Energy management method and system based on electrical energy group

Abstract

The invention belongs to the technical field of energy management, and discloses an electric energy group-based energy management method and system, and the method comprises the steps: integrating dispersed energy devices into an electric energy group based on an informatization technology, and setting a sensor to monitor the energy state of each energy device in real time; judging the correlation degree between the energy supply parameter and each energy device based on the historical operation information, and predicting the power demand in the future time period according to the historical operation information and the climate change condition; based on the real-time energy state, the energy supply condition of each energy device in the electric power energy group is managed for the first time by combining the electric power demand and the correlation degree result; and evaluating the advantages and disadvantages of the energy supply condition, carrying out energy supply secondary management operation according to the advantages and disadvantages result, and optimizing the energy management result. According to the method, the association degree between the energy supply parameters and each energy device is analyzed through the historical operation information, and a solid foundation is provided for system optimization and resource allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an energy management method and system based on electric energy groups. Background Art

[0002] A large energy group refers to an energy system composed of multiple energy devices and resources, usually including various power generation equipment (such as solar energy, wind energy, gas generators, nuclear power plants, etc.), energy storage systems (such as batteries, pumped storage), and power transmission and distribution equipment. These devices and systems are coordinated and managed through information technology and intelligent scheduling platforms to achieve efficient production, transmission, storage and distribution of energy.

[0003] Energy management of large energy groups is the coordination and optimization of multiple energy resources to ensure efficient supply, reliable transmission and sustainable use of energy. Since large energy groups usually include a variety of different energy equipment (such as generators, energy storage equipment, renewable energy facilities, etc.), management complexity is relatively high. Therefore, advanced information technology, intelligent scheduling systems, and optimization algorithms play a vital role in energy management.

[0004] However, existing energy management technologies are often unable to predict electricity demand based on climate change conditions within the supply range, making it impossible to efficiently dispatch energy based on real-time operating conditions and unable to adapt to future changes in demand, thereby reducing the effectiveness of energy management.

[0005] Therefore, how to provide an energy management method and system based on electric energy groups is a problem that needs to be solved urgently. Summary of the invention

[0006] The embodiment of the present invention provides an energy management method and system based on electric energy groups to solve the problem in the prior art that energy cannot be efficiently dispatched according to real-time operating conditions.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not a general review, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, there is provided an energy management method based on an electric energy group.

[0009] In one embodiment, an energy management method based on an electric energy group includes: Based on information technology, dispersed energy devices are integrated into electric energy groups, and sensors and data collectors are set up to monitor the energy status of each energy device in real time; Determine the correlation between energy supply parameters and various energy equipment based on historical operation information, and predict the power demand in the future time period based on historical operation information and climate change conditions; Based on the real-time energy status, combined with the power demand and correlation results, the energy supply status of each energy device in the power energy group is initially managed; Evaluate the quality of energy supply conditions, and perform secondary management operations on energy supply based on the results to optimize energy management results.

[0010] In one embodiment, determining the correlation between energy supply parameters and various energy devices based on historical operation information, and predicting the power demand in a future time period based on historical operation information and climate change conditions includes: Obtain demand parameters and load parameters of energy supply events based on historical operation information, and use the demand parameters and load parameters as energy supply parameters affecting energy supply events; Obtain the output parameters of each energy device during the historical operation process, and use the energy supply parameters as the cause sequence and the output parameters as the result sequence to form a correlation matrix; Based on the correlation matrix, the grey correlation analysis of the cause sequence and the effect sequence is performed to obtain the correlation weight between the effect sequence and the energy supply event, and the correlation between the energy supply parameters and each energy device is determined according to the correlation weight; Obtain meteorological data for future time periods within the energy supply range, and build a demand forecasting model based on meteorological data and load parameters to predict electricity demand in future time periods.

[0011] In one embodiment, performing grey correlation analysis on the cause sequence and the effect sequence based on the correlation matrix to obtain the correlation weight between the effect sequence and the energy supply event, and judging the correlation between the energy supply parameter and each energy device according to the correlation weight includes: The correlation matrix is ​​processed to obtain a normalized matrix, and the minimum and maximum differences between the result sequence and the corresponding cause sequence are determined based on the normalized matrix. The correlation between the effect sequence and the cause sequence is measured according to the difference results corresponding to the minimum difference and the maximum difference, and the correlation coefficient between the effect sequence and the cause sequence is analyzed based on the measurement results; The preliminary correlation between the effect sequence and the cause sequence is analyzed based on the correlation coefficient, and the correlation weight between the effect sequence and the cause sequence is determined based on the preliminary correlation and the correlation matrix; The secondary correlation between the result sequence and the energy supply event is analyzed based on the correlation weight and correlation matrix, and the correlation sequence between each energy supply parameter and each energy device is determined according to the secondary correlation.

