Distributed cooperative control methods, apparatus, computer equipment and media for wind and solar energy storage

Abstract

The present application relates to the field of new energy technology and provides a distributed cooperative control method, device, computer device, and medium for wind and solar energy storage, wherein the distributed cooperative control method for wind and solar energy storage includes the steps of obtaining a topology structure between a plurality of wind and solar energy storage nodes in a wind and solar energy storage station, classifying each wind and solar energy storage node according to the topology structure to obtain at least one wind and solar energy storage node set, determining first energy storage data for each wind and solar energy storage node in each wind and solar energy storage node set, where the first energy storage data maximizes a sum of complementary coefficients between each wind and solar energy storage node in the wind and solar energy storage node set, inputting each first energy storage data into a pre-constructed optimization configuration model to determine optimized power for each wind and solar energy storage node, and controlling each wind and solar energy storage node based on each optimized power. The present application optimizes the allocation of distributed energy output among wind and solar energy storage stations, smooths out fluctuations in distributed energy output power, and maximizes the profits of wind and solar energy storage stations.

Description

[Technical Field]

[0001] This application relates to the field of new energy technologies, and more particularly to distributed cooperative control methods, apparatus, computer equipment and media for wind and solar energy storage. [Background technology]

[0002] The development and utilization of renewable energy is a crucial technology for addressing the current dual energy and environmental crises, driving the energy revolution, and achieving sustainable energy development. However, directly connecting large-scale, decentralized renewable energy sources to the power grid presents new challenges to the stability of the power system. On the one hand, the variability and randomness of the output power of intermittent new energy sources such as wind and solar energy means that simple grid connection can cause power shocks to the power system. On the other hand, the connection of decentralized energy alters the unidirectional flow patterns of energy storage systems, making it difficult for conventional energy storage systems to adapt to new control needs. Therefore, how to rationalize and optimize the allocation of decentralized energy output to maximize its benefits is of particular importance. [Overview of the Initiative] [Problems that the invention aims to solve]

[0003] To optimize the distribution of distributed energy output in wind and solar energy storage stations, smooth out fluctuations in distributed energy output power, and maximize the benefits of wind and solar energy storage stations, this application provides a distributed coordinated control method, computer equipment, and medium for wind and solar energy storage. [Means for solving the problem]

[0004] In a first aspect, this application provides a distributed coordinated control method for wind and solar energy storage, the method being: The steps include obtaining the topology structure between multiple wind and solar energy storage nodes in a wind and solar energy storage station, The steps include: dividing each wind and solar energy storage node according to the topological structure to obtain at least one set of wind and solar energy storage nodes; A step of determining first energy storage data for each wind and solar energy storage node in each wind and solar energy storage node set, wherein the sum of the interpolation coefficients between each wind and solar energy storage node in each wind and solar energy storage node set is maximized by the first energy storage data, The steps include inputting each first energy storage data into a pre-built optimized configuration model to determine the optimized power for each wind and solar energy storage node, The process includes the step of controlling each wind and solar energy storage node based on its optimized power output.

[0005] By the above method, each wind and solar energy storage node is divided according to the topological structure between each wind and solar energy storage node in the wind and solar energy storage station, and multiple sets of wind and solar energy storage nodes are obtained. Based on the complementation coefficient between the wind and solar energy storage nodes, energy storage data between each wind and solar energy storage node in each set of wind and solar energy storage nodes is determined. Next, the optimized power of each wind and solar energy storage node is determined according to each energy storage data and the optimization configuration model, thereby controlling each wind and solar energy storage node. By complementing the power between adjacent nodes of each wind and solar energy storage node, fluctuations in the output power of distributed energy are smoothed, the distribution of distributed energy output of the wind and solar energy storage station is optimized, and the profits of the wind and solar energy storage station are maximized.

[0006] In one selectable embodiment, the step of partitioning each wind and solar energy storage node according to a topological structure to obtain at least one set of wind and solar energy storage nodes is: The steps include determining the adjacency matrix of each wind and solar energy storage node according to the topological structure, The process includes the steps of partitioning each wind and solar energy storage node according to each adjacency matrix to obtain at least one set of wind and solar energy storage nodes.

[0007] In the above embodiment, the adjacency matrix of each wind and solar energy storage node is determined according to the topological structure of each wind and solar energy storage node, and the adjacent nodes of each wind and solar energy storage node are configured into a wind and solar energy storage node set according to the adjacency matrix, and in the wind and solar energy storage node set, fluctuations in the output power of distributed energy are smoothed by the power complementation between each wind and solar energy storage node, thereby improving the stability of the wind and solar energy storage station.

