Wind power plant energy management system and method, electronic equipment and storage medium
By designing a wind farm energy management system that integrates data acquisition, communication, prediction and optimization technologies, it solves the problem that traditional systems are difficult to adapt to complex grid environments and energy needs, and achieves efficient energy prediction and optimized scheduling, which improves the efficiency and reliability of energy management.
Patent Information
- Application Number
- CN202411862948.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-04-29
AI Technical Summary
Traditional wind farm energy management systems are difficult to adapt to the complex and changeable grid environment and energy needs, and cannot effectively integrate multi-source data for efficient energy prediction and optimized scheduling.
A wind farm energy management system is designed, including a data acquisition module, a communication and edge computing module, an energy prediction module, a scheduling optimization module and a control execution module. By collecting and preprocessing data in real time, energy prediction is performed using a seasonal ARIMA model, and real-time optimization scheduling is performed based on a dynamic rule optimization algorithm.
It realizes efficient management of wind farm energy, can collect and process multi-source data in real time, conduct accurate energy prediction, and realizes real-time optimization scheduling of wind power, photovoltaics, energy storage and load requirements through optimization algorithms, improving the efficiency and reliability of energy management.
Smart Images
Figure CN119905993A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of electric power technology, and in particular to a wind farm energy management system, method, electronic equipment and storage medium. Background Art
[0002] With the rapid development of renewable energy, wind farms are an important part of it, and the efficiency and reliability of their energy management have an important impact on the stability and economic benefits of the entire power system. Traditional wind farm energy management systems often rely on a single data source and a simple prediction model, which is difficult to adapt to the complex and changing power grid environment and energy demand. Therefore, it is particularly important to develop a wind farm energy management system that can integrate multi-source data and has efficient energy prediction and optimization scheduling capabilities. Summary of the invention
[0003] The present disclosure provides a wind farm energy management system, method, electronic device and storage medium to solve the problems in the related art.
[0004] The first aspect of the present disclosure provides a wind farm energy management system, including a data acquisition module, a communication and edge computing module, an energy prediction module, a scheduling optimization module, and a control execution module connected in sequence;
[0005] The data acquisition module is used to collect real-time data of the wind farm, and the real-time data includes wind speed, light, and energy storage status data;
[0006] The communication and edge computing module is used to preprocess the real-time data to obtain preprocessed real-time data; the preprocessing includes at least one of the following: cleaning, formatting, and outlier removal;
[0007] The energy prediction module is used to make short-term and long-term predictions on the power generation capacity of wind power and photovoltaic power based on preset historical data, and obtain short-term prediction results and long-term prediction results;
[0008] The scheduling optimization module is used to obtain an optimized scheduling strategy according to the short-term prediction result, the long-term prediction result and the processed real-time data;
[0009] The control execution module is used to generate a scheduling instruction based on the optimized scheduling strategy, and send the scheduling instruction to the device layer so that the devices in the device layer respond to the scheduling instruction action.
[0010] In one embodiment, the system further comprises a data storage module;
[0011] The data storage module is used to store the pre-processed real-time data and preset historical data.
[0012] In one embodiment, the energy prediction module is used to perform short-term and long-term predictions on wind power and photovoltaic power generation capacity based on preset historical data using a seasonal ARIMA model to obtain short-term prediction results and long-term prediction results;
[0013] The dispatch optimization module is used to perform real-time optimization dispatch of wind power, photovoltaic, energy storage and load demand based on the short-term prediction results, the long-term prediction results and the processed real-time data, and obtain an optimized dispatch strategy based on a dynamic rule optimization algorithm.
[0014] The second aspect of the present disclosure provides a method for implementing the wind farm energy management system provided in the first aspect of the present disclosure, comprising the following steps:
[0015] Collecting real-time data of the wind farm through the data acquisition module, the real-time data including wind speed, light intensity, and energy storage status data;
[0016] Preprocessing the real-time data through the communication and edge computing module to obtain preprocessed real-time data; the preprocessing includes at least one of the following: cleaning, formatting, and outlier removal;
[0017] Through the energy prediction module, based on the preset historical data, the short-term and long-term predictions of wind power and photovoltaic power generation capacity are carried out to obtain short-term prediction results and long-term prediction results;
[0018] Obtaining an optimized scheduling strategy according to the short-term prediction results, the long-term prediction results and the processed real-time data through a scheduling optimization module;
[0019] A scheduling instruction is generated based on the optimized scheduling strategy through a control execution module, and the scheduling instruction is sent to the device layer so that the devices in the device layer respond to the scheduling instruction action.
