Distributed coordinated control method, apparatus, computer device and medium 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] The present application relates to the field of new energy technologies, and in particular to a distributed cooperative control method, apparatus, computer device and medium for wind and solar energy storage. [Background technology]

[0002] The development and utilization of renewable energy can help resolve the dual energy and environmental crises we currently face, and is one of the key technologies for promoting the energy revolution and achieving sustainable energy development. However, directly connecting large-scale renewable distributed energy sources to the power grid poses new challenges to the stability of the power system. On the one hand, the output power of intermittent new energy sources such as wind and solar energy is fluctuating and random, so simple connection to the power grid can cause power shocks to the power system. On the other hand, the connection of distributed energy sources changes the unidirectional power flow pattern of energy storage systems, making it difficult for traditional energy storage systems to adapt to new control needs. Therefore, how to rationalize and optimize the allocation of distributed energy output to maximize its benefits is particularly important. Summary of the Invention [Problem to be solved by the invention]

[0003] In order to optimize the allocation of distributed energy output in wind and solar energy storage stations, smooth the fluctuations in the output power of distributed energy, and maximize the profits of wind and solar energy storage stations, the present application provides a distributed coordinated control method, computer device, and medium for wind and solar energy storage. [Means for solving the problem]

[0004] In a first aspect, the present application provides a distributed coordinated control method for wind and solar energy storage, the method comprising: Obtaining a topology structure between a plurality of wind and solar energy storage nodes in a wind and solar energy storage station; Partitioning each wind and solar energy storage node according to a 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 set of wind and solar energy storage nodes, the first energy storage data maximizing a sum of complementarity coefficients between each wind and solar energy storage node in the set of wind and solar energy storage nodes; Inputting each first energy storage data into a pre-built optimization configuration model to determine an optimized power of each wind and solar energy storage node; and controlling each wind and solar energy storage node based on each optimized power.

[0005] According to the above method, each wind and solar energy storage node is divided according to the topology structure between each wind and solar energy storage node in the wind and solar energy storage station, to obtain a plurality of wind and solar energy storage node sets, and based on the complementary coefficients between the wind and solar energy storage nodes, energy storage data between each wind and solar energy storage node in each wind and solar energy storage node set is determined, and then, according to each energy storage data and the optimization configuration model, optimized power of the wind and solar energy storage node is determined, thereby controlling each wind and solar energy storage node, and through the power complementary between adjacent nodes of each wind and solar energy storage node, the fluctuation of distributed energy output power is smoothed, the allocation 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 alternative embodiment, the step of partitioning each wind and solar energy storage node according to a topology structure to obtain at least one wind and solar energy storage node set comprises: determining an adjacency matrix of each wind and solar energy storage node according to the topology structure; 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] According to the above embodiment, the adjacent matrix of each wind and solar energy storage node is determined according to the topology 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 adjacent matrix, and in the wind and solar energy storage node set, power is complemented between each wind and solar energy storage node, thereby smoothing the fluctuation of the output power of distributed energy and improving the stability of the wind and solar energy storage station.

[0008] In one alternative 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, where the first energy storage data maximizes a sum of complementarity coefficients between each wind and solar energy storage node in the set of wind and solar energy storage nodes, comprises: The step includes determining first energy storage data of each wind and solar energy storage node in each wind and solar energy storage node set according to a pre-constructed auxiliary model, wherein the first energy storage data maximizes the sum of complementary coefficients between each wind and solar energy storage node in the wind and solar energy storage node set, 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.

[0009] In one alternative embodiment, determining the first energy storage data for each wind and solar energy storage node in the set of wind and solar energy storage nodes according to the pre-established auxiliary model includes: determining a plurality of sets of operating states for the set of wind and solar energy storage nodes, each set of operating states including an operating state for each wind and solar energy storage node; determining an energy storage data set corresponding to each set of operating conditions according to each set of operating conditions, wherein each energy storage data set includes second energy storage data of each wind and solar energy storage node; Calculating a sum of complementary coefficients between each wind and solar energy storage node corresponding to each energy storage data set according to each energy storage data set and the auxiliary model; selecting the energy storage data set with the largest sum of the complementary coefficients as a final energy storage data set, and setting each second energy storage data in the final energy storage data set as the first energy storage data of each wind and solar energy storage node.

