Multi-dimensional index fused power market evaluation method, device and equipment and medium

Abstract

The invention relates to a power market evaluation method and device based on multi-dimensional index fusion, equipment and a medium. The method comprises the steps of obtaining multi-dimensional data; performing static evaluation on the multi-dimensional data to obtain a static evaluation result; performing dynamic evaluation on the multi-dimensional data to obtain a dynamic evaluation result; inputting the static evaluation result and the dynamic evaluation result into a multi-dimensional fusion engine to obtain an evaluation score; judging the early warning grade based on the evaluation score; and outputting early warning signals based on the early warning grades. The method has the effect of accurately evaluating the risk of the electricity market.

Description

Technical Field

[0001] This application relates to the technical field of electricity trading, and in particular to a method, apparatus, equipment and medium for electricity market assessment that integrates multi-dimensional indicators. Background Technology

[0002] The electricity spot market refers to a market where eligible operators conduct day-ahead, intraday, and real-time electricity trading. The electricity spot market uses competition to establish market-clearing prices that reflect spatial and temporal value, and also facilitates the trading of ancillary services such as frequency regulation and reserve.

[0003] Certain risks exist in electricity market transactions. Due to factors such as resource and information asymmetry, fair and impartial transactions are difficult to achieve. Therefore, how to assess electricity market risks has become an urgent problem to be solved. Summary of the Invention

[0004] In order to accurately assess electricity market risks, this application provides a method, apparatus, equipment and medium for electricity market assessment that integrates multiple dimensions of indicators.

[0005] Firstly, the electricity market assessment method that integrates multiple indicators provided in this application adopts the following technical solution: A multi-dimensional indicator fusion method for electricity market assessment includes: Obtain multi-dimensional data; Static evaluation is performed on the multi-dimensional data to obtain static evaluation results; The multi-dimensional data is dynamically evaluated to obtain dynamic evaluation results; The static evaluation results and the dynamic evaluation results are input into a multi-dimensional fusion engine to obtain an evaluation score; The warning level is determined based on the assessment score; Early warning signals are output based on the aforementioned early warning classification.

[0006] By adopting the above technical solutions, multi-dimensional data covers various types of data related to the electricity market, overcoming the limitations of traditional assessment methods that rely on only a single or a few indicators. This multi-dimensional data foundation makes the assessment more comprehensive, reflecting market health, stability, and efficiency from different perspectives, effectively avoiding misjudgments caused by missing information. Static assessment focuses on "state-related" indicators such as market structure, allocation efficiency, and price rationality at a specific point in time or period, helping to identify whether there are obvious irrationalities in the current market. Dynamic assessment focuses on "behavioral" indicators such as market trends, volatility, and response speed over time, revealing the dynamic characteristics and potential risks of market operation. The combination of these two approaches allows for both a grasp of the "current health status of the market" and insight into the "potential future risk evolution," improving the timeliness and forward-looking nature of the assessment. Furthermore, quantifying the assessment results of the electricity market enables users to have a clearer understanding of the electricity market situation, facilitating subsequent hierarchical judgments and decisions, and enhancing the overall level of intelligence.

[0007] Optionally, a static evaluation is performed on the multi-dimensional data to obtain a static evaluation result, including: The HHI index is calculated based on the aforementioned multi-dimensional data. Obtain market data; Key suppliers were identified and extracted based on the market data. Static evaluation results were obtained based on the HHI index and the key suppliers.

[0008] Optionally, the HHI index is calculated based on the multi-dimensional data, including: The formula for calculating the HHI index is: ; in, For HHI index; The total number of generating units on the supply side of the electricity market; Indicates the unit Maximum output; This represents the maximum output of all generating units on the supply side of the electricity market; Indicates the unit Blocking sensitive factors.

[0009] Optionally, identifying and extracting key suppliers based on the market data includes: The market data is judged based on the identification formula; If the identification formula is satisfied, then the suppliers that satisfy the identification formula are extracted, and the extracted suppliers are designated as key suppliers. The recognition formula is: ; in, Indicates exclusion of the unit The remaining available capacity; express Total available capacity of all units during the time period; express The marginal price of electricity at the node in the electricity market; This represents the threshold for blocking sensitive factors; Indicates the unit exist The actual adjustable capacity for a given time period; Indicates the unit exist The actual output for the time period; The formula for calculating the actual adjustable capacity is as follows: ; in, Indicates the unit exist Loss of capacity due to scheduled maintenance or real-time failures during certain periods; Indicates the unit exist The first reduction in capacity due to heating constraints during certain periods; Indicates the unit exist The second reduction in capacity was due to power constraints for the plant during certain periods.

