-storage system. This application can realize the quantitative evaluation of the frequency regulation capability of distributed wind-storage systems in power systems.

Description

Technical Field

[0001] This invention relates to a method for calculating the frequency regulation capacity of a power system, and more particularly to a method for calculating the frequency regulation capacity of a distributed wind-storage system. Background Technology

[0002] With the increasing penetration of renewable energy sources such as wind and solar power into the power system, especially the rapid development of wind power generation, wind farms are required to be equipped with a certain scale of energy storage in order to reduce the impact of their intermittency and volatility on grid stability. This storage can release energy to support the grid when needed, helping to balance supply and demand and improve the flexibility and stability of the system.

[0003] However, the adjustable capacity of distributed wind-storage systems is closely linked to the complex relationship between its wind energy input, energy storage capacity, and system demand, making its inherent frequency stabilization mechanism complex and variable. In actual operation, its frequency regulation capability is inaccurate due to various factors such as meteorological conditions, load changes, operating strategies, and energy storage technologies. Assessing this capability can help predict and avoid potential stability problems, ensuring the normal operation of the power grid.

[0004] The frequency regulation capacity of a distributed wind and energy storage system is an evaluation indicator used to measure the frequency regulation capability of such a system within a power system. Therefore, it is essential to study the calculation method for the frequency regulation capacity of distributed wind and energy storage systems for their frequency regulation purposes. Summary of the Invention

[0005] The purpose of this application is to provide a method for calculating the frequency regulation capacity of a distributed wind and energy storage system, which can realize the quantitative evaluation of the frequency regulation capability of the distributed wind and energy storage system in the power system.

[0006] The present invention adopts the following technical solution:

[0007] In a first aspect, this application provides a method for calculating the frequency regulation capacity of a distributed wind-storage system, including:

[0008] Obtain the distribution network topology of the area where the distributed wind and energy storage system is located, and determine the nodes to which the distributed wind and energy storage system is connected; the distributed wind and energy storage system includes multiple single-node wind and energy storage systems, and each single-node wind and energy storage system includes a wind turbine and an energy storage device connected to the same node in the power grid;

[0009] Collect measured wind speed and wind power data from wind farms, and establish a wind power prediction model based on the measured wind speed and wind power data; based on the wind power prediction model, predict the output power of all wind turbines.

[0010] Based on the distribution network topology, the physical and mathematical models of the distributed wind and energy storage system, and the output power of the wind turbine, the power flow distribution is calculated. The objective function is to reduce the power fluctuation of the single-node wind and energy storage system and the construction and operation and maintenance costs of the energy storage device. The optimal configuration of the single-node wind and energy storage system is obtained by solving the problem.

[0011] Based on the optimized configuration of the single-node wind and energy storage system, and considering the reactive power balance of the distribution network and the node voltage limit, the configuration of each single-node wind and energy storage system is further optimized to obtain the optimal configuration of each single-node wind and energy storage system.

[0012] Based on the output power of the wind turbine and the optimal configuration of each single-node wind-storage system, the frequency regulation capacity of the distributed wind-storage system is calculated.

[0013] In one possible implementation, the objective function is:

[0014] minαF1+βF2

[0015]

[0016] Where F1 represents the power fluctuation of the wind-storage system; n is the number of scheduling cycles; P w,i P represents the output power of the wind turbine during the i-th dispatch cycle. bat,i Let be the charging and discharging power of the energy storage device in the i-th scheduling cycle, and be the variable to be optimized.

[0017] F2 represents the construction and operation and maintenance cost of the energy storage device; m1 represents the unit capacity cost of the energy storage device, including the cost of its supporting equipment; m2 represents the unit power cost of the energy storage device participating in long-term grid dispatch; E bat Let be the capacity of the energy storage device, and be the variable to be optimized; η bat P′ represents the energy conversion efficiency of the energy storage device. bat The average charging and discharging power of the energy storage device participating in long-term grid dispatch;

[0018] α and β are the weighting factors for F1 and F2, respectively.