[0010] In one alternative embodiment, the energy storage data includes a minimum output, and the auxiliary model calculates the complement coefficients according to the following formula:

number

[0011] In one alternative embodiment, the optimization configuration model is expressed as:

number

[0012] According to the above embodiment, an optimization configuration model is used to determine the optimized power of each wind and solar energy storage node in a 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 allocation of distributed energy output in the wind and solar energy storage stations and maximize the profits of the wind and solar energy storage stations.

[0013] In one alternative embodiment, the optimization configuration model includes at least one of a power balance constraint, a unit operation constraint, and a wind and solar generation abandonment constraint.

[0014] According to the above embodiment, the power balance constraint, the unit operation constraint, and the wind and solar power generation abandonment constraint are incorporated into the constraint conditions, which improves the practicability of the optimization configuration model, meets the control needs of multi-tasks and multi-nodes, and determines that the power system operates safely, stably, and reliably.

[0015] In a second aspect, the present application further provides a distributed cooperative controller for wind and solar energy storage, the controller comprising: an acquisition module for acquiring a topology structure between a plurality of wind and solar energy storage nodes in the wind and solar energy storage station; a partitioning module for partitioning each wind and solar energy storage node according to a topology structure to obtain at least one wind and solar energy storage node set; a first determination module used to determine first energy storage data of each wind and solar energy storage node in each set of wind and solar energy storage nodes, the first energy storage data maximizing a sum of complementary coefficients between each wind and solar energy storage node in the set of wind and solar energy storage nodes; a second determination module for inputting each first energy storage data into a pre-constructed optimization configuration model to determine an optimized power of each wind and solar energy storage node; and a control module for controlling each wind and solar energy storage node based on each optimized power.

[0016] The above device divides each wind and solar energy storage node according to the topology structure between each wind and solar energy storage node in the wind and solar energy storage station to obtain a plurality of wind and solar energy storage node sets, determines energy storage data between each wind and solar energy storage node in each wind and solar energy storage node set based on the complementary coefficient between the wind and solar energy storage nodes, and then determines optimized power of the wind and solar energy storage node according to each energy storage data and the optimization configuration model, thereby controlling each wind and solar energy storage node, and smoothing the fluctuation of distributed energy output power through the complementary power between adjacent nodes of each wind and solar energy storage node, optimizing the allocation of distributed energy output in the wind and solar energy storage station, and maximizing the profits of the wind and solar energy storage station.

[0017] In a third aspect, the present application further provides a computing device comprising a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the steps of the method for distributed coordinated control for wind and solar energy storage of the first aspect or any embodiment of the first aspect.

[0018] In a fourth aspect, the present application further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the steps of the method for distributed coordinated control for wind and solar energy storage of the first aspect or any embodiment of the first aspect to be realized. [Brief explanation of the drawings]

[0019] In order to more clearly describe the specific embodiments of the present application or the technical solutions of the prior art, the following will briefly introduce the drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings based on these drawings without any creative efforts.

[0020] [Figure 1] 1 is a flowchart of a distributed coordinated control method for wind and solar energy storage according to one exemplary embodiment. [Figure 2] 1 is a structural schematic diagram of a distributed cooperative control device for wind and solar energy storage according to one exemplary embodiment; [Figure 3] 1 is a schematic diagram of a hardware structure of a computer device according to one exemplary embodiment; DETAILED DESCRIPTION OF THE INVENTION

[0021] The technical solutions of the present application will be described below clearly and completely in conjunction with the drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments, and all other embodiments that a person skilled in the art can obtain based on the embodiments of the present application without any creative efforts belong to the protection scope of the present application.

[0022] Furthermore, the technical features according to different embodiments of the present application described below can be combined with each other unless they contradict each other.

[0023] In order to optimize the allocation of distributed energy output in wind and solar energy storage stations, smooth the fluctuations in the output power of distributed energy, and maximize the profits of wind and solar energy storage stations, the present application provides a distributed coordinated control method, computer device, and medium for wind and solar energy storage.

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

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

[0026] In one alternative embodiment, the 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 location where wind energy is used to generate electricity and typically includes wind power generating units, transmission lines, a control system, etc. The wind power generating units 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 power generating units to ensure their normal operation. A solar power plant refers to a location where solar energy is used to generate electricity and typically includes solar panels, inverters, transmission lines, etc. The solar panels convert solar energy into direct current, the inverter converts 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 the wind and solar energy storage station and can store energy when power generation is excessive and release energy when power generation is insufficient. This allows the station to balance power generation with demand and ensure a stable power supply. The energy storage system typically uses a battery energy storage system that includes a battery pack, a battery management system, a charging / discharging device, and the like.

