A method and device for identifying development motivation of an energy power system

Abstract

This invention proposes a method, device, electronic device, and storage medium for identifying the driving forces of energy and power system development. The identification method employs fuzzy cognitive graph theory to construct a fuzzy cognitive graph of the driving forces of future power system development, screening the core structural forms of the power system. Based on the screened power system structural forms, a power system planning model adapted to the corresponding structural forms is constructed. Among the driving forces in the fuzzy cognitive graph, those with deep uncertainty are selected for consideration, and the uncertainty range is estimated. Based on the power system planning model adapted to the corresponding structural forms, a computer experiment method is used to generate a massive number of scenarios, combining the deep uncertainty driving forces and the uncertainty range. A scenario discovery technique based on data mining is used to analyze the generated massive number of scenarios to identify the core driving forces of power system development. This invention proposes a method for identifying the driving forces of power system development that comprehensively considers deep uncertainty and the characteristics of the power system.

Description

Technical Field

[0001] This invention relates to the field of energy system optimization technology, and in particular to a method, apparatus, electronic device and storage medium for identifying the driving forces of energy and power system development. Background Technology

[0002] Identifying the driving forces behind power system development is crucial for both predicting future trends in power system planning and supporting decision-making in power system operation strategies, possessing significant theoretical and practical value. Currently, the power system is undergoing unprecedented transformations. First, external drivers such as fossil fuel depletion and climate change have replaced internal drivers like technological innovation as the primary driving force of this round of energy and power transition, resulting in often abrupt rather than gradual systemic changes. Second, challenges arise from deep uncertainty, including Knight's uncertainty (which cannot be described by probabilistic methods) regarding technological advancements and morphological changes in the power system, as well as uncertainties regarding scenarios, decision consequences, and decision schemes, making risk-based analysis methods difficult to apply. Currently, three types of methods are involved in driver identification research: power system planning models, complex network theory, and exploratory modeling based on system dynamics. However, these methods either fail to consider deep uncertainty or the characteristics of the power system itself. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, electronic device and storage medium for identifying the driving forces of energy and power system development, and to propose a method for identifying the driving forces of power system development that comprehensively considers deep uncertainty and the characteristics of power system.

[0004] In a first aspect, embodiments of the present invention provide a method for identifying the driving forces behind the development of energy and power systems, the method comprising:

[0005] S1. Using fuzzy cognitive graph theory, construct a fuzzy cognitive graph of the driving forces of future power system development, and screen the core structural forms of the power system;

[0006] S2. Based on the selected power system structure, construct a power system planning model that adapts to the corresponding structure.

[0007] S3. Among the driving forces behind the development of fuzzy cognitive maps, select those with deep uncertainty and estimate the range of uncertainty.

[0008] S4. Based on the power system planning model adapted to the corresponding structural form, a large number of scenarios are generated by combining computer experiments to address the driving forces and range of deep uncertainty.

[0009] S5. Employ data mining-based scenario discovery techniques to analyze and generate massive amounts of scenarios, and identify the core drivers of power system development.

[0010] Secondly, embodiments of the present invention provide an energy and power system development driver identification device, the identification device comprising:

[0011] The filtering module is used to construct a fuzzy cognitive graph of the driving forces of future power system development using fuzzy cognitive graph theory, and to filter the core structural forms of the power system.

[0012] The model building module constructs a power system planning model that adapts to the selected power system structure.

[0013] The range estimation module is used to select an investigation with deep uncertainty from the development drivers of fuzzy cognitive maps and estimate the range of uncertainty.

[0014] The combined module, based on a power system planning model adapted to the corresponding structural form, uses computer experiments to generate a massive number of scenarios, targeting the driving forces and range of deep uncertainty.

[0015] The identification module analyzes massive amounts of scenarios generated by data mining scenario discovery technology to identify the core drivers of power system development.

[0016] Thirdly, embodiments of the present invention provide an electronic device, including:

[0017] At least one processor; and,

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for identifying the driving forces of energy and power system development.

[0020] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the above-described method for identifying the driving forces of energy and power system development.