[0019] In one possible implementation, the constraints of the objective function include:

[0020] 30% < SOC < 80%;

[0021]

[0022] P bat,i +P w,i =P L,i +P loss,i ;

[0023] Wherein, SOC represents the state of charge of the energy storage device;

[0024] P bat,min P is the minimum allowable charge and discharge power of the energy storage device. bat,max P represents the maximum permissible charge and discharge power of the energy storage device. max To ensure the maximum charging and discharging power required for the normal operation of the power grid within a certain time period;

[0025] P L,i P represents the power at the DC bus during the i-th scheduling cycle. loss,i Let be the line loss power during the i-th scheduling cycle.

[0026] In one possible implementation, solving for the frequency regulation capacity of the distributed wind and energy storage system based on the output power of the wind turbine and the optimal configuration of each individual node wind and energy storage system includes: calculating the frequency regulation capacity E for the i-th scheduling cycle based on the following formula. i :

[0027]

[0028] Where m is the total number of wind turbines connected to the distribution network; w is the total number of energy storage devices connected to the distribution network; P w,i,j Let E be the output power of the j-th wind turbine during the i-th dispatch cycle; Δh is the duration of the dispatch cycle; E batn,k This represents the optimal capacity configuration for the k-th energy storage device.

[0029] Secondly, this application provides a frequency regulation capacity calculation system for a distributed wind-storage system, comprising:

[0030] The topology acquisition module is used to acquire the distribution network topology of the area where the distributed wind and energy storage system is located, and to determine the nodes to which the distributed wind and energy storage system is connected; the distributed wind and energy storage system includes multiple single-node wind and energy storage systems, and each single-node wind and energy storage system includes a wind turbine and an energy storage device connected to the same node in the power grid;

[0031] The power prediction module is used to collect measured wind speed and wind power data from wind farms, establish a wind power prediction model, and predict the output power of all wind turbines, i.e., the output power P. w ;

[0032] The first optimization module is used to calculate the power flow distribution based on the distribution network topology, the physical and mathematical models of the distributed wind and energy storage system, and the output power of the wind turbine. The objective function is to reduce the power fluctuation of the single-node wind and energy storage system and the construction and operation and maintenance costs of the energy storage device. The optimal configuration of the single-node wind and energy storage system is obtained by solving the problem.

[0033] The second optimization module is used to optimize the configuration of each single-node wind and energy storage system based on the optimized configuration of the single-node wind and energy storage system, taking into account the reactive power balance of the distribution network and the node voltage limit, in some embodiments to obtain the optimal configuration of each single-node wind and energy storage system.

[0034] The frequency regulation capacity calculation module is used to solve the frequency regulation capacity of a distributed wind and energy storage system based on the output power of the wind turbine and the optimal configuration of each individual node wind and energy storage system.

[0035] In one possible implementation, the objective function is:

[0036] minαF1+βF2

[0037]

[0038] Where F1 represents the power fluctuation of the wind-storage system; n is the number of scheduling cycles; P w,i P represents the output power of the wind turbine during the i-th dispatch cycle. bat,i Let be the charging and discharging power of the energy storage device in the i-th scheduling cycle, and be the variable to be optimized.

[0039] F2 represents the construction and operation and maintenance cost of the energy storage device; m1 represents the unit capacity cost of the energy storage device, including the cost of its supporting equipment; m2 represents the unit power cost of the energy storage device participating in long-term grid dispatch; E bat Let be the capacity of the energy storage device, and be the variable to be optimized; η bat P′ represents the energy conversion efficiency of the energy storage device. bat The average charging and discharging power of the energy storage device participating in long-term grid dispatch;

[0040] α and β are the weighting factors for F1 and F2, respectively.

[0041] In one possible implementation, the constraints of the objective function include:

[0042] 30% < SOC < 80%;

[0043]

[0044] P bat,i +P w,i =P L,i +P loss,i ;

[0045] Wherein, SOC represents the state of charge of the energy storage device;

[0046] P bat,min P is the minimum allowable charge and discharge power of the energy storage device. bat,max P represents the maximum permissible charge and discharge power of the energy storage device.max To ensure the maximum charging and discharging power required for the normal operation of the power grid within a certain time period;

[0047] P L,i P represents the power at the DC bus during the i-th scheduling cycle. loss,i Let be the line loss power during the i-th scheduling cycle.