[0008] In one selectable embodiment, the step of determining first energy storage data for each wind and solar energy storage node in each set of wind and solar energy storage nodes is to maximize the sum of the interpolation coefficients between each wind and solar energy storage node in each set of wind and solar energy storage nodes using the first energy storage data. The steps include determining first energy storage data for each wind and solar energy storage node in each set of wind and solar energy storage nodes according to a pre-constructed auxiliary model, wherein the first energy storage data maximizes the sum of the complementary coefficients between each wind and solar energy storage node in each set of wind and solar energy storage nodes, and the auxiliary model is used to represent the complementary relationships between each wind and solar energy storage node in each set of wind and solar energy storage nodes.

[0009] In one selectable embodiment, according to a pre-constructed auxiliary model, the step of determining the first energy storage data of each wind and solar energy storage node in the wind and solar energy storage node set includes determining a plurality of sets of operating states of the wind and solar energy storage node set, each set of operating states including the operating states of each wind and solar energy storage node, according to each set of operating states, determining a set of energy storage data corresponding to each set of operating states, each set of energy storage data including the second energy storage data of each wind and solar energy storage node, calculating the sum of the complementary coefficients between each wind and solar energy storage node corresponding to each set of energy storage data according to each set of energy storage data and the auxiliary model, selecting the set of energy storage data with the maximum sum of complementary coefficients as the final set of energy storage data, and using each second energy storage data in the final set of energy storage data as the first energy storage data of each wind and solar energy storage node.

[0010] In one selectable embodiment, the energy storage data includes a minimum output, and the auxiliary model calculates the complementary coefficient according to the following formula

Equation

[0011] In one selectable embodiment, the optimization configuration model is represented by the following formula

Equation

[0012] According to the above embodiment, using the optimization configuration model, the optimized power of each wind power and solar energy storage node in the wind power and solar energy storage node set is determined. Based on the optimized power, each wind power and solar energy storage node is controlled to optimize the distribution of distributed energy output in the wind power and solar energy storage station, and maximize the profit of the wind power and solar energy storage station.

[0013] In one selectable embodiment, the optimization configuration model includes at least one of power balance constraints, unit operation constraints, and wind power and photovoltaic abandonment constraints.

[0014] According to the above embodiment, the power balance constraints, unit operation constraints, and wind power and photovoltaic abandonment constraints are incorporated into the constraint conditions, improving the practicality of the optimization configuration model, meeting the control needs of multi-task and multi-node, and determining that the power system operates safely, stably and reliably.

[0015] In the second aspect, this application further provides a distributed cooperative control device for wind power and solar energy storage, and this device includes an acquisition module for acquiring the topological structure among multiple wind power and solar energy storage nodes in the wind power and solar energy storage station, and A partitioning module for partitioning each wind and solar energy storage node according to the topological structure and obtaining at least one set of wind and solar energy storage nodes, A first determination module is used to determine the first energy storage data for each wind and solar energy storage node in each wind and solar energy storage node set, and the first energy storage data maximizes the sum of the interpolation coefficients between each wind and solar energy storage node in each wind and solar energy storage node set. Each first energy storage data is input into a pre-built optimized configuration model, and a second decision module is used to determine the optimized power for each wind and solar energy storage node. It includes a control module for controlling each wind and solar energy storage node based on its optimized power output.

[0016] The above-described device divides each wind and solar energy storage node according to the topological structure between each wind and solar energy storage node in the wind and solar energy storage station, obtains multiple sets of wind and solar energy storage nodes, determines the energy storage data between each wind and solar energy storage node in each set of wind and solar energy storage nodes based on the complementation coefficient between the wind and solar energy storage nodes, then determines the optimized power of each wind and solar energy storage node according to the energy storage data and the optimization configuration model, thereby controlling each wind and solar energy storage node, smoothing fluctuations in the output power of distributed energy by complementing the power between adjacent nodes of each wind and solar energy storage node, optimizing the distribution of distributed energy output in the wind and solar energy storage station, and maximizing the benefits of the wind and solar energy storage station.

[0017] In a third aspect, the application further provides a computer device comprising a memory and a processor, wherein the memory and the processor are connected to each other in a manner that allows for communication, the memory stores computer instructions, and the processor executes computer instructions to perform steps of the distributed cooperative control method for wind and solar energy storage of the first aspect or any embodiment of the first aspect.

