Virtual power plant auxiliary analysis system and method, storage medium and electronic device

Abstract

The invention discloses a virtual power plant auxiliary analysis system and method, a storage medium and an electronic device, and relates to the field of computers, and the virtual power plant auxiliary analysis system comprises a data integration module which is used for obtaining the operation data of a plurality of energy data sources, and obtaining a plurality of first operation data of a first time period; the prediction scheduling module is connected with the data integration module, and is used for carrying out energy supply and demand analysis according to the various first operation data, and generating a first scheduling strategy according to an obtained energy supply and demand analysis result; the energy operation and maintenance management module is connected with the prediction scheduling module and used for performing energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, and the second time period is later than the first time period; through adoption of the scheme, the problem that the existing energy prediction and scheduling algorithm lacks deep understanding and accurate prediction of a complex energy system in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of computers, and more specifically, to a virtual power plant auxiliary analysis system and method, a storage medium, and an electronic device. Background Art

[0002] Globally, the energy industry is undergoing profound changes. As global climate change and environmental issues become increasingly severe, the industry is seeking more efficient and environmentally friendly energy solutions. The energy industry is moving towards a more intelligent, integrated and green direction.

[0003] With the rapid development of the economy and the improvement of people's living standards, energy demand continues to grow. At the same time, my country attaches great importance to environmental protection and climate change issues, and has put forward the goals of "carbon peak" and "carbon neutrality" to promote the optimization and upgrading of the energy structure. China is vigorously developing renewable energy such as wind power and solar energy, and actively promoting the application of advanced technologies such as smart grids and energy Internet to achieve efficient management and utilization of energy.

[0004] However, existing energy management systems have obvious deficiencies in data integration, real-time monitoring, dynamic adjustment and accurate prediction. These problems limit the performance and efficiency of energy management systems and cannot meet the needs of modern power systems for efficiency, flexibility and environmental protection.

[0005] Regarding the problem that existing energy forecasting and scheduling algorithms in related technologies lack in-depth understanding and accurate prediction of complex energy systems, no effective solution has been proposed so far.

[0006] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0007] The embodiments of the present application provide a virtual power plant auxiliary analysis system and method, storage medium and electronic device to at least solve the problem in the related art that the existing energy forecasting and scheduling algorithms lack in-depth understanding and accurate prediction of complex energy systems.

[0008] According to one aspect of an embodiment of the present application, a virtual power plant auxiliary analysis system is provided, including: a data integration module, used to acquire operating data of multiple energy data sources, and obtain multiple first operating data in a first time period; a forecasting and scheduling module, connected to the data integration module, used to perform energy supply and demand analysis based on the multiple first operating data, and generate a first scheduling strategy based on the obtained energy supply and demand analysis results; an energy operation and maintenance management module, connected to the forecasting and scheduling module, used to perform energy scheduling on the multiple energy data sources in a second time period according to the first scheduling strategy, wherein the second time period is later than the first time period.

[0009] In an exemplary embodiment, the virtual power plant auxiliary analysis system also includes a dynamic adjustment module, which is connected to the data integration module and the predictive scheduling module, and is used to: obtain a plurality of second operating data of the data integration module in the second time period, wherein the plurality of first operating data are of the same category as the plurality of second operating data; determine a plurality of third operating data according to the first scheduling strategy, wherein the plurality of third operating data are expected operating data of the plurality of energy data sources; perform deviation analysis on the plurality of second operating data and the plurality of third operating data, and send the obtained deviation analysis results to the predictive scheduling module.

[0010] In an exemplary embodiment, the dynamic adjustment module is also used to: analyze the causes of deviations of the deviation analysis results by a quick analysis method to obtain real-time analysis results; and send the real-time analysis results to the prediction and scheduling module to instruct the prediction and scheduling module to adjust the first scheduling strategy.

[0011] In an exemplary embodiment, the prediction and scheduling module is also used to: receive the real-time analysis results sent by the dynamic adjustment module; adjust the first scheduling strategy according to the real-time analysis results to obtain a second scheduling strategy; and send the second scheduling strategy to the energy operation and maintenance management module to instruct the energy operation and maintenance management module to perform energy scheduling on the multiple energy data sources according to the second scheduling strategy during the second time period.