[0012] In one embodiment, the preliminary correlation between the effect sequence and the cause sequence is analyzed based on the correlation coefficient, and the correlation weight between the effect sequence and the cause sequence is determined based on the preliminary correlation and the correlation matrix, including: Based on the correlation coefficient and combined with causal analysis technology, the preliminary correlation between the cause sequence and the effect sequence is determined, and the best effect sequence and the worst effect sequence are determined according to the preliminary correlation; Determine direct parameters and indirect parameters according to the best fruit sequence and the worst fruit sequence, and determine a first distance between each parameter point and the direct parameter based on the fruit sequence and the direct parameter; Determine the second distance between each parameter point and the indirect parameter based on the result sequence and the indirect factor, and define the comprehensive evaluation factor with the first distance and the second distance; The association weight between the effect sequence and the cause sequence is determined according to the comprehensive evaluation factors, and the contribution of the force parameters to the energy supply event is reflected based on the association weight.

[0013] In one embodiment, the calculation formula of the association weight is: ; ; In the formula, R r represents the association weight between the result sequence and the cause sequence, E r represents the comprehensive evaluation factor, r represents the energy supply parameter point, m represents the total number of energy supply parameters, α represents the bias, T r2 represents the second spacing of the rth energy supply parameter point, T r1 Represents the first spacing of the rth energy supply parameter point.

[0014] In one embodiment, obtaining meteorological data in a future time period within the energy supply range, and constructing a demand prediction model based on the meteorological data and load parameters to predict the power demand in the future time period includes: Collect meteorological data within the future time period within the energy supply range, perform preprocessing operations on the meteorological data, and use random forest technology to screen meteorological factors that affect electricity demand; Based on meteorological factors, a training set is selected from preprocessed meteorological data, and the initial structure of the demand forecasting model is determined by optimizing the parameter information corresponding to the least squares vector machine using the sparrow search technique; The training set is used to perform training operations on the initial structure, and nonlinear discrete mapping is introduced according to the training results to adjust the distribution uniformity of each node in the initial structure and the hyperparameter information to determine the demand forecasting model; The meteorological data corresponding to the meteorological factors are used as the input of the demand forecasting model, and the power demand is used as the output of the demand forecasting model to predict the power demand in the future time period.

[0015] In one embodiment, based on the real-time energy status, the energy supply status of each energy device in the power energy group is initially managed in combination with the power demand and the correlation result, including: Obtain real-time energy status from the power energy group, extract the operating status and output of each energy device from the implemented energy status, and obtain the electricity price within the energy supply range; An energy supply allocation model is constructed based on the operating status, production capacity and electricity price, and the objective function and constraint conditions of the energy supply allocation model are generated by combining the power demand and correlation results; The energy supply allocation model is solved based on the optimization method to generate the optimal energy supply allocation plan, and the optimal energy supply allocation plan is converted into control instructions for energy equipment.

[0016] In one embodiment, the objective function of the energy supply allocation model is expressed as: ; Where MinT (a, b, c) represents the objective function, a, b, c represent the operating status, production capacity and electricity price respectively, and Y n represents the energy consumption of the nth energy device, P represents the weight, b n represents the energy output of the nth energy device, c o Qc represents the penalty factor, a 0 represents the operating status of the energy device at the previous moment, h represents the weight, d represents the constant, xyz represents the determinant of the matrix, and G(a) represents the energy supply allocation matrix.

[0017] In one embodiment, the objective function and constraints of the energy supply allocation model include supply-demand balance constraints, equipment capacity constraints, economic cost constraints, and scheduling time constraints.

[0018] According to a second aspect of an embodiment of the present invention, an energy management system based on electric energy groups is provided.

[0019] In one embodiment, an energy management system based on electric energy groups includes: The real-time energy status acquisition module is used to integrate the scattered energy devices into a power energy group based on information technology, and set sensors and data collectors to monitor the energy status of each energy device in real time; The power demand prediction module is used to determine the correlation between energy supply parameters and various energy devices based on historical operation information, and to predict the power demand in the future time period based on historical operation information and climate change conditions; The energy preliminary management module is used to initially manage the energy supply status of each energy device in the power energy group based on the real-time energy status and combined with the power demand and correlation results; The energy management optimization module is used to evaluate the quality of energy supply conditions, and perform secondary management operations on energy supply based on the quality results to optimize energy management results.