[0010] Optionally, the multi-dimensional data is dynamically evaluated to obtain dynamic evaluation results, including: Calculate the estimated marginal cost; Thermoelectric coupling correction factor is obtained based on the aforementioned key suppliers; The Lerner index is calculated based on the marginal cost estimate, the thermocouple, and the correction factor. The dynamic evaluation results are obtained based on the Lerner index.

[0011] Optionally, the method further includes: The formula for calculating the Lerner index is as follows: ; in, for Time-critical units Lerner index; express The clearing price of the electricity market during a given period; Indicates the unit exist Marginal cost estimates for the time period; Indicates the unit exist The winning bid volume for the specified time period; express Total market trading volume during the period; Indicates the unit Thermoelectric coupling correction factor; The formula for calculating the estimated marginal cost is as follows: ; in, This is an estimate of the marginal cost; express The average price of fuel delivered to the factory during a given period; Indicates the unit The fuel consumption coefficient; Indicates the unit carbon emission intensity; express Carbon emission prices for a given period; Indicates the unit The cost per boot; This represents the cost amortization rate, which is the rate at which the unit's startup cost is amortized over time. Indicates the unit The no-load operating cost rate; Indicates the unit Minimum output; Indicates the unit exist The actual output for the time period; The formula for calculating the thermoelectric coupling correction factor is as follows: ; in, This is a thermoelectric coupling correction factor; Indicates the thermoelectric decoupling coefficient; It is a non-linear correction exponent; express Time-of-use units The actual heating load; Indicates the unit The maximum heating capacity.

[0012] Optionally, the static evaluation results and the dynamic evaluation results are input into a multi-dimensional fusion engine to obtain an evaluation score, including: The price impact index is calculated based on the dynamic evaluation results; Obtain the capacity control index, collusion risk index, and congestion premium index; The weighted sum value is calculated based on the static evaluation results, price impact index, capacity control index, collusion risk index, and congestion premium index. The evaluation score is calculated based on the weighted sum value.

[0013] Secondly, the multimodal content filtering device provided in this application adopts the following technical solution: A power market assessment device that integrates multiple dimensions of indicators, comprising: The first acquisition module is used to acquire multi-dimensional data; The first evaluation module is used to perform static evaluation on the multi-dimensional data to obtain static evaluation results; The second evaluation module is used to dynamically evaluate the multi-dimensional data to obtain dynamic evaluation results. The input module is used to input the static evaluation results and the dynamic evaluation results into the multi-dimensional fusion engine to obtain the evaluation score; The judgment module is used to determine the warning level based on the evaluation score; The output module is used to output early warning signals based on the early warning classification.

[0014] Thirdly, the electronic device provided in this application adopts the following technical solution: An electronic device includes a processor coupled to a memory; the processor is configured to execute a computer program stored in the memory such that the electronic device performs the method as described in the first aspect.

[0015] Fourthly, the computer-readable storage medium provided in this application adopts the following technical solution: A computer-readable storage medium includes a computer program or instructions that, when executed on a computer, cause the computer to perform the method as described in the first aspect.

[0016] In summary, this application includes at least one of the following beneficial technical effects: Multi-dimensional data covers various types of data related to the electricity market, overcoming the limitations of traditional assessment methods that rely on only a single or a few indicators. This multi-dimensional data foundation makes the assessment more comprehensive, reflecting market health, stability, and efficiency from different perspectives and effectively avoiding misjudgments caused by missing information. Static assessment focuses on "state-related" indicators such as market structure, allocation efficiency, and price rationality at a specific point in time or period, helping to identify any obvious irrationalities in the current market. Dynamic assessment focuses on "behavioral" indicators such as market trends, volatility, and response speed over time, revealing the dynamic characteristics and potential risks of market operation. Combining these two approaches allows for both a grasp of the "current health status of the market" and insight into the "potential future risk evolution," improving the timeliness and forward-looking nature of the assessment. Furthermore, quantifying the assessment results of the electricity market enables users to have a clearer understanding of the electricity market situation, facilitating subsequent hierarchical judgments and decisions, and enhancing the overall level of intelligence. Attached Figure Description

[0017] Figure 1This is a flowchart of the power market assessment method that integrates multiple indicators according to embodiments of this application.

