A method and system for intelligent generation and checking of power grid operation mode considering maintenance and power market constraints

Abstract

The application discloses a kind of considering overhaul and power market constraint grid operation mode intelligent generation and checking method and system, belong to grid overhaul technical field.The method of the present application comprises: obtaining the multi-source data of grid, based on the multi-source data of standardization processing, joint constraint modeling is carried out;Generation market overhaul coordination grid operation mode, for the operation mode data of the grid operation mode, power flow calculation and convergence adjustment are carried out, and target grid operation mode meeting the target is screened out;For the target grid operation mode, safety and market coordination checking is carried out, for the target grid operation mode after checking, block management and rescheduling simulation are carried out, to select the optimal target grid operation mode in the target grid operation mode.The implementation of the present application improves the automation and intelligent level of grid operation mode generation and checking, and provides technical support for power market transaction meeting the requirements of grid safe operation.

Description

Technical Field

[0001] This invention relates to the field of power grid maintenance technology, and more specifically, to a method and system for intelligent generation and verification of power grid operation modes that take into account maintenance and electricity market constraints. Background Technology

[0003] Currently, power grid dispatching departments generally use offline simulation software-based technical processes when compiling annual, quarterly, and day-ahead operating plans. This process typically includes: loading basic power grid data → overlaying maintenance plans → manually adjusting generation output → invoking power flow calculations → verifying N-1 safety constraints → manually correcting non-convergence or limit-crossing methods. This process exhibits significant fragmentation and lag, mainly in the following aspects: 1) The maintenance plan is out of sync with system operation and lacks forward-looking analysis. Equipment maintenance (such as the shutdown of main transformers, lines, and generating units) directly alters the power grid topology, affecting power flow distribution, cross-sectional transmission capacity, and voltage stability. Current methods often employ a sequential approach of "first determining maintenance, then calculating power flow," lacking a systematic scan of the safety risks across multiple scenarios under maintenance combinations. Especially during periods of intensive maintenance, the overlapping shutdowns of multiple devices can lead to overload on critical sections or even cascading failures. However, traditional methods struggle to automatically identify such high-risk combinations, relying on dispatcher experience and thus risking missed detections.

[0004] 2) The electricity market mechanism has not been effectively integrated into the generation process. my country has established several provincial-level pilot electricity spot markets and fully implemented the "six-signature" mechanism for medium- and long-term transactions. Market clearing results (such as unit output in bidding and nodal marginal price (LMP)) have become an important basis for the actual operation of the power grid. However, the current operation mode generation process generally ignores market clearing signals, leading to: inconsistencies between dispatch execution plans and market settlement results, causing settlement disputes; forced deployment of high-priced units, violating the principle of economic dispatch; inability to realize the same amount of electricity at designated nodes, affecting the performance of inter-provincial transactions; and a lack of prediction of LMP spatial differences, making it impossible to identify congestion points in advance, resulting in frequent rescheduling in the real-time stage and increasing system operating costs.

[0005] 3) The market and safety verification processes are disconnected, lacking a closed-loop feedback mechanism. Currently, power flow calculation, security and stability verification, and market compliance assessment are handled by different systems and professional positions. For example, the methodology specialist is responsible for power flow and N-1 verification, while the market specialist is responsible for contract execution and congestion analysis. Data exchange between the two relies on manual export / import, which is inefficient and prone to errors. More seriously, when a methodology is rejected due to market factors (such as contract non-execution), there is a lack of an automatic feedback mechanism to drive the regeneration of the methodology, resulting in an inefficient cycle of "generation—rejection—manual adjustment—resubmission," which seriously affects the methodology development progress.

[0006] 4) Congestion management is a passive response, lacking proactive prevention capabilities. Transmission congestion is a core issue in electricity market operation. Traditional methods typically focus only on physical exceedances during the generation phase, neglecting the indicative role of market signals such as LMP spreads and shadow prices in indicating congestion. Congestion is often only detected during market clearing or real-time operation, leading to economic losses through the reallocation of higher-priced units or payment of congestion surpluses. An ideal approach should proactively adjust generation distribution or activate backup channels during the planning phase, based on LMP predictions and sensitivity analysis, achieving "feedforward control" of congestion.

[0007] 5) Insufficient level of intelligence, making it difficult to cope with complex scene combinations. As the penetration rate of new energy sources increases, the operation mode needs to take into account the uncertainty of wind power and photovoltaic output, often requiring the generation of multiple scenario sets of "high / low new energy + different maintenance combinations". Traditional manual methods are difficult to efficiently handle dozens or even hundreds of scenario combinations, and lack the ability to automatically repair non-converging power flows, heavily relying on repeated debugging by technicians, resulting in long mode development cycles and high labor costs.

[0008] 6) Lack of unified data model and system platform support Existing systems such as PSASP, EMS, and market clearing platforms use different data formats, and data conversion relies on scripts or manual processing. The lack of standardized interfaces makes it difficult to achieve integrated "data-model-computation-feedback" processes. This is especially true in inter-provincial interconnected power grids, where coordination involves multiple dispatching agencies, further complicating data sharing and collaborative modeling.

