Distributed wind storage system frequency modulation capacity calculation method

Abstract

The invention discloses a distributed wind storage system frequency modulation capacity calculation method, which comprises the steps of calculating load flow distribution based on a power distribution network topological structure, a physical and mathematical model of a distributed wind storage system and a predicted output power condition of a wind driven generator, taking reduction of power fluctuation of the single-node wind storage system and construction, operation and maintenance cost of an energy storage device as objective functions, and solving to obtain optimal configuration of the single-node wind storage system; further optimizing the configuration of each single-node wind storage system by taking the optimal configuration of the single-node wind storage system as the basis and considering the reactive power balance and node voltage limitation of the power distribution network to obtain the optimal configuration of each single-node wind storage system; and solving the frequency modulation capacity of the distributed wind storage system based on the output power condition of the wind driven generator and the optimal configuration of each single-node wind storage system. According to the invention, quantitative evaluation of the frequency regulation capability of the distributed wind storage system in the power system can be realized.

Description

Technical Field

[0001] The present invention relates to a method for calculating the frequency regulation capacity of an electric power system, and in particular to a method for calculating the frequency regulation capacity of a distributed wind power storage system. Background Art

[0002] With the increasing penetration of renewable energy such as wind and solar energy in the power system, especially the rapid development of wind power generation, in order to reduce the impact of its intermittent and volatile nature on the stability of the power grid, wind farms are required to be equipped with a certain scale of energy storage, which can release energy when needed to support the power grid, help balance supply and demand, and improve the flexibility and stability of the system.

[0003] However, the adjustable capacity of the distributed wind storage system is closely linked to the complex relationship between its wind energy input, energy storage capacity and system requirements, making its inherent frequency stability mechanism complex and changeable. In actual operation, its frequency regulation capability is affected by various factors such as meteorological conditions, load changes, operation strategies, energy storage technology, etc., and there are inaccuracies in its frequency regulation capability. Evaluating this capability can help predict and avoid potential stability problems and ensure the normal operation of the power grid.

[0004] The frequency regulation capacity of the distributed wind-storage system is an evaluation indicator used to measure the frequency regulation capability of the distributed wind-storage system in the power system. Therefore, it is very necessary to study the calculation method of the frequency regulation capacity of the distributed wind-storage system for the frequency regulation of the distributed wind-storage system. 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-storage system, which can realize a quantitative evaluation of the frequency regulation capability of a distributed wind-storage system in a power system.

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

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

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

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

[0010] Based on the topological structure of the distribution network, the physical and mathematical models of the distributed wind 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 storage system and the construction and operation and maintenance costs of the energy storage device, and the optimal configuration of the single-node wind storage system is solved.

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

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

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

[0014] minαF 1 +βF 2

[0015]

[0016] Among them, F 1 is the power fluctuation of the wind storage system; n is the number of dispatch cycles; P w,i is the output power of the wind turbine in the i-th scheduling period; P bat,i is the charging and discharging power of the energy storage device in the i-th scheduling period, and is the variable to be optimized;

[0017] F 2 is the construction and operation and maintenance cost of the energy storage device; m 1 is the unit capacity cost of the energy storage device, including the cost of its supporting equipment, m 2 E is the unit power cost of the energy storage device participating in the long-term dispatch of the power grid; bat is the capacity of the energy storage device, is the variable to be optimized; η bat is the energy conversion efficiency of the energy storage device; P′ bat The average charging and discharging power of the energy storage device participating in the long-term dispatch of the power grid;

[0018] α and β are F 1 and F 2 The weight factor of .

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

[0020] 30%<SOC<80%;

[0021]

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

[0023] Among them, SOC is the state of charge of the energy storage device;

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

[0025] P L,i is the power at the DC bus in the ith scheduling period; P loss,i is the line loss power in the i-th scheduling period.

[0026] In a possible implementation, solving the frequency regulation capacity of the distributed wind storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind storage system includes: calculating the frequency regulation capacity E of the i-th scheduling period 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 is the output power of the jth wind turbine in the i-th scheduling period; Δh is the duration of the scheduling period; E batn,k is the optimal configuration of the capacity of the kth energy storage device.

