Power grid operation optimization method and system based on multi-time scale coupling

Abstract

The invention discloses a power grid operation optimization method and system based on multi-time scale coupling, and relates to the technical field of power grid operation optimization, and the method comprises the steps: receiving power contract data corresponding to a preset multi-time scale hierarchy, inputting the power contract data corresponding to the preset multi-time scale hierarchy into a pre-established time scale coupling rule model, and based on a preset contract curve decomposition strategy, performing decomposition and aggregation processing on the power contract data corresponding to the preset multi-time scale hierarchy to obtain a total contract electric quantity; and receiving the actual power generation and consumption data, calculating a deviation ratio between the actual power generation and consumption data and the total contract power, and when the deviation ratio is greater than or equal to a preset deviation ratio threshold, generating a power adjustment instruction, otherwise, not performing instruction generation, and performing power adjustment based on the power adjustment instruction so as to realize operation optimization of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid operation optimization technology, specifically a power grid operation optimization method and system based on multi-timescale coupling. Background Technology

[0002] In the process of power market reform, medium- and long-term power contracts are a core tool for connecting the generation and consumption sides, stabilizing market prices, and ensuring the supply and demand balance of the power system. Its core technical logic revolves around the decomposition of contracted electricity volume, multi-timescale coordination, and market price linkage. Specifically, it is necessary to combine historical load data to determine the allocation ratio of contracted electricity volume at different time scales such as annual, quarterly, monthly, and daily, and to achieve the connection of contracts at all levels through trading mechanisms (such as bilateral negotiation, centralized bidding, and rolling matching), ultimately forming a time-based contract curve that can guide the actual execution of power generation and consumption, providing a basis for market settlement, system dispatch, and power grid operation optimization.

[0003] However, traditional centralized computing architectures are unable to meet the real-time generation needs of contract curves for a massive number of market participants, which restricts market operation efficiency and affects the optimization of power grid operation. Summary of the Invention

[0004] To address the shortcomings mentioned in the background section, the present invention aims to provide a power grid operation optimization method and system based on multi-timescale coupling.

[0005] Firstly, the objective of this invention can be achieved through the following technical solution: a power grid operation optimization method based on multi-time-scale coupling, the method comprising the following steps: Receive electricity contract data corresponding to a preset multi-timescale hierarchy, wherein the multi-timescale hierarchy includes a medium-to-long-term foundation layer, a medium-term adjustment layer, and a short-term execution layer. The medium-to-long-term foundation layer corresponds to annual and quarterly cycles, the medium-term adjustment layer corresponds to monthly and ten-day cycles, and the short-term execution layer corresponds to daily cycles. The electricity contract data corresponding to the preset multi-time scale levels is input into the pre-established time scale coupling rule model. Based on the preset contract curve decomposition strategy, the electricity contract data corresponding to the preset multi-time scale levels is decomposed and aggregated to obtain the total contract electricity. The time scale coupling rule includes the electricity constraint of the upper time scale on the lower time scale and the deviation electricity feedback relationship of the lower time scale on the upper time scale. Receive actual power generation and consumption data, calculate the deviation rate between the actual power generation and consumption data and the total contracted power volume, and generate a power adjustment command when the deviation rate is greater than or equal to a preset deviation rate threshold; otherwise, no command generation is required. Power adjustment is performed based on the power adjustment command to optimize the operation of the power grid.