[0009] In summary, existing technologies have shown significant limitations in adaptability when dealing with the complex operating environment involving the multi-dimensional coupling of "maintenance, market, safety, and new energy". Summary of the Invention

[0010] To address the aforementioned problems, this invention aims to solve key technical challenges in existing technologies, such as the separation of maintenance and market, non-closed-loop verification processes, and low levels of intelligence. It provides a highly efficient, safe, economical, and market-compliant intelligent generation and verification solution for modern power systems, proposing an intelligent generation and verification method for power grid operation modes that considers maintenance and electricity market constraints, including: Acquire multi-source data of the power grid, standardize the multi-source data of the power grid, and perform joint constraint modeling based on the standardized multi-source data; Based on the model established by joint constraints, a grid operation mode for market-based maintenance coordination is generated. Power flow calculation and convergence adjustment are performed on the operation mode data of the grid operation mode to select the target grid operation mode that meets the objective. A safety and market collaborative verification is performed on the target power grid operation mode to select a compliant target power grid operation mode. Congestion management and rescheduling simulations are then performed on the compliant target power grid operation modes to obtain management simulation results. Based on the management simulation results, the optimal target power grid operation mode is selected from the compliant target power grid operation modes.

[0011] Optional, multi-source data, including at least one of the following: basic power grid operation data, equipment maintenance plan data, electricity market data, and forecast data.

[0012] Optional, standardized processing includes: Based on the names of equipment components in the basic operation data of the power grid, the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data are adjusted to make the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data consistent with the names of equipment components in the basic operation data of the power grid.

[0013] Optional, joint constraint modeling, including: Establish grid operation constraints, including maintenance topology constraints and electricity market constraints.

[0014] Optionally, maintenance topology constraints are implemented, and the network structure is updated based on the out-of-service equipment to generate a set of topologies for multiple scenarios.

[0015] Optional electricity market constraints include: electricity price guidance constraints, contract execution constraints, and market clearing consistency constraints; The electricity price guidance constraint includes: prioritizing the use of low-priced generator units and limiting the output of high-priced generator units based on generator price data, and setting an output deviation tolerance threshold α1; The contract execution constraints include: a tolerance threshold α2 for the power deviation of the inter-provincial transaction tie line, set to ensure that the contracted electricity volume in the power market is completed on time at the designated node; The market clearing consistency constraint includes: the unit output in the generated grid operation mode should be close to the market clearing result, and the set deviation tolerance threshold α3 between the output of all units and the market clearing result.

[0016] Optional, the power grid operation mode constraints include: the set electricity price guidance constraint coefficient F1, the contract execution constraint coefficient F2, and the market clearing consistency constraint coefficient F3; The total tolerance threshold for operational deviation is α = F1×α1 + F2×α2 + F3×α3, where F1 + F2 + F3 = 1; Among them, F1, F2 and F3 take different values ​​according to the different trading plans corresponding to the power grid operation mode.

[0017] Optionally, the power grid operation modes that generate market-based maintenance coordination include: For each maintenance combination, generate the corresponding topology of power grid operation mode data; Initialize the unit output in the power grid operation mode data based on the market clearing results; Set power control targets for cross-sections; Based on the volatility of new energy sources, a set of typical power output scenarios is generated; Output the unit output plan, tie line power target, key section power target, and new energy output status for each power grid operation mode.

[0018] Optionally, power flow calculation and convergence adjustment are performed on the operation mode data of the power grid operation mode, including: The power flow calculation program is invoked to perform power flow calculation based on the power grid operation mode data. For power flow convergence data, check whether the tie-line power target and the critical section power target have been achieved. If not, call the power flow intelligent adjustment model based on electricity market constraints, set the tie-line power and critical section power as targets, adjust the operation mode data, and call the power flow calculation program again to perform power flow calculation until the tie-line power and critical section power reach the target, or the target is still not reached after reaching the upper limit of adjustment times. For non-convergent power flow data, the power flow convergence adjustment model is invoked to bring the non-convergent power flow data to convergence. Then, the power flow intelligent adjustment model based on electricity market constraints is invoked to adjust the tie line power and key section power targets.

[0019] Optionally, a safety and market-based collaborative verification is performed for the target power grid operation mode, including: Safety and electricity market verifications are performed on the converged power grid operation mode data that meet the target in terms of tie line power and critical section power, in order to determine the safety constraints and electricity market constraints of the target power grid operation mode. The safety verification includes: setting an N-1 power flow verification range, performing N-1 power flow fault scanning calculations on the power grid operation mode data of each set of target power grid operation modes that meet the requirements, and verifying whether the line power and cross-sectional power exceed the limits; The electricity market verification includes: performing market compliance verification on the power grid operation mode data of each target power grid operation mode after completing the N-1 security verification; The market compliance verification includes at least one of the following: checking the contracted electricity volume completion rate, verifying the deviation between unit output and market clearing, and assessing the consistency of unit node electricity prices.

[0020] Optionally, check the contracted electricity volume completion rate, including: calculating the ratio β1 of the actual traded electricity volume to the market traded contracted electricity volume based on the operating time of the target grid operation mode that has completed the N-1 verification in the actual market transaction; The deviation between the verified unit output and the market clearing includes: comparing the output of the generator unit in the target grid operation mode that has completed the N-1 safety verification with the output of the actual generator unit in the corresponding market clearing data, and calculating its output deviation β2. The unit node price consistency assessment includes: calculating whether the generator price for the target grid operation mode that has completed the N-1 safety verification and participates in the actual electricity market transaction is consistent with the generator price data, and calculating the deviation β3 between the generator price data and the generator price data.