[0027] In one alternative embodiment, the topology structure between wind and solar energy storage nodes refers to the connection relationship and operation method between each device in the wind and solar energy storage station. In the wind and solar energy storage station, devices such as wind turbines, solar panels, and energy storage systems need to be connected and interact with each other to realize energy conversion, storage, and distribution. The topology structure between the wind and solar energy storage nodes can use different structural forms, such as a tree structure, a star structure, or a mesh structure. Here, the tree structure is the most commonly used structural form and has advantages such as a simple structure, easy maintenance, and easy expansion. In the tree structure, individual nodes are connected according to a certain hierarchy to form a hierarchical network structure.

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

[0029] In one alternative embodiment, according to the topology structure, obtain the adjacent wind and solar energy storage nodes of the wind and solar energy storage nodes, and configure the adjacent wind and solar energy storage nodes into one wind and solar energy storage node set.

[0030] In one alternative embodiment, the topology structure, power supply characteristics and operation methods of each wind and solar energy storage node can be combined to partition each wind and solar energy storage node to obtain multiple wind and solar energy storage node sets.

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

[0032] In one alternative embodiment, the complementarity coefficient between the wind and solar energy storage nodes represents the complementarity between the wind and solar energy storage nodes, which mainly reflects the complementarity between wind energy and solar energy, the complementarity between wind energy and stored energy, and the complementarity between solar energy and stored energy.

[0033] In one alternative embodiment, wind energy and solar energy are complementary in time and season. For example, during the daytime in summer, sunlight is sufficient, and the solar power generation system can generate a large amount of electricity, and wind energy can be relatively low. However, during the night in winter, wind is strong, and the wind power generation system can generate a large amount of electricity, and the solar power generation system cannot generate electricity. Therefore, by combining wind energy and solar energy, stable power output can be achieved throughout the year.

[0034] In one alternative embodiment, the complementarity of wind energy and stored energy refers to the combination of wind energy and stored energy to achieve complementarity. Wind energy is intermittent and unpredictable. When the wind is strong, the wind power generation system generates a large amount of power, which may exceed the consumption capacity of the power grid. When the wind is weak, the wind power generation system generates insufficient power, which may lead to a power shortage. The energy storage system can store excess electrical energy when the wind is strong and release it when the wind is weak, thereby balancing the supply and needs of the power grid.

[0035] In one alternative embodiment, like wind energy, solar energy is intermittent and unpredictable. When sunlight is sufficient, solar power generation systems can generate large amounts of electricity, potentially exceeding the power grid's consumption capacity. When sunlight is insufficient, solar power generation systems cannot generate enough electricity, potentially leading to power shortages. An energy storage system can balance the needs and supply of the power grid by storing excess electrical energy when sunlight is sufficient and releasing it when sunlight is insufficient.

[0036] In one alternative embodiment, the larger the complementarity coefficient between the wind and solar energy storage nodes, the stronger the complementarity between the wind and solar energy storage nodes, and the better the optimization and stable supply of energy can be realized. The larger the sum of the complementarity coefficients between the wind and solar energy storage nodes in the wind and solar energy storage node set, the stronger the complementarity between each wind and solar energy storage node in the wind and solar energy storage node set.

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

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

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

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

[0041] In one alternative embodiment, for each wind and solar energy storage node, its operating state and power output can be adjusted based on its optimized power by means of a control system, a monitoring system, etc. For example, in the case of a wind turbine generator, its output power can be controlled by adjusting parameters such as the rotation speed and pitch angle of the wind turbine, in the case of a solar 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] According to the above method, each wind and solar energy storage node is divided according to the topology structure between each wind and solar energy storage node in the wind and solar energy storage station, to obtain a plurality of wind and solar energy storage node sets, and energy storage data between each wind and solar energy storage node in each wind and solar energy storage node set is determined based on the complementary coefficient between the wind and solar energy storage nodes, and then optimized power of the wind and solar energy storage nodes is determined according to each first energy storage data and the optimization configuration model, thereby controlling each wind and solar energy storage node, and before calculating using the optimization configuration model, the first energy storage data of each wind and solar energy storage node is all converged to a high-density operating state, and the complementary power between adjacent nodes of each wind and solar energy storage node smooths the output power fluctuation of distributed energy, optimizes the allocation of distributed energy output in the wind and solar energy storage station, and maximizes the profits of the wind and solar energy storage station.