[0012] In an exemplary embodiment, the prediction and scheduling module is further used to: update the prediction and scheduling module according to the deviation analysis result.

[0013] In an exemplary embodiment, the dynamic adjustment module is also used to: determine whether the deviation analysis result is greater than a preset threshold; if it is determined that the deviation analysis result is greater than the preset threshold, send the deviation analysis result to the predictive scheduling module, and adjust the first scheduling strategy according to the deviation analysis result.

[0014] According to another aspect of an embodiment of the present application, a virtual power plant auxiliary analysis method is also provided, including: acquiring operating data from multiple energy data sources to obtain multiple first operating data in a first time period; performing energy supply and demand analysis based on the multiple first operating data, and generating a first scheduling strategy based on the obtained energy supply and demand analysis results; performing energy scheduling on the multiple energy data sources in a second time period according to the first scheduling strategy, wherein the second time period is later than the first time period.

[0015] According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned virtual power plant auxiliary analysis method when running.

[0016] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the virtual power plant auxiliary analysis method through the computer program.

[0017] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0018] Through the present application, a virtual power plant auxiliary analysis system is proposed, which specifically includes: a data integration module, which is used to obtain operating data of multiple energy data sources, and obtain multiple first operating data within a first time period; a prediction and scheduling module, which is connected to the data integration module, and is used to perform energy supply and demand analysis based on the multiple first operating data to obtain energy supply and demand analysis results, and generate a first scheduling strategy based on the energy supply and demand analysis results; an energy operation and maintenance management module, which is connected to the prediction and scheduling module, and is used to perform energy scheduling on the multiple energy data sources according to the first scheduling strategy within a second time period, wherein the second time period is later than the first time period; through this system, a suitable scheduling strategy can be comprehensively decided based on the operating data of multiple energy data sources to complete the unified scheduling of the energy system; thereby solving the problem that the existing energy prediction and scheduling algorithms in the relevant technology lack in-depth understanding and accurate prediction of complex energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 This is a hardware structure block diagram of a virtual power plant auxiliary analysis system according to an embodiment of the present application;

[0022] Figure 2 is a structural block diagram of an optional virtual power plant auxiliary analysis system according to an embodiment of the present application;

[0023] Figure 3 is a structural block diagram of another optional virtual power plant auxiliary analysis system according to an embodiment of the present application;

[0024] Figure 4 is an overall architecture diagram of an optional virtual electric field assisted analysis system according to an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of an auxiliary function module architecture of an optional energy operation and maintenance management platform according to an embodiment of the present application;

[0026] Figure 6 is a functional architecture diagram of an optional virtual power plant auxiliary analysis system according to an embodiment of the present application;

[0027] Figure 7 is a data architecture diagram of an optional virtual power plant auxiliary analysis system according to an embodiment of the present application;

[0028] Figure 8 is a data interaction schematic diagram of an optional virtual power plant auxiliary analysis system according to an embodiment of the present application;

[0029] Fig. 9 It is a flowchart of an optional virtual power plant auxiliary analysis method according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of a virtual power plant auxiliary analysis method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the virtual power plant auxiliary analysis method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0035] In this embodiment, a virtual power plant auxiliary analysis system is provided. Figure 2is a structural block diagram of a virtual power plant auxiliary analysis system according to an embodiment of the present application, such as Figure 2 As shown, the system includes:

[0036] A data integration module 22, used to obtain operation data from multiple energy data sources to obtain multiple first operation data in a first time period;

[0037] A prediction and scheduling module 24, connected to the data integration module, configured to perform energy supply and demand analysis based on the plurality of first operation data, and generate a first scheduling strategy based on the obtained energy supply and demand analysis results;

[0038] The energy operation and maintenance management module 26 is connected to the prediction and scheduling module, and is used to perform energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, wherein the second time period is later than the first time period.