[0021] Optionally, congestion management and rescheduling simulation includes: determining whether line congestion has occurred based on the line power upper limit threshold; if congestion has occurred, performing rescheduling simulation calculations to confirm that the congestion has been eliminated. The line blockage includes: the absolute value of the active power flow of the transmission line or transformer exceeds the safety limit, causing the electrical energy to be unable to be transmitted along the planned path; The rescheduling simulation includes: readjusting the power grid operation mode data of the target power grid and performing power flow calculations to determine that the active power of the blocked lines or transformers has been reduced to within the safe limit.

[0022] Optionally, selecting the optimal target power grid operation mode from the target power grid operation modes includes: Calculate the deviation value β of the generated target power grid operation mode. Let the contract power completion rate coefficient be f1, the unit output and market clearing deviation coefficient be f2, and the unit node price consistency coefficient be f3. Then β = f1×β1 + f2×β2 + f3×β3, f1 + f2 + f3 = 1. The target power grid is sorted by the size of β according to the target power grid operation mode, and the target power grid operation mode with the smallest β is the optimal mode.

[0023] Furthermore, this invention also proposes an intelligent generation and verification system for power grid operation modes that considers maintenance and electricity market constraints, comprising: An initial unit is used to acquire multi-source data of the power grid, standardize the multi-source data of the power grid, and perform joint constraint modeling based on the standardized multi-source data; The computing unit is used to generate a grid operation mode for market maintenance coordination based on the model established by joint constraints, perform power flow calculation and convergence adjustment on the operation mode data of the grid operation mode, and screen out the target grid operation mode that meets the objectives. The output unit is used to perform safety and market coordination verification for the target power grid operation mode, select compliant target power grid operation modes, perform congestion management and rescheduling simulation for the compliant target power grid operation modes, obtain management simulation results, and select the optimal target power grid operation mode among the compliant target power grid operation modes based on the management simulation results.

[0024] Optional, multi-source data, including at least one of the following: basic power grid operation data, equipment maintenance plan data, electricity market data, and forecast data.

[0025] Optional, standardized processing includes: Based on the names of equipment components in the basic operation data of the power grid, the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data are adjusted to make the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data consistent with the names of equipment components in the basic operation data of the power grid.

[0026] Optional, joint constraint modeling, including: Establish grid operation constraints, including maintenance topology constraints and electricity market constraints.

[0027] Optionally, maintenance topology constraints are implemented, and the network structure is updated based on the out-of-service equipment to generate a set of topologies for multiple scenarios.

[0028] Optional electricity market constraints include: electricity price guidance constraints, contract execution constraints, and market clearing consistency constraints; The electricity price guidance constraint includes: prioritizing the use of low-priced generator units and limiting the output of high-priced generator units based on generator price data, and setting an output deviation tolerance threshold α1; The contract execution constraints include: a tolerance threshold α2 for the power deviation of the inter-provincial transaction tie line, set to ensure that the contracted electricity volume in the power market is completed on time at the designated node; The market clearing consistency constraint includes: the unit output in the generated grid operation mode should be close to the market clearing result, and the set deviation tolerance threshold α3 between the output of all units and the market clearing result.

[0029] Optional, the power grid operation mode constraints include: the set electricity price guidance constraint coefficient F1, the contract execution constraint coefficient F2, and the market clearing consistency constraint coefficient F3; The total tolerance threshold for operational deviation is α = F1×α1 + F2×α2 + F3×α3, where F1 + F2 + F3 = 1; Among them, F1, F2 and F3 take different values ​​according to the different trading plans corresponding to the power grid operation mode.

[0030] Optionally, the power grid operation modes that generate market-based maintenance coordination include: For each maintenance combination, generate the corresponding topology of power grid operation mode data; Initialize the unit output in the power grid operation mode data based on the market clearing results; Set power control targets for cross-sections; Based on the volatility of new energy sources, a set of typical power output scenarios is generated; Output the unit output plan, tie line power target, key section power target, and new energy output status for each power grid operation mode.

[0031] Optionally, power flow calculation and convergence adjustment are performed on the operation mode data of the power grid operation mode, including: The power flow calculation program is invoked to perform power flow calculation based on the power grid operation mode data. For power flow convergence data, check whether the tie-line power target and the critical section power target have been achieved. If not, call the power flow intelligent adjustment model based on electricity market constraints, set the tie-line power and critical section power as targets, adjust the operation mode data, and call the power flow calculation program again to perform power flow calculation until the tie-line power and critical section power reach the target, or the target is still not reached after reaching the upper limit of adjustment times. For non-convergent power flow data, the power flow convergence adjustment model is invoked to bring the non-convergent power flow data to convergence. Then, the power flow intelligent adjustment model based on electricity market constraints is invoked to adjust the tie line power and key section power targets.

[0032] Optionally, a safety and market-based collaborative verification is performed for the target power grid operation mode, including: Safety and electricity market verifications are performed on the converged power grid operation mode data that meet the target in terms of tie line power and critical section power, in order to determine the safety constraints and electricity market constraints of the target power grid operation mode. The safety verification includes: setting an N-1 power flow verification range, performing N-1 power flow fault scanning calculations on the power grid operation mode data of each set of target power grid operation modes that meet the requirements, and verifying whether the line power and cross-sectional power exceed the limits; The electricity market verification includes: performing market compliance verification on the power grid operation mode data of each target power grid operation mode after completing the N-1 security verification; The market compliance verification includes at least one of the following: checking the contracted electricity volume completion rate, verifying the deviation between unit output and market clearing, and assessing the consistency of unit node electricity prices.