[0048] In one possible implementation, solving for the frequency regulation capacity of the distributed wind and energy storage system based on the output power of the wind turbine and the optimal configuration of each individual node wind and energy storage system includes: calculating the frequency regulation capacity E for the i-th scheduling cycle based on the following formula. i :

[0049]

[0050] Where m is the total number of wind turbines connected to the distribution network; w is the total number of energy storage devices connected to the distribution network; P w,i,j Let E be the output power of the j-th wind turbine during the i-th dispatch cycle; Δh is the duration of the dispatch cycle; E batn,k This represents the optimal capacity configuration for the k-th energy storage device.

[0051] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0052] The memory is used to store computer programs;

[0053] The processor is used to invoke the computer program to execute the method described above.

[0054] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed on an electronic device, causes the electronic device to perform the method described above.

[0055] Fifthly, this application provides a computer program product, including a computer program that, when run on an electronic device, causes the electronic device to perform the method described above.

[0056] The specific implementation methods of the third to fifth aspects of this application can refer to the implementation methods of the first aspect mentioned above, and will not be elaborated here.

[0057] Beneficial effects:

[0058] This application proposes a method for calculating the frequency regulation capacity of a distributed wind-storage system. Taking the reduction of power fluctuations in single-node wind-storage systems and the construction and operation costs of energy storage devices as the objective function, the method solves for the optimal configuration of energy storage capacity in a single-node wind-storage system, fully considering the impact of power fluctuations in the grid and the construction and operation costs of energy storage devices. Furthermore, based on the reactive power balance of the distribution network and node voltage limitations, the configuration of each single-node wind-storage system is further optimized, resulting in the optimal configuration of energy storage device capacity in each single-node wind-storage system within the distributed system. This application quantifies the frequency regulation capability from the perspective of energy storage device capacity in wind-storage systems, constructing an evaluation index for grid operation. This allows for the quantitative evaluation of the frequency regulation capability of distributed wind-storage systems in the power system, helping to predict and avoid potential stability problems and ensuring the normal operation of the power grid. Attached Figure Description

[0059] Figure 1 This is a flowchart of one embodiment of this application.

[0060] Figure 2 This is a physical model of a single-node wind-storage system in one embodiment of this application.

[0061] Figure 3 This is a schematic diagram of the filtering and smoothing control strategy of an energy storage device according to an embodiment of this application. Detailed Implementation

[0062] The specific embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0063] This application analyzes the interaction mechanism of constraints such as grid strength, unit output characteristics, and frequency / voltage safety boundaries. It optimizes the energy storage capacity configuration of the wind-storage system with power fluctuation and cost as the objective function, and studies the frequency regulation capacity calculation method of the distributed wind-storage system based on wind power probability prediction.

[0064] The embodiments of this application are described in detail below.

[0065] Example 1

[0066] like Figure 1 As shown, this invention provides a method for calculating the frequency regulation capacity of a distributed wind-storage system, the method comprising:

[0067] S1: Obtain the distribution network topology of the area where the distributed wind and energy storage system is located, and determine the nodes to which the distributed wind and energy storage system is connected; the distributed wind and energy storage system includes multiple single-node wind and energy storage systems, and each single-node wind and energy storage system includes a wind turbine and an energy storage device connected to the same node in the power grid;

[0068] S2: Collect measured wind speed and wind power data from wind farms, and establish a wind power prediction model based on the measured wind speed and wind power data; based on the wind power prediction model, predict the output power of all wind turbines.

[0069] In some embodiments, measured wind speed and wind power data of wind farms can be collected, and a wind power prediction model can be established using a copula quantile regression model to perform wind power probability prediction and solve for the predicted output power of wind turbines under different confidence probabilities.

[0070] S3: Based on the distribution network topology, the physical and mathematical models of the distributed wind and energy storage system, and the output power of the wind turbine, calculate the power flow distribution. With the objective function of reducing the power fluctuation of the single-node wind and energy storage system and the construction and operation and maintenance costs of the energy storage device, solve for the optimal configuration of the single-node wind and energy storage system.

[0071] Figure 2 The figure shown is a physical model of a single-node wind-storage system in one embodiment of this application. The single-node wind-storage system includes a wind turbine, a battery, a converter, and a transformer, etc.