[0018] Figure 2 This is a table illustrating the specific parameters in the feature vector in the embodiments of this application.

[0019] Figure 3 This is an example of an embodiment of this application. and A table of parameters.

[0020] Figure 4 This is a block diagram of a power market assessment device that integrates multi-dimensional indicators according to an embodiment of this application.

[0021] Figure 5 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0022] This specific embodiment is merely an explanation of this application and is not intended to limit it. Users skilled in the art can make modifications to this embodiment without contributing any inventive step after reading this specification, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by users of ordinary skills in the art without creative effort are within the scope of protection of this application.

[0024] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0025] This application discloses a method for assessing the power market by fusing multi-dimensional indicators. This method can be executed by an electronic device. The electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these.

[0026] This application discloses a multi-dimensional indicator fusion method for electricity market assessment. (Refer to...) Figure 1 The main processes of a multi-dimensional indicator fusion power market assessment method are described below (S100~S600): Step S100: Obtain multi-dimensional data; Step S200: Perform static evaluation on the multi-dimensional data to obtain static evaluation results; Step S300: Perform dynamic evaluation on the multi-dimensional data to obtain dynamic evaluation results; Step S400: Input the static evaluation results and dynamic evaluation results into the multi-dimensional fusion engine to obtain the evaluation score; Step S500: Determine the warning level based on the evaluation score; Step S600: Output early warning signals based on early warning levels.

[0027] Electronic devices acquire multi-dimensional data, including but not limited to unit physical parameters, market operation data, and grid topology. Based on this data, the devices perform static and dynamic assessments, obtaining static and dynamic results respectively. These results are then input into a multi-dimensional fusion engine to generate an assessment score. This score comprehensively reflects the electricity market situation, quantifying it to provide users with a clear understanding. The devices then determine the appropriate warning level based on the score and output a corresponding warning signal, automating the process and allowing users to quickly receive warning information. By integrating various electricity market data, the system provides a more realistic assessment of the electricity market, ensuring the results are more consistent with reality and providing users with a more accurate understanding of the market situation.

[0028] Specifically, a static evaluation is performed on multi-dimensional data to obtain the static evaluation results, including: calculating the HHI index based on the multi-dimensional data; acquiring market data; identifying and extracting key suppliers based on the market data; and obtaining the static evaluation results based on the HHI index and key suppliers. The calculated HHI index is an indicator that can measure market concentration. In this embodiment, the output share of generating units in the electricity market is used to measure concentration.

[0029] Specifically, the HHI index is calculated based on multi-dimensional data, including the following formula: ;in, For HHI index; The total number of generating units on the supply side of the electricity market; Indicates the unit Maximum output; This represents the maximum output of all generating units on the supply side of the electricity market; Indicates the unit Blocking sensitive factors.

[0030] Identifying and extracting key suppliers based on market data includes: judging market data based on an identification formula; if the identification formula is satisfied, extracting suppliers that satisfy the formula and designating them as key suppliers; the identification formula is: ;in, Indicates exclusion of the unit The remaining available capacity; express Total available capacity of all units during the time period; express The marginal price of electricity at the node in the electricity market; This represents the threshold for blocking sensitive factors; Indicates the unit exist The actual adjustable capacity for a given time period; Indicates the unit exist The actual output for the time period; The formula for calculating the actual adjustable capacity is: ;in, Indicates the unit exist Loss of capacity due to scheduled maintenance or real-time failures during certain periods; Indicates the unit exist The first reduction in capacity due to heating constraints during certain periods; Indicates the unit exist The second reduction in capacity was due to power constraints for the plant during certain periods.

[0031] The HHI (Higher Hierarchical Index) is used to statically assess the concentration of the electricity market, reflecting its long-term supply structure. Therefore, market players with a high market share may influence the market through activities such as capacity hoarding and collusion, which can generate market risks. Thus, it is necessary to identify these high-market-share players. This is done using an identification formula to screen out such players and designate them as key suppliers. Through these steps, key suppliers that require close monitoring and are prone to causing market risks can be identified.