[0033] Optionally, check the contracted electricity volume completion rate, including: calculating the ratio β1 of the actual traded electricity volume to the market traded contracted electricity volume based on the operating time of the target grid operation mode that has completed the N-1 verification in the actual market transaction; The deviation between the verified unit output and the market clearing includes: comparing the output of the generator unit in the target grid operation mode that has completed the N-1 safety verification with the output of the actual generator unit in the corresponding market clearing data, and calculating its output deviation β2. The unit node price consistency assessment includes: calculating whether the generator price for the target grid operation mode that has completed the N-1 safety verification and participates in the actual electricity market transaction is consistent with the generator price data, and calculating the deviation β3 between the generator price data and the generator price data.

[0034] Optionally, congestion management and rescheduling simulation includes: determining whether line congestion has occurred based on the line power upper limit threshold; if congestion has occurred, performing rescheduling simulation calculations to confirm that the congestion has been eliminated. The line blockage includes: the absolute value of the active power flow of the transmission line or transformer exceeds the safety limit, causing the electrical energy to be unable to be transmitted along the planned path; The rescheduling simulation includes: readjusting the power grid operation mode data of the target power grid and performing power flow calculations to determine that the active power of the blocked lines or transformers has been reduced to within the safe limit.

[0035] Optionally, selecting the optimal target power grid operation mode from the target power grid operation modes includes: Calculate the deviation value β of the generated target power grid operation mode. Let the contract power completion rate coefficient be f1, the unit output and market clearing deviation coefficient be f2, and the unit node price consistency coefficient be f3. Then β = f1×β1 + f2×β2 + f3×β3, f1 + f2 + f3 = 1. The target power grid is sorted by the size of β according to the target power grid operation mode, and the target power grid operation mode with the smallest β is the optimal mode.

[0036] In another aspect, the present invention also provides a computing device, comprising: one or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described above is implemented.

[0037] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described above.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides an intelligent generation and verification method for power grid operation modes considering maintenance and electricity market constraints. The method includes: acquiring multi-source data of the power grid and standardizing the data; performing joint constraint modeling based on the standardized data; generating a market-maintenance coordinated power grid operation mode based on the model established by the joint constraints; performing power flow calculation and convergence adjustment on the operation mode data of the selected operation modes to identify target operation modes that meet the objectives; performing safety and market coordination verification on the target operation modes to select compliant ones; performing congestion management and rescheduling simulation on the compliant target operation modes to obtain management simulation results; and selecting the optimal target operation mode from the compliant target operation modes based on the management simulation results. The implementation of this invention improves the automation and intelligence level of power grid operation mode generation and verification, providing technical support for electricity market transactions that meet the requirements of safe power grid operation. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0040] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0041] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0042] Example 1: A smart generation and verification method S100 for power grid operation modes considering maintenance and electricity market constraints is proposed, such as... Figure 1 As shown, it includes: S101, acquire multi-source data of the power grid, and perform standardization processing on the multi-source data of the power grid. Based on the standardized multi-source data, perform joint constraint modeling. S102, Based on the model established by joint constraints, generate a grid operation mode for market-based maintenance coordination, perform power flow calculation and convergence adjustment on the operation mode data of the grid operation mode, and select the target grid operation mode that meets the objective; S103, perform safety and market coordination verification on the target power grid operation mode, select compliant target power grid operation modes, perform congestion management and rescheduling simulation on the compliant target power grid operation modes, obtain management simulation results, and select the optimal target power grid operation mode among the compliant target power grid operation modes based on the management simulation results.

[0043] The following is a further explanation of S101-S103: Specifically, S101-S103 mainly include: Multi-source data integration and standardized processing, joint constraint modeling, generation of market-maintenance collaborative operation modes, power flow calculation and intelligent convergence adjustment, safety and market collaborative verification, congestion management and rescheduling simulation, optimal mode recommendation and output.

[0044] As a further aspect of the present invention: the multi-source data integration and standardization processing includes basic power grid operation mode data (power grid topology, parameters, generator and load models), equipment maintenance plan data (planned outage time and scope of generator sets, lines, and transformers), power market data (market clearing results, medium and long-term trading plans, inter-provincial tie line power plans, unit price curves), and forecast data (load, wind power, and photovoltaic output forecasts).

[0045] As a further aspect of the present invention: the standardization process refers to the fact that since the present invention involves multiple systems, which are all constructed independently and the same equipment has different names in different systems, a multi-system equipment component name correspondence table is established based on the names of each equipment component in the power grid basic operation mode data, so as to facilitate the correspondence between equipment components in other systems and equipment component names in the power grid basic operation mode data.

[0046] As a further aspect of the present invention: the joint constraint modeling establishes power grid operation mode constraints, including maintenance topology constraints and electricity market constraints.

[0047] As a further aspect of the present invention: the maintenance topology constraint is generated by updating the network structure based on the out-of-service equipment to create a multi-scenario topology set.

[0048] As a further aspect of the present invention, electricity market constraints include electricity price guidance constraints, contract execution constraints, and market clearing consistency constraints.

[0049] As a further aspect of the present invention: the electricity price guidance constraint refers to prioritizing the use of low-priced generator units and limiting the output of high-priced generator units based on generator price data, and setting an output deviation tolerance threshold α1.

[0050] As a further aspect of the present invention: the contract execution constraint refers to setting a power deviation tolerance threshold α2 for the inter-provincial transaction tie line power target to ensure that the contracted electricity volume in the electricity market is completed on time at the designated node.