[0029] In a second aspect, the present application provides a distributed wind-storage system frequency regulation capacity calculation system, comprising:

[0030] A topology acquisition module is used to acquire the topology of the distribution network in the area where the distributed wind storage system is located, and determine the node to which the distributed wind storage system is connected; the distributed wind storage system includes multiple single-node wind storage systems, each of which 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 wind speed and wind power measured data of the wind farm, establish a wind power prediction model, and predict the output power of all wind turbines, that is, the output power P w ;

[0032] The first optimization module is used to calculate the power flow distribution based on the topological structure of the distribution network, the physical and mathematical models of the distributed wind storage system, and the output power of the wind turbine generator, and to reduce the power fluctuation of the single-node wind storage system and the construction and operation and maintenance costs of the energy storage device as the objective function, and to solve the optimal configuration of the single-node wind storage system;

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

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

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

[0036] minαF 1 +βF 2

[0037]

[0038] Among them, F 1 is the power fluctuation of the wind storage system; n is the number of dispatch cycles; P w,i is the output power of the wind turbine in the i-th scheduling period; P bat,i is the charging and discharging power of the energy storage device in the i-th scheduling period, and is the variable to be optimized;

[0039] F 2 is the construction and operation and maintenance cost of the energy storage device; m 1 is the unit capacity cost of the energy storage device, including the cost of its supporting equipment, m 2 E is the unit power cost of the energy storage device participating in the long-term dispatch of the power grid; bat is the capacity of the energy storage device, is the variable to be optimized; η bat is the energy conversion efficiency of the energy storage device; P′ bat The average charging and discharging power of the energy storage device participating in the long-term dispatch of the power grid;

[0040] α and β are F 1 and F 2 The weight factor of .

[0041] In a 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] Among them, SOC is the state of charge of the energy storage device;

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

[0047] P L,i is the power at the DC bus in the ith scheduling period; P loss,i is the line loss power in the i-th scheduling period.

[0048] In a possible implementation, solving the frequency regulation capacity of the distributed wind storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind storage system includes: calculating the frequency regulation capacity E of the i-th scheduling period 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 is the output power of the jth wind turbine in the i-th scheduling period; Δh is the duration of the scheduling period; E batn,k is the optimal configuration of the capacity of the kth energy storage device.

[0051] In a third aspect, the present 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 call the computer program to execute the method as described above.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed on an electronic device, the electronic device implements the method described above.

[0055] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is run on an electronic device, the electronic device implements the method as described above.

[0056] The specific implementation methods of the third to fifth aspects of the present application can refer to the implementation methods of the first aspect, which will not be repeated here.

[0057] Beneficial effects:

[0058] This application proposes a method for calculating the frequency regulation capacity of a distributed wind storage system, which takes reducing the power fluctuation of a single-node wind storage system and the construction and operation and maintenance costs of the energy storage device as the objective function, and solves to obtain the optimal configuration of the energy storage capacity in the single-node wind storage system, fully considering the impact of the power fluctuation of the wind storage system in the power grid and the construction and operation and maintenance costs of the energy storage device. And further based on 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 the energy storage device capacity in each single-node wind storage system in the distributed wind storage system. This application quantifies the frequency regulation capability from the perspective of the energy storage device capacity of the wind storage system, and constructs an evaluation index for the operation of the power grid, which can realize the quantitative evaluation of the frequency regulation capability of the distributed wind storage system in the power system, can help predict and avoid potential stability problems, and ensure the normal operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of an embodiment of the present application.

[0060] Figure 2 It is a physical model of a single-node wind storage system in an embodiment of the present application.

[0061] Figure 3 It is a schematic diagram of a filtering and smoothing control strategy for an energy storage device according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings.

[0063] This application analyzes the mutual influence mechanism of constraints such as grid strength, unit output characteristics, frequency / voltage safety boundaries, etc., optimizes the energy storage capacity configuration of the wind storage system with the power fluctuation and cost of the wind storage system 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 following is a detailed description of the embodiments of the present application.