[0020] In one embodiment, the short-term and long-term forecasts of wind power and photovoltaic power generation capacity are performed based on preset historical data to obtain short-term forecast results and long-term forecast results, including:
[0021] Based on the preset historical data, the seasonal ARIMA model is used to make short-term and long-term forecasts of wind power and photovoltaic power generation capacity, and obtain short-term and long-term forecast results;
[0022] The step of obtaining an optimized scheduling strategy based on the short-term prediction result, the long-term prediction result and the processed real-time data includes:
[0023] According to the short-term prediction results, the long-term prediction results and the processed real-time data, wind power, photovoltaics, energy storage and load demand are optimized and dispatched in real time based on a dynamic rule optimization algorithm to obtain an optimized dispatching strategy.
[0024] In one embodiment, the ARIMA model is:
[0025]
[0026] Among them, B is the backoff operator, φ i is the autoregressive coefficient, θ j is the moving average coefficient, d is the difference order, ε t is white noise; the seasonal part is expressed as:
[0027]
[0028] Where s is the seasonal period, Φ i and θ j are seasonal autoregressive and moving average coefficients, d s is the seasonal differencing order.
[0029] In one embodiment, the dynamic rule optimization algorithm is:
[0030]
[0031] Among them, f(x) is the objective function, g i (x) is an inequality constraint, h j (x) is an equality constraint, and x represents a decision variable.
[0032] In one embodiment, after sending the scheduling instruction to the device layer, the method further includes:
[0033] Acquire device operating parameters of the device layer, and detect abnormal behavior of the device through an anomaly detection algorithm;
[0034] Among them, the anomaly detection algorithm is:
[0035]
[0036] Among them, Z t is the control chart statistic at time t, X t is the observed value at time t, is the smoothing constant, Z t When the control limit is exceeded, the corresponding equipment is considered abnormal.
[0037] A third aspect of the present disclosure provides an electronic device, including:
[0038] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the embodiment of the second aspect of the present disclosure.
[0039] The fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the second aspect embodiment of the present disclosure.
[0040] In summary, the present disclosure proposes a wind farm energy management system, method, electronic device and storage medium, the wind farm energy management system includes a data acquisition module, a communication and edge computing module, an energy prediction module, a scheduling optimization module, a control execution module, and a monitoring and feedback module connected in sequence; the data acquisition module is used to collect real-time data of the wind farm, and the real-time data includes wind speed, light, and energy storage status data; the communication and edge computing module is used to pre-process the real-time data to obtain pre-processed real-time data; the pre-processing includes at least one of the following: cleaning, formatting, and outlier removal; the energy prediction module is used to perform short-term and long-term predictions on the power generation capacity of wind power and photovoltaic power based on preset historical data to obtain short-term prediction results and long-term prediction results; the scheduling optimization module is used to obtain an optimized scheduling strategy based on the short-term prediction results, the long-term prediction results and the processed real-time data; the control execution module is used to generate a scheduling instruction based on the optimized scheduling strategy, and send the scheduling instruction to the device layer so that the device at the device layer responds to the scheduling instruction action.
[0041] The solution provided by the present disclosure realizes efficient management of wind farm energy by integrating advanced data collection, communication, prediction, optimization, and control technologies. The wind farm energy management system can collect and process multi-source data such as wind speed, light and energy storage status in real time, make accurate energy predictions, and realize real-time optimization and scheduling of wind power, photovoltaic, energy storage and load demand through optimization algorithms.
[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.
[0044] Figure 1 A schematic diagram of the structure of a wind farm energy management system provided by an embodiment of the present disclosure;
[0045] Figure 2 A schematic diagram of a flow chart of a wind farm energy management method provided by an embodiment of the present disclosure;
[0046] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0047] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.
[0048] With the rapid development of renewable energy, wind farms are an important part of it, and the efficiency and reliability of their energy management have an important impact on the stability and economic benefits of the entire power system. Traditional wind farm energy management systems often rely on a single data source and a simple prediction model, which is difficult to adapt to the complex and changing power grid environment and energy demand. Therefore, it is particularly important to develop a wind farm energy management system that can integrate multi-source data and has efficient energy prediction and optimization scheduling capabilities.