[0072] In some embodiments, the physical and mathematical models of the distributed wind storage system include:

[0073] (1) Wind turbine:

[0074] A wind turbine is an electrical device that converts wind energy into mechanical work, which drives a rotor to rotate and ultimately outputs alternating current (AC). It includes components such as blades, gearbox, generator (e.g., a double-fed induction generator, DFIG), and converter. The output power P of a wind turbine is... w for:

[0075]

[0076] Where ρ is the air density; R is the wind turbine radius; V ωt C represents wind speed. p λ is the power efficiency coefficient; λ is the tip speed ratio; β is the blade pitch angle.

[0077] (2) Energy storage device:

[0078] Energy storage devices in wind farms typically use lithium batteries, which offer advantages such as high energy density, high frequency regulation efficiency, and low operating and maintenance costs. The state of charge (SBC) of the energy storage device is as follows:

[0079]

[0080] Where SOC is the state of charge of the energy storage device, and S0 is the initial value of the state of charge of the energy storage device; E bat P represents the capacity of the energy storage device. bat t represents the charging and discharging power of the energy storage device, and t represents time.

[0081] It should be understood that the energy storage device in the wind-storage system is responsible for smoothing fluctuations in the output power of the wind turbine, ensuring that when the output power of the wind turbine fluctuates significantly, the DC bus power of the power grid does not fluctuate significantly, and the power grid can operate reliably. The principle of the filtering and smoothing control strategy of the energy storage device is as follows: Figure 3 As shown. Therefore, the charging and discharging power fluctuation of the energy storage device can be obtained from the output power fluctuation value of the wind turbine, as shown in the following formula:

[0082]

[0083] Among them, P bat_ref For the charging and discharging power fluctuations of energy storage batteries; P ref is the output power fluctuation value of the wind turbine; T is the filtering time constant of the low-pass filter; and s is a complex variable.

[0084] In some embodiments, the optimal configuration of a single-node energy storage device is obtained by taking the reduction of power fluctuations in a single-node wind-storage system and the construction and operation and maintenance costs of the energy storage device as the objective function, including:

[0085] (1) Taking the reduction of power fluctuation in a single-node wind-storage system as the objective function

[0086]

[0087] Where F1 represents the power fluctuation of the wind-storage system; n is the number of scheduling cycles; P w,i P represents the output power of the wind turbine during the i-th dispatch cycle. bat,i Let be the charging and discharging power of the energy storage device in the i-th scheduling cycle, and be the variable to be optimized. Considering the rapid changes in the output power of the wind turbine, in some embodiments, a scheduling cycle of 1 minute can be set.

[0088] (2) The objective function is to reduce the construction and operation and maintenance costs of energy storage devices.

[0089] Currently, the construction and operation of power grids require high costs. While configuring larger energy storage capacity could meet the normal operation needs of the power grid, it would incur higher construction, operation, and maintenance costs. Conversely, configuring smaller energy storage capacity would be insufficient to meet the normal operation needs of the power grid. Therefore, taking the construction and operation costs of energy storage devices as the objective function, as shown in the following equation:

[0090]

[0091] Where F2 represents the construction and operation and maintenance cost of the energy storage device; m1 represents the unit capacity cost of the energy storage device, including the cost of its supporting equipment; m2 represents the unit power cost of the energy storage device participating in long-term grid dispatch; E bat Let be the capacity of the energy storage device, and be the variable to be optimized; η bat P′ represents the energy conversion efficiency of the energy storage device. bat This refers to the average charging and discharging power of the energy storage device participating in long-term grid dispatch.

[0092] (3) In summary, based on the two objective functions above, the overall objective function is:

[0093] minαF1+βF2

[0094] Where α and β are the weighting factors for F1 and F2, respectively.

[0095] In some embodiments, the constraints on the objective function include:

[0096] (1) To prevent overcharging and over-discharging of the energy storage device, the remaining capacity of the energy storage device is limited, as shown in the following formula:

[0097] 30% < SOC < 80%

[0098] (2) Considering the uncertainty of wind power generation in the power grid, the energy storage device in the wind-storage system needs to be able to output a relatively large power within a certain period of time to meet the normal operation of the power grid. At the same time, the minimum and maximum power that the energy storage device can withstand also need to be considered, as shown in the following formula:

[0099]

[0100] Among them, P bat,min P is the minimum allowable charge and discharge power of the energy storage device. bat,max P represents the maximum permissible charge and discharge power of the energy storage device. max The maximum charging and discharging power required to meet the normal operation of the power grid within a certain period of time.