[0065] Embodiment 1

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

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

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

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

[0070] S3: Based on the topological structure of the distribution network, the physical and mathematical models of the distributed wind storage system, and the output power of the wind turbine, the power flow distribution is calculated, and the objective function is to reduce the power fluctuation of the single-node wind storage system and the construction and operation and maintenance costs of the energy storage device, and the optimal configuration of the single-node wind storage system is obtained;

[0071] Figure 2 The figure shows a physical model of a single-node wind storage system in one embodiment of the present application, wherein 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 the rotor to rotate and finally outputs alternating current. It includes blades, gearboxes, generators (such as double-fed asynchronous wind turbines, DFIG for short), and converters. The output power P of a wind turbine is w for:

[0075]

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

[0077] (2) Energy storage device:

[0078] The energy storage devices equipped in wind farms usually use lithium batteries, which have the advantages of high energy density, high frequency modulation efficiency, and low operation and maintenance costs. The charge state of the energy storage device is:

[0079]

[0080] Among them, SOC is the state of charge of the energy storage device, S 0 is the initial value of the state of charge of the energy storage device; E bat is the capacity of the energy storage device; P bat is the charging and discharging power of the energy storage device, and t is the time.

[0081] It should be understood that the energy storage device in the wind storage system is responsible for smoothing the output power fluctuation of the wind turbine generator, so that when the output power of the wind turbine generator fluctuates greatly, the DC bus power of the power grid will not fluctuate greatly, and the power grid can operate reliably. Figure 3 As shown. Therefore, the charging and discharging power fluctuation of the energy storage device can be obtained according to the output power fluctuation value of the wind turbine generator, as shown in the following formula:

[0082]

[0083] Among them, P bat_ref is the charging and discharging power fluctuation of the energy storage battery; 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 objective function is to reduce the power fluctuation of the single-node wind storage system and the construction and operation and maintenance costs of the energy storage device, and to solve the optimal configuration of the single-node energy storage device, including:

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

[0086]

[0087] Among them, F 1 is the power fluctuation of the wind storage system; n is the number of dispatch cycles; P w,i is the output power of the wind turbine in the i-th scheduling period; P bat,i is the charge and discharge power of the energy storage device in the i-th scheduling cycle, and is the variable to be optimized. Considering that the output power of the wind turbine generator changes rapidly, in some embodiments, 1 minute can be set as a scheduling cycle.

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

[0089] At present, the construction and operation of the power grid require high costs. If a larger energy storage capacity is configured, the normal operation of the power grid can be met, but there will be higher construction, operation and maintenance costs. If a smaller energy storage capacity is configured, it will not be able to meet the normal operation of the power grid. Therefore, the construction and operation and maintenance costs of the energy storage device are used as the objective function, as shown in the following formula:

[0090]

[0091] Among them, F 2 is the construction and operation and maintenance cost of the energy storage device; m 1 is the unit capacity cost of the energy storage device, including the cost of its supporting equipment, m 2 E is the unit power cost of the energy storage device participating in the long-term dispatch of the power grid; bat is the capacity of the energy storage device, is the variable to be optimized; η bat is the energy conversion efficiency of the energy storage device; P′ bat It is the average charging and discharging power of the energy storage device participating in the long-term dispatch of the power grid.

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

[0093] minαF 1 +βF 2

[0094] Among them, α and β are F 1 and F 2 The weight factor of .

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

[0096] (1) In order 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 relatively large power within a certain period of time to meet the normal operation of the power grid. At the same time, it is also necessary to consider the minimum and maximum power that the energy storage device can withstand, as shown in the following formula:

[0099]

[0100] Among them, P bat,min is the minimum allowable charging and discharging power of the energy storage device, P bat,max is the maximum allowable charging and discharging power of the energy storage device; P max To meet the maximum charging and discharging power required for the normal operation of the power grid within a certain period of time.

[0101] (3) At any time, the power in the power grid needs to be balanced. The power relationship is shown in the following formula:

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

[0103] Among them, P L,i is the power at the DC bus in the ith scheduling period; P loss,i is the line loss power in the i-th scheduling period.

[0104] In some embodiments, the particle swarm algorithm is used to optimize and 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 optimal configuration of the single-node wind-storage system, the configuration of each single-node wind-storage system is further optimized considering the reactive power balance of the distribution network and the node voltage limit to obtain the optimal configuration of each single-node wind-storage system;

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

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

[0108] In some embodiments, based on the output power of wind turbines and the optimal configuration of each single-node wind storage system, the frequency regulation capacity of the distributed wind storage system is solved. 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 is the output power of the jth wind turbine in the i-th scheduling period; Δh is the duration of the scheduling period; E batn,k is the optimal configuration of the capacity of the kth energy storage device.