[0049] In order to solve the defects in the related technologies, the solution provided by the present disclosure realizes efficient management of wind farm energy by integrating advanced data collection, communication, prediction, optimization, and control technologies. The wind farm energy management system can collect and process multi-source data such as wind speed, light and energy storage status in real time, make accurate energy predictions, and realize real-time optimization and scheduling of wind power, photovoltaic, energy storage and load demand through optimization algorithms.
[0050] like Figure 1 As shown, Figure 1 A schematic diagram of the structure of a wind farm energy management system provided by an embodiment of the present disclosure. The wind farm energy management system provided by an embodiment of the present disclosure includes:
[0051] A data acquisition module 101, a communication and edge computing module 102, an energy prediction module 103, a scheduling optimization module 104, and a control execution module 105 connected in sequence;
[0052] The data acquisition module 101 is used to collect real-time data of the wind farm, and the real-time data includes wind speed, light, and energy storage status data;
[0053] The communication and edge computing module 102 is used to preprocess the real-time data to obtain preprocessed real-time data; the preprocessing includes at least one of the following: cleaning, formatting, and outlier removal;
[0054] The energy prediction module 103 is used to perform short-term and long-term predictions on the power generation capacity of wind power and photovoltaic power based on preset historical data to obtain short-term prediction results and long-term prediction results;
[0055] The scheduling optimization module 104 is used to obtain an optimized scheduling strategy according to the short-term prediction result, the long-term prediction result and the processed real-time data;
[0056] The control execution module 105 is used to generate a scheduling instruction based on the optimized scheduling strategy, and send the scheduling instruction to the device layer so that the devices in the device layer respond to the scheduling instruction.
[0057] In summary, the present disclosure proposes a wind farm energy management system, including a data acquisition module 101, a communication and edge computing module 102, an energy prediction module 103, a scheduling optimization module 104, and a control execution module 405 connected in sequence; the data acquisition module 101 is used to collect real-time data of the wind farm, and the real-time data includes wind speed, light, and energy storage status data; the communication and edge computing module 102 is used to preprocess the real-time data to obtain preprocessed real-time data; the preprocessing includes at least one of the following: cleaning, formatting, and outlier removal; the energy prediction module 103 is used to perform short-term and long-term predictions on the power generation capacity of wind power and photovoltaic power based on preset historical data to obtain short-term prediction results and long-term prediction results; the scheduling optimization module 104 is used to obtain an optimized scheduling strategy based on the short-term prediction results, the long-term prediction results and the processed real-time data; the control execution module 105 is used to generate a scheduling instruction based on the optimized scheduling strategy, and send the scheduling instruction to the device layer so that the device at the device layer responds to the scheduling instruction action.
[0058] The solution provided by the present disclosure realizes efficient management of wind farm energy by integrating advanced data collection, communication, prediction, optimization, and control technologies. The wind farm energy management system can collect and process multi-source data such as wind speed, light and energy storage status in real time, make accurate energy predictions, and realize real-time optimization and scheduling of wind power, photovoltaic, energy storage and load demand through optimization algorithms.
[0059] In one embodiment, the system further comprises a data storage module;
[0060] The data storage module is used to store the pre-processed real-time data and preset historical data.
[0061] In one embodiment, the preset historical data is stored in a long-term storage manner to provide basic data for the energy prediction module; the preprocessed real-time data is stored in a short-term cache manner to provide basic data for the scheduling optimization module.
[0062] In one embodiment, the energy prediction module is used to perform short-term and long-term predictions on wind power and photovoltaic power generation capacity based on preset historical data using a seasonal ARIMA model to obtain short-term prediction results and long-term prediction results;
[0063] The dispatch optimization module is used to perform real-time optimization dispatch of wind power, photovoltaic, energy storage and load demand based on the short-term prediction results, the long-term prediction results and the processed real-time data, and obtain an optimized dispatch strategy based on a dynamic rule optimization algorithm.