[0018] In a fourth aspect, the application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed on a processor, steps of a distributed cooperative control method for wind and solar energy storage of the first aspect or any embodiment of the first aspect are realized. [Brief explanation of the drawing]

[0019] To more clearly describe specific embodiments of this application or technical solutions of the prior art, the following briefly introduces the drawings that may be used to describe specific embodiments or the prior art. Clearly, the drawings in the following description are some embodiments of this application, and those skilled in the art can obtain other drawings based on these without any creative effort.

[0020] [Figure 1] This is a flowchart of a distributed coordinated control method for wind and solar energy storage according to one exemplary embodiment. [Figure 2] This is a schematic diagram of a distributed cooperative control system for wind and solar energy storage according to one exemplary embodiment. [Figure 3] This is a schematic diagram of the hardware structure of a computer device according to one exemplary embodiment. [Modes for carrying out the invention]

[0021] The technical solutions of this application will be clearly and completely described below with reference to the drawings, and it is clear that the embodiments described are only a part of the embodiments of this application, not all of them. All other embodiments that a person skilled in the art could obtain without creative effort based on the embodiments of this application are within the scope of protection of this application.

[0022] Furthermore, the technical features of the different embodiments of this application described below can be combined with each other, insofar as they do not contradict each other.

[0023] To optimize the distribution of distributed energy output in wind and solar energy storage stations, smooth out fluctuations in distributed energy output power, and maximize the benefits of wind and solar energy storage stations, this application provides a distributed coordinated control method, computer equipment, and medium for wind and solar energy storage.

[0024] Figure 1 is a flowchart of a distributed coordinated control method for wind and solar energy storage according to one exemplary embodiment. As shown in Figure 1, the distributed coordinated control method for wind and solar energy storage includes steps S101 to S105.

[0025] In step S101, the topology structure between multiple wind and solar energy storage nodes of the wind and solar energy storage station is obtained.

[0026] In one selectable embodiment, a wind and solar energy storage station includes a wind power plant, a solar power plant, and an energy storage system. A wind power plant refers to a place that generates electricity using wind energy and typically consists of wind turbines, transmission lines, and a control system. The wind turbines convert wind energy into electrical energy, the transmission lines transport the electrical energy to the power grid, and the control system monitors and controls the wind turbines to ensure their normal operation. A solar power plant refers to a place that generates electricity using solar energy and typically consists of solar panels, inverters, and transmission lines. The solar panels convert solar energy into direct current, the inverters convert the direct current into alternating current, and the transmission lines transport the electrical energy to the power grid. An energy storage system is an important component of a wind and solar energy storage station, capable of storing energy when generation is in excess and releasing energy when generation is insufficient. This allows the station to balance generation with needs and ensure the stability of the power supply. Energy storage systems typically utilize battery energy storage systems that consist of battery packs, battery management systems, and charging / discharging equipment.

[0027] In one possible embodiment, the topology structure between wind and solar energy storage nodes refers to the connection relationships and operating methods between each piece of equipment in a wind and solar energy storage station. In a wind and solar energy storage station, equipment such as wind turbines, solar panels, and energy storage systems need to be connected and interact with each other to achieve energy conversion, storage, and distribution. Different structural forms can be used for the topology structure between wind and solar energy storage nodes, such as tree, star, and mesh. Here, the tree topology structure is the most commonly used structural form and has advantages such as a simple structure and ease of maintenance and expansion. In a tree topology structure, individual nodes are connected according to a certain hierarchy, forming a hierarchical network structure.

[0028] In step S102, each wind and solar energy storage node is divided according to the topological structure to obtain at least one set of wind and solar energy storage nodes.

[0029] In one selectable embodiment, adjacent wind and solar energy storage nodes are acquired according to a topological structure, and these adjacent wind and solar energy storage nodes are configured into a single set of wind and solar energy storage nodes.

[0030] In one selectable embodiment, the topology structure, power characteristics of each wind and solar energy storage node, and operating method can be combined to compartmentalize each wind and solar energy storage node and obtain multiple sets of wind and solar energy storage nodes.

[0031] In step S103, the first energy storage data for each wind and solar energy storage node in each wind and solar energy storage node set is determined, and the sum of the interpolation coefficients between each wind and solar energy storage node in each wind and solar energy storage node set is maximized by the first energy storage data.

[0032] In one selectable embodiment, the complementarity coefficient between wind and solar energy storage nodes represents the complementarity between wind and solar energy storage nodes. The complementarity between wind and solar energy storage nodes primarily reflects the complementarity between wind energy and solar energy, wind energy and stored energy, and solar energy and stored energy.