[0032] Specifically, dynamic evaluation of multi-dimensional data is performed to obtain dynamic evaluation results, including: calculating marginal cost estimates; obtaining thermocouple correction factors based on key suppliers; calculating the Lerner index based on marginal cost estimates, thermocouples, and correction factors; and obtaining dynamic evaluation results based on the Lerner index.

[0033] The Lerner index quantifies the degree of deviation from editing costs in pricing, thereby reflecting market monopoly. It also incorporates thermocouples and correction factors for dynamic assessment, namely, assessing the degree of unit monopoly of key suppliers.

[0034] The formula for calculating the Lerner index is: ;in, for Time-of-use units Lerner index; express The clearing price of the electricity market during a given period; Indicates the unit exist Marginal cost estimates for the time period; Indicates the unit exist The winning bid volume for the specified time period; express Total market trading volume during the period; Indicates the unit Thermoelectric coupling correction factor; The formula for calculating the marginal cost estimate is: ;in, This is an estimate of the marginal cost; express The average price of fuel delivered to the factory during a given period; Indicates the unit The fuel consumption coefficient; Indicates the unit carbon emission intensity; express Carbon emission prices for a given period; Indicates the unit The cost per boot; This represents the cost amortization rate, which is the rate at which the unit's startup cost is amortized over time. Indicates the unit The no-load operating cost rate; Indicates the unit Minimum output; This represents the actual output of unit i during time period t; The formula for calculating the thermoelectric coupling correction factor is: ;in, This is a thermoelectric coupling correction factor; represents the thermoelectric decoupling coefficient; in this embodiment, the typical value for a coal-fired unit can be selected as 0.8; represents the nonlinear correction index; in this embodiment, the value of the nonlinear correction index can be selected as 1.2-1.5. express Time-of-use units The actual heating load; Indicates the unit The maximum heating capacity.

[0035] The above data includes the total number of generating units, the number of generating units, and the number of generating units. Maximum output, maximum output of all units, unit The congestion sensitivity factors, remaining available capacity, total available capacity, nodal marginal electricity prices, etc., are all data from multi-dimensional data. That is, apart from the data to be calculated, the other data substituted into the calculation formula can be obtained and extracted from multi-dimensional data. Therefore, based on the above steps and methods, it is possible to combine various types of data in the electricity market, that is, to comprehensively evaluate the electricity market situation using multi-dimensional data.

[0036] Specifically, the static and dynamic evaluation results are input into a multi-dimensional fusion engine to obtain an evaluation score, including: calculating the price impact index based on the dynamic evaluation results; obtaining the capacity control index, collusion risk index, and congestion premium index; calculating a weighted sum based on the static evaluation results, price impact index, capacity control index, collusion risk index, and congestion premium index; and calculating the evaluation score based on the weighted sum.

[0037] The Lerner index is a dynamic assessment result; the formula for calculating the price impact index is as follows: ; The specific process for obtaining the capacity control index, collusion risk index, and congestion premium index is as follows: The formula for calculating the capacity control index is: ;in, Indicates the unit exist The actual application capacity for the time period.

[0038] The formula for calculating the conspiracy risk index is: ;in, express Time period Units and The risk index of collusion among the generating units; This indicates the total number of price segments corresponding to a given time period; express During the period The unit's first Segmented pricing; express During the period The unit's first Segmented pricing; Indicates the electricity market on that day Within the time period The highest bid among all bids in the segment; Indicates the electricity market on that day Within the time period The lowest price among all offers in the segment.

[0039] The formula for calculating the congestion premium index is: ;in, This represents the Euclidean distance between unit i and unit j in the power grid topology; and This represents the feature vector of the unit's bidding behavior.

[0040] Specifically, ; Refer to the specific parameters in the feature vector Figure 2 The table in the middle; Similarly, refer to That's all.

[0041] The specific calculation process for the weighted composite value is as follows: The formula for calculating the weighted composite value is: ; among them, of Indicates the first Weights for different risk types; Then it means the first The values ​​of various characterization indicators. as well as For specific parameters, please refer to Figure 3 The table in the middle; This is the ratio of total network congestion cost to total network power consumption, used to represent the congestion premium. To ensure dimensional consistency, it is necessary to... Scale to the [0,1] range and reassign the scaled values. ; The formula for assigning values ​​is: ;in, Represents the set of all nodes in the power grid in the electricity market; Represents a node Marginal electricity price at the node; Indicates time period Reference time-of-use electricity price; Represents a node Net injected power.