[0064] The present disclosure also provides a wind farm energy management method, which is applied to a wind farm energy management system, such as Figure 2 As shown, the method comprises the following steps:
[0065] Step 201, collecting real-time data of the wind farm through a data collection module, wherein the real-time data includes wind speed, light intensity, and energy storage status data;
[0066] Step 202: preprocessing the real-time data through the communication and edge computing module to obtain preprocessed real-time data;
[0067] In one embodiment, the preprocessing includes at least one of the following: cleaning, formatting, and outlier removal;
[0068] Step 203, using an energy prediction module, based on preset historical data, short-term and long-term predictions are performed on the power generation capacity of wind power and photovoltaic power to obtain short-term prediction results and long-term prediction results;
[0069] In one embodiment, step 103 includes:
[0070] Based on the preset historical data, the seasonal ARIMA model is used to make short-term and long-term forecasts of wind power and photovoltaic power generation capacity, and obtain short-term and long-term forecast results;
[0071] In one embodiment, the ARIMA model is:
[0072]
[0073] Among them, B is the backoff operator, φ i is the autoregressive coefficient, θ j is the moving average coefficient, d is the difference order, ε t is white noise; the seasonal part is expressed as:
[0074]
[0075] Where s is the seasonal period, Φ i and θ j are the seasonal autoregressive and moving average coefficients, and ds is the seasonal difference order.
[0076] Step 204, obtaining an optimized scheduling strategy according to the short-term prediction result, the long-term prediction result and the processed real-time data through a scheduling optimization module;
[0077] In one embodiment, step 104 includes:
[0078] According to the short-term prediction results, the long-term prediction results and the processed real-time data, wind power, photovoltaics, energy storage and load demand are optimized and dispatched in real time based on a dynamic rule optimization algorithm to obtain an optimized dispatching strategy.
[0079] The dynamic rule optimization algorithm is:
[0080]
[0081] Among them, f(x) is the objective function, g i (x) is an inequality constraint, h j (x) is an equality constraint, and x represents a decision variable.
[0082] Step 205 , generating a scheduling instruction based on the optimized scheduling strategy through a control execution module, and sending the scheduling instruction to the device layer so that the devices in the device layer respond to the scheduling instruction action.
[0083] In one embodiment, after step 105, the method further includes:
[0084] Acquire device operating parameters of the device layer, and detect abnormal behavior of the device through an anomaly detection algorithm;
[0085] Among them, the anomaly detection algorithm is:
[0086]
[0087] Among them, Z t is the control chart statistic at time t, X t is the observed value at time t, is the smoothing constant, Z t When the control limit is exceeded, the corresponding equipment is considered abnormal.
[0088] Figure 3A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure is shown in FIG. Figure 3 As shown, the electronic device 300 includes at least one processor 302; and a memory 301 that is communicatively connected to the at least one processor 302; wherein the memory 301 stores instructions that can be executed by the at least one processor 302, and the instructions are executed by the at least one processor 302 to implement the steps of the DCS-based thermal equipment inspection method described in the embodiment of the present disclosure.
[0089] Optionally, the electronic device may specifically be a DCS-based thermal equipment inspection device of an embodiment of the present application, and the electronic device may implement the corresponding processes implemented by the DCS-based thermal equipment inspection device in each method of the embodiment of the present application, which will not be described in detail here for the sake of brevity.
[0090] It is understood that the electronic device also includes a communication interface 303. The various components in the electronic device are coupled together through a bus system 304. It is understood that the bus system 304 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 304 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 3 Various buses are labeled as bus system 304 .
[0091] It can be understood that the memory 301 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 301 described in the embodiment of the present invention is intended to include but is not limited to these and any other suitable types of memories.
[0092] The method disclosed in the above embodiment of the present disclosure can be applied to the processor 302, or implemented by the processor 302. The processor 302 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 302 or the instruction in the form of software. The above processor 302 may be a general-purpose processor, a DSP, or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 302 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 301, and the processor 302 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.
[0093] In an exemplary embodiment, the electronic device may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general purpose processors, controllers, MCUs, microprocessors, or other electronic components to execute the aforementioned method.
[0094] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the steps of the DCS-based thermal equipment inspection method described in the embodiment of the present disclosure when executed.
[0095] The embodiment of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the DCS-based thermal equipment inspection method described in the embodiment of the present disclosure.
[0096] Optionally, the computer-readable storage medium can be applied to the DCS-based thermal equipment inspection device in the embodiments of the present application, and the computer instructions enable the computer to execute the corresponding processes implemented by the DCS-based thermal equipment inspection device in each method of the embodiments of the present application. For the sake of brevity, they will not be repeated here.
[0097] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.
[0098] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.