[0006] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the medium- and long-term foundation layer adopts a combination of bilateral negotiated transactions or listing transactions combined with centralized bidding transactions or rolling matching transactions, wherein bilateral negotiated transactions and listing transactions use a custom horizontal curve, and the transaction results are averaged into 24 time periods per day according to the number of calendar days of the corresponding period; centralized bidding transactions and rolling matching transactions use commonly used decomposition curves, wherein the commonly used decomposition curves include annual commonly used decomposition curves. Commonly used decomposition curves for quarterly periods The transaction results are divided into 24 time periods per day based on the number of calendar days in the corresponding period; The intermediate adjustment layer only uses centralized bidding and rolling matching transactions, and employs commonly used decomposition curves. The transaction results are divided into 24 time periods per day based on the number of calendar days in the corresponding period; The short-term execution layer employs rolling matching transactions, with the transaction target being... From the date Electricity consumption during the 72 time periods of the day, Transactions are conducted daily, and the results directly correspond to the time period without needing to be broken down.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: within the commonly used decomposition curve, The daily average curve is obtained by decomposing the daily electricity consumption into a 24-hour electricity consumption curve. The peak-hour curve is calculated by averaging the daily electricity consumption across the peak periods, with zero values ​​for the flat and off-peak periods.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the clearing method for the centralized competitive bidding transaction is: Market participants independently declare their electricity purchase and sales volume and price. After the declaration deadline, a unified marginal clearing process is implemented to form a unified clearing price for the current period. When the declared price equals the unified clearing price, transactions are completed proportionally based on the declared volume, as shown in the following formula: in, To standardize the clearing price, The declared price for market entity j For the declared electricity volume of market entity j, The total market demand for electricity is used as the basis for the order of transactions in the rolling matching transactions, which is price priority and time priority, and is based on the unified clearing price of the corresponding centralized bidding transactions.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the contract electricity at the upper time scale level of the pre-established time scale coupling rule model provides electricity constraint boundaries for transactions at the lower time scale level; the transaction results at the lower time scale level provide feedback to correct the deviation electricity at the upper time scale level; and the constraint relationships in the coupling rules satisfy: in, The upper limit of contracted electricity volume allocated to time period i at the upper time scale. The contracted electricity volume for the i-th time period on day d at the lower time scale.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the calculation process of the total contracted electricity volume, comprising: To calculate the total contracted volume of a market participant during period i on day D, the medium- and long-term contract curve is obtained using the following formula: in, For a certain market entity, the total contracted electricity volume during time period i on day D. The total contracted electricity volume for the i-period of annual bilateral negotiation or listed trading. This refers to the total contract volume during the i-period of the annual centralized bidding or rolling matching transaction. This represents the total contracted electricity volume during the i-th trading period of the quarter. This represents the total contract volume during the i-th trading period of the month. This represents the total contract volume during the i-th trading period of the ten-day period. The daily trading volume is represented by D for the contract electricity during the i-th trading period, Y for the annual calendar days, S for the quarterly calendar days, M for the monthly calendar days, and X for the ten-day calendar days.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of calculating the deviation rate between the actual electricity generation and consumption data and the total contracted electricity volume, comprising: 100% in, Let be the deviation rate for the i-th time period on day D. This represents the actual power generation and consumption during the i-th time period on day D. In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the power adjustment based on the power adjustment command, comprising: The correction method for power adjustment is to adjust the contract power for the corresponding period in the next round of trading in the short-term execution layer, with a processing cycle of less than or equal to 24 hours. The calculation formula is as follows: in, The adjustment amount for time period i in the next round of rolling matching transactions. This is a correction factor; The contract execution deviation is corrected by the next round of rolling matching transactions in the short-term execution layer. A distributed computing architecture is used to reduce the overall computing time. The calculation formula is as follows: in, Calculate the total time spent on contract curves for all market participants. For the number of market entities, Calculate the number of nodes, The computing tasks of each market entity are allocated to Each node processes... There are 1 subject, and each subject needs to calculate 24 time periods. The overall time complexity is O(N), and / 100.

[0012] Secondly, in order to achieve the above objectives, this invention discloses a power grid operation optimization system based on multi-time-scale coupling, comprising: The data receiving module is used to receive power contract data corresponding to a preset multi-timescale level, wherein the multi-timescale level includes a medium- and long-term foundation level, a medium-term adjustment level, and a short-term execution level. The medium- and long-term foundation level corresponds to annual and quarterly cycles, the medium-term adjustment level corresponds to monthly and ten-day cycles, and the short-term execution level corresponds to daily cycles. The power processing module is used to input the power contract data corresponding to the preset multi-time scale levels into the pre-established time scale coupling rule model. Based on the preset contract curve decomposition strategy, the power contract data corresponding to the preset multi-time scale levels is decomposed and aggregated to obtain the total contract power. The time scale coupling rule includes the power constraint of the upper time scale on the lower time scale and the deviation power feedback relationship of the lower time scale on the upper time scale. The power grid optimization module receives actual power generation and consumption data, calculates the deviation rate between the actual power generation and consumption data and the total contracted power volume, and generates a power adjustment command when the deviation rate is greater than or equal to a preset deviation rate threshold; otherwise, no command generation is required, and power adjustment is performed based on the power adjustment command to achieve power grid operation optimization.