[0101] (3) At all times, the power in the power grid needs to be kept in balance. The power relationship is shown in the following equation:

[0102] P bat,i +P w,i =P L,i +P loss,i

[0103] Among them, P L,i P represents the power at the DC bus during the i-th scheduling cycle. loss,i Let be the line loss power during the i-th scheduling cycle.

[0104] In some embodiments, the particle swarm optimization algorithm is used to solve the above objective function to obtain the optimal configuration of the single-node wind-storage system, including P bat,i and E bat Optimized configuration.

[0105] S4: Based on the optimized configuration of the single-node wind and energy storage system, considering the reactive power balance of the distribution network and the node voltage limit, the configuration of each single-node wind and energy storage system is further optimized to obtain the optimal configuration of each single-node wind and energy storage system.

[0106] In some embodiments, the optimal configuration of a single-node wind-storage system includes E bat The optimal configuration, the optimal configuration of the energy storage device capacity, is denoted as E. batn,k .

[0107] S5: Based on the output power of the wind turbine and the optimal configuration of each single-node wind-storage system, solve for the frequency regulation capacity of the distributed wind-storage system.

[0108] In some embodiments, the frequency regulation capacity of a distributed wind-storage system is solved based on the output power of the wind turbine and the optimal configuration of each single-node wind-storage system. Wherein, the frequency regulation capacity E in the i-th scheduling cycle... i The calculation formula is as follows:

[0109]

[0110] Where m is the total number of wind turbines connected to the distribution network; w is the total number of energy storage devices connected to the distribution network; P w,i,j Let E be the output power of the j-th wind turbine during the i-th dispatch cycle; Δh is the duration of the dispatch cycle; E batn,k This represents the optimal capacity configuration for the k-th energy storage device.

[0111] It should be understood that the numbers S1 to S5 above are only used to distinguish and facilitate the expression of different steps, and do not constitute a restriction on the execution order of the steps. The execution order of the steps in this application is not limited by the order described above.

[0112] Example 2

[0113] Secondly, this application provides a frequency regulation capacity calculation system for a distributed wind-storage system, comprising:

[0114] The topology acquisition module is used to acquire the distribution network topology of the area where the distributed wind and energy storage system is located, and to determine the nodes to which the distributed wind and energy storage system is connected; the distributed wind and energy storage system includes multiple single-node wind and energy storage systems, and each single-node wind and energy storage system includes a wind turbine and an energy storage device connected to the same node in the power grid;

[0115] The power prediction module is used to collect measured data on wind speed and wind power in wind farms, establish a wind power prediction model, and predict the output power of all wind turbines.

[0116] The first optimization module is used to calculate the power flow distribution based on the distribution network topology, the physical and mathematical models of the distributed wind and energy storage system, and the output power of the wind turbine. The objective function is to reduce the power fluctuation of the single-node wind and energy storage system and the construction and operation and maintenance costs of the energy storage device. The optimal configuration of the single-node wind and energy storage system is obtained by solving the problem.

[0117] In one possible implementation, the objective function is:

[0118] minαF1+βF2

[0119]

[0120] Where F1 represents the power fluctuation of the wind-storage system; n is the number of scheduling cycles; P w,i P represents the output power of the wind turbine during the i-th dispatch cycle. bat,i Let be the charging and discharging power of the energy storage device in the i-th scheduling cycle, and be the variable to be optimized.

[0121] F2 represents the construction and operation and maintenance cost of the energy storage device; m1 represents the unit capacity cost of the energy storage device, including the cost of its supporting equipment; m2 represents the unit power cost of the energy storage device participating in long-term grid dispatch; E bat Let be the capacity of the energy storage device, and be the variable to be optimized; η bat P′ represents the energy conversion efficiency of the energy storage device. bat The average charging and discharging power of the energy storage device participating in long-term grid dispatch;

[0122] α and β are the weighting factors for F1 and F2, respectively.