[0033] In one possible embodiment, wind energy and solar energy are complementary in terms of time and season. For example, in summer, daytime sunlight may be sufficient, resulting in high power generation from a solar power system and relatively low wind energy. However, in winter, strong winds at night may result in high power generation from a wind power system and no power generation from a solar power system. Therefore, by combining wind energy and solar energy, a stable power output can be achieved throughout the year.

[0034] In one possible embodiment, the complementarity of wind energy and stored energy refers to the combination of wind energy and stored energy achieving complementarity. Wind energy is intermittent and unpredictable; when the wind is strong, wind power systems generate a large amount of electricity, but this may exceed the consumption capacity of the power grid, and when the wind is weak, wind power systems generate insufficient electricity, which can lead to power shortages. Energy storage systems can balance the relationship between the needs and supply of the power grid by storing surplus electrical energy when the wind is strong and releasing it when the wind is weak.

[0035] In one possible embodiment, solar energy, like wind energy, is intermittent and unpredictable. When there is sufficient sunlight, solar power systems generate a large amount of electricity, but this may exceed the consumption capacity of the power grid. When there is insufficient sunlight, solar power systems generate insufficient electricity, which can lead to power shortages. Energy storage systems can balance the relationship between the needs and supply of the power grid by storing surplus electrical energy when there is sufficient sunlight and releasing it when there is insufficient sunlight.

[0036] In one selectable embodiment, a larger complementarity coefficient between wind and solar energy storage nodes leads to stronger complementarity between the wind and solar energy storage nodes, resulting in better energy optimization and stable supply. This demonstrates that a larger sum of the complementarity coefficients between wind and solar energy storage nodes in a set of wind and solar energy storage nodes leads to stronger complementarity between each wind and solar energy storage node in that set.

[0037] In one selectable embodiment, the first energy storage data for wind and solar energy storage nodes includes, but is not limited to, work data, power data, etc., where work data includes the maximum output, minimum output, etc., of the wind and solar energy storage nodes; and power data includes the generated power, etc.

[0038] In step S104, each first energy storage data is input into a pre-built optimized configuration model to determine the optimized power for each wind and solar energy storage node.

[0039] In one selectable embodiment, the goal of the optimized configuration model is to optimize the power output of each wind and solar energy storage node to ensure the stability and reliability of the power system while meeting the needs of the power system. The optimization process must consider factors such as the operating characteristics, constraints, and target functions of each wind and solar energy storage node.

[0040] In step S105, each wind and solar energy storage node is controlled based on its optimized power output.

[0041] In one selectable embodiment, the operating state and power output of each wind and solar energy storage node can be adjusted by means such as a control system and a monitoring system, based on its optimized power. For example, in the case of a wind turbine, its output power can be controlled by adjusting parameters such as the rotational speed and pitch angle of the wind turbine; in the case of a solar power panel, its output power can be controlled by adjusting parameters such as the operating voltage and current; and in the case of an energy storage system, its energy storage and release can be controlled by adjusting parameters such as the charge / discharge current and voltage.

[0042] By the above method, each wind and solar energy storage node is divided according to the topological structure between each wind and solar energy storage node in the wind and solar energy storage station, and multiple sets of wind and solar energy storage nodes are obtained. Based on the interpolation coefficients between the wind and solar energy storage nodes, the energy storage data between each wind and solar energy storage node in each set of wind and solar energy storage nodes is determined. Next, the optimized power of the wind and solar energy storage nodes is determined according to each first energy storage data and the optimized configuration model, thereby controlling each wind and solar energy storage node. Before calculations are performed using the optimized configuration model, all first energy storage data for each wind and solar energy storage node is converged to a high-density operating state. By interpolating the power between adjacent nodes of each wind and solar energy storage node, fluctuations in the output power of distributed energy are smoothed, the distribution of distributed energy output in the wind and solar energy storage station is optimized, and the benefits of the wind and solar energy storage station are maximized.

[0043] In one example, in step S102 above, each wind and solar energy storage node is divided according to the following method to obtain at least one set of wind and solar energy storage nodes, First, the adjacency matrix of each wind and solar energy storage node is determined according to the topological structure.

[0044] In one selectable embodiment, the set of adjacent nodes of each wind and solar energy storage node is

Number

Number

Number

[0045] Next, according to each adjacency matrix, each wind and solar energy storage node is classified to obtain at least one set of wind and solar energy storage nodes.