[0013] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it employs the power grid operation optimization method based on multi-timescale coupling as described above.

[0014] The beneficial effects of this invention are: This invention utilizes a three-level time-scale architecture, two-layer coupling rules, and a distributed computing architecture to improve the accuracy of matching contracted electricity volume with actual power generation and consumption demand, the efficiency of correcting contract deviations, the ability to smooth out spot market fluctuations, and the computational efficiency of contract curve generation, thereby ensuring the efficient and stable operation of the electricity market. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0017] Example 1: like Figure 1 As shown, a power grid operation optimization method based on multi-time-scale coupling includes the following steps: S101: Receive electricity contract data corresponding to a preset multi-timescale layer, wherein the multi-timescale layer includes a medium-to-long-term foundation layer, a medium-term adjustment layer, and a short-term execution layer. The medium-to-long-term foundation layer corresponds to annual and quarterly cycles, the medium-term adjustment layer corresponds to monthly and ten-day cycles, and the short-term execution layer corresponds to daily cycles. The medium- and long-term basic tier adopts a combination of "bilateral negotiated transactions / listed transactions + centralized bidding transactions / rolling matching transactions". Among them, bilateral negotiated transactions and listed transactions use a custom horizontal curve, and their transaction results are averaged into 24 time periods per day according to the number of calendar days in the corresponding period; centralized bidding transactions and rolling matching transactions use commonly used decomposition curves, including annual commonly used decomposition curves. Commonly used decomposition curves for quarterly periods The transaction results are divided into 24 time periods per day based on the number of calendar days in the corresponding period; The intermediate adjustment layer only uses centralized bidding and rolling matching transactions, and employs commonly used decomposition curves. The transaction results are divided into 24 time periods per day based on the number of calendar days in the corresponding period; The short-term execution layer employs rolling matching transactions, with the transaction target being... From the date Electricity consumption during the 72 time periods of the day, Transactions are conducted daily, and the results directly correspond to specific time periods without needing to be broken down. Commonly used decomposition curves The daily average curve is obtained by decomposing the daily electricity consumption into a 24-hour electricity consumption curve. The peak-hour curve is calculated by dividing the daily electricity consumption into daily peak periods, with zero values ​​for the flat and valley periods. The annual monthly and monthly daily proportions are determined by the trading center in conjunction with the dispatching agency based on historical load data from the past three years.

[0018] In the medium- and long-term basic tier, bilateral negotiated transactions and listed transactions are conducted before centralized bidding transactions and rolling matching transactions. Furthermore, before monthly transactions, annual and quarterly bilateral negotiated transactions and listed transaction contracts need to be standardized by time period and evenly divided into contracts for 24 time periods.

[0019] The clearing process for centralized competitive bidding is as follows: market participants independently declare their purchase and sale volume and price; after the declaration deadline, a unified marginal clearing is established, forming a unified clearing price for that period; when the declared price equals the unified clearing price, transactions are completed proportionally based on the declared volume. in, To standardize the clearing price, The declared price for market entity j For the declared electricity volume of market entity j, The total market demand for electricity is used as the basis for the order of transactions in the rolling matching transactions, which is price priority and time priority, and is based on the unified clearing price of the corresponding centralized bidding transactions.