[0051] As a further aspect of the present invention: the market clearing consistency constraint means that the unit output in the generated operating mode should be as close as possible to the market clearing result, and a deviation tolerance threshold α3 is set between the output of all units and the market clearing result.

[0052] As a further aspect of the present invention: the power grid operation mode constraints refer to setting a price guidance constraint coefficient F1, a contract execution constraint coefficient F2, and a market clearing consistency constraint coefficient F3, and calculating the total tolerance threshold for operation mode deviation α = F1×α1 + F2×α2 + F3×α3, where F1 + F2 + F3 = 1. The coefficients F1, F2, and F3 should take different values ​​according to the different trading plans (e.g., day-ahead, intraday, medium-to-long-term) corresponding to the operation mode.

[0053] As a further aspect of the present invention: the generation of market-maintenance coordinated operation modes refers to generating multiple candidate operation modes by combining maintenance plans and market constraints. Specifically, this includes: generating the topology of the corresponding power grid operation mode data for each maintenance combination; initializing the unit output in the power grid operation mode data based on the market clearing results; setting power control targets for cross-regional sections; generating typical output scenario sets (such as high / low penetration rate new energy output) considering the volatility of new energy sources; and outputting the unit output plan, tie-line power target, key section power target, and new energy output status for each power grid operation mode.

[0054] As a further aspect of this invention: the power flow calculation and convergence intelligent adjustment involve calling a power flow calculation program for each set of power grid operation mode data. For converged power flow data, it is verified whether the tie-line power target and the critical section power target have been achieved. If not, a power flow intelligent adjustment model based on electricity market constraints is invoked, setting the tie-line power and critical section power as targets, adjusting the operation mode data, and then calling the power flow calculation program again until the tie-line power and critical section power reach the targets, or until the targets are still not reached after reaching the upper limit of adjustment times. For non-convergent power flow data, the power flow convergence adjustment model is first invoked to achieve convergence, and then the power flow intelligent adjustment model based on electricity market constraints is invoked to adjust the tie-line power and critical section power targets.

[0055] As a further aspect of the present invention: the aforementioned safety and market collaborative verification refers to performing safety verification and electricity market verification on the grid operation mode data that has converged and whose tie-line power and key section power both meet the target, in order to ensure compliance with safety constraints and electricity market constraints.

[0056] As a further aspect of the present invention: the safety verification refers to setting an N-1 power flow verification range, performing power flow N-1 fault scan calculations on each set of power grid operation mode data that meets the requirements, and verifying whether the line power and cross-sectional power exceed the limits.

[0057] As a further aspect of the present invention: the electricity market verification is to perform market compliance verification on each set of power grid operation mode data after completing the N-1 security verification, including checking the contracted electricity volume completion rate, verifying the deviation between unit output and market clearing, and assessing the consistency of unit node electricity prices.

[0058] As a further aspect of the present invention: the contract electricity completion rate refers to the ratio β1 of the actual transaction electricity to the market transaction contract electricity, calculated based on the operating time of the grid operation mode that has completed N-1 verification in the actual market transaction.

[0059] As a further aspect of the present invention: the deviation between the verified unit output and the market clearing is calculated by comparing the output of the generator unit in the grid operation mode that has completed the N-1 safety check with the actual output of the generator unit in the corresponding market clearing data, and then calculating the output deviation β2.

[0060] As a further aspect of the present invention: the unit node price consistency assessment is to calculate whether the generator price that has completed the N-1 safety verification grid operation mode and participated in the actual electricity market transaction is consistent with the generator price data, and to calculate the deviation β3 between the generator price data and the generator price data.

[0061] As a further aspect of the present invention: the congestion management and rescheduling simulation refers to determining whether a line congestion has occurred based on the upper limit threshold of the line power. If a congestion occurs, a rescheduling simulation calculation is performed to ensure that the congestion is eliminated and the line can be cleared smoothly.

[0062] As a further aspect of the present invention: the line blockage refers to the situation where the absolute value of the active power flow of a transmission line or transformer exceeds its safety limit, resulting in the inability of electrical energy to be transmitted along the planned path.

[0063] As a further aspect of the present invention: the rescheduling simulation refers to readjusting the power grid operation mode data and performing power flow calculations to ensure that the active power of the blocked lines or transformers is reduced to within safe limits.

[0064] As a further aspect of the present invention: the optimal method recommendation and output refers to, based on the above calculation results, performing an optimal evaluation on all the finally generated methods, outputting all methods and indicating the recommendation priority order.

[0065] As a further aspect of the present invention: the optimal evaluation is calculated using the generated operational mode deviation value β. Let the contracted electricity completion rate coefficient be f1, the unit output and market clearing deviation coefficient be f2, and the unit node price consistency coefficient be f3. β = f1 × β1 + f2 × β2 + f3 × β3, where f1 + f2 + f3 = 1. The operational modes are sorted according to the magnitude of β, with the mode having the smallest β being the optimal mode and the mode having the largest β being the worst mode. Any operational mode with β greater than α needs to be specifically highlighted. Example 2: Furthermore, this invention also proposes an intelligent generation and verification system 200 for power grid operation modes that considers maintenance and electricity market constraints, such as... Figure 2 As shown, it includes: The initial unit 201 is used to acquire multi-source data of the power grid, and to perform standardization processing on the multi-source data of the power grid, and to perform joint constraint modeling based on the standardized multi-source data; The calculation unit 202 is used to generate a grid operation mode for market maintenance coordination based on the model established by joint constraints, perform power flow calculation and convergence adjustment on the operation mode data of the grid operation mode, and screen out the target grid operation mode that meets the objective. The output unit 203 is used to perform safety and market coordination verification on the target power grid operation mode, select a compliant target power grid operation mode, perform congestion management and rescheduling simulation on the compliant target power grid operation mode, obtain management simulation results, and select the optimal target power grid operation mode among the compliant target power grid operation modes based on the management simulation results.