[0123] In some embodiments, the constraints of the objective function include:

[0124] 30% < SOC < 80%;

[0125]

[0126] P bat,i +P w,i =P L,i +P loss,i ;

[0127] Wherein, SOC represents the state of charge of the energy storage device;

[0128] P bat,min P is the minimum allowable charge and discharge power of the energy storage device. bat,maxP represents the maximum permissible charge and discharge power of the energy storage device. max To ensure the maximum charging and discharging power required for the normal operation of the power grid within a certain time period;

[0129] P L,i P represents the power at the DC bus during the i-th scheduling cycle. loss,i Let be the line loss power during the i-th scheduling cycle.

[0130] The second optimization module is used to optimize the configuration of each single-node wind and energy storage system based on the optimized configuration of the single-node wind and energy storage system, taking into account the reactive power balance of the distribution network and the node voltage limit, in some embodiments to obtain the optimal configuration of each single-node wind and energy storage system.

[0131] The frequency regulation capacity calculation module is used to solve the frequency regulation capacity of a distributed wind and energy storage system based on the output power of the wind turbine and the optimal configuration of each individual node wind and energy storage system.

[0132] In some embodiments, solving for the frequency regulation capacity of the distributed wind and energy storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind and energy storage system includes: calculating the frequency regulation capacity E for the i-th scheduling cycle based on the following formula. i :

[0133]

[0134] Where m is the total number of wind turbines connected to the distribution network; w is the total number of energy storage devices connected to the distribution network; P w,i,j Let E be the output power of the j-th wind turbine during the i-th dispatch cycle; Δh is the duration of the dispatch cycle; E batn,k This represents the optimal capacity configuration for the k-th energy storage device.

[0135] Example 3

[0136] This application also provides an electronic device, including: a memory and a processor;

[0137] The memory is used to store computer programs;

[0138] The processor is used to invoke the computer program to execute the method described above.

[0139] Example 4

[0140] This application also provides a computer-readable storage medium storing a computer program that, when run on an electronic device, causes the electronic device to perform the method described above.

[0141] Example 5

[0142] This application also provides a computer program product, including a computer program that, when run on an electronic device, causes the electronic device to perform the method described above.

[0143] This application also provides specific implementations of a system, electronic device, computer-readable storage medium, and computer program product. These specific implementations can be referred to in the above-described methods and will not be repeated here.

[0144] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, electronic devices, computer-readable storage media, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0145] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, electronic devices, computer-readable storage media, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0149] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for calculating the frequency regulation capacity of a distributed wind-storage system, characterized in that, include: Obtain the distribution network topology of the area where the distributed wind and energy storage system is located, and determine the nodes to which the distributed wind and energy storage system is connected; the distributed wind and energy storage system includes multiple single-node wind and energy storage systems, and each single-node wind and energy storage system includes a wind turbine and an energy storage device connected to the same node in the power grid; Collect measured wind speed and wind power data from wind farms, and establish a wind power prediction model based on the measured wind speed and wind power data; based on the wind power prediction model, predict the output power of all wind turbines. Based on the power distribution network topology, the physical and mathematical models of the distributed wind-storage system, and the output power of the wind turbines, the power flow distribution is calculated. The optimal configuration of the single-node wind-storage system is obtained by solving for the objective function of reducing power fluctuations in the single-node system and the construction and operation costs of the energy storage devices. The objective function is: ； ； ； ； in, The power fluctuation of the wind-storage system; n is the number of scheduling cycles; Let be the output power of the wind turbine during the i-th scheduling cycle; Let be the charging and discharging power of the energy storage device in the i-th scheduling cycle, and be the variable to be optimized. The construction and operation and maintenance costs of energy storage devices; The unit capacity cost of the energy storage device, including the cost of its supporting equipment. The unit power cost of energy storage devices participating in long-term grid dispatch; Let be the capacity of the energy storage device, and be the variable to be optimized. The energy conversion efficiency of the energy storage device; The average charging and discharging power of the energy storage device participating in long-term grid dispatch; and They are respectively and Weighting factors; Based on the optimized configuration of the single-node wind-storage system, and considering the reactive power balance of the distribution network and the node voltage limit, the configuration of each single-node wind-storage system is further optimized to obtain the optimal configuration of each single-node wind-storage system. Based on the output power of the wind turbine and the optimal configuration of each single-node wind-storage system, the frequency regulation capacity of the distributed wind-storage system is calculated.