[0021] Beneficial effects

[0022] This invention proposes a method, device, electronic device, and storage medium for identifying the driving forces of energy and power system development. The identification method employs fuzzy cognitive graph theory to construct a fuzzy cognitive graph of the driving forces of future power system development, screening the core structural forms of the power system. Based on the screened power system structural forms, a power system planning model adapted to the corresponding structural forms is constructed. Among the driving forces in the fuzzy cognitive graph, those with deep uncertainty are selected for consideration, and the uncertainty range is estimated. Based on the power system planning model adapted to the corresponding structural forms, a computer experiment method is used to generate a massive number of scenarios, combining the deep uncertainty driving forces and the uncertainty range. A scenario discovery technique based on data mining is used to analyze the generated massive number of scenarios to identify the core driving forces of power system development. This invention proposes a method for identifying the driving forces of power system development that comprehensively considers deep uncertainty and the characteristics of the power system. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for identifying the driving forces of energy and power system development according to an embodiment of the present invention;

[0024] Figure 2 for Figure 1 A flowchart of the specific method for step S1;

[0025] Figure 3 This is a structural block diagram of an energy and power system development driver identification device according to an embodiment of the present invention;

[0026] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Figure 1 A flowchart of a method for identifying the driving forces of energy and power system development is shown; for example... Figure 1 As shown, the identification method includes:

[0029] S1. Using fuzzy cognitive graph theory, construct a fuzzy cognitive graph of the driving forces of future power system development, and screen the core structural forms of the power system;

[0030] S2. Based on the selected power system structure, construct a power system planning model that adapts to the corresponding structure.

[0031] S3. Among the driving forces behind the development of fuzzy cognitive maps, select those with deep uncertainty and estimate the range of uncertainty.

[0032] S4. Based on the power system planning model adapted to the corresponding structural form, a large number of scenarios are generated by combining computer experiments to address the driving forces and range of deep uncertainty.

[0033] S5. Employ data mining-based scenario discovery techniques to analyze and generate massive amounts of scenarios, and identify the core drivers of power system development.

[0034] like Figure 2 As shown, step S1 includes:

[0035] S11. Selecting the target concept

[0036] The morphology of source-network-load is listed as a target concept. It should be noted that, unlike the usual fuzzy cognitive map which only has one target concept, a set of target concepts will be presented.

[0037] S12, Driving Forces of Literature Review Development

[0038] The literature review step aims to identify the drivers influencing the target concept as comprehensively as possible. This avoids overlooking any influential concepts and reduces the workload of the expert panel in subsequent steps.

[0039] S13. Organize expert discussions to determine the final concept.

[0040] The main objectives of the expert seminar included confirming that concepts reflect future paradigm shifts, that concepts follow mutually exclusive and complete set rules, that each concept is appropriately granular, and that causal relationships and their directions are determined. The expert panel consisted of mid-level managers from power generation and grid companies, researchers from power industry associations, consultants from consulting firms, and associate / professors from universities.

[0041] S14. Organize experts to discuss and evaluate causal weights to form a fuzzy cognitive map.

[0042] Based on the given concept nodes and their causal relationships, a second expert discussion was organized to assess the causal weights. The final causal weights were obtained by averaging the values ​​of the participating experts. This resulted in a complete fuzzy cognitive graph topology.

[0043] S15. Screening the core structural forms of the power system

[0044] It is important to note that the order of the concept states is crucial, rather than the value of each concept state. Examining the ranking of target concept states after the fuzzy cognitive graph iterates into a steady state, the states ranked higher are considered to represent the core structural form of the future power system.

[0045] This study comprehensively examines the correlation between the observed development drivers and structural morphological outcomes. Among all generated scenarios, two scenarios are randomly selected. If the observed deep uncertainty development drivers change in these two scenarios, and the observed evolutionary outcome variables are also inconsistent, then a correlation is considered to exist between the observed deep uncertainty drivers and the observed structural morphological outcome variables. The correlation frequency is counted, and the ratio of the normalized changes is counted as the correlation strength. After statistical calculations for all randomly selected scenarios, the correlation frequency explores the frequency of mutual influence between various attributes, and the correlation strength analyzes the strength of the correlation between various attributes. Development drivers with both high correlation frequency and high correlation strength are considered core development drivers of the power system.