[0066] The multi-source data includes at least one of the following: basic power grid operation data, equipment maintenance plan data, electricity market data, and forecast data.

[0067] Standardization processes include: Based on the names of equipment components in the basic operation data of the power grid, the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data are adjusted to make the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data consistent with the names of equipment components in the basic operation data of the power grid.

[0068] Joint constraint modeling includes: Establish grid operation constraints, including maintenance topology constraints and electricity market constraints.

[0069] Among them, maintenance topology constraints are generated by updating the network structure based on out-of-service equipment, resulting in a set of topologies for multiple scenarios.

[0070] Among them, electricity market constraints include: electricity price guidance constraints, contract execution constraints, and market clearing consistency constraints; The electricity price guidance constraint includes: prioritizing the use of low-priced generator units and limiting the output of high-priced generator units based on generator price data, and setting an output deviation tolerance threshold α1; The contract execution constraints include: a tolerance threshold α2 for the power deviation of the inter-provincial transaction tie line, set to ensure that the contracted electricity volume in the power market is completed on time at the designated node; The market clearing consistency constraint includes: the unit output in the generated grid operation mode should be close to the market clearing result, and the set deviation tolerance threshold α3 between the output of all units and the market clearing result.

[0071] Among them, the constraints on power grid operation mode include: the set electricity price guidance constraint coefficient F1, the contract execution constraint coefficient F2, and the market clearing consistency constraint coefficient F3; The total tolerance threshold for operational deviation is α = F1×α1 + F2×α2 + F3×α3, where F1 + F2 + F3 = 1; Among them, F1, F2 and F3 take different values ​​according to the different trading plans corresponding to the power grid operation mode.

[0072] Among them, the power grid operation mode that generates market-based maintenance coordination includes: For each maintenance combination, generate the corresponding topology of power grid operation mode data; Initialize the unit output in the power grid operation mode data based on the market clearing results; Set power control targets for cross-sections; Based on the volatility of new energy sources, a set of typical power output scenarios is generated; Output the unit output plan, tie line power target, key section power target, and new energy output status for each power grid operation mode.

[0073] The process of performing power flow calculations and convergence adjustments on the operation mode data of the aforementioned power grid operation mode includes: The power flow calculation program is invoked to perform power flow calculation based on the power grid operation mode data. For power flow convergence data, check whether the tie-line power target and the critical section power target have been achieved. If not, call the power flow intelligent adjustment model based on electricity market constraints, set the tie-line power and critical section power as targets, adjust the operation mode data, and call the power flow calculation program again to perform power flow calculation until the tie-line power and critical section power reach the target, or the target is still not reached after reaching the upper limit of adjustment times. For non-convergent power flow data, the power flow convergence adjustment model is invoked to bring the non-convergent power flow data to convergence. Then, the power flow intelligent adjustment model based on electricity market constraints is invoked to adjust the tie line power and key section power targets.

[0074] Among these, the safety and market-based collaborative verification of the target power grid operation mode includes: Safety and electricity market verifications are performed on the converged power grid operation mode data that meet the target in terms of tie line power and critical section power, in order to determine the safety constraints and electricity market constraints of the target power grid operation mode. The safety verification includes: setting an N-1 power flow verification range, performing N-1 power flow fault scanning calculations on the power grid operation mode data of each set of target power grid operation modes that meet the requirements, and verifying whether the line power and cross-sectional power exceed the limits; The electricity market verification includes: performing market compliance verification on the power grid operation mode data of each target power grid operation mode after completing the N-1 security verification; The market compliance verification includes at least one of the following: checking the contracted electricity volume completion rate, verifying the deviation between unit output and market clearing, and assessing the consistency of unit node electricity prices.

[0075] Among them, checking the contracted electricity volume completion rate includes: calculating the ratio β1 of the actual traded electricity volume to the market traded contracted electricity volume based on the operating time of the target power grid operation mode that has completed N-1 verification in the actual market transaction; The deviation between the verified unit output and the market clearing includes: comparing the output of the generator unit in the target grid operation mode that has completed the N-1 safety verification with the output of the actual generator unit in the corresponding market clearing data, and calculating its output deviation β2. The unit node price consistency assessment includes: calculating whether the generator price for the target grid operation mode that has completed the N-1 safety verification and participates in the actual electricity market transaction is consistent with the generator price data, and calculating the deviation β3 between the generator price data and the generator price data.

[0076] The congestion management and rescheduling simulation includes: determining whether line congestion has occurred based on the line power upper limit threshold; if congestion has occurred, performing rescheduling simulation calculations to confirm that the congestion has been eliminated. The line blockage includes: the absolute value of the active power flow of the transmission line or transformer exceeds the safety limit, causing the electrical energy to be unable to be transmitted along the planned path; The rescheduling simulation includes: readjusting the power grid operation mode data of the target power grid and performing power flow calculations to determine that the active power of the blocked lines or transformers has been reduced to within the safe limit.