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

[0112] Embodiment 2

[0113] In a second aspect, the present application provides a distributed wind-storage system frequency regulation capacity calculation system, comprising:

[0114] A topology acquisition module is used to acquire the topology of the distribution network in the area where the distributed wind storage system is located, and determine the node to which the distributed wind storage system is connected; the distributed wind storage system includes multiple single-node wind storage systems, each of which 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 the wind speed and wind power measured data of the wind farm, 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 topological structure of the distribution network, the physical and mathematical models of the distributed wind storage system, and the output power of the wind turbine generator, and to reduce the power fluctuation of the single-node wind storage system and the construction and operation and maintenance costs of the energy storage device as the objective function, and to solve the optimal configuration of the single-node wind storage system;

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

[0118] minαF 1 +βF 2

[0119]

[0120] Among them, F 1 is the power fluctuation of the wind storage system; n is the number of dispatch cycles; P w,i is the output power of the wind turbine in the i-th scheduling period; P bat,i is the charging and discharging power of the energy storage device in the i-th scheduling period, and is the variable to be optimized;

[0121] F 2 is the construction and operation and maintenance cost of the energy storage device; m 1 is the unit capacity cost of the energy storage device, including the cost of its supporting equipment, m 2 E is the unit power cost of the energy storage device participating in the long-term dispatch of the power grid; bat is the capacity of the energy storage device, is the variable to be optimized; η bat is the energy conversion efficiency of the energy storage device; P′ bat The average charging and discharging power of the energy storage device participating in the long-term dispatch of the power grid;

[0122] α and β are F 1 and F 2The weight factor of .

[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] Among them, SOC is the state of charge of the energy storage device;

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

[0129] P L,i is the power at the DC bus in the ith scheduling period; P loss,i is the line loss power in the i-th scheduling period.

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

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

[0132] In some embodiments, the method of solving the frequency regulation capacity of the distributed wind storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind storage system includes: calculating the frequency regulation capacity E of the i-th scheduling period 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 is the output power of the jth wind turbine in the i-th scheduling period; Δh is the duration of the scheduling period; E batn,k is the optimal configuration of the capacity of the kth energy storage device.

[0135] Embodiment 3

[0136] The embodiment of the present 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 call the computer program to execute the method as described above.

[0139] Embodiment 4

[0140] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed on an electronic device, the electronic device implements the method as described above.

[0141] Embodiment 5

[0142] An embodiment of the present application further provides a computer program product, including a computer program. When the computer program is executed on an electronic device, the electronic device implements the method as described above.

[0143] The embodiments of the present application also provide a system, an electronic device, a computer-readable storage medium, and a computer program product. The specific implementation methods can refer to the specific embodiments of the above methods and will not be repeated here.

[0144] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, electronic devices, computer-readable storage media or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0145] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, electronic devices, computer-readable storage media, and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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 memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions 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 the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0149] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these 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 topological structure of the distribution network in the area where the distributed wind storage system is located, and determine the node to which the distributed wind storage system is connected; the distributed wind storage system includes multiple single-node wind storage systems, each single-node wind storage system includes a wind turbine and an energy storage device connected to the same node in the power grid; Collect the wind speed and wind power measured data of the wind farm, and establish a wind power prediction model based on the wind speed and wind power measured data; based on the wind power prediction model, predict the output power of all wind turbines; Based on the topological structure of the distribution network, the physical and mathematical models of the distributed wind 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 storage system and the construction and operation and maintenance costs of the energy storage device, and the optimal configuration of the single-node wind storage system is solved. Based on the optimal configuration of the single-node wind-storage system, the configuration of each single-node wind-storage system is further optimized by considering the reactive power balance of the distribution network and the node voltage limit, and the optimal configuration of each single-node wind-storage system is obtained. Based on the output power of wind turbines and the optimal configuration of each single-node wind-storage system, the frequency regulation capacity of the distributed wind-storage system is solved.

2. The method according to claim 1, characterized in that: The objective function is: minαF1+βF2 Where F1 is the power fluctuation of the wind storage system; n is the number of dispatch cycles; P w,i is the output power of the wind turbine in the i-th scheduling period; P bat,i is the charging and discharging power of the energy storage device in the i-th scheduling period, and is the variable to be optimized; F2 is the construction and operation and maintenance cost of the energy storage device; m1 is the unit capacity cost of the energy storage device, including the cost of its supporting equipment; m2 is the unit power cost of the energy storage device participating in the long-term dispatch of the power grid; E bat is the capacity of the energy storage device, is the variable to be optimized; η bat is the energy conversion efficiency of the energy storage device; P' bat The average charging and discharging power of the energy storage device participating in the long-term dispatch of the power grid; α and β are the weight factors of F1 and F2 respectively.