[0046] In the embodiment of the present application, based on the adjacency matrix, each wind and solar energy storage node is divided into groups of two. That is, one set of wind and solar energy storage nodes includes two wind and solar energy storage nodes.

[0047] In the embodiments of this application, the adjacency matrix of each wind and solar energy storage node is determined according to the topological structure of each wind and solar energy storage node, and the adjacent nodes of each wind and solar energy storage node are configured into a wind and solar energy storage node set according to the adjacency matrix, and the stability of the wind and solar energy storage station is enhanced by smoothing out fluctuations in the output power of distributed energy through power complementation between each wind and solar energy storage node in the wind and solar energy storage node set.

[0048] In one example, in step S103 above, first energy storage data for each wind and solar energy storage node in each wind and solar energy storage node set is determined according to a pre-constructed auxiliary model, and the sum of the complementary coefficients between each wind and solar energy storage node in the wind and solar energy storage node set is maximized by the first energy storage data, and the auxiliary model is used to represent the complementary relationship between each wind and solar energy storage node in the wind and solar energy storage node set.

[0049] In one selectable embodiment, the following steps determine the first energy storage data for each wind and solar energy storage node in the set of wind and solar energy storage nodes.

[0050] In step a1, multiple sets of operating states for the wind and solar energy storage node set are determined, and each set of operating states includes the operating state of each wind and solar energy storage node.

[0051] In one selectable embodiment, the operating status of the wind and solar energy storage nodes includes data such as the rotational speed, wind direction, and power factor of the wind turbine units, data such as the temperature, voltage, and current of the photovoltaic modules of the solar power plant, and data such as the charge-discharge efficiency, charge-discharge rate, and capacity of the energy storage system.

[0052] In step a2, according to each set of operating states, the energy storage dataset corresponding to each set of operating states is determined, and each energy storage dataset includes the second energy storage data for each wind and solar energy storage node.

[0053] In one selectable embodiment, the operating status of wind and solar energy storage nodes corresponds to secondary energy storage data. The operating status of each wind and solar energy storage node is associated with energy storage data, and the corresponding secondary energy storage data is obtained based on the operating status of the wind and solar energy storage nodes. Exemplaryly, the corresponding secondary energy storage data is obtained by a field acquisition method depending on the operating status.

[0054] In one selectable embodiment, the second energy storage data includes maximum power, minimum power, etc.

[0055] In step a3, the sum of the interpolation coefficients between each wind and solar energy storage node corresponding to each energy storage dataset is calculated according to each energy storage dataset and auxiliary model.

[0056] In step a4, the energy storage dataset with the maximum sum of interpolation coefficients is selected as the final energy storage dataset, and each second energy storage data in the final energy storage dataset is set as the first energy storage data for each wind and solar energy storage node.

[0057] In one selectable embodiment, in step a3 above, the energy storage data includes the minimum output, and the auxiliary model calculates the interpolation coefficients by the following equation:

number

[0058] In one selectable embodiment, if the number of wind and solar energy storage nodes in a wind and solar energy storage node set is 2, the corresponding energy storage dataset when the complementarity coefficient between the two wind and solar energy storage nodes is maximized is defined as the final energy storage dataset, where the complementarity relationship between the two wind and solar energy storage nodes reaches its best state, and at this time the power data (power generated) of the two wind and solar energy storage nodes are the same.

[0059] In one example, in step S104 above, the optimized configuration model is expressed by the following equation:

number

[0060] In embodiments of this application, an optimized configuration model is used to determine the optimized power of each wind and solar energy storage node in each set of wind and solar energy storage nodes, and based on the optimized power, each wind and solar energy storage node is controlled to optimize the distribution of distributed energy output in the wind and solar energy storage station and maximize the benefits of the wind and solar energy storage station.

[0061] In one example, in step S104 above, the optimized configuration model includes at least one of the following: power balance constraints, unit operation constraints, and wind and solar power abandonment constraints. In the embodiments of this application, the power balance constraints, unit operation constraints, and wind and solar power abandonment constraints are incorporated into the constraints, improving the practicality of the optimized configuration model, addressing multitasking and multinode control needs, and determining safe, stable, and reliable operation of the power system.

[0062] In one selectable embodiment, the power balance constraint is expressed as follows:

number

[0063] In one selectable embodiment, the unit operation constraints are expressed as follows:

number

[0064] In one selectable embodiment, the constraints on abandoning wind and solar power are expressed as follows:

number

[0065] In one selectable embodiment, if the operating state of any wind and solar energy storage node converges to a Nash equilibrium point, then, correspondingly, the neighboring nodes adjacent to this wind and solar energy storage node also converge to a Nash equilibrium solution. At this point, the optimized constitutive model also reaches a Nash equilibrium solution.