[0042] The formula for calculating the assessment score is: ;in, It is the weighted sum of multiple risk types.

[0043] Specifically, the warning level is determined based on the assessment score; a warning signal is output based on the warning level, including: Once the assessment score is determined to be within a certain range, the corresponding warning level can be obtained, and then the corresponding warning signal can be output.

[0044] Distance description: The range can include: [0,0.4], (0.4,0.6], (0.6,0.8]; [0,0.4] corresponds to green warning, (0.4,0.6] corresponds to yellow warning, and (0.6,0.8] corresponds to orange warning.

[0045] When the evaluation score is determined to be within (0.4, 0.6], the warning level is yellow, so the corresponding warning signal is output. The warning signal corresponding to the yellow warning can be to activate the upper limit of the price and forcibly release the standby capacity.

[0046] It should be noted that after the key suppliers are selected, all data obtained and selected in subsequent calculations are data from the key suppliers.

[0047] Figure 4 A structural block diagram of a multi-dimensional index fusion power market assessment device 700 provided in this application embodiment is shown below. Figure 4 As shown, the multi-dimensional indicator fusion power market assessment device 700 includes: The first acquisition module 701 is used to acquire multi-dimensional data; The first evaluation module 702 is used to perform static evaluation on multi-dimensional data to obtain static evaluation results; The second evaluation module 703 is used to dynamically evaluate multi-dimensional data to obtain dynamic evaluation results; Input module 704 is used to input static evaluation results and dynamic evaluation results into the multi-dimensional fusion engine to obtain an evaluation score; Judgment module 705 is used to determine the warning level based on the evaluation score; Output module 706 is used to output early warning signals based on early warning levels.

[0048] Figure 5 This is a structural block diagram of an electronic device 800 provided in an embodiment of this application. The electronic device 800 can be a mobile phone, tablet computer, PC, server, or other similar device. Figure 5As shown, the electronic device 800 includes a memory 801, a processor 802, and a communication bus 803; the memory and the processor 802 are connected via the communication bus 803. The memory 801 stores a computer program that can be loaded by the processor 802 and executed as described in the above embodiments, which is a multi-dimensional indicator fusion power market assessment method.

[0049] The memory 801 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 801 may include a stored program area and a stored managed data area. The stored program area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the power market assessment method for multi-dimensional indicator fusion provided in the above embodiments, etc. The stored managed data area may store managed data involved in the power market assessment method for multi-dimensional indicator fusion provided in the above embodiments, etc.

[0050] Processor 802 may include one or more processing cores. Processor 802 executes instructions, programs, code sets, or instruction sets stored in memory 801, and calls managed data stored in memory 801 to perform various functions of this application and process managed data. Processor 802 may be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that for different devices, the electronic devices used to implement the functions of processor 802 may also be other types, and this application embodiment does not specifically limit the specific devices used.

[0051] The communication bus 803 may include a path for transmitting information between the aforementioned components. The communication bus 803 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 803 can be divided into an address bus, a managed data bus, a control bus, etc. For ease of representation, Figure 5The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0052] This application provides a computer storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which integrates multi-dimensional indicators for power market assessment.

[0053] In this embodiment, the computer storage medium can be a tangible device that holds and stores instructions used by the instruction execution device. The computer storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), speaker random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0054] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A method for power market evaluation based on multi-dimensional index fusion, characterized in that, The method comprises: acquiring multi-dimensional data; performing static evaluation on the multi-dimensional data to obtain a static evaluation result; performing dynamic evaluation on the multi-dimensional data to obtain a dynamic evaluation result; inputting the static evaluation result and the dynamic evaluation result into a multi-dimensional fusion engine to obtain an evaluation score; judging a warning classification based on the evaluation score; outputting a warning signal based on the warning classification.

2. The method of claim 1, wherein, The static evaluation on the multi-dimensional data to obtain a static evaluation result comprises: calculating an HHI index based on the multi-dimensional data; acquiring market data; identifying and extracting key suppliers based on the market data; obtaining a static evaluation result based on the HHI index and the key suppliers.