[0020] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0021] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0022] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0023] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0024] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: 1. The present invention integrates scattered energy devices into electric energy groups based on information technology, and identifies the performance of different energy devices under different load demands by analyzing historical operating data. It can accurately determine whether energy devices provide the optimal power output under different conditions, thereby achieving efficient scheduling. At the same time, combining historical data and climate change models can predict future power demand, facilitate the subsequent adjustment of scheduling plans, and flexibly respond to the impact of climate on renewable energy supply.

[0025] 2. The present invention analyzes the correlation between energy supply parameters and various energy equipment through historical operation information, and combines meteorological data to predict the future electricity demand process, which not only makes energy supply management more accurate and efficient, but also provides a solid foundation for system optimization and resource allocation, making the later energy supply management process more intelligent and efficient, and having the ability to adapt to future demand changes, thereby improving the overall benefits of the entire energy system.

[0026] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0028] Figure 1 is a flow chart showing an energy management method based on electric energy groups according to an exemplary embodiment; Figure 2 is a principle block diagram of an energy management system based on electric energy groups according to an exemplary embodiment; Figure 3 It is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0029] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0030] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0031] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0032] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0033] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0034] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0035] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0036] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0037] Figure 1An embodiment of the energy management method based on electric energy groups of the present invention is shown.

[0038] In this optional embodiment, the energy management method based on the electric energy group includes: Step S101, integrating the dispersed energy devices into an electric energy group based on information technology, and setting sensors and data collectors to monitor the energy status of each energy device in real time; Step S103, determining the correlation between energy supply parameters and various energy devices based on historical operation information, and predicting the power demand in the future time period based on the historical operation information and climate change conditions; Step S105, based on the real-time energy status, combined with the power demand and the correlation result, initially managing the energy supply status of each energy device in the power energy group; Step S107, evaluating the quality of the energy supply status, and performing secondary management operations on the energy supply according to the quality results to optimize the energy management results.

[0039] In this optional embodiment, when decentralized energy devices are integrated into an electric energy group based on information technology, and sensors and data collectors are set to monitor the energy status of each energy device in real time, decentralized energy devices (such as solar panels, wind turbines, traditional power plants, etc.) can be integrated into a manageable electric energy group through information technology. Specifically, energy devices distributed in different geographical locations can be interconnected through Internet of Things (IoT) technology, industrial Ethernet, wireless communication and other means to form a unified communication network.

[0040] Appropriate sensors and data collectors are installed for each energy device to monitor its status and operating parameters in real time. These monitoring devices can collect key information such as temperature, pressure, power generation, voltage, current, equipment health status, etc. Through a centralized data monitoring platform, data transmitted from each energy device is received and analyzed in real time to understand the energy status and operating status of each device.

[0041] In this optional embodiment, when judging the correlation between energy supply parameters and each energy device based on historical operation information, and predicting the electricity demand in the future time period based on the historical operation information and climate change conditions, the demand parameters and load parameters of the energy supply event can be obtained based on the historical operation information, and the demand parameters and load parameters are used as energy supply parameters affecting the energy supply event; the output parameters of each energy device in the historical operation process are obtained, and the energy supply parameters are used as the cause sequence and the output parameters are used as the result sequence to form a correlation matrix; based on the correlation matrix, a grey correlation analysis is performed on the cause sequence and the result sequence to obtain the correlation weight between the result sequence and the energy supply event, and the correlation between the energy supply parameters and each energy device is judged based on the correlation weight; the meteorological data in the future time period within the energy supply range is obtained, and a demand prediction model is constructed based on the meteorological data and load parameters to predict the electricity demand in the future time period.