[0066] Based on the same inventive concept, embodiments of this application further provide a distributed cooperative control device for wind and solar energy storage, which comprises the following modules, as shown in Figure 2.

[0067] The acquisition module 201 is used to acquire the topological structure between multiple wind and solar energy storage nodes in a wind and solar energy storage station. For details, please refer to the description of step S101 in the above embodiment, and the description will not be repeated here.

[0068] The partition module 202 is used to partition each wind and solar energy storage node according to the topological structure to obtain at least one set of wind and solar energy storage nodes. For details, please refer to the description of step S102 in the above embodiment, which will not be repeated here.

[0069] The first determination module 203 is used to determine the first energy storage data for each wind and solar energy storage node in each wind and solar energy storage node set. The first energy storage data maximizes the sum of the interpolation coefficients between each wind and solar energy storage node in the wind and solar energy storage node set. For details, please refer to the description of step S103 in the above embodiment, which will not be repeated here.

[0070] The second decision module 204 inputs each first energy storage data into a pre-built optimized configuration model and is used to determine the optimized power for each wind and solar energy storage node. For details, please refer to the description of step S104 in the above embodiment, which will not be repeated here.

[0071] The control module 205 is used to control each wind and solar energy storage node based on its optimized power. For details, please refer to the description of step S105 in the above embodiment, which will not be repeated here.

[0072] For example, section module 202 includes the following submodules:

[0073] The first determination submodule is used to determine the adjacency matrix of each wind and solar energy storage node according to the topological structure. For details, please refer to the description of the above embodiment, and the description will not be repeated here.

[0074] The partition submodules are used to partition each wind and solar energy storage node according to each adjacent matrix, in order to obtain at least one set of wind and solar energy storage nodes. For detailed information, please refer to the description of the embodiment above, which will not be repeated here.

[0075] In one example, the first decision module 203 includes the following submodules:

[0076] The determination submodule is used to determine the first energy storage data for each wind and solar energy storage node in each wind and solar energy storage node set, according to a pre-built auxiliary model. The first energy storage data maximizes the sum of the complementary coefficients between each wind and solar energy storage node in each wind and solar energy storage node set, and the auxiliary model is used to represent the complementary relationships between each wind and solar energy storage node in each wind and solar energy storage node set. For detailed information, please refer to the description of the above embodiment, which will not be repeated here.

[0077] In one example, the decision submodule includes the following units:

[0078] The first decision unit is used to determine multiple operating state sets for the wind and solar energy storage node set, each operating state set containing the operating state of each wind and solar energy storage node. For details, please refer to the description of the embodiment above, which will not be repeated here.

[0079] The second decision unit is used to determine the energy storage dataset corresponding to each operating state set, according to each operating state set. Each energy storage dataset includes the second energy storage data for each wind and solar energy storage node. For details, please refer to the description of the embodiment above, which will not be repeated here.

[0080] The calculation unit is used to calculate the sum of the interpolation coefficients between each wind and solar energy storage node corresponding to each energy storage dataset, according to each energy storage dataset and auxiliary model. For details, please refer to the description of the above example, which will not be repeated here.

[0081] The selection unit selects the energy storage dataset with the largest sum of interpolation coefficients as the final energy storage dataset, and uses each second energy storage data in the final energy storage dataset as the first energy storage data for each wind and solar energy storage node. For detailed information, please refer to the description of the embodiment above, and a repeated explanation will be omitted here.

[0082] In one example, in the computing unit, the energy storage data includes the minimum output, and the auxiliary model calculates the interpolation coefficients using the following formula:

number

[0083] In one example, in the second decision module 204, the optimized configuration model is expressed by the following equation:

number

[0084] In one example, in the second decision module 204, the optimized configuration model includes at least one of the following constraints: power balance constraints, unit operation constraints, and wind and solar power abandonment constraints. For detailed information, please refer to the description of the above embodiment, which will not be repeated here.

[0085] The specific limitations and beneficial effects of the above-described apparatus can be found by referring to the above-described limitations for distributed coordinated control methods for wind and solar energy storage, and therefore will not be repeated here. Each of the above-described modules can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-described modules may be incorporated into a processor in a computer device in hardware form, or may be independent of a processor in a computer device in hardware form, or may be stored in memory in a computer device in software form so that the processor can easily call each of the above-described modules and have them perform operations corresponding to each of the above-described modules.