[0020] S102: Input the electricity contract data corresponding to the preset multi-time scale level into the pre-established time scale coupling rule model. Based on the preset contract curve decomposition strategy, decompose and aggregate the electricity contract data corresponding to the preset multi-time scale level to obtain the total contract electricity. Among them, the time scale coupling rule includes the electricity constraint of the upper time scale on the lower time scale and the deviation electricity feedback relationship of the lower time scale on the upper time scale. The contract electricity levels at the upper timescale level of the pre-established timescale coupling rule model provide electricity constraint boundaries for transactions at the lower timescale level. The transaction results at the lower timescale level provide feedback to correct the deviation electricity levels at the upper timescale level. The constraint relationships in the coupling rules satisfy: in, The upper limit of contracted electricity volume allocated to time period i at the upper time scale. The contracted electricity volume for the i-th time period on day d at the lower time scale.

[0021] Each time scale level uses 24 time periods per day as the trading unit, forming 24 independent sub-markets; For contracts for difference (CFDs), only the decomposed contract curve results are used for market settlement; for physical contracts, the decomposed contract curve results are used as both the basis for market settlement and the constraints for power system operation. S103: Receive actual power generation and consumption data, calculate the deviation rate between the actual power generation and consumption data and the total contracted power volume, and generate a power adjustment command when the deviation rate is greater than or equal to the preset deviation rate threshold; otherwise, no command generation is required. Power adjustment is performed based on the power adjustment command to optimize the operation of the power grid.

[0022] The calculation process for the total contracted electricity volume includes: To calculate the total contracted volume of a market participant during period i on day D, the medium- and long-term contract curve is obtained using the following formula: in, For a certain market entity, the total contracted electricity volume during time period i on day D. The total contracted electricity volume for the i-period of annual bilateral negotiation or listed trading. This refers to the total contract volume during the i-period of the annual centralized bidding or rolling matching transaction. This represents the total contracted electricity volume during the i-th trading period of the quarter. This represents the total contract volume during the i-th trading period of the month. This represents the total contract volume during the i-th trading period of the ten-day period. The daily trading volume is represented by D for the contract electricity during the i-th trading period, Y for the annual calendar days, S for the quarterly calendar days, M for the monthly calendar days, and X for the ten-day calendar days.

[0023] The process of calculating the deviation rate between the actual electricity generation and consumption data and the total contracted electricity volume includes: 100% in, Let be the deviation rate for the i-th time period on day D. This represents the actual power generation and consumption during the i-th time period on day D; Adjusting battery power based on battery adjustment commands includes: The correction method for power adjustment is to adjust the contract power for the corresponding period in the next round of trading in the short-term execution layer, with a processing cycle of less than or equal to 24 hours. The calculation formula is as follows: in, The adjustment amount for time period i in the next round of rolling matching transactions. This is a correction factor; The contract execution deviation is corrected by the next round of rolling matching transactions in the short-term execution layer. A distributed computing architecture is used to reduce the overall computing time. The calculation formula is as follows: in, Calculate the total time spent on contract curves for all market participants. For the number of market entities, Calculate the number of nodes, The computing tasks of each market entity are allocated to Each node processes... There are 1 subject, and each subject needs to calculate 24 time periods. The overall time complexity is O(N), and / 100.

[0024] Specifically, the present invention will be further illustrated below through embodiments: To further illustrate the specific operation of the electricity medium- and long-term contract curve formation mechanism based on multi-timescale coupling described in this invention, this example uses a typical day in July, the peak electricity load month, as the target period. The installed capacity is set at 600MW, primarily participating in medium- and long-term contract trading, with a small amount of spot trading also considered. The target date is July 15th, denoted as D day, which falls in mid-July. July is a peak electricity consumption month, and this day is a working day, with peak load concentrated between 18:00 and 22:00. The target time period i=18 corresponds to 18:00-19:00. Data statistics show that this period has the highest load of the day, with a significantly higher peak electricity consumption percentage than other periods. Time scale periodic parameters: annual calendar days Y = 365 days, third quarter (Q3) calendar days S = 92 days, July calendar days M = 31 days; mid-July (July 11 - July 20) calendar days X = 10 days; Contract decomposition curve parameters, decomposition percentage rules for the 18:00-19:00 period (peak period): annual commonly used decomposition curve (peak period curve): peak period percentage 15%; quarterly commonly used decomposition curve (peak period curve): peak period percentage 16% (Q3 is the peak load season, and the percentage is slightly higher than the annual percentage); monthly commonly used decomposition curve (peak period curve): peak period percentage 17% (July is the month with the highest load in Q3, and the percentage is further increased). The computing power optimization parameters are as follows: there are N=1500 market entities (including power generation companies, electricity sales companies, and large users), and K=20 computing nodes are configured, which meets the requirement of "K≥N / 100" of this invention.