[0042] In this optional embodiment, when grey correlation analysis is performed on the cause sequence and the effect sequence based on the correlation matrix to obtain the correlation weight between the effect sequence and the energy supply event, and the correlation between the energy supply parameters and each energy device is judged based on the correlation weight, the correlation matrix can be outlined to obtain a normalized matrix, and the minimum difference and maximum difference between the effect sequence and the corresponding cause sequence are judged based on the normalized matrix; the correlation between the effect sequence and the cause sequence is measured based on the difference results corresponding to the minimum difference and the maximum difference, and the correlation coefficient between the effect sequence and the cause sequence is analyzed based on the measurement results; the preliminary correlation between the effect sequence and the cause sequence is analyzed based on the correlation coefficient, and the correlation weight between the effect sequence and the cause sequence is judged based on the preliminary correlation and the correlation matrix; the secondary correlation between the effect sequence and the energy supply event is analyzed based on the correlation weight and the correlation matrix, and the correlation sequence between each energy supply parameter and each energy device is determined based on the secondary correlation.

[0043] In this optional embodiment, when analyzing the preliminary correlation between the effect sequence and the cause sequence based on the correlation coefficient, and judging the correlation weight between the effect sequence and the cause sequence based on the preliminary correlation and the correlation matrix, the preliminary correlation between the cause sequence and the effect sequence can be determined based on the correlation coefficient in combination with the causal analysis technology, and the optimal effect sequence and the worst effect sequence can be determined based on the preliminary correlation; direct parameters and indirect parameters are determined based on the optimal effect sequence and the worst effect sequence, and the first distance between each parameter point and the direct parameter is determined based on the effect sequence and the direct parameter; the second distance between each parameter point and the indirect parameter is determined based on the effect sequence and the indirect factor, and a comprehensive evaluation factor is defined by the first distance and the second distance; the correlation weight between the effect sequence and the cause sequence is determined based on the comprehensive evaluation factor, and the contribution of the force parameter to the energy supply event is reflected based on the correlation weight.

[0044] In this optional embodiment, the calculation formula of the association weight is: ; ; In the formula, R r represents the association weight between the result sequence and the cause sequence, E r represents the comprehensive evaluation factor, r represents the energy supply parameter point, m represents the total number of energy supply parameters, α represents the bias, T r2 represents the second spacing of the rth energy supply parameter point, T r1 Represents the first spacing of the rth energy supply parameter point.

[0045] In this optional embodiment, when obtaining meteorological data for a future time period within the energy supply range and constructing a demand prediction model based on the meteorological data and load parameters to predict the electricity demand in the future time period, meteorological data for a future time period within the energy supply range can be collected, and the meteorological data can be preprocessed, and the meteorological factors that affect the electricity demand can be screened using random forest technology; a training set is selected from the preprocessed meteorological data based on the meteorological factors, and the sparrow search technology is used to optimize the parameter information corresponding to the least squares vector machine to determine the initial structure of the demand prediction model; the initial structure is trained using the training set, and a nonlinear discrete mapping is introduced according to the training results to adjust the distribution uniformity and hyperparameter information of each node in the initial structure to determine the demand prediction model; the meteorological data corresponding to the meteorological factors are used as the input of the demand prediction model, and the electricity demand is used as the output of the demand prediction model to predict the electricity demand in the future time period.

[0046] In this optional embodiment, when initially managing the energy supply status of each energy device in the electric energy group based on the real-time energy status and combined with the power demand and correlation results, the real-time energy status can be obtained from the electric energy group, and the operating status and output of each energy device can be extracted from the implementation energy status, and the electricity price within the energy supply range can be obtained at the same time; an energy supply allocation model is constructed based on the operating status, output and electricity price, and the objective function and constraints of the energy supply allocation model are generated in combination with the power demand and correlation results; the energy supply allocation model is solved based on the optimization method to generate the optimal energy supply allocation plan, and the optimal energy supply allocation plan is converted into control instructions for the energy equipment.

[0047] In this optional embodiment, the objective function of the energy supply allocation model is expressed as: ; Where MinT (a, b, c) represents the objective function, a, b, c represent the operating status, production capacity and electricity price respectively, and Y n represents the energy consumption of the nth energy device, P represents the weight, b nrepresents the energy output of the nth energy device, c o Qc represents the penalty factor, a 0 represents the operating status of the energy device at the previous moment, h represents the weight, d represents the constant, xyz represents the determinant of the matrix, and G(a) represents the energy supply allocation matrix.

[0048] In this optional embodiment, the objective function and constraints of the energy supply allocation model include supply and demand balance constraints, equipment capacity constraints, economic cost constraints, and scheduling time constraints.