[0086] Figure 3 is a schematic diagram of the hardware structure of a computer device according to one exemplary embodiment. As shown in Figure 3, the device comprises one or more processors 310 and memory 320, the memory 320 including persistent memory, volatile memory and hard disk, and in Figure 3, one processor 310 is used as an example. The device may further include an input device 330 and an output device 340.

[0087] The processor 310, memory 320, input device 330, and output device 340 may be connected via a bus or other means, and Figure 3 shows a connection via a bus as an example.

[0088] The processor 310 may be a central processing unit (CPU). The processor 310 may 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 gates or transistor logic devices, discrete hardware components, or a combination of the above chips. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor.

[0089] Memory 320 includes persistent memory, volatile memory, and hard disk as non-temporary computer-readable storage media and may be used to store non-temporary software programs, non-temporary computer executable programs, and modules, such as program instructions / modules corresponding to the distributed cooperative control methods for wind and solar energy storage in the embodiments of this application. The processor 310 executes various functional applications and data processing of the server by executing the non-temporary software programs, instructions, and modules stored in memory 1120, thereby realizing any of the above-described distributed cooperative control methods for wind and solar energy storage.

[0090] The memory 320 may include a program storage area and a data storage area, where the program storage area can store the operating system, application programs required for at least one function, etc., and the data storage area can store data used as needed, etc. The memory 320 may also include high-speed random access memory and may further include at least one non-temporary memory such as a magnetic disk storage device, a flash memory device, or other non-temporary solid-state memory device. In some embodiments, the memory 320 may optionally include memory located remotely from the processor 310, and these remote memories may be connected to the data processing device via a network. Embodiments of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0091] The input device 330 can receive input numerical or character information and generate signal inputs related to user settings and function control. The output device 340 may include a display device such as a display screen.

[0092] One or more modules are stored in memory 320 and, when executed by one or more processors 310, perform the method shown in Figure 1.

[0093] The above-described product can perform the method provided by the embodiments of this application and has corresponding functional modules and beneficial effects for performing that method. For technical details not described in detail in these embodiments, refer to the relevant descriptions of the embodiments shown in Figure 1.

[0094] Embodiments of this application further provide a non-temporary computer storage medium for storing computer executable instructions capable of performing the method in any embodiment of the above-described method. The storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), random access memory (RAM), flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), and the storage medium may further include combinations of the above types of memory.

[0095] In this specification, relational terms such as “First” and “Second” are used solely to distinguish one entity or operation from another entity and operation, and do not necessarily require or suggest that any actual relationship or order exists between these entities or operations. Furthermore, since the terms “compose,” “include,” or any other variations are intended to cover non-exclusive inclusion, a process, method, article, or apparatus that includes a set of elements includes not only those elements but also other elements not explicitly indicated, or elements specific to such a process, method, article, or apparatus. Unless otherwise specified, an element limited by the phrase “includes one…” does not preclude the presence of another identical element in a process, method, article, or apparatus that includes the element.

[0096] The above are merely specific embodiments of the present application, intended to enable those skilled in the art to understand or implement it. Various modifications of these embodiments will be obvious to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Accordingly, the present application is not limited to these embodiments shown herein, but rather conforms to the broadest scope that matches the principles and novel features disclosed herein.

Claims

1. A distributed coordinated control method for wind and solar energy storage, The process involves a processor obtaining the topology structure between multiple wind and solar energy storage nodes in a wind and solar energy storage station, The processor divides each of the wind and solar energy storage nodes according to the topology structure and obtains at least one set of wind and solar energy storage nodes. The processor determines first energy storage data for each wind and solar energy storage node in each set of wind and solar energy storage nodes, the first energy storage data being used to maximize the sum of the interpolation coefficients between each wind and solar energy storage node in the set of wind and solar energy storage nodes. The processor inputs each of the first energy storage data into a pre-built optimized configuration model and determines the optimized power of each of the wind and solar energy storage nodes. The steps include controlling each of the wind and solar energy storage nodes via a control system and a monitoring system using the processor based on each of the optimized powers, The interpolation coefficient is calculated using the following formula: [Math 1] A distributed cooperative control method for wind and solar energy storage, characterized in that β is a interpolation coefficient, Xg i,t represents the minimum output of the i-th wind and solar energy storage node at time t, Pl represents work power, Pv represents load power, S0 is the topological structure density of the wind and solar energy storage nodes, and αj is the rated capacity of adjacent nodes.

