Multi-source energy collaborative optimization scheduling method and device for energy enterprise and electronic equipment

Abstract

The embodiment of the invention provides an energy enterprise multi-source energy collaborative optimization scheduling method and device and electronic equipment, and relates to the technical field of energy scheduling. The method comprises the following steps: establishing an energy facility model for each energy infrastructure, wherein the energy facility model comprises an operation state and historical data of the energy infrastructure; establishing an energy storage forecasting model and a load demand response model based on the energy facility model; determining a scheduled target area, obtaining a real-time load demand in the target area and an energy supply condition of each energy infrastructure, and obtaining a current adjustable load capacity of the target area; determining a scheduling objective function and a constraint condition, and inputting the scheduling objective function and the constraint condition into the two-stage multi-energy complementary scheduling decision model to obtain a long-term scheduling scheme and a short-term scheduling scheme output by the two-stage multi-energy complementary scheduling decision model; and integrating the long-term scheduling scheme and the short-term scheduling scheme to obtain a comprehensive scheduling scheme. According to the embodiment of the invention, unified optimization scheduling of various energy sources is realized.

Description

Technical Field

[0001] The present application relates to the technical field of energy scheduling, and specifically relates to a multi-source energy collaborative optimization scheduling method for energy enterprises, a multi-source energy collaborative optimization scheduling device for energy enterprises, an electronic device, and a corresponding storage medium. Background Art

[0002] Under the background of the rapid economic development in China, the increasing demand for energy day by day, and the serious damage to the global ecological environment, developing the clean energy industry, promoting the revolution of energy production and consumption, and building a clean, low-carbon, safe and efficient energy system are the directions of China's future energy development. The coordinated optimization of the integrated electric-thermal energy system can effectively improve the energy consumption structure during winter heating in northern China and improve the environmental pollution problem.

[0003] For example, a multi-energy collaborative optimization scheduling method for an isolated power grid system, a multi-energy collaborative optimization scheduling device for an isolated power grid system, a computer-readable storage medium, and an electronic device in an existing patent. This utility invention can only perform short-term energy scheduling on the power grid, and the energy scheduling does not have the function of long-term planning, resulting in large scheduling fluctuations, no macro-control function, and being unfavorable for the overall allocation of resources.

[0004] Currently, existing traditional energy scheduling schemes usually only schedule the common energy in a single region, and do not incorporate new energy sources such as solar energy and hydrogen energy into the unified scheduling. In addition, the energy scheduling usually adopts a unified long-term plan, so that it is impossible to respond in a timely manner under the condition of large short-term dynamic changes, and thus multi-energy cannot be optimized and scheduled. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a multi-source energy collaborative optimization scheduling method, device, and electronic device for energy enterprises, which realizes the scheduling of multiple energy sources and provides a short-term optimization scheme while making a long-term plan, so as to solve at least some of the problems in the background art.

[0006] To achieve the above object, a multi-source energy collaborative optimization scheduling method for energy enterprises is provided in this application. The method includes: establishing an energy facility model for each energy infrastructure, where the energy facility model includes the operating status and historical data of the energy infrastructure; establishing a energy storage prediction model and a load demand response model based on the energy facility model, where the energy storage prediction model is used to predict the expected energy output of the energy infrastructure, and the load demand response model is used to output the operating status of the energy infrastructure based on the input load; determining the target area for scheduling, obtaining the real-time load demand and the energy supply situation of each energy infrastructure in the target area, and obtaining the current adjustable load capacity of the target area; determining the scheduling objective function and constraints, and inputting the current adjustable load capacity, energy scheduling requirements, scheduling objective function and constraints of the target area into a two-level multi-energy complementary scheduling decision model to obtain the long-term scheduling plan and short-term scheduling plan output by the two-level multi-energy complementary scheduling decision model; synthesizing the long-term scheduling plan and the short-term scheduling plan to obtain a comprehensive scheduling plan, and scheduling the energy infrastructure in the target area based on the comprehensive scheduling plan, energy storage prediction model and load demand response model.