[0049] In this optional embodiment, when evaluating the quality of energy supply conditions and performing secondary management operations on energy supply based on the quality results to optimize energy management results, various data on energy supply can be collected in real time during the operation of energy equipment, including power generation, energy consumption, equipment operating efficiency, equipment failure rate, load fluctuation, energy storage equipment status, etc.; preliminary analysis and evaluation are performed on the collected energy supply data to identify the quality of energy supply and determine whether the system operation has achieved the set goals (such as high efficiency, safety, economy, environmental protection, etc.); based on different energy equipment and system goals, standards for evaluating the quality of energy supply are determined, and these standards can measure the effect of energy supply based on multiple dimensions (such as economy, environmental protection, reliability); based on the preliminary evaluation results, in-depth correlation analysis is performed to diagnose the specific reasons for the differences in the quality of energy supply, which may include equipment performance problems, unreasonable allocation of energy resources, inaccurate demand forecasts, etc.; based on the quality evaluation results and problem diagnosis, corresponding secondary management strategies are formulated.

[0050] Figure 2 An embodiment of an energy management system based on electric energy groups of the present invention is shown.

[0051] In this optional embodiment, the energy management system based on the electric energy group includes: The real-time energy status acquisition module 201 is used to integrate the dispersed energy devices into an electric energy group based on information technology, and set sensors and data collectors to monitor the energy status of each energy device in real time; The power demand prediction module 203 is used to determine the correlation between energy supply parameters and various energy devices based on historical operation information, and to predict the power demand in the future time period based on historical operation information and climate change conditions; The energy preliminary management module 205 is used to initially manage the energy supply status of each energy device in the power energy group based on the real-time energy status and in combination with the power demand and the correlation result; The energy management optimization module 207 is used to evaluate the quality of energy supply conditions and perform secondary management operations on energy supply according to the quality results to optimize energy management results.

[0052] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0053] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0054] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0055] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0056] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0057] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. An energy management method based on electric energy groups, characterized in that: This energy management approach includes: Based on information technology, dispersed energy devices are integrated into electric energy groups, and sensors and data collectors are set up to monitor the energy status of each energy device in real time; Determine the correlation between energy supply parameters and various energy equipment based on historical operation information, and predict the power demand in the future time period based on historical operation information and climate change conditions; Based on the real-time energy status, combined with the power demand and correlation results, the energy supply status of each energy device in the power energy group is initially managed; Evaluate the quality of energy supply conditions, and perform secondary management operations on energy supply based on the results to optimize energy management results.

2. The energy management method based on electric energy groups according to claim 1 is characterized in that: The method of judging the correlation between energy supply parameters and various energy devices based on historical operation information, and predicting the power demand in the future time period based on historical operation information and climate change conditions includes: Obtain demand parameters and load parameters of energy supply events based on historical operation information, and use the demand parameters and load parameters as energy supply parameters affecting energy supply events; Obtain the output parameters of each energy device during the historical operation process, and use the energy supply parameters as the cause sequence and the output parameters as the result sequence to form a correlation matrix; Based on the correlation matrix, the grey correlation analysis of the cause sequence and the effect sequence is performed to obtain the correlation weight between the effect sequence and the energy supply event, and the correlation between the energy supply parameters and each energy device is determined according to the correlation weight; Obtain meteorological data for future time periods within the energy supply range, and build a demand forecasting model based on meteorological data and load parameters to predict electricity demand in future time periods.

3. The energy management method based on electric energy groups according to claim 2 is characterized in that: The grey correlation analysis of the cause sequence and the effect sequence based on the correlation matrix is ​​performed to obtain the correlation weight between the effect sequence and the energy supply event, and the correlation between the energy supply parameter and each energy device is determined according to the correlation weight, including: The correlation matrix is ​​processed to obtain a normalized matrix, and the minimum and maximum differences between the result sequence and the corresponding cause sequence are determined based on the normalized matrix. The correlation between the effect sequence and the cause sequence is measured according to the difference results corresponding to the minimum difference and the maximum difference, and the correlation coefficient between the effect sequence and the cause sequence is analyzed based on the measurement results; The preliminary correlation between the effect sequence and the cause sequence is analyzed based on the correlation coefficient, and the correlation weight between the effect sequence and the cause sequence is determined based on the preliminary correlation and the correlation matrix; The secondary correlation between the result sequence and the energy supply event is analyzed based on the correlation weight and correlation matrix, and the correlation sequence between each energy supply parameter and each energy device is determined according to the secondary correlation.