[0039] The above-mentioned system specifically includes: a data integration module, which is used to obtain the operating data of multiple energy data sources and obtain multiple first operating data within a first time period; a prediction and scheduling module, which is connected to the data integration module and is used to perform energy supply and demand analysis based on the multiple first operating data to obtain energy supply and demand analysis results, and generate a first scheduling strategy based on the energy supply and demand analysis results; an energy operation and maintenance management module, which is connected to the prediction and scheduling module and is used to perform energy scheduling on the multiple energy data sources according to the first scheduling strategy within a second time period, wherein the second time period is later than the first time period; through this system, it is possible to comprehensively decide on a suitable scheduling strategy based on the operating data of multiple energy data sources to complete the unified scheduling of the energy system; thereby solving the problem that the existing energy prediction and scheduling algorithms in the relevant technology lack in-depth understanding and accurate prediction of complex energy systems.

[0040] In an exemplary embodiment, the virtual power plant auxiliary analysis system further includes a dynamic adjustment module 28, such as Figure 3 As shown, the dynamic adjustment module 28 is connected to the data integration module 22 and the prediction and scheduling module 24, and is used to: obtain a variety of second operating data of the data integration module in the second time period, wherein the variety of first operating data is of the same category as the variety of second operating data; determine a variety of third operating data according to the first scheduling strategy, wherein the variety of third operating data is the expected operating data of the variety of energy data sources; perform deviation analysis on the variety of second operating data and the variety of third operating data, and send the obtained deviation analysis results to the prediction and scheduling module.

[0041] The dynamic adjustment module continuously monitors the real-time operating data of the energy system, including energy consumption, energy storage status (such as SOC), equipment operating status, environmental parameters (such as temperature, humidity), etc., which can be uploaded in real time through the Internet of Things interface. Then the real-time monitored data (i.e., the second operating data) is compared with the scheduling plan generated by the predictive scheduling module (i.e., the first scheduling strategy, or the third operating data corresponding to the first scheduling strategy) to identify the deviation between the plan and the actual operating status. For example, if the actual load exceeds the predicted value, or the remaining power of the energy storage system is lower than the planned requirements, the dynamic adjustment module will immediately detect these deviations and generate deviation analysis results.

[0042] Optionally, the dynamic adjustment module 28 is also used to: analyze the causes of deviations of the deviation analysis results by a quick analysis method to obtain real-time analysis results; and send the real-time analysis results to the prediction and scheduling module to instruct the prediction and scheduling module to adjust the first scheduling strategy.

[0043] Once a deviation is detected, the dynamic adjustment module will immediately start the fast analysis algorithm to quickly identify the cause of the deviation and obtain the real-time analysis result; then the real-time analysis result will be sent to the predictive scheduling module, which will calculate the most appropriate adjustment strategy based on the real-time data and the real-time analysis result, and adjust the first scheduling strategy.

[0044] Optionally, the prediction and scheduling module 24 is also used to: receive the real-time analysis results sent by the dynamic adjustment module; adjust the first scheduling strategy according to the real-time analysis results to obtain a second scheduling strategy; and send the second scheduling strategy to the energy operation and maintenance management module to instruct the energy operation and maintenance management module to perform energy scheduling on the multiple energy data sources according to the second scheduling strategy during the second time period.

[0045] After receiving the real-time analysis result sent by the dynamic adjustment module, the prediction scheduling module will immediately adjust the first scheduling strategy according to the real-time analysis result to obtain the second scheduling strategy, and execute the second scheduling strategy through the energy operation and maintenance management module to adjust the relevant equipment in the energy system. For example, if it is found that the power demand exceeds the forecast, the module may generate instructions to start additional power generation equipment or adjust the discharge rate of the energy storage system.

[0046] Optionally, the prediction and scheduling module 24 is further used to: update the prediction and scheduling module according to the deviation analysis result.

[0047] Based on the results of deviation analysis, the prediction and scheduling module uses machine learning or deep learning algorithms (such as neural networks, decision trees, support vector machines, etc.) to train and optimize the prediction and scheduling module. Among them, the prediction and scheduling module consists of two parts, one is the prediction model, and the other is the scheduling algorithm; the purpose of prediction model training is to improve the prediction accuracy so that the prediction results are closer to the actual operation situation. The optimization of the scheduling algorithm aims to more effectively adjust energy resources based on the prediction to achieve supply and demand balance, while considering factors such as cost, efficiency and environmental impact.