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

[0044] In one alternative embodiment, the set of neighboring nodes for each wind and solar energy storage node is:

number

number

number

[0045] Then, each wind and solar energy storage node is partitioned according to each adjacency matrix to obtain at least one wind and solar energy storage node set.

[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 wind and solar energy storage node set includes two wind and solar energy storage nodes.

[0047] In the embodiment of the present application, an adjacency matrix of each wind and solar energy storage node is determined according to the topology 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, power is complemented between each wind and solar energy storage node, thereby smoothing the fluctuation of the output power of distributed energy and improving the stability of the wind and solar energy storage station.

[0048] In one example, in the above step S103, first energy storage data of 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 first energy storage data maximizes the sum of the complementary coefficients between each wind and solar energy storage node in the wind and solar energy storage node set, 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 alternative embodiment, the following steps are performed to determine 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, a plurality of operation state sets of the wind and solar energy storage node set are determined, and each operation state set includes the operation state of each wind and solar energy storage node.

[0051] In one alternative embodiment, the operating status of the wind and solar energy storage nodes includes data such as the rotation speed, wind direction, and power factor of the wind power generation unit, data such as the temperature, voltage, and current of the solar power generation module of the solar power plant, and data such as the charge and discharge efficiency, charge and discharge rate, and capacity of the energy storage system.

[0052] In step a2, determine an energy storage data set corresponding to each operating state set according to each operating state set, where each energy storage data set includes second energy storage data of each wind and solar energy storage node.

[0053] In one alternative embodiment, the operating status of the wind and solar energy storage nodes corresponds to the second energy storage data. The operating status of each wind and solar energy storage node is associated with the energy storage data, and the corresponding second energy storage data is obtained according to the operating status of the wind and solar energy storage nodes. Illustratively, the corresponding second energy storage data is obtained in a field acquisition manner according to the operating status.

[0054] In one alternative embodiment, the second energy storage data includes a maximum output, a minimum output, and the like.

[0055] In step a3, calculate the sum of the complementary coefficients between each wind and solar energy storage node corresponding to each energy storage data set according to each energy storage data set and the auxiliary model.

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

[0057] In one alternative embodiment, in the above step a3, the energy storage data includes a minimum output, and the auxiliary model calculates the complementary coefficients according to the following formula:

number

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

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

number

[0060] In an embodiment of the present application, an optimization configuration model is used to determine the optimized power of each wind and solar energy storage node in each wind and solar energy storage node set, and based on the optimized power, each wind and solar energy storage node is controlled to optimize the allocation of distributed energy output in the wind and solar energy storage stations and maximize the profits of the wind and solar energy storage stations.

[0061] In one example, in the above step S104, the optimization configuration model includes at least one of a power balance constraint, a unit operation constraint, and a wind and solar power generation abandonment constraint. In the embodiment of the present application, the power balance constraint, the unit operation constraint, and the wind and solar power generation abandonment constraint are incorporated into the constraint conditions, thereby improving the practicality of the optimization configuration model, meeting the control needs of multi-tasks and multi-nodes, and determining the safe, stable, and reliable operation of the power system.

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

number

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

number

[0064] In one alternative embodiment, the wind and solar power abandonment constraints are expressed as follows:

number

[0065] In one alternative embodiment, when the operating state of any wind and solar energy storage node converges to a Nash equilibrium point, the neighboring nodes adjacent to this wind and solar energy storage node also converge to a Nash equilibrium solution, and the optimization configuration model also reaches a Nash equilibrium solution.

[0066] Based on the same inventive concept, an embodiment of the present application further provides a distributed cooperative control device for wind and solar energy storage, as shown in FIG. 2, the device comprises the following modules:

[0067] The acquisition module 201 is used to acquire the topology structure between multiple wind and solar energy storage nodes in the 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 partitioning module 202 is used to partition each wind and solar energy storage node according to the topology structure to obtain at least one wind and solar energy storage node set, for details, please refer to the description of step S102 in the above embodiment, and the description will not be repeated here.

[0069] The first determination module 203 is used to determine first energy storage data of 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 complementary 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, and the description will not be repeated here.