3. The method according to claim 2, characterized in that The constraints of the objective function include: 30%<SOC<80%; P bat,i +P w,i =P L,i +P loss,i ; Among them, SOC is the state of charge of the energy storage device; P bat,min is the minimum allowable charging and discharging power of the energy storage device, P bat,max is the maximum allowable charging and discharging power of the energy storage device; P max To meet the maximum charging and discharging power required for the normal operation of the power grid within a certain period of time; P L,i is the power at the DC bus in the ith scheduling period; P loss,i is the line loss power in the i-th scheduling period.

4. The method according to claim 3, characterized in that The method of solving the frequency regulation capacity of the distributed wind storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind storage system includes: calculating the frequency regulation capacity E of the i-th scheduling period based on the following formula: i : 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 is the output power of the jth wind turbine in the i-th scheduling period; Δh is the duration of the scheduling period; E batn,k is the optimal configuration of the capacity of the kth energy storage device.

5. A distributed wind storage system frequency modulation capacity calculation system, characterized in that: include: A topology acquisition module is used to acquire the topology of the distribution network in the area where the distributed wind storage system is located, and determine the node to which the distributed wind storage system is connected; the distributed wind storage system includes multiple single-node wind storage systems, each of which 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 wind speed and wind power measured data of the wind farm, establish a wind power prediction model, and predict the output power of all wind turbines, that is, the output power P w ; The first optimization module is used to calculate the power flow distribution based on the topological structure of the distribution network, the physical and mathematical models of the distributed wind storage system, and the output power of the wind turbine generator, and to reduce the power fluctuation of the single-node wind storage system and the construction and operation and maintenance costs of the energy storage device as the objective function, and to solve the optimal configuration of the single-node wind storage system; The second optimization module is used to optimize the configuration of each single-node wind storage system based on the optimized configuration of the single-node wind storage system, taking into account the reactive power balance of the distribution network and the node voltage limit, and to obtain the optimal configuration of each single-node wind storage system in some embodiments; The frequency regulation capacity calculation module is used to solve the frequency regulation capacity of the distributed wind storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind storage system.

6. The system according to claim 5, characterized in that The objective function is: minαF1+βF2 Where F1 is the power fluctuation of the wind storage system; n is the number of dispatch cycles; P w,i is the output power of the wind turbine in the i-th scheduling period; P bat,i is the charging and discharging power of the energy storage device in the i-th scheduling period, and is the variable to be optimized; F2 is the construction and operation and maintenance cost of the energy storage device; m1 is the unit capacity cost of the energy storage device, including the cost of its supporting equipment; m2 is the unit power cost of the energy storage device participating in the long-term dispatch of the power grid; E bat is the capacity of the energy storage device, is the variable to be optimized; η bat is the energy conversion efficiency of the energy storage device; P' bat The average charging and discharging power of the energy storage device participating in the long-term dispatch of the power grid; α and β are the weight factors of F1 and F2 respectively.

7. The system according to claim 6, characterized in that The constraints of the objective function include: 30%<SOC<80%; P bat,i +P w,i =P L,i +P loss,i ; Among them, SOC is the state of charge of the energy storage device; P bat,min is the minimum allowable charging and discharging power of the energy storage device, P bat,max is the maximum allowable charging and discharging power of the energy storage device; P max To meet the maximum charging and discharging power required for the normal operation of the power grid within a certain period of time; P L,i is the power at the DC bus in the ith scheduling period; P loss,i is the line loss power in the i-th scheduling period.

8. The system according to claim 7, characterized in that The method of solving the frequency regulation capacity of the distributed wind storage system based on the output power of the wind turbine and the optimal configuration of each single-node wind storage system includes: calculating the frequency regulation capacity E of the i-th scheduling period based on the following formula: i : 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 is the output power of the jth wind turbine in the i-th scheduling period; Δh is the duration of the scheduling period; E batn,k is the optimal configuration of the capacity of the kth energy storage device.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is configured to call the computer program to execute the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed on an electronic device, the electronic device implements the method according to any one of claims 1 to 4.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed on an electronic device, the electronic device implements the method according to any one of claims 1 to 4.

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