[0007] Optionally, establishing a energy storage prediction model and a load demand response model based on the energy facility model includes: obtaining the power generation environment data and the corresponding energy output of the power generation station in the energy facility model; establishing a energy storage prediction model including a machine learning algorithm, where the energy storage prediction model obtains the corresponding relationship between the power generation environment data and the energy output of the power generation station through training; the energy storage prediction model is used to obtain the expected energy output during the prediction period based on the obtained power generation environment data of the power generation station during the prediction period; obtaining the energy output of the energy infrastructure in the energy facility model and the operating status at the time of this energy output; establishing a energy infrastructure model including a machine learning algorithm, where the energy infrastructure model obtains the corresponding relationship between the energy output and the operating status at the time of this energy output through training; the energy infrastructure model is used to obtain the operating status of the energy infrastructure under the input load based on the input load.

[0008] Optionally, the constraints include: energy supply quantity constraint, energy price constraint, environmental protection requirement constraint and safe operation constraint.

[0009] Optionally, the long-term scheduling scheme includes a scheduling plan on a daily basis, and the short-term scheduling scheme includes a scheduling plan on an hourly basis; by integrating the long-term scheduling scheme and the short-term scheduling scheme, a comprehensive scheduling scheme is obtained, including: dividing the long-term scheduling scheme into several scheduling plan sub-schemes on an hourly basis; aligning in time the sub-schemes in the scheduling plan sub-schemes that are temporally correlated with the short-term scheduling scheme and the short-term scheduling scheme; obtaining an intermediate scheduling scheme based on the average value of the scheduling parameters in the sub-schemes after time alignment and the short-term scheduling scheme; obtaining the scheduling objective function of the intermediate scheduling scheme, and determining whether the intermediate scheduling scheme meets the constraint conditions; when the scheduling objective function is within a preset interval and the constraint conditions are met, using the intermediate scheduling scheme as the comprehensive scheduling scheme.

[0010] Optionally, the method further includes: during the execution of the comprehensive scheduling scheme, when a specific event is monitored, re-obtaining the current adjustable load capacity of the target area and inputting it into the two-level multi-energy complementary scheduling decision model to obtain a new short-term scheduling scheme, adjusting the current comprehensive scheduling scheme based on the new short-term scheduling scheme, and continuing to execute the adjusted comprehensive scheduling scheme.

[0011] Optionally, the specific event includes an event that triggers an alarm when the warning threshold is reached, and the warning threshold is set according to service rules and risks for equipment operation data, the number of times of gate opening, reservoir water level, and grid load. The warning types of the specific event include abnormal operation of equipment and facilities, abandonment power loss, benefit risk, load risk, and supply-demand imbalance.

[0012] Optionally, the method further includes: obtaining the execution result of the comprehensive scheduling scheme; when the deviation between the execution result and the expected result is within the first deviation range, correcting and updating the energy facility model; when the deviation between the execution result and the expected result is within the second deviation range, correcting and updating the two-level multi-energy complementary scheduling decision model.

[0013] In this application, an energy enterprise multi-source energy collaborative optimization scheduling device is also provided. The device includes: an equipment model module for establishing an energy facility model for each energy infrastructure, where the energy facility model includes the operating status and historical data of the energy infrastructure; a data model module for establishing a energy storage prediction model and a load demand response model based on the energy facility model, where the energy storage prediction model is used to predict the expected energy output of the energy infrastructure, and the load demand response model is used to output the operating status of the energy infrastructure based on the input load; a capacity determination module for determining the target area of the scheduling, obtaining the real-time load demand in the target area and the energy supply situation of each energy infrastructure, and obtaining the current adjustable load capacity of the target area; a scheme output module for determining the scheduling objective function and constraints, inputting the current adjustable load capacity, energy scheduling requirements, scheduling objective function and constraints of the target area into a two-level multi-energy complementary scheduling decision model, and obtaining the long-term scheduling scheme and short-term scheduling scheme output by the two-level multi-energy complementary scheduling decision model; and a comprehensive scheduling module for synthesizing the long-term scheduling scheme and the short-term scheduling scheme to obtain a comprehensive scheduling scheme, and scheduling the energy infrastructure in the target area based on the comprehensive scheduling scheme, the energy storage prediction model and the load demand response model.