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

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

[0072] In one example, the partition module 202 includes the following sub-modules:

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

[0074] The partitioning submodule is used to partition each wind and solar energy storage node according to each adjacency matrix, and obtain at least one wind and solar energy storage node set. For details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0075] In one example, the first determination module 203 includes the following sub-modules:

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

[0077] In one example, the decision sub-module includes the following units:

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

[0079] The second determination unit is used to determine an energy storage data set corresponding to each operating state set according to each operating state set, and each energy storage data set includes second energy storage data of each wind and solar energy storage node. For detailed content, please refer to the description of the above embodiment, and the description will not be repeated here.

[0080] The calculation unit is used to calculate the sum of the complementary coefficients between each wind and solar energy storage node corresponding to each energy storage data set according to each energy storage data set and the auxiliary model, for details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0081] The selection unit is used to select the energy storage data set with the largest sum of the complementary coefficients as the final energy storage data set, and set each second energy storage data in the final energy storage data set as the first energy storage data of each wind and solar energy storage node. For details, please refer to the description of the above embodiment, and repeated description will be omitted here.

[0082] In one example, in the calculation unit, the energy storage data includes a minimum output, and the auxiliary model calculates the complementary coefficients according to the following formula:

number

[0083] In one example, in the second determination module 204, the optimized configuration model is expressed as:

number

[0084] In one example, in the second determination module 204, the optimization configuration model includes at least one of a power balance constraint, a unit operation constraint, and a wind and solar power abandonment constraint. For details, please refer to the description of the above embodiment, and the description will not be repeated here.

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

[0086] 3 is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. As shown in FIG. 3, the device includes one or more processors 310 and a memory 320, where the memory 320 includes persistent memory, volatile memory, and a hard disk. In FIG. 3, one processor 310 is taken 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 FIG. 3 illustrates the connection via a bus as an example.

[0088] Processor 310 may be a central processing unit (CPU). Processor 310 may also be other general-purpose processors, chips such as digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of various chips described above. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0089] The memory 320 may include persistent memory, volatile memory, and a hard disk as a non-transitory computer-readable storage medium, and may be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions / modules corresponding to the distributed cooperative control method for wind and solar energy storage in the embodiments of the present application. The processor 310 executes the non-transitory software programs, instructions, and modules stored in the memory 1120 to perform various functional applications and data processing of the server, i.e., to realize any of the distributed cooperative control methods for wind and solar energy storage described above.

[0090] The memory 320 may include a program storage area and a data storage area, where the program storage area may store an operating system, an application program required for at least one function, etc., and the data storage area may 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-transitory memory, such as a magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 320 optionally includes memory located remotely from the processor 310, and these remote memories may be connected to the data processing device via a network. Examples of such networks 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 numeric or textual information and generate signal inputs for user settings and function control. The output device 340 can include a display device such as a display screen.

[0092] The one or more modules are stored in memory 320 and, when executed by one or more processors 310, perform the method illustrated in FIG.

[0093] The above product can implement the method provided by the embodiment of the present application, and has corresponding functional modules and beneficial effects for implementing the method. For technical details not described in detail in this embodiment, please refer to the relevant description of the embodiment shown in Figure 1.

[0094] An embodiment of the present application further provides a non-transitory computer storage medium storing computer-executable instructions capable of executing the method of any of the above method embodiments, wherein the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), a solid-state drive (SSD), etc., and the storage medium may further include a combination of the above types of memory.

[0095] It should be noted that, in this specification, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another and do not necessarily require or imply any actual relationship or order between those entities or operations. Furthermore, the terms "comprise," "include," or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not expressly set forth or inherent in such process, method, article, or device. Absent further limitations, an element qualified by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, article, or device that includes the element.

[0096] The foregoing are merely specific embodiments of the present application, intended to enable those skilled in the art to understand or realize the present application. Various modifications to these examples will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other examples without departing from the spirit or scope of the present application. Therefore, the present application is not intended to be limited to the examples set forth herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. 1. A distributed coordinated control method for wind and solar energy storage, comprising: Obtaining a topology structure between a plurality of wind and solar energy storage nodes in a wind and solar energy storage station; Partitioning each of the wind and solar energy storage nodes according to the topology structure to obtain at least one wind and solar energy storage node set; determining first energy storage data for each of the wind and solar energy storage nodes in each of the sets of wind and solar energy storage nodes, the first energy storage data maximizing a sum of complementarity coefficients between each of the wind and solar energy storage nodes in the set of wind and solar energy storage nodes; inputting each of the first energy storage data into a pre-built optimization configuration model to determine an optimized power of each of the wind and solar energy storage nodes; and controlling each of the wind and solar energy storage nodes based on each of the optimized powers.