[0077] Among these, selecting the optimal target power grid operation mode includes: Calculate the deviation value β of the generated target power grid operation mode. Let the contract power completion rate coefficient be f1, the unit output and market clearing deviation coefficient be f2, and the unit node price consistency coefficient be f3. Then β = f1×β1 + f2×β2 + f3×β3, f1 + f2 + f3 = 1. The target power grid is sorted by the size of β according to the target power grid operation mode, and the target power grid operation mode with the smallest β is the optimal mode.

[0078] The implementation of this invention unifies the descriptions and data formats of the same power grid element in power grid operation mode data, electricity market data, equipment maintenance plan data, and forecast data. It can generate operation modes based on maintenance plans and electricity market constraints, and perform power flow calculations and adjustments that meet tie-line power and critical section power targets through artificial intelligence methods. After completing safety and market collaborative verification, congestion management, and rescheduling, it realizes the automatic generation and verification of power grid operation modes that take into account maintenance and electricity market constraints, improves the automation and intelligence level of mode generation and verification, and provides technical support for electricity market transactions that meet the requirements of power grid safe operation.

[0079] Example 3: Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. 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, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the methods in the above embodiments.

[0080] Example 4: Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiments.

[0081] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0086] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent generation and verification of power grid operation modes considering maintenance and electricity market constraints, characterized in that, include: Acquire multi-source data of the power grid, standardize the multi-source data of the power grid, and perform joint constraint modeling based on the standardized multi-source data; Based on the model established by joint constraints, a grid operation mode for market-based maintenance coordination is generated. Power flow calculation and convergence adjustment are performed on the operation mode data of the grid operation mode to select the target grid operation mode that meets the objective. A safety and market collaborative verification is performed on the target power grid operation mode to select a compliant target power grid operation mode. Congestion management and rescheduling simulations are then performed on the compliant target power grid operation modes to obtain management simulation results. Based on the management simulation results, the optimal target power grid operation mode is selected from the compliant target power grid operation modes.

2. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The multi-source data includes at least one of the following: basic power grid operation mode data, equipment maintenance plan data, electricity market data, and forecast data.

3. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The standardization process includes: Based on the names of equipment components in the basic operation data of the power grid, the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data are adjusted to make the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data consistent with the names of equipment components in the basic operation data of the power grid.

4. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The joint constraint modeling includes: Establish grid operation constraints, including maintenance topology constraints and electricity market constraints.

5. The method for intelligent generation and verification of power grid operation modes according to claim 4, characterized in that, The maintenance topology constraint is a set of multi-scenario topologies generated by updating the network structure based on the out-of-service equipment.

6. The method for intelligent generation and verification of power grid operation modes according to claim 4, characterized in that, The electricity market constraints include: electricity price guidance constraints, contract execution constraints, and market clearing consistency constraints; The electricity price guidance constraint includes: prioritizing the use of low-priced generator units and limiting the output of high-priced generator units based on generator price data, and setting an output deviation tolerance threshold α1; The contract execution constraints include: a tolerance threshold α2 for the power deviation of the inter-provincial transaction tie line, set to ensure that the contracted electricity volume in the power market is completed on time at the designated node; The market clearing consistency constraint includes: the unit output in the generated grid operation mode should be close to the market clearing result, and the set deviation tolerance threshold α3 between the output of all units and the market clearing result.

7. The method for intelligent generation and verification of power grid operation modes according to claim 4, characterized in that, The constraints on the power grid operation mode include: the set electricity price guidance constraint coefficient F1, the contract execution constraint coefficient F2, and the market clearing consistency constraint coefficient F3; The total tolerance threshold for operational deviation is α = F1×α1 + F2×α2 + F3×α3, where F1 + F2 + F3 = 1; Among them, F1, F2 and F3 take different values ​​according to the different trading plans corresponding to the power grid operation mode.

8. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The power grid operation mode that generates market-based maintenance coordination includes: For each maintenance combination, generate the corresponding topology of power grid operation mode data; Initialize the unit output in the power grid operation mode data based on the market clearing results; Set power control targets for cross-sections; Based on the volatility of new energy sources, a set of typical power output scenarios is generated; Output the unit output plan, tie line power target, key section power target, and new energy output status for each power grid operation mode.

9. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The process of performing power flow calculations and convergence adjustments on the power grid operation mode data includes: The power flow calculation program is invoked to perform power flow calculation based on the power grid operation mode data. For power flow convergence data, check whether the tie-line power target and the critical section power target have been achieved. If not, call the power flow intelligent adjustment model based on electricity market constraints, set the tie-line power and critical section power as targets, adjust the operation mode data, and call the power flow calculation program again to perform power flow calculation until the tie-line power and critical section power reach the target, or until the target is still not reached after reaching the upper limit of adjustment times. For non-convergent power flow data, the power flow convergence adjustment model is invoked to bring the non-convergent power flow data to convergence. Then, the power flow intelligent adjustment model based on electricity market constraints is invoked to adjust the tie line power and key section power targets.

10. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The security and market-based collaborative verification of the target power grid operation mode includes: Safety and electricity market verifications are performed on the converged grid operation mode data that meet the target in terms of tie line power and critical section power, in order to determine the safety constraints and electricity market constraints of the target grid operation mode. The safety verification includes: setting an N-1 power flow verification range, performing N-1 power flow fault scanning calculations on the power grid operation mode data of each set of target power grid operation modes that meet the requirements, and verifying whether the line power and cross-sectional power exceed the limits; The electricity market verification includes: performing market compliance verification on the power grid operation mode data of each target power grid operation mode after completing the N-1 security verification; The market compliance verification includes at least one of the following: checking the contracted electricity volume completion rate, verifying the deviation between unit output and market clearing, and assessing the consistency of unit node electricity prices.