[0048] It should be noted that after model training and updating, the forecasting and scheduling module can regenerate a scheduling strategy that will comprehensively consider the latest forecast data, resource status, and optimization goals to more accurately guide the operation and resource allocation of the energy system. Strategy generation may involve multi-objective optimization problems, such as minimizing operating costs or reducing carbon emissions while meeting energy supply requirements.

[0049] Optionally, the dynamic adjustment module 28 is also used to: determine whether the deviation analysis result is greater than a preset threshold; if it is determined that the deviation analysis result is greater than the preset threshold, send the deviation analysis result to the prediction scheduling module, and adjust the first scheduling strategy according to the deviation analysis result.

[0050] If the deviation analysis result indicates that the gap is too large, the deviation analysis result is sent to the prediction and scheduling module so that the prediction and scheduling module can be trained and updated; and the first scheduling strategy is adjusted according to the deviation analysis result.

[0051] In an optional embodiment, the present application provides a virtual electric field assisted analysis system, the overall architecture of which is as follows: Figure 4 As shown, it includes four functional modules: virtual power plant, asset maintenance, energy manager, and basic data management, which integrate data from the application level and the energy operation and maintenance management platform.

[0052] (1) Platform service layer

[0053] The platform service layer is the infrastructure of the entire platform. The networking and deployment of server hardware and network equipment are all at this layer. The stability and economy of this layer directly affect the future use and expansion cost of the platform.

[0054] The platform has planned a cloud base (local hyper-converged edge node), which mainly uses virtualization technology to improve the utilization efficiency of hardware resources. The virtualized cloud platform mainly supports the deployment of big data platforms, AI platforms, and smart application runtime environments.

[0055] The big data platform is built based on a virtualized cluster, mainly providing storage, computing, query and data governance capabilities for massive data, and providing support for model algorithms and data mining.

[0056] The AI ​​platform is built based on a virtualized cluster, mainly providing algorithm modeling, training, and reasoning capabilities, as well as model algorithm library management capabilities to provide support for intelligent application algorithms.

[0057] This layer mainly provides data and user authority services for the virtual power plant auxiliary analysis system.

[0058] (2) Application Service Layer

[0059] The business application layer is mainly a virtual power plant auxiliary analysis system, including four functional modules: virtual power plant, asset maintenance, energy manager, and basic data management. The construction and operating environment of this layer of applications will rely on the basic capabilities provided by the platform service layer.

[0060] (3) Security guarantee mechanism

[0061] Build and simultaneously improve a security assurance system covering all levels including applications, data, and networks, as well as a supporting information security assurance system.

[0062] The following combination Figure 5 Introduce the above energy operation and maintenance management platform. Figure 5 It is a schematic diagram of the auxiliary function module architecture of the energy operation and maintenance management platform, including the Internet of Things access layer, platform service layer, application service layer, interactive display layer and security assurance system.

[0063] Specifically, the IoT access layer provides device access capabilities, accesses underlying devices and third-party systems, uploads device data to the data acquisition and transmission system and stores it, and the application layer obtains device data from the platform service layer.

[0064] Platform service layer: The platform service layer integrates the big data platform, AI platform, and model algorithm library, and provides the application service layer with unified data gateway, configuration gateway, and message gateway capabilities.

[0065] Application service layer: The auxiliary function module of the energy operation and maintenance management platform is built on the application service layer based on the security assurance system and operation and maintenance management system, and obtains equipment data from the platform service layer through a unified data gateway.

[0066] Interactive display layer: The interactive display layer is for system users. The overall system adopts B / S architecture and the system is accessed through a PC browser.

[0067] Security assurance system: Provides a security assurance system covering applications, data, networks and other levels.

[0068] Figure 6 The functional architecture of the virtual power plant auxiliary analysis system is shown, which mainly includes four major functions: virtual power plant operation, asset maintenance, energy manager and basic data management.