[0014] In this application, an electronic device is also provided, including: at least one processor; a memory connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the at least one processor implements the foregoing energy enterprise multi-source energy collaborative optimization scheduling method by executing the instructions stored in the memory.

[0015] In this application, a machine-readable storage medium is also provided, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute and implement the foregoing energy enterprise multi-source energy collaborative optimization scheduling method.

[0016] In this application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the foregoing energy enterprise multi-source energy collaborative optimization scheduling method is implemented.

[0017] The above technical solutions have the following beneficial effects:

[0018] The energy enterprise multi-source energy collaborative optimization scheduling method realizes the unified optimization scheduling of various energies, saves more costs, meets the electricity demand, and at the same time, through the two-level scheduling method, long-term planning and short-term planning are carried out to better cope with emergencies.

[0019] Other features and advantages of the embodiments of this application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the following specific implementation manners, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the accompanying drawings:

[0021] Figure 1 Schematically shows a schematic diagram of the steps of the multi-source energy collaborative optimization scheduling method for energy enterprises according to an embodiment of the present application;

[0022] Figure 2 Schematically shows a schematic diagram of the structure of the multi-source energy collaborative optimization scheduling device for energy enterprises according to an embodiment of the present application;

[0023] Figure 3 Schematically shows an internal structure diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The following will detail the specific implementation manners of the embodiments of the present application with reference to the accompanying drawings. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of the present application, and do not limit the embodiments of the present application.

[0025] Figure 1 Schematically shows a schematic diagram of the steps of the multi-source energy collaborative optimization scheduling method for energy enterprises according to an embodiment of the present application. As Figure 1 shown, a multi-source energy collaborative optimization scheduling method for energy enterprises, the method includes:

[0026] S01. Establish an energy facility model for each energy infrastructure, where the energy facility model includes the operating status and historical data of the energy infrastructure;

[0027] S02. Based on the energy facility model, establish a energy storage prediction model and a load demand response model. The energy storage prediction model is used to predict the expected energy output of the energy infrastructure, and the load demand response model is used to output the operating status of the energy infrastructure based on the input load;

[0028] S03. Determine the target area for scheduling, obtain the real-time load demand and the energy supply situation of each energy infrastructure in the target area, and obtain the current adjustable load capacity of the target area;

[0029] S04. Determine the scheduling objective function and constraint conditions, and input the current adjustable load capacity, energy scheduling requirements, scheduling objective function and constraint conditions of the target area into a two-level multi-energy complementary scheduling decision model to obtain a long-term scheduling plan and a short-term scheduling plan output by the two-level multi-energy complementary scheduling decision model;

[0030] S05. Combine the long-term scheduling plan and the short-term scheduling plan to obtain an integrated scheduling plan, and schedule the energy infrastructure in the target area based on the integrated scheduling plan, the energy storage prediction model, and the load demand response model.

[0031] Through the above implementation manners, the scheduling plan has the advantages of being more optimized and more energy-saving, solving the problems that the existing traditional energy scheduling plans usually only schedule the common energy in a single area, and new energy such as solar energy and hydrogen energy is not included in the unified scheduling. In addition, the energy scheduling usually adopts a unified long-term plan, so that in the case of large dynamic changes in the short term, corresponding actions cannot be taken in a timely manner, and further, the multi-energy cannot be optimally scheduled.

[0032] In the foregoing step S01, three-dimensional visualization technology is used to construct three-dimensional models of new energy power generation infrastructures such as hydropower stations, wind power generation, and photovoltaic power generation, and intuitive visual displays are made of indicators such as their operation data, energy storage, energy utilization efficiency, energy waste, and energy pollution, and past electricity consumption data is collected to generate a database. Further, the foregoing historical data also includes the collection of power generation environment data of power generation stations, which may include weather data, temperature, wind speed, solar irradiance, etc., equipment status data such as generator efficiency, equipment failure rate, etc., and the collected data is cleaned and preprocessed to ensure data integrity.