[0046] like Figure 3 As shown, this embodiment of the invention provides a device for identifying the driving forces of energy and power system development. The device includes:

[0047] The screening module 20 is used to construct a fuzzy cognitive graph of the driving forces of future power system development using fuzzy cognitive graph theory, and to screen the core structural forms of the power system.

[0048] Model building module 40 constructs a power system planning model that adapts to the selected power system structure based on the selected power system structure.

[0049] The range estimation module 60 is used to select an investigation with deep uncertainty from the development drivers of the fuzzy cognitive map and estimate the range of uncertainty.

[0050] The combined module 80, based on a power system planning model adapted to the corresponding structural form, uses computer experiments to generate a large number of scenarios, targeting the driving forces and range of deep uncertainty.

[0051] The identification module 100 analyzes massive amounts of scenarios generated by data mining scenario discovery technology to identify the core driving forces of power system development.

[0052] This invention also provides an electronic device. Figure 4 A schematic diagram of the structure of an electronic device to which embodiments of the present invention can be applied is shown, such as... Figure 4 As shown, this computer electronic device includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0053] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0054] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0055] The present invention also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the energy and power system development driver identification device described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into an electronic device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the energy and power system development driver identification method described in the present invention.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the driving forces behind the development of energy and power systems, characterized in that, The identification method includes: S1. Using fuzzy cognitive graph theory, construct a fuzzy cognitive graph of the driving forces for future power system development, and screen the core structural forms of the power system; wherein, the core structural forms of the power system are screened based on the following steps: S11, the form of source-grid-load is selected as the target concept; S12, Identify the driving forces influencing the target concept through the literature review step; S13, organize experts to discuss and determine the causal relationships and directions between target concepts; S14. Based on the target concept and its causal relationship and direction, organize a second expert discussion to conduct a causal weight assessment. The final causal weight is obtained by the average value of the experts who participated in the discussion, so as to form a fuzzy cognitive graph topology. S15. After the fuzzy cognitive map iterates and enters a steady state, the states of the target concepts are sorted, and the target concepts with the highest sorting are determined to be the core structural form of the power system. S2. Based on the selected power system structure, construct a power system planning model that adapts to the corresponding structure. S3. Among the driving forces behind the development of fuzzy cognitive maps, select those with deep uncertainty and estimate the range of uncertainty. S4. Based on the power system planning model adapted to the corresponding structural form, a large number of scenarios are generated by combining computer experiments to address the driving forces and range of deep uncertainty. S5. Employ data mining-based scenario discovery techniques to analyze and generate massive amounts of scenarios, and identify the core drivers of power system development.

2. A device for identifying the driving forces of energy and power system development, characterized in that, The identification device includes: The screening module is used to construct a fuzzy cognitive graph of the driving forces of future power system development using fuzzy cognitive graph theory, and to screen the core structural forms of the power system. Specifically, the screening module is used to: select the forms of power source, grid, and load as target concepts; identify the driving forces affecting the target concepts through a literature review; organize expert discussions to determine the causal relationships and directions between target concepts; based on the target concepts and their causal relationships and directions, organize a second expert discussion to conduct a causal weight assessment, and the final causal weight is obtained by the average of the participating experts to form the fuzzy cognitive graph topology; after the fuzzy cognitive graph iterates to a steady state, the states of the target concepts are ranked, and the target concepts ranked higher are determined as the core structural forms of the power system. The model building module constructs a power system planning model that adapts to the selected power system structure. The range estimation module is used to select an investigation with deep uncertainty from the development drivers of fuzzy cognitive maps and estimate the range of uncertainty. The combined module, based on a power system planning model adapted to the corresponding structural form, uses computer experiments to generate a massive number of scenarios, targeting the driving forces and range of deep uncertainty. The identification module analyzes massive amounts of scenarios generated by data mining scenario discovery technology to identify the core drivers of power system development.

3. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the energy and power system development driver identification method as described in claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the energy and power system development driver identification method as described in claim 1.

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