[0069] Virtual power plant operation: This module is the core of the platform and is responsible for the comprehensive management and evaluation and analysis of adjustable resources within the park. It analyzes the short-term potential of resources such as energy storage and air conditioning through the adjustable potential evaluation function to ensure the effective use of resources. The load forecasting function combines the energy consumption data and resource data of the park, and uses big data and deep learning technology to predict short-term and ultra-short-term load conditions to provide an accurate basis for energy scheduling. The day-ahead scheduling function formulates a detailed energy supply and demand allocation plan based on information such as load forecasts, energy prices and equipment status. The control strategy management function optimizes the operating mode of the equipment and improves the efficiency of energy operation and scheduling by setting targeted control strategies. In addition, the trading strategy function reserves an interface for docking with the provincial power virtual power plant platform.

[0070] Asset maintenance: This module focuses on intelligent analysis, including power quality analysis and historical data query. The power quality analysis function monitors and evaluates key power quality indicators such as current, voltage, and power factor to ensure the stability and safety of power supply. The historical data query function provides historical power data of specific circuits in the energy station, which facilitates operation and maintenance personnel to perform fault diagnosis and performance optimization.

[0071] Energy Shopkeeper: This module is responsible for transaction settlement and cost analysis. The metering data statistics function is based on the user energy measurement system to achieve hierarchical and itemized energy metering. The settlement form function generates user settlement forms with one click, which is convenient for fee collection and account management. The cost analysis function provides energy cost data under different time dimensions, supports export and push, and helps administrators and related personnel to control costs and make decisions.

[0072] Basic data management: This module is the data foundation of the platform, including energy user management, billing template management and business parameter configuration. The energy user management function supports the addition, editing and deletion of users to ensure the accuracy of user information. The billing template management function provides flexible billing methods and parameter configuration to meet different business needs. The business parameter configuration function uniformly manages key business parameters that affect energy billing and services.

[0073] Figure 7 The data architecture of the virtual power plant auxiliary analysis system is shown in FIG. Figure 7 As shown in the figure, the virtual power plant and auxiliary function module database of the energy operation and maintenance management platform are deployed separately, and data interaction is completed with the energy operation and maintenance management platform through the data interface:

[0074] Virtual power plant and auxiliary function modules: provide the energy operation and maintenance management platform with data such as virtual power plant control strategy, power quality analysis results, energy user settlement, cost analysis results, etc. The data flow is one-way transmission.

[0075] Energy operation and maintenance management platform: provides user permissions, energy consumption and real-time equipment operation data for virtual power plants and auxiliary function modules, and receives and forwards control instructions in virtual power plant strategies. Data flow is bidirectional.

[0076] The data of virtual power plants and auxiliary function modules include structured data and unstructured data. Structured data includes business data and supporting data, which are stored in the MySQL relational database, including control strategies, equipment operation, billing bills, energy users, business parameters, user permissions, etc. Unstructured data is stored in Minio objects, including notification templates, uploaded pictures, documents, and other data.

[0077] The data interaction between the virtual power plant and the energy operation and maintenance management platform is as follows Figure 8 As shown, equipment operation and user authority data are obtained from the energy operation and maintenance platform through the data interface, and the control strategy is sent to the energy operation and maintenance platform through the data interface.

[0078] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0079] In this embodiment, a virtual power plant auxiliary analysis method is also provided. Fig. 9 A virtual power plant auxiliary analysis method according to an embodiment of the present application includes:

[0080] Step S902, acquiring operation data from multiple energy data sources to obtain multiple first operation data in a first time period;

[0081] Step S904, performing energy supply and demand analysis according to the plurality of first operation data, and generating a first scheduling strategy according to the obtained energy supply and demand analysis results;

[0082] Step S906: performing energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, wherein the second time period is later than the first time period.

[0083] Through the above method, the operating data of multiple energy data sources are obtained to obtain multiple first operating data within a first time period; energy supply and demand analysis is performed based on the multiple first operating data to obtain energy supply and demand analysis results, and a first scheduling strategy is generated based on the energy supply and demand analysis results; energy scheduling is performed on the multiple energy data sources according to the first scheduling strategy within a second time period, wherein the second time period is later than the first time period; through the above scheme, a suitable scheduling strategy can be comprehensively decided based on the operating data of multiple energy data sources to complete the unified scheduling of the energy system; thereby solving the problem that the existing energy prediction and scheduling algorithms in related technologies lack in-depth understanding and accurate prediction of complex energy systems.