[0033] The foregoing energy storage prediction model includes the following parts: 1) A scheduling model that simulates the operation of energy storage under specific market conditions and predicts the revenue performance of the energy storage system by calculating the revenue and cycle rate of the energy storage participating in various power transactions; 2) A production cost model that calculates the power generation output, auxiliary service procurement, and energy prices in each region in each period, providing a reference basis for the optimal economic scheduling simulation to meet the predicted demand; 3) Physical model construction. The energy storage display sand table model needs to be designed with a reduced scale according to the actual energy storage facilities to ensure the accurate scale and complete structure of the model; 4) Data collection and monitoring: Sensors are embedded inside the model to simulate and monitor the working state of the model, such as the charging level of the battery, temperature change, etc.

[0034] In some embodiments of the present application, an energy storage prediction model and a load demand response model are established based on the energy facility model, including: obtaining the power generation environment data and the corresponding energy output in the energy facility model; establishing an energy storage prediction model including a machine learning algorithm, where the energy storage prediction model obtains the corresponding relationship between the power generation environment data and the energy output through training; the energy storage prediction model is used to obtain the expected energy output during the prediction period based on the power generation environment data of the power generation station obtained during the prediction period; obtaining the energy output in the energy facility model and the operating state at the time of this energy output; establishing an energy facility model including a machine learning algorithm, where the energy facility model obtains the corresponding relationship between the energy output and the operating state at the time of this energy output through training; the energy facility model is used to obtain the operating state of the energy infrastructure under the input load based on the input load. The energy storage prediction model and the load demand response model in the present application can be created based on machine learning algorithms. By analyzing the operation data of the new energy power generation infrastructure combined with meteorological data, such as wind direction and speed, rainfall, solar radiation, etc., the available energy, benefit risks, and curtailment losses can be reasonably predicted; through the modeling analysis of historical data: based on historical data, a power grid operation load assessment model, a volatility prediction model of wind energy and photovoltaic energy, a risk assessment model, etc. are built, and a three-dimensional model is constructed to make the data more intuitive and more convenient for macro allocation. The energy storage prediction model can monitor the equipment and calculate the production cost at the same time, ensure the normal operation of the equipment, and provide data support for the collaborative optimization scheduling of multi-source energy. Adjust the load demand response model to ensure that the actual demand is adapted to the data of the load demand response model, and at the same time guide users to use electricity flexibly to avoid excessive power consumption during peak hours. In this embodiment, it also includes the optimization of the model. Input the data within a preset time collected into the load demand response model, compare it with the actual data, and train and adjust the load demand response model to make the prediction result more accurate.

[0035] In some embodiments of the present application, the constraint conditions include: energy supply quantity constraint, energy price constraint, environmental protection requirement constraint, and safe operation constraint. The energy supply quantity constraint means that it should not work at the maximum output of the energy supply equipment for a long time, but the output energy constraint within the rated output. The energy price constraint includes selecting energy supply equipment in the order of increasing energy price. The environmental protection requirement constraint includes the constraint with the strategy of giving priority to clean energy, that is, giving priority to wind, light, and water, and using thermal power generation as a supplement for the supply quantity constraint. The safe operation constraint includes the constraint of giving priority to low-risk energy supply and using high-risk energy functions as a supplement.

[0036] In some embodiments of the present application, the long-term scheduling scheme includes a scheduling plan on a daily basis, and the short-term scheduling scheme includes a scheduling plan on an hourly basis; by integrating the long-term scheduling scheme and the short-term scheduling scheme, an integrated scheduling scheme is obtained, including: dividing the long-term scheduling scheme into several scheduling plan sub-schemes on an hourly basis; aligning in time the sub-schemes in the scheduling plan sub-schemes that are temporally correlated with the short-term scheduling scheme and the short-term scheduling scheme; obtaining an intermediate scheduling scheme based on the average value of the scheduling parameters in the sub-schemes after time alignment and the short-term scheduling scheme; obtaining the scheduling objective function of the intermediate scheduling scheme and determining whether the intermediate scheduling scheme meets the constraint conditions; when the scheduling objective function is within a preset range and meets the constraint conditions, using the intermediate scheduling scheme as the integrated scheduling scheme. This embodiment provides the integration of the long-term scheduling scheme and the short-term scheduling scheme, and the integration is based on temporal correlation. When there is a first value corresponding to the scheduling plan sub-scheme of the long-term scheduling scheme and a second value corresponding to the short-term scheduling scheme at the same moment, an intermediate scheduling scheme is generated based on the average value of the two. This intermediate scheduling scheme is only a compromise result, but it still needs to meet the scheduling objective function and the constraint conditions. When it meets, the intermediate scheduling scheme is used as the integrated scheduling scheme. When it does not meet, the intermediate scheduling scheme needs to be corrected. The correction method includes approaching some values in the intermediate scheduling scheme to the scheduling plan sub-scheme of the long-term scheduling scheme or the short-term scheduling scheme so that it meets the scheduling objective function and the constraint conditions within the range of the two.