2. The step of using the processor to divide each of the wind and solar energy storage nodes according to the topology structure and obtain at least one set of wind and solar energy storage nodes is: The processor performs the steps of determining the adjacency matrix of each wind and solar energy storage node according to the topology structure, The method according to claim 1, characterized in that the processor divides each of the wind and solar energy storage nodes according to each of the adjacent matrices to obtain at least one set of wind and solar energy storage nodes.

3. A step of determining first energy storage data for each wind and solar energy storage node in each set of wind and solar energy storage nodes using the processor, wherein the sum of the interpolation coefficients between each wind and solar energy storage node in the set of wind and solar energy storage nodes is maximized by the first energy storage data, The method according to claim 1, comprising the step of determining first energy storage data for each wind and solar energy storage node in each set of wind and solar energy storage nodes using the processor, according to a pre-built auxiliary model, wherein the first energy storage data maximizes the sum of the complementation coefficients between each wind and solar energy storage node in the set of wind and solar energy storage nodes, and the auxiliary model is used to represent the complementary relationship between each wind and solar energy storage node in the set of wind and solar energy storage nodes.

4. The step of the processor determining first energy storage data for each of the wind and solar energy storage nodes in the set of wind and solar energy storage nodes, according to a pre-built auxiliary model, The steps include determining a plurality of operating state sets for the wind and solar energy storage node set using the processor, wherein each operating state set includes the operating state of each wind and solar energy storage node, The steps include: determining an energy storage dataset corresponding to each set of operating states according to each set of operating states using the processor, wherein each energy storage dataset includes second energy storage data for each of the wind and solar energy storage nodes; The processor performs the steps of calculating the sum of interpolation coefficients between each wind and solar energy storage node corresponding to each energy storage dataset, according to each energy storage dataset and the auxiliary model. The method according to claim 3, characterized in that the processor selects the energy storage dataset with the maximum sum of the interpolation coefficients as the final energy storage dataset, and sets each second energy storage data in the final energy storage dataset as the first energy storage data for each of the wind and solar energy storage nodes.

5. The aforementioned optimized configuration model is expressed by the following equation: [Math 2] However, ｖｏｒ Here, is the optimized power, Q is the peak power output of the wind and solar energy storage nodes among the adjacent nodes, Pn represents the discharge power of the i-th node at time t, and Xm ｉ，ｔ Xg represents the maximum output of the i-th wind and solar energy storage node at time t, and Xg ｉ，ｔ ηw represents the minimum output of the i-th wind and solar energy storage node at time t, and ηw ｉ，ｔ The method according to claim 1, characterized in that represents the discarded solar power at time t of the i-th wind and solar energy storage node.

6. The method according to claim 1, characterized in that the optimized configuration model includes at least one of the following: power balance constraints, unit operation constraints, and wind and solar power generation abandonment constraints.

7. A distributed coordinated control system for wind and solar energy storage, An acquisition module for obtaining the topology structure between multiple wind and solar energy storage nodes in a wind and solar energy storage station, A partitioning module for partitioning each of the wind and solar energy storage nodes according to the topological structure and obtaining at least one set of wind and solar energy storage nodes, A first determination module used to determine first energy storage data for each wind and solar energy storage node in each set of wind and solar energy storage nodes, wherein the sum of the interpolation coefficients between each wind and solar energy storage node in the set of wind and solar energy storage nodes is maximized by the first energy storage data, A second decision module inputs each of the first energy storage data into a pre-built optimized configuration model and determines the optimized power of each of the wind and solar energy storage nodes, The system includes a control module for controlling each of the wind and solar energy storage nodes based on the optimized power, The interpolation coefficient is calculated using the following formula: [Math 3] A distributed cooperative control device for wind and solar energy storage, characterized in that β is a interpolation coefficient, Xg i,t represents the minimum output of the i-th wind and solar energy storage node at time t, Pl represents work power, Pv represents load power, S0 is the topological structure density of the wind and solar energy storage nodes, and αj is the rated capacity of adjacent nodes.

8. Computer equipment, A computer device comprising memory and a processor, wherein the memory and the processor are connected to each other in a manner that allows for communication, the memory stores computer instructions, and the processor executes the steps of the distributed cooperative control method for wind and solar energy storage described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium on which a computer program is stored, A computer-readable storage medium characterized in that, when the computer program is executed on the processor, the steps of the distributed cooperative control method for wind and solar energy storage described in any one of claims 1 to 6 are realized.

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