[0084] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0085] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0086] S1, acquiring operation data from multiple energy data sources to obtain multiple first operation data in a first time period;

[0087] S2, performing energy supply and demand analysis according to the plurality of first operation data, and generating a first scheduling strategy according to the obtained energy supply and demand analysis results;

[0088] S3: Perform energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, wherein the second time period is later than the first time period.

[0089] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0090] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0091] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0092] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0093] S1, acquiring operation data from multiple energy data sources to obtain multiple first operation data in a first time period;

[0094] S2, performing energy supply and demand analysis according to the plurality of first operation data, and generating a first scheduling strategy according to the obtained energy supply and demand analysis results;

[0095] S3: Perform energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, wherein the second time period is later than the first time period.

[0096] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0097] An embodiment of the present application further provides a computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program product, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0098] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0099] S1, acquiring operation data from multiple energy data sources to obtain multiple first operation data in a first time period;

[0100] S2, performing energy supply and demand analysis according to the plurality of first operation data, and generating a first scheduling strategy according to the obtained energy supply and demand analysis results;

[0101] S3: Perform energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, wherein the second time period is later than the first time period.

[0102] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0103] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0104] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A virtual power plant auxiliary analysis system, characterized in that: include: A data integration module, used to obtain operation data from multiple energy data sources to obtain multiple first operation data in a first time period; A prediction and scheduling module, connected to the data integration module, configured to perform energy supply and demand analysis based on the plurality of first operation data, and generate a first scheduling strategy based on the obtained energy supply and demand analysis results; An energy operation and maintenance management module is connected to the prediction and scheduling module, and is used to perform energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, wherein the second time period is later than the first time period.

2. The system according to claim 1, characterized in that The virtual power plant auxiliary analysis system further includes a dynamic adjustment module connected to the data integration module and the prediction and scheduling module, and configured to: Acquire a plurality of second operation data of the data integration module in the second time period, wherein the plurality of first operation data and the plurality of second operation data are of the same category; Determining a plurality of third operating data according to the first scheduling strategy, wherein the plurality of third operating data are expected operating data of the plurality of energy data sources; Perform deviation analysis on the multiple second operating data and the multiple third operating data, and send the obtained deviation analysis results to the prediction and scheduling module.

3. The system according to claim 2, characterized in that The dynamic adjustment module is also used for: Performing deviation cause analysis on the deviation analysis results by a rapid analysis method to obtain real-time analysis results; The real-time analysis result is sent to the prediction and scheduling module to instruct the prediction and scheduling module to adjust the first scheduling strategy.

4. The system according to claim 3, characterized in that The prediction and scheduling module is also used for: Receiving the real-time analysis result sent by the dynamic adjustment module; The first scheduling strategy is adjusted according to the real-time analysis results to obtain a second scheduling strategy; the second scheduling strategy is sent to the energy operation and maintenance management module to instruct the energy operation and maintenance management module to perform energy scheduling on the multiple energy data sources according to the second scheduling strategy during the second time period.

5. The system according to claim 2, characterized in that The prediction and scheduling module is also used for: The prediction and scheduling module is updated according to the deviation analysis result.

6. The system according to claim 3, characterized in that The dynamic adjustment module is also used for: Determine whether the deviation analysis result is greater than a preset threshold; When it is determined that the deviation analysis result is greater than the preset threshold, the deviation analysis result is sent to the prediction scheduling module, and the first scheduling strategy is adjusted according to the deviation analysis result.

7. A virtual power plant auxiliary analysis method, characterized in that: The virtual power plant auxiliary analysis system applied to any one of claims 1 to 6 comprises: Acquire operation data of multiple energy data sources to obtain multiple first operation data in a first time period; perform energy supply and demand analysis based on the multiple first operation data, and generate a first scheduling strategy based on the obtained energy supply and demand analysis results; Energy scheduling is performed on the multiple energy data sources according to the first scheduling strategy in a second time period, wherein the second time period is later than the first time period.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method of claim 7 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method of claim 7 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 7 are implemented.

Images