[0025] Based on steps S1 and S2 of this invention, the transaction execution status and contract power decomposition results at each time scale level are as follows: Medium- to long-term foundation tier (annual, Q3): Annual bilateral negotiated transactions: The total transaction volume agreed in the annual bilateral negotiated contract is 50,000 MWh. The transaction method is bilateral negotiation + custom horizontal curve. According to the decomposition rules, the total contract volume for this level i=18 period is 9,000 MWh. Annual centralized bidding transaction: The annual centralized bidding transaction involves a declared electricity sales volume of 30,000 MWh at a price of 0.38 yuan / kWh. The final transaction will be completed at a unified clearing price of 0.38 yuan / kWh. The transaction method is centralized bidding plus the annual commonly used decomposition curve. The total contracted electricity volume for this tier (i=18 time period) is 4,500 MWh. Q3 rolling matching transactions: Total transaction volume was 15,000 MWh, referencing the Q3 centralized bidding unified clearing price of RMB 0.39 / kWh, with a bid price of RMB 0.388 / kWh. The transaction method was rolling matching + quarterly common decomposition curve (peak period curve). The total contracted volume for this level i=18 period was 2,400 MWh. Mid-term adjustment layer (July monthly, mid-July): July monthly centralized bidding transaction: Total transaction volume 6000MWh, unified clearing price 0.40 yuan / kWh, transaction method is centralized bidding + monthly common decomposition curve (peak period curve), the total contracted volume for this level i=18 period is 1020MWh. Mid-July rolling matching transactions: Total transaction volume of 2000MWh, referencing the July monthly centralized bidding unified clearing price of 0.40 yuan / kWh, and a bid price of 0.398 yuan / kWh. The transaction method is rolling matching + monthly common decomposition curve (peak period curve). The total contracted volume for period i=18 in this tier is 340MWh. Short-term execution layer (daily trading on July 15): The trading target is the electricity volume of 24 time periods from 0:00 to 24:00 on July 15. Among them, the electricity volume declared for the i=18 time period (18:00-19:00) is 80MWh, the electricity price is 0.41 yuan / kWh, and the transaction is completed in full. The contract electricity volume for the i=18 time period of this layer is 80MWh.

[0026] The total contracted electricity volume is calculated by substituting the contracted electricity volume data for each level (i=18) into the formula. The total contracted electricity volume for the period from 18:00 to 19:00 on July 15th is then calculated. The total contracted electricity volume is 209.98 MWh. Similarly, by calculating the total contracted electricity volume for the other 23 time periods on that day, the complete medium- and long-term contract curve for July 15th can be formed.

[0027] Table 1 According to the distributed computing architecture of the present invention, the contract curve calculation task for the 1500 market participants is evenly distributed across 20 computing nodes, with each node handling 75 participants; each market participant needs to calculate 24 time periods. The computational workload for a single node is 1800 calculations.