[0037] In some embodiments of the present application, the method further includes: during the execution of the integrated scheduling scheme, when a specific event is monitored to occur, re-obtaining the current adjustable load capacity of the target area and inputting it into the two-level multi-energy complementary scheduling decision model to obtain a new short-term scheduling scheme, adjusting the current integrated scheduling scheme based on the new short-term scheduling scheme, and continuing to execute the adjusted integrated scheduling scheme. In this embodiment, abnormal situations during execution are considered. When a specific event that affects the execution of the scheme occurs, if the integrated scheduling scheme continues to be executed, it is very likely that the scheduling objective cannot be achieved. Therefore, when a specific event occurs, the previous integrated scheduling scheme needs to be corrected. The correction method provided in this embodiment includes: regenerating a short-term scheduling scheme based on the new adjustable load capacity and correcting the current integrated scheduling scheme based on this short-term scheduling scheme.

[0038] In some embodiments of the present application, the specific event includes an event that reaches a warning threshold to trigger a warning, and the warning threshold is set according to business rules and risks for equipment operation data, gate opening times, reservoir water level, and grid load, and the warning type of the specific event includes abnormal operation of equipment and facilities, power abandonment loss, benefit risk, load risk, and supply and demand imbalance. This embodiment provides a definition of specific events. Among them, abnormal operation of equipment and facilities will affect their energy output, and non-technical risks such as power abandonment loss, benefit risk, load risk, and supply and demand imbalance will lead to the failure to achieve the predetermined goals.

[0039] In some embodiments of the present application, the method further includes: obtaining the execution result of the comprehensive scheduling scheme; when the deviation between the execution result and the expected result is within a first deviation range, correcting and updating the energy facility model; when the deviation between the execution result and the expected result is within a second deviation range, correcting and updating the two-level multi-energy complementary scheduling decision model. The present application also provides a backfill operation on the aforementioned model based on the execution result. When the deviation between the execution result and the expected result is small, it can be corrected by adjusting the energy facility model. When the deviation between the execution result and the expected result is large, it is necessary to correct it by adjusting the two-level multi-energy complementary scheduling decision model. When adjusting the model, it is necessary to first determine the parameters to be adjusted in the model and the adjustment function of the parameters. By fine-tuning the parameters in the model, the scheduling accuracy is improved.

[0040] Through the above implementation methods, unified optimization and scheduling of various energy sources are achieved, which is more cost-effective and meets electricity demand. At the same time, through a two-level scheduling method, long-term and short-term planning are carried out to better respond to emergencies.

[0041] Based on the same inventive concept, the present application also provides a multi-source energy collaborative optimization scheduling device for an energy enterprise, Figure 2 The schematic diagram shows the structure of the multi-source energy collaborative optimization scheduling device of the energy enterprise according to the implementation mode of the present application. Figure 2As shown, the device includes: a device model module for establishing an energy facility model for each energy infrastructure, where the energy facility model includes the operating status and historical data of the energy infrastructure; a data model module for establishing a energy storage prediction model and a load demand response model based on the energy facility model, where the energy storage prediction model is used to predict the expected energy output of the energy infrastructure, and the load demand response model is used to output the operating status of the energy infrastructure based on the input load; a capacity determination module for determining the target area of scheduling, obtaining the real-time load demand in the target area and the energy supply situation of each energy infrastructure, and obtaining the current adjustable load capacity of the target area; a solution output module for determining the scheduling objective function and constraint conditions, inputting the current adjustable load capacity, energy scheduling requirements, scheduling objective function and constraint conditions of the target area into a two-level multi-energy complementary scheduling decision model, and obtaining the long-term scheduling plan and short-term scheduling plan output by the two-level multi-energy complementary scheduling decision model; and a comprehensive scheduling module for synthesizing the long-term scheduling plan and the short-term scheduling plan to obtain a comprehensive scheduling plan, and scheduling the energy infrastructure in the target area based on the comprehensive scheduling plan, the energy storage prediction model and the load demand response model.