2. Partitioning each of the wind and solar energy storage nodes according to the topology structure to obtain at least one wind and solar energy storage node set includes: determining an adjacency matrix of each of the wind and solar energy storage nodes according to the topology structure; and partitioning each of the wind and solar energy storage nodes according to each of the adjacency matrices to obtain at least one set of wind and solar energy storage nodes.

3. determining first energy storage data for each of the wind and solar energy storage nodes in each of the wind and solar energy storage node sets, wherein the first energy storage data maximizes a sum of complementarity coefficients between each of the wind and solar energy storage nodes in the wind and solar energy storage node set, 2. The method of claim 1, further comprising: determining first energy storage data for each of the wind and solar energy storage nodes in each of the wind and solar energy storage node sets according to a pre-established auxiliary model, wherein the first energy storage data maximizes a sum of complementary coefficients between each of the wind and solar energy storage nodes in the wind and solar energy storage node set, and the auxiliary model is used to represent a complementary relationship between each of the wind and solar energy storage nodes in the wind and solar energy storage node set.

4. Determining first energy storage data of each of the wind and solar energy storage nodes in the set of wind and solar energy storage nodes according to a pre-established auxiliary model includes: determining a plurality of sets of operating states for the set of wind and solar energy storage nodes, each set of operating states including an operating state of each of the wind and solar energy storage nodes; determining an energy storage data set corresponding to each of the operating condition sets according to each of the operating condition sets, each energy storage data set including second energy storage data of each of the wind and solar energy storage nodes; Calculating a sum of complementary coefficients between each of the wind and solar energy storage nodes corresponding to each of the energy storage data sets according to each of the energy storage data sets and the auxiliary model; selecting the energy storage data set with the largest sum of the complementation coefficients as a final energy storage data set, and setting each second energy storage data in the final energy storage data set as the first energy storage data of each of the wind and solar energy storage nodes.

5. The energy storage data includes a minimum output, and the auxiliary model calculates the interpolation coefficients according to the following formula: [Equation 1] where β is the complementary coefficient and Xg ｉ，ｔ represents the minimum output of the i-th wind and solar energy storage node at time t, Pl represents the work power, Pv represents the load power, and S ０ is the topology structure density of wind and solar energy storage nodes, and α ｊ 5. The method of claim 4, wherein: is the nominal capacity of the adjacent node.

6. The optimization configuration model is expressed by the following equation: [Equation 2] However, η ｖｏｒ is the optimized power, Q is the peak output rate of the wind and solar energy storage nodes among the neighboring nodes, Pn is the discharge power of the i-th node at time t, and Xm ｉ，ｔ represents the maximum output of the i-th wind and solar energy storage node at time t, and Xg ｉ，ｔ represents the minimum output of the i-th wind and solar energy storage node at time t, and ηw ｉ，ｔ 2. The method of claim 1, wherein t represents the photovoltaic power relinquishment power of the i-th wind and solar energy storage node at time t.

7. The method of claim 5 , wherein the optimization configuration model includes at least one of a power balance constraint, a unit operation constraint, and a wind and solar generation abandonment constraint.

8. 1. A distributed cooperative controller for wind and solar energy storage, comprising: an acquisition module for acquiring a topology structure between a plurality of wind and solar energy storage nodes in the wind and solar energy storage station; a partitioning module for partitioning each of the wind and solar energy storage nodes according to the topology structure to obtain at least one wind and solar energy storage node set; a first determination module used to determine first energy storage data for each of the wind and solar energy storage nodes in each of the wind and solar energy storage node sets, the first energy storage data maximizing a sum of complementary coefficients between each of the wind and solar energy storage nodes in the wind and solar energy storage node set; a second determination module for inputting each of the first energy storage data into a pre-built optimization configuration model to determine an optimized power of each of the wind and solar energy storage nodes; and a control module for controlling each of the wind and solar energy storage nodes based on each of the optimized power.

9. A computer device comprising: A computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other; computer instructions stored in the memory; and the processor executing the computer instructions to perform the steps of the distributed cooperative control method for wind and solar energy storage according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, A computer-readable storage medium, characterized in that, when the computer program is executed by a processor, the steps of the distributed cooperative control method for wind and solar energy storage according to any one of claims 1 to 7 are realized.

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