2. The method according to claim 1, characterized in that, The constraints of the objective function include: ； ； ； in, The state of charge of the energy storage device; This refers to the minimum permissible charge and discharge power of the energy storage device. This refers to the maximum permissible charge and discharge power of the energy storage device. To ensure the maximum charging and discharging power required for the normal operation of the power grid within a certain time period; The power at the DC bus during the i-th scheduling cycle; Let be the line loss power during the i-th scheduling cycle.

3. The method according to claim 2, characterized in that, The calculation of the frequency regulation capacity of the distributed wind and energy storage system, based on the output power of the wind turbine and the optimal configuration of each single-node wind and energy storage system, includes: calculating the frequency regulation capacity for the i-th scheduling cycle based on the following formula. : ； in, This refers to the total number of wind turbines connected to the power distribution network. This represents the total number of energy storage devices connected to the distribution network. For the first The first wind turbine was in the... Output power within a scheduling cycle; The duration of the scheduling cycle; For the first Optimal configuration of the capacity of each energy storage device.

4. A frequency regulation capacity calculation system for a distributed wind-storage system, characterized in that, include: The topology acquisition module is used to acquire the distribution network topology of the area where the distributed wind and energy storage system is located, and to determine the nodes to which the distributed wind and energy storage system is connected; the distributed wind and energy storage system includes multiple single-node wind and energy storage systems, and each single-node wind and energy storage system includes a wind turbine and an energy storage device connected to the same node in the power grid; The power prediction module is used to collect measured wind speed and wind power data from wind farms, establish a wind power prediction model, and predict the output power of all wind turbines, i.e., the output power. ; The first optimization module is used to calculate the power flow distribution based on the distribution network topology, the physical and mathematical models of the distributed wind-storage system, and the output power of the wind turbines. The objective function is to reduce the power fluctuations of the single-node wind-storage system and the construction and operation costs of the energy storage devices, thus obtaining the optimal configuration of the single-node wind-storage system. The objective function is: ； ； ； ； in, The power fluctuation of the wind-storage system; n is the number of scheduling cycles; Let be the output power of the wind turbine during the i-th scheduling cycle; Let be the charging and discharging power of the energy storage device in the i-th scheduling cycle, and be the variable to be optimized. The construction and operation and maintenance costs of energy storage devices; The unit capacity cost of the energy storage device, including the cost of its supporting equipment. The unit power cost of energy storage devices participating in long-term grid dispatch; Let be the capacity of the energy storage device, and be the variable to be optimized. The energy conversion efficiency of the energy storage device; The average charging and discharging power of the energy storage device participating in long-term grid dispatch; and They are respectively and Weighting factors; The second optimization module is used to optimize the configuration of each single-node wind and energy storage system based on the optimized configuration of the single-node wind and energy storage system, taking into account the reactive power balance of the distribution network and the node voltage limit, in some embodiments to obtain the optimal configuration of each single-node wind and energy storage system. The frequency regulation capacity calculation module is used to solve the frequency regulation capacity of a distributed wind and energy storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind and energy storage system.

5. The system according to claim 4, characterized in that, The constraints of the objective function include: ； ； ； in, The state of charge of the energy storage device; This refers to the minimum permissible charge and discharge power of the energy storage device. This refers to the maximum permissible charge and discharge power of the energy storage device. To ensure the maximum charging and discharging power required for the normal operation of the power grid within a certain time period; The power at the DC bus during the i-th scheduling cycle; Let be the line loss power during the i-th scheduling cycle.

6. The system according to claim 5, characterized in that, The calculation of the frequency regulation capacity of the distributed wind and energy storage system, based on the output power of the wind turbine and the optimal configuration of each single-node wind and energy storage system, includes: calculating the frequency regulation capacity for the i-th scheduling cycle based on the following formula. : ； in, This refers to the total number of wind turbines connected to the power distribution network. This represents the total number of energy storage devices connected to the distribution network. For the first The first wind turbine was in the... Output power within a scheduling cycle; The duration of the scheduling cycle; For the first Optimal configuration of the capacity of each energy storage device.

7. An electronic device, characterized in that, include: memory and processor; The memory is used to store computer programs; The processor is configured to invoke the computer program to perform the method as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed on an electronic device, causes the electronic device to perform the method as described in any one of claims 1 to 3.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is run on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1 to 3.

Images