[0042] In some alternative embodiments of the present application, establishing an energy storage prediction model and a load demand response model based on the energy facility model includes: obtaining the power generation environment data and the corresponding energy output in the energy facility model; establishing an energy storage prediction model including a machine learning algorithm, where the energy storage prediction model obtains the corresponding relationship between the power generation environment data and the energy output through training; the energy storage prediction model is used to obtain the expected energy output during the prediction period based on the obtained power generation environment data of the power generation station during the prediction period; obtaining the energy output in the energy facility model and the operating status at the time of the energy output; establishing an energy facility model including a machine learning algorithm, where the energy facility model obtains the corresponding relationship between the energy output and the operating status at the time of the energy output through training; the energy facility model is used to obtain the operating status of the energy infrastructure under the input load based on the input load.

[0043] In some alternative embodiments of the present application, the constraint conditions include: energy supply quantity constraint, energy price constraint, environmental protection requirement constraint and safe operation constraint.

[0044] In some alternative embodiments of the present application, the long-term scheduling scheme includes a daily scheduling plan, and the short-term scheduling scheme includes an hourly scheduling plan; by integrating the long-term scheduling scheme and the short-term scheduling scheme, a comprehensive scheduling scheme is obtained, including: dividing the long-term scheduling scheme into several hourly scheduling plan sub-schemes; aligning in time the sub-schemes in the scheduling plan sub-schemes that are temporally correlated with the short-term scheduling scheme and the short-term scheduling scheme; obtaining an intermediate scheduling scheme based on the average values of the scheduling parameters in the sub-schemes after time alignment and the short-term scheduling scheme; obtaining the scheduling objective function of the intermediate scheduling scheme, and determining whether the intermediate scheduling scheme meets the constraint conditions; when the scheduling objective function is within a preset interval and the constraint conditions are met, using the intermediate scheduling scheme as the comprehensive scheduling scheme.

[0045] In some alternative embodiments of the present application, the device further includes a burst correction module, which is used for: during the execution of the comprehensive scheduling scheme, when a specific event is detected, re-obtaining the current adjustable load capacity of the target area and inputting it into the two-level multi-energy complementary scheduling decision model to obtain a new short-term scheduling scheme, adjusting the current comprehensive scheduling scheme based on the new short-term scheduling scheme, and continuing to execute the adjusted comprehensive scheduling scheme.

[0046] In some alternative embodiments of the present application, the specific event includes an event that triggers an alarm when the warning threshold is reached. The warning threshold is set according to service rules and risks for equipment operation data, gate opening times, reservoir water levels, and grid loads. The warning types of the specific event include abnormal operation of equipment and facilities, abandonment power loss, benefit risk, load risk, and supply-demand imbalance.

[0047] In some alternative embodiments of the present application, the device further includes a model compensation module, which is used for: obtaining the execution result of the comprehensive scheduling scheme; when the deviation between the execution result and the expected result is within the first deviation range, correcting and updating the energy facility model; when the deviation between the execution result and the expected result is within the second deviation range, correcting and updating the two-level multi-energy complementary scheduling decision model.

[0048] The specific definitions of the various functional modules in the above-mentioned multi-source energy collaborative optimization scheduling device for energy enterprises can be referred to the definitions of the multi-source energy collaborative optimization scheduling method for energy enterprises in the foregoing text, and will not be elaborated here. Each module in the above system can be implemented in whole or in part by software, hardware, and their combinations. The above-mentioned modules can be embedded in the processor of the electronic device in the form of hardware or be independent of it, or can be stored in the memory of the electronic device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules. It also realizes the advantage of unified optimization scheduling of various energies.