[0028] Example 2: To achieve the above objective, such as Figure 2 As shown, based on Embodiment 1, this invention discloses a power grid operation optimization system based on multi-time-scale coupling, comprising: Data receiving module 11 is used to receive power contract data corresponding to a preset multi-timescale level, wherein the multi-timescale level includes a medium- and long-term basic level, a medium-term adjustment level and a short-term execution level, the medium- and long-term basic level corresponds to an annual and quarterly cycle, the medium-term adjustment level corresponds to a monthly and ten-day cycle, and the short-term execution level corresponds to a daily cycle. The power processing module 12 is used to input the power contract data corresponding to the preset multi-time scale level into the pre-established time scale coupling rule model. Based on the preset contract curve decomposition strategy, the power contract data corresponding to the preset multi-time scale level is decomposed and aggregated to obtain the total contract power. The time scale coupling rule includes the power constraint of the upper time scale on the lower time scale and the deviation power feedback relationship of the lower time scale on the upper time scale. The power grid optimization module 13 is used to receive actual power generation and consumption data, calculate the deviation rate between the actual power generation and consumption data and the total contracted power volume, and generate a power adjustment command when the deviation rate is greater than or equal to a preset deviation rate threshold; otherwise, no command generation is required, and power adjustment is performed based on the power adjustment command to achieve power grid operation optimization.

[0029] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0030] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0031] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0032] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A power grid operation optimization method based on multi-time-scale coupling, characterized in that, The method includes the following steps: Receive electricity contract data corresponding to a preset multi-timescale hierarchy, wherein the multi-timescale hierarchy includes a medium-to-long-term foundation layer, a medium-term adjustment layer, and a short-term execution layer. The medium-to-long-term foundation layer corresponds to annual and quarterly cycles, the medium-term adjustment layer corresponds to monthly and ten-day cycles, and the short-term execution layer corresponds to daily cycles. The electricity contract data corresponding to the preset multi-time scale levels is input into the pre-established time scale coupling rule model. Based on the preset contract curve decomposition strategy, the electricity contract data corresponding to the preset multi-time scale levels is decomposed and aggregated to obtain the total contract electricity. The time scale coupling rule includes the electricity constraint of the upper time scale on the lower time scale and the deviation electricity feedback relationship of the lower time scale on the upper time scale. Receive actual power generation and consumption data, calculate the deviation rate between the actual power generation and consumption data and the total contracted power volume, and generate a power adjustment command when the deviation rate is greater than or equal to a preset deviation rate threshold; otherwise, no command generation is required. Power adjustment is performed based on the power adjustment command to optimize the operation of the power grid.

2. The power grid operation optimization method based on multi-time-scale coupling according to claim 1, characterized in that, The medium- and long-term foundation tier adopts a combination of bilateral negotiated transactions or listed transactions, along with centralized bidding transactions or rolling matching transactions. Bilateral negotiated transactions and listed transactions use a custom horizontal curve, with transaction results averaged across 24 daily time periods based on the calendar days of the corresponding period. Centralized bidding transactions and rolling matching transactions use commonly used decomposition curves, including annual commonly used decomposition curves. Commonly used decomposition curves for quarterly periods The transaction results are divided into 24 time periods per day based on the number of calendar days in the corresponding period; The intermediate adjustment layer only uses centralized bidding and rolling matching transactions, and employs commonly used decomposition curves. The transaction results are divided into 24 time periods per day based on the number of calendar days in the corresponding period; The short-term execution layer employs rolling matching transactions, with the transaction target being... From the date Electricity consumption during the 72 time periods of the day, Transactions are conducted daily, and the results directly correspond to the time period without needing to be broken down.

3. The power grid operation optimization method based on multi-time-scale coupling according to claim 2, characterized in that, Within the commonly used decomposition curves, The daily average curve is obtained by decomposing the daily electricity consumption into a 24-hour electricity consumption curve. The peak-hour curve is calculated by averaging the daily electricity consumption across the peak periods, with zero values ​​for the flat and off-peak periods.

4. The power grid operation optimization method based on multi-time-scale coupling according to claim 3, characterized in that, The clearing method for the centralized bidding transactions is as follows: Market participants independently declare their electricity purchase and sales volume and price. After the declaration deadline, a unified marginal clearing process is implemented to form a unified clearing price for the current period. When the declared price equals the unified clearing price, transactions are completed proportionally based on the declared volume, as shown in the following formula: in, To standardize the clearing price, The declared price for market entity j For the declared electricity volume of market entity j, The total market demand for electricity is used as the basis for the order of transactions in the rolling matching transactions, which is price priority and time priority, and is based on the unified clearing price of the corresponding centralized bidding transactions.