[0099] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.
[0100] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.
[0101] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.
[0102] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.
Claims
1. A wind farm energy management system, characterized in that: It includes a data acquisition module, a communication and edge computing module, an energy prediction module, a scheduling optimization module, and a control execution module which are connected in sequence; The data acquisition module is used to collect real-time data of the wind farm, and the real-time data includes wind speed, light, and energy storage status data; The communication and edge computing module is used to preprocess the real-time data to obtain preprocessed real-time data; the preprocessing includes at least one of the following: cleaning, formatting, and outlier removal; The energy prediction module is used to make short-term and long-term predictions on the power generation capacity of wind power and photovoltaic power based on preset historical data, and obtain short-term prediction results and long-term prediction results; The scheduling optimization module is used to obtain an optimized scheduling strategy according to the short-term prediction result, the long-term prediction result and the processed real-time data; The control execution module is used to generate a scheduling instruction based on the optimized scheduling strategy, and send the scheduling instruction to the device layer so that the devices in the device layer respond to the scheduling instruction action.
2. The system according to claim 1, characterized in that The system also includes a data storage module; The data storage module is used to store the pre-processed real-time data and preset historical data.
3. The system according to claim 1, characterized in that The energy prediction module is used to make short-term and long-term predictions on the power generation capacity of wind power and photovoltaic power based on preset historical data and using a seasonal ARIMA model to obtain short-term prediction results and long-term prediction results; The dispatch optimization module is used to perform real-time optimization dispatch of wind power, photovoltaic, energy storage and load demand based on the short-term prediction results, the long-term prediction results and the processed real-time data, and obtain an optimized dispatch strategy based on a dynamic rule optimization algorithm.
4. A method for implementing the wind farm energy management system as claimed in claim 3, characterized in that: The following steps are involved: Collecting real-time data of the wind farm through the data acquisition module, the real-time data including wind speed, light intensity, and energy storage status data; Preprocessing the real-time data through the communication and edge computing module to obtain preprocessed real-time data; the preprocessing includes at least one of the following: cleaning, formatting, and outlier removal; Through the energy prediction module, based on the preset historical data, the short-term and long-term predictions of wind power and photovoltaic power generation capacity are carried out to obtain short-term prediction results and long-term prediction results; Obtaining an optimized scheduling strategy according to the short-term prediction results, the long-term prediction results and the processed real-time data through a scheduling optimization module; A scheduling instruction is generated based on the optimized scheduling strategy through a control execution module, and the scheduling instruction is sent to the device layer so that the devices in the device layer respond to the scheduling instruction action.
5. The method according to claim 3, characterized in that: The short-term and long-term forecasts of wind power and photovoltaic power generation capacity are performed based on preset historical data to obtain short-term forecast results and long-term forecast results, including: Based on the preset historical data, the seasonal ARIMA model is used to make short-term and long-term forecasts of wind power and photovoltaic power generation capacity, and obtain short-term and long-term forecast results; The step of obtaining an optimized scheduling strategy based on the short-term prediction result, the long-term prediction result and the processed real-time data includes: According to the short-term prediction results, the long-term prediction results and the processed real-time data, wind power, photovoltaics, energy storage and load demand are optimized and dispatched in real time based on a dynamic rule optimization algorithm to obtain an optimized dispatching strategy.
6. The method according to claim 5, characterized in that The ARIMA model is: Among them, B is the backoff operator, φ i is the autoregressive coefficient, θ j is the moving average coefficient, d is the difference order, ε t is white noise; the seasonal part is expressed as: Where s is the seasonal period, Φ i and θ j are the seasonal autoregressive and moving average coefficients, and ds is the seasonal difference order.
7. The method according to claim 5, characterized in that The dynamic rule optimization algorithm is: Among them, f(x) is the objective function, g i (x) is an inequality constraint, h j (x) is an equality constraint, and x represents a decision variable.
8. The method according to claim 4, characterized in that After sending the scheduling instruction to the device layer, the method further includes: Acquire device operating parameters of the device layer, and detect abnormal behavior of the device through an anomaly detection algorithm; Among them, the anomaly detection algorithm is: Among them, Z t is the control chart statistic at time t, X t is the observed value at time t, is the smoothing constant, Z t When the control limit is exceeded, the corresponding equipment is considered abnormal.
9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 4 to 8.
10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 4 to 8.