[0049] In this application, a multi-source energy collaborative optimization scheduling system for energy enterprises is also provided. The system includes: a main controller module, which is wirelessly connected to the data acquisition and monitoring module, load forecasting module, automatic generation control module, energy scheduling module, safety warning module, and energy efficiency analysis module, and controls each module to achieve collaborative work and ensure more accurate energy scheduling. The data acquisition and monitoring module is responsible for real-time acquisition of various data in the power system; the load forecasting module predicts the load changes in the future period by analyzing historical data and the current system state; the automatic generation control module realizes load frequency control and economic scheduling to ensure the stability of the system frequency and optimize the operation of the units; the energy scheduling module makes long-term and short-term plans according to demands to achieve optimal energy scheduling; the safety warning module warns of equipment losses and sudden power consumption situations; the energy efficiency analysis module automatically calculates energy indicators based on energy basic data and various basic reports, including unit consumption of equipment, process unit consumption, company energy consumption, etc., and then calculates the energy consumption cost. The foregoing system is wirelessly connected to the data acquisition and monitoring module, load forecasting module, automatic generation control module, energy scheduling module, safety warning module, and energy efficiency analysis module through the main controller module, controls each module, and thus realizes collaborative work to ensure more accurate energy scheduling. Each module cooperates with each other to make energy scheduling more optimized.

[0050] In some embodiments of this application, an electronic device is also provided, including: at least one processor; a memory connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the at least one processor executes the foregoing multi-source energy collaborative optimization scheduling method for energy enterprises. Its internal structure diagram can be as Figure 3 shown. Figure 3 Schematically shows the internal structure diagram of the electronic device according to the embodiments of this application. The electronic device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The network interface A02 of the electronic device is used to communicate with external terminals through a network connection. When the computer program B02 is executed by the processor A01, it realizes a multi-source energy collaborative optimization scheduling method for energy enterprises.

[0051] Those skilled in the art can understand,Figure 3 The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0052] In an implementation provided by this application, a machine-readable storage medium is provided. Instructions are stored on the machine-readable storage medium, and when the instructions are executed by a processor, the processor is configured to execute the aforementioned multi-source energy collaborative optimization scheduling method for energy enterprises.

[0053] In an implementation provided by this application, a computer program product is provided, including a computer program that implements the aforementioned multi-source energy collaborative optimization scheduling method for energy enterprises when executed by a processor.

[0054] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0056] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 steps for implementing the functions specified in one block or multiple blocks.

[0058] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0059] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0060] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0061] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, commodity or device comprising the element.

[0062] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for coordinated optimization and scheduling of multi-source energy in energy enterprises, characterized in that: The method includes: Establishing an energy facility model for each energy infrastructure, wherein the energy facility model includes the operation status and historical data of the energy infrastructure; Establishing an energy storage forecasting model and a load demand response model based on the energy facility model, wherein the energy storage forecasting model is used to predict the expected energy output of the energy infrastructure, and the load demand response model is used to output the operating status of the energy infrastructure based on the input load; Determine the target area for scheduling, obtain the real-time load demand in the target area and the energy supply status of each energy infrastructure, and obtain the current adjustable load capacity of the target area; Determine the scheduling objective function and constraints, input the current adjustable load capacity, energy scheduling demand, scheduling objective function and constraints of the target area into the two-level multi-energy complementary scheduling decision model, and obtain the long-term scheduling plan and short-term scheduling plan output by the two-level multi-energy complementary scheduling decision model; The long-term scheduling plan and the short-term scheduling plan are combined to obtain a comprehensive scheduling plan, and the energy infrastructure in the target area is scheduled based on the comprehensive scheduling plan, the energy storage forecasting model and the load demand response model.

2. The method according to claim 1, characterized in that An energy storage forecasting model and a load demand response model are established based on the energy facility model, including: Acquire power generation environment data of the power station and corresponding energy output in the energy facility model; Establishing an energy storage forecasting model including a machine learning algorithm, wherein the energy storage forecasting model obtains a corresponding relationship between power generation environment data of a power station and energy output through training; the energy storage forecasting model is used to obtain the expected energy output within the forecasting time period based on the power generation environment data of the power station within the forecasting time period obtained; Obtaining energy output in the energy facility model and the operating status of the energy output; An energy facility model including a machine learning algorithm is established, wherein the energy facility model obtains the correspondence between energy output and the operating status at the time of energy output through training; the energy facility model is used to obtain the operating status of the energy infrastructure under the input load based on the input load.