5. The power grid operation optimization method based on multi-time-scale coupling according to claim 1, characterized in that, The contract electricity volume at the upper time scale level of the pre-established time scale coupling rule model provides electricity constraint boundaries for transactions at the lower time scale level. The transaction results at the lower time scale level provide feedback to correct the deviation electricity volume at the upper time scale level. The constraint relationships in the coupling rules satisfy: in, The upper limit of contracted electricity volume allocated to time period i at the upper time scale. The contracted electricity volume for the i-th time period on day d at the lower time scale.

6. The power grid operation optimization method based on multi-time-scale coupling according to claim 1, characterized in that, The calculation process for the total contracted electricity volume includes: To calculate the total contracted volume of a market participant during period i on day D, the medium- and long-term contract curve is obtained using the following formula: in, For a certain market entity, the total contracted electricity volume during time period i on day D. The total contracted electricity volume for the i-period of annual bilateral negotiation or listed trading. This refers to the total contract volume during the i-period of the annual centralized bidding or rolling matching transaction. This represents the total contracted electricity volume during the i-th trading period of the quarter. This represents the total contract volume during the i-th trading period of the month. This represents the total contract volume during the i-th trading period of the ten-day period. The daily trading volume is represented by D for the contract electricity during the i-th trading period, Y for the annual calendar days, S for the quarterly calendar days, M for the monthly calendar days, and X for the ten-day calendar days.

7. The power grid operation optimization method based on multi-time-scale coupling according to claim 1, characterized in that, The process of calculating the deviation rate between the actual electricity generation and consumption data and the total contracted electricity volume includes: 100% in, Let be the deviation rate for the i-th time period on day D. This represents the actual power generation and consumption during the i-th time period on day D.

8. The power grid operation optimization method based on multi-time-scale coupling according to claim 1, characterized in that, The power adjustment based on the power adjustment command includes: The correction method for power adjustment is to adjust the contract power for the corresponding period in the next round of trading in the short-term execution layer, with a processing cycle of less than or equal to 24 hours. The calculation formula is as follows: in, The adjustment amount for time period i in the next round of rolling matching transactions. This is a correction factor; The contract execution deviation is corrected by the next round of rolling matching transactions in the short-term execution layer. A distributed computing architecture is used to reduce the overall computing time. The calculation formula is as follows: in, Calculate the total time spent on contract curves for all market participants. For the number of market entities, Calculate the number of nodes, The computing tasks of each market entity are allocated to Each node processes... There are 1 subject, and each subject needs to calculate 24 time periods. The overall time complexity is O(N), and / 100.

9. A power grid operation optimization system based on multi-time-scale coupling, employing the power grid operation optimization method based on multi-time-scale coupling as described in any one of claims 1 to 8, characterized in that, include: The data receiving module is used to receive power contract data corresponding to a preset multi-timescale level, wherein the multi-timescale level includes a medium- and long-term foundation level, a medium-term adjustment level, and a short-term execution level. The medium- and long-term foundation level corresponds to annual and quarterly cycles, the medium-term adjustment level corresponds to monthly and ten-day cycles, and the short-term execution level corresponds to daily cycles. The power processing module is used to input the power contract data corresponding to the preset multi-time scale levels into the pre-established time scale coupling rule model. Based on the preset contract curve decomposition strategy, the power contract data corresponding to the preset multi-time scale levels is decomposed and aggregated to obtain the total contract power. The time scale coupling rule includes the power constraint of the upper time scale on the lower time scale and the deviation power feedback relationship of the lower time scale on the upper time scale. The power grid optimization module receives actual power generation and consumption data, calculates the deviation rate between the actual power generation and consumption data and the total contracted power volume, and generates a power adjustment command when the deviation rate is greater than or equal to a preset deviation rate threshold; otherwise, no command generation is required, and power adjustment is performed based on the power adjustment command to achieve power grid operation optimization.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, it employs the power grid operation optimization method based on multi-timescale coupling as described in any one of claims 1 to 8.

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