4. The energy management method based on electric energy groups according to claim 3 is characterized in that: The method of analyzing the preliminary correlation between the result sequence and the cause sequence based on the correlation coefficient, and judging the correlation weight between the result sequence and the cause sequence according to the preliminary correlation and the correlation matrix includes: Based on the correlation coefficient and combined with causal analysis technology, the preliminary correlation between the cause sequence and the effect sequence is determined, and the best effect sequence and the worst effect sequence are determined according to the preliminary correlation; Determine direct parameters and indirect parameters according to the best fruit sequence and the worst fruit sequence, and determine a first distance between each parameter point and the direct parameter based on the fruit sequence and the direct parameter; Determine the second distance between each parameter point and the indirect parameter based on the result sequence and the indirect factor, and define the comprehensive evaluation factor with the first distance and the second distance; The correlation weight between the effect sequence and the cause sequence is determined according to the comprehensive evaluation factors, and the contribution of the force parameters to the energy supply event is reflected based on the correlation weight.

5. The energy management method based on electric energy groups according to claim 4 is characterized in that: The calculation formula of the association weight is: ； ； In the formula, R r represents the association weight between the result sequence and the cause sequence, E r represents the comprehensive evaluation factor, r represents the energy supply parameter point, m represents the total number of energy supply parameters, α represents the bias, T r2 represents the second spacing of the rth energy supply parameter point, T r1 Represents the first spacing of the rth energy supply parameter point.

6. The energy management method based on electric energy groups according to claim 5 is characterized in that: The acquisition of meteorological data in a future time period within the energy supply range and the construction of a demand prediction model based on the meteorological data and load parameters to predict the power demand in the future time period include: Collect meteorological data within the future time period within the energy supply range, perform preprocessing operations on the meteorological data, and use random forest technology to screen meteorological factors that affect electricity demand; Based on meteorological factors, a training set is selected from preprocessed meteorological data, and the initial structure of the demand forecasting model is determined by optimizing the parameter information corresponding to the least squares vector machine using the sparrow search technique; The training set is used to perform training operations on the initial structure, and nonlinear discrete mapping is introduced according to the training results to adjust the distribution uniformity of each node in the initial structure and the hyperparameter information to determine the demand forecasting model; The meteorological data corresponding to the meteorological factors are used as the input of the demand forecasting model, and the power demand is used as the output of the demand forecasting model to predict the power demand in the future time period.

7. The energy management method based on electric energy groups according to claim 6 is characterized in that: The initial management of the energy supply status of each energy device in the electric energy group based on the real-time energy status and combined with the power demand and the correlation result includes: Obtain real-time energy status from the power energy group, extract the operating status and output of each energy device from the implemented energy status, and obtain the electricity price within the energy supply range; An energy supply allocation model is constructed based on the operating status, production capacity and electricity price, and the objective function and constraint conditions of the energy supply allocation model are generated by combining the power demand and correlation results; The energy supply allocation model is solved based on the optimization method to generate the optimal energy supply allocation plan, and the optimal energy supply allocation plan is converted into control instructions for energy equipment.

8. The energy management method based on electric energy groups according to claim 7 is characterized in that: The expression of the objective function of the energy supply allocation model is: ； Where MinT (a, b, c) represents the objective function, a, b, c represent the operating status, production capacity and electricity price respectively, and Y n represents the energy consumption of the nth energy device, P represents the weight, b n represents the energy output of the nth energy device, c o Qc represents the penalty factor, a0 represents the operating status of the energy device at the previous moment, h represents the weight, d represents the constant, xyz represents the determinant of the matrix, and G(a) represents the energy supply allocation matrix.

9. The energy management method based on electric energy groups according to claim 8 is characterized in that: The objective function and constraints of the energy supply allocation model include supply and demand balance constraints, equipment capacity constraints, economic cost constraints and scheduling time constraints.

10. An energy management system based on electric energy groups, characterized in that: The energy management system comprises: The real-time energy status acquisition module is used to integrate the scattered energy devices into a power energy group based on information technology, and set sensors and data collectors to monitor the energy status of each energy device in real time; The power demand prediction module is used to determine the correlation between energy supply parameters and various energy devices based on historical operation information, and to predict the power demand in the future time period based on historical operation information and climate change conditions; The energy preliminary management module is used to initially manage the energy supply status of each energy device in the power energy group based on the real-time energy status and combined with the power demand and correlation results; The energy management optimization module is used to evaluate the quality of energy supply conditions, and perform secondary management operations on energy supply based on the quality results to optimize energy management results.

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