3. The method according to claim 1, characterized in that The constraints include: energy supply constraints, energy price constraints, environmental protection requirements constraints and safe operation constraints.

4. The method according to claim 1, characterized in that The long-term scheduling plan includes a scheduling plan based on days, and the short-term scheduling plan includes a scheduling plan based on hours; Combining the long-term scheduling plan and the short-term scheduling plan, a comprehensive scheduling plan is obtained, including: Dividing the long-term scheduling plan into a number of scheduling planning sub-plans in hours; Time-aligning the short-term scheduling plan with the sub-plan in the scheduling plan that has a time correlation with the short-term scheduling plan; An intermediate scheduling plan is obtained based on the average values ​​of scheduling parameters in the time-aligned sub-plans and the short-term scheduling plan; Obtaining a scheduling objective function of the intermediate scheduling solution, and determining whether the intermediate scheduling solution satisfies a constraint condition; When the scheduling objective function is within a preset interval and meets the constraint conditions, the intermediate scheduling plan is used as the comprehensive scheduling plan.

5. The method according to claim 1, characterized in that The method also includes: during the execution of the comprehensive scheduling plan, when a specific event is monitored, the current adjustable load capacity of the target area is re-acquired and input into a two-level multi-energy complementary scheduling decision model to obtain a new short-term scheduling plan, the current comprehensive scheduling plan is adjusted based on the new short-term scheduling plan, and the adjusted comprehensive scheduling plan continues to be executed.

6. The method according to claim 5, characterized in that The specific events include events that trigger warnings when the warning threshold is reached. The warning threshold is set based on business rules and risks for equipment operation data, gate opening times, reservoir water level, and grid load. The warning types of the specific events include abnormal operation of equipment and facilities, power abandonment losses, benefit risks, load risks, and supply and demand imbalances.

7. The method according to claim 1, characterized in that The method further comprises: obtaining an execution result of the comprehensive scheduling scheme; When the deviation between the execution result and the expected result is within a first deviation range, the energy facility model is corrected and updated; When the deviation between the execution result and the expected result is within a second deviation range, the two-level multi-energy complementary scheduling decision model is corrected and updated.

8. A multi-source energy collaborative optimization scheduling device for an energy enterprise, characterized in that: The device includes: An equipment model module, used to establish an energy facility model for each energy infrastructure, wherein the energy facility model includes the operation status and historical data of the energy infrastructure; A data model module, used to establish an energy storage forecasting model and a load demand response model based on the energy facility model, wherein the energy storage forecasting model is used to predict the expected energy output of the energy infrastructure, and the load demand response model is used to output the operating status of the energy infrastructure based on the input load; A capacity determination module is used to determine the target area for scheduling, obtain the real-time load demand in the target area and the energy supply status of each energy infrastructure, and obtain the current adjustable load capacity of the target area; a scheme output module, used to determine the scheduling objective function and constraints, input the current adjustable load capacity, energy scheduling demand, scheduling objective function and constraints of the target area into the two-level multi-energy complementary scheduling decision model, and obtain the long-term scheduling scheme and short-term scheduling scheme output by the two-level multi-energy complementary scheduling decision model; and The comprehensive scheduling module is used to integrate the long-term scheduling plan and the short-term scheduling plan to obtain a comprehensive scheduling plan, and schedule the energy infrastructure in the target area based on the comprehensive scheduling plan, the energy storage forecasting model and the load demand response model.

9. An electronic device, characterized in that: include: at least one processor; a memory connected to the at least one processor; Among them, the memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the steps of the multi-source energy collaborative optimization scheduling method for an energy enterprise as described in any one of claims 1 to 7 by executing the instructions stored in the memory.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method for collaborative optimization and scheduling of multi-source energy for an energy enterprise as described in any one of claims 1 to 7 are implemented.

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