3. The method of claim 2, wherein, The calculation of the HHI index based on the multi-dimensional data comprises: The formula for calculating the HHI index is: ; wherein, is the HHI index; is the total number of units on the supply side of the electricity market; represents the maximum output of a unit ; represents the maximum output of all units on the supply side of the electricity market; represents the congestion sensitivity factor of a unit .

4. The multi-dimensional index fused power market evaluation method according to claim 2 or 3, characterized in that, The identification and extraction of the key suppliers based on the market data comprises: judging the market data based on an identification formula; if the identification formula is met, extracting the suppliers meeting the identification formula, and taking the extracted suppliers as the key suppliers. The recognition formula is: ; wherein, denotes the remaining available capacity of the unit after the rest of the available capacity; denotes the total available capacity of the unit for the period; denotes the nodal marginal price of the electricity market at the time; denotes a threshold value for the congestion sensitivity factor; denotes the available capacity of the unit at the actual adjustable capacity of the unit for the period; denotes the available capacity of the unit at the actual output of the unit for the period; The formula for calculating the actual adjustable capacity is: ; wherein, representing the units In the loss capacity in the period due to planned maintenance, real-time forced outage reasons; representing the units In the first curtailment capacity in the period due to heating constraints; representing the units In the second curtailment capacity in the period due to station service constraints.

5. The multi-dimensional index fusion-based power market evaluation method according to claim 2, characterized in that, The dynamic evaluation on the multi-dimensional data to obtain a dynamic evaluation result comprises: calculating a marginal cost estimate value; acquiring a thermoelectric coupling correction factor based on the key suppliers; calculating a Lerner index based on the marginal cost estimate value and the thermoelectric coupling correction factor; obtaining a dynamic evaluation result based on the Lerner index.

6. The multi-dimensional index fusion-based power market evaluation method according to claim 5, characterized in that, The method further comprises: The Lerner index is calculated as follows: ; in, for Time-of-use units Lerner index; express The clearing price of the electricity market during a given period; Indicates the unit exist Marginal cost estimates for the time period; Indicates the unit exist The winning bid volume for the specified time period; express Total market trading volume during the period; Indicates the unit Thermoelectric coupling correction factor; The marginal cost estimate is calculated by the formula: ; wherein, is the marginal cost estimate; denotes the fuel composite in-plant price for the period; denotes the fuel consumption factor of the unit ; denotes the carbon emission intensity of the unit ; denotes the carbon emission price for the period; denotes the single start-up cost of the unit ; denotes the cost apportionment decay factor, i.e. the rate of apportionment of the start-up cost of the unit over time; denotes the no-load operation cost rate of the unit ; denotes the minimum output of the unit ; denotes the actual output of the unit for the period ; The calculation formula of the thermocouple correction factor is: ; wherein, is a thermoelectric coupling correction factor; represents a thermoelectric decoupling coefficient; is a non-linear correction exponent; represents a period unit of actual heating load; represents a maximum heating capacity of a unit .

7. The multi-dimensional index fusion-based power market evaluation method according to claim 1, characterized in that, The inputting of the static evaluation result and the dynamic evaluation result into the multi-dimensional fusion engine to obtain an evaluation score comprises: calculating a price influence index based on the dynamic evaluation result; acquiring a capacity control index, a collusion risk index and a congestion premium index; calculating a weighted sum value based on the static evaluation result, the price influence index, the capacity control index, the collusion risk index and the congestion premium index; calculating an evaluation score based on the weighted sum value.

8. A multi-dimensional index fusion-based power market evaluation device, characterized by, The method comprises: a first acquisition module configured to acquire multi-dimensional data; a first evaluation module configured to perform static evaluation on the multi-dimensional data to obtain a static evaluation result; a second evaluation module configured to perform dynamic evaluation on the multi-dimensional data to obtain a dynamic evaluation result; an input module configured to input the static evaluation result and the dynamic evaluation result into a multi-dimensional fusion engine to obtain an evaluation score; a judgment module configured to judge a warning classification based on the evaluation score; an output module configured to output a warning signal based on the warning classification.

9. An electronic device, comprising: The electronic device comprises a processor coupled with a memory; the processor is configured to execute a computer program stored in the memory, so that the electronic device performs the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program or instructions, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 7.

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