11. The method for intelligent generation and verification of power grid operation modes according to claim 10, characterized in that, The check of the contract electricity completion rate includes: calculating the ratio β1 of the actual transaction electricity to the electricity agreed in the market transaction contract based on the operating time of the target power grid operation mode that has completed the N-1 verification in the actual market transaction; The deviation between the verified unit output and the market clearing includes: comparing the output of the generator unit in the target grid operation mode that has completed the N-1 safety verification with the output of the actual generator unit in the corresponding market clearing data, and calculating its output deviation β2. The unit node price consistency assessment includes: calculating whether the generator price for the target grid operation mode that has completed the N-1 safety verification and participates in the actual electricity market transaction is consistent with the generator price data, and calculating the deviation β3 between the generator price data and the generator price data.

12. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The congestion management and rescheduling simulation includes: determining whether line congestion has occurred based on the line power upper limit threshold; if congestion has occurred, performing rescheduling simulation calculations to confirm that the congestion has been eliminated. The line blockage includes: the absolute value of the active power flow of the transmission line or transformer exceeds the safety limit, causing the power to be unable to be transmitted along the planned path; The rescheduling simulation includes: readjusting the power grid operation mode data of the target power grid and performing power flow calculations to determine that the active power of the blocked lines or transformers has been reduced to within the safe limit.

13. The method for intelligent generation and verification of power grid operation modes according to claim 1, characterized in that, The step of selecting the optimal target power grid operation mode from the target power grid operation modes includes: Calculate the deviation value β of the generated target power grid operation mode. Let the contract power completion rate coefficient be f1, the unit output and market clearing deviation coefficient be f2, and the unit node price consistency coefficient be f3. Then β = f1×β1 + f2×β2 + f3×β3, f1 + f2 + f3 = 1. The target power grid is sorted by the size of β according to the target power grid operation mode, and the target power grid operation mode with the smallest β is the optimal mode.

14. A smart generation and verification system for power grid operation modes considering maintenance and electricity market constraints, characterized in that, include: An initial unit is used to acquire multi-source data of the power grid, standardize the multi-source data of the power grid, and perform joint constraint modeling based on the standardized multi-source data; The computing unit is used to generate a grid operation mode for market maintenance coordination based on the model established by joint constraints, perform power flow calculation and convergence adjustment on the operation mode data of the grid operation mode, and screen out the target grid operation mode that meets the objectives. Output unit, used for A safety and market collaborative verification is performed on the target power grid operation mode to select a compliant target power grid operation mode. Congestion management and rescheduling simulations are then performed on the compliant target power grid operation modes to obtain management simulation results. Based on the management simulation results, the optimal target power grid operation mode is selected from the compliant target power grid operation modes.

15. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The multi-source data includes at least one of the following: basic power grid operation mode data, equipment maintenance plan data, electricity market data, and forecast data.

16. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The standardization process includes: Based on the names of equipment components in the basic operation data of the power grid, the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data are adjusted to make the names of equipment components in the power grid's equipment maintenance plan data, electricity market data, and forecast data consistent with the names of equipment components in the basic operation data of the power grid.

17. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The joint constraint modeling includes: Establish grid operation constraints, including maintenance topology constraints and electricity market constraints.

18. The intelligent generation and verification system for power grid operation modes according to claim 17, characterized in that, The maintenance topology constraint is a set of multi-scenario topologies generated by updating the network structure based on the out-of-service equipment.

19. The intelligent generation and verification system for power grid operation modes according to claim 17, characterized in that, The electricity market constraints include: electricity price guidance constraints, contract execution constraints, and market clearing consistency constraints; The electricity price guidance constraint includes: prioritizing the use of low-priced generator units and limiting the output of high-priced generator units based on generator price data, and setting an output deviation tolerance threshold α1; The contract execution constraints include: a tolerance threshold α2 for the power deviation of the inter-provincial transaction tie line, set to ensure that the contracted electricity volume in the power market is completed on time at the designated node; The market clearing consistency constraint includes: the unit output in the generated grid operation mode should be close to the market clearing result, and the set deviation tolerance threshold α3 between the output of all units and the market clearing result.

20. The intelligent generation and verification system for power grid operation modes according to claim 17, characterized in that, The constraints on the power grid operation mode include: the set electricity price guidance constraint coefficient F1, the contract execution constraint coefficient F2, and the market clearing consistency constraint coefficient F3; The total tolerance threshold for operational deviation is α = F1×α1 + F2×α2 + F3×α3, where F1 + F2 + F3 = 1; Among them, F1, F2 and F3 take different values ​​according to the different trading plans corresponding to the power grid operation mode.

21. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The power grid operation mode that generates market-based maintenance coordination includes: For each maintenance combination, generate the corresponding topology of power grid operation mode data; Initialize the unit output in the power grid operation mode data based on the market clearing results; Set power control targets for cross-sections; Based on the volatility of new energy sources, a set of typical power output scenarios is generated; Output the unit output plan, tie line power target, key section power target, and new energy output status for each power grid operation mode.

22. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The process of performing power flow calculations and convergence adjustments on the power grid operation mode data includes: The power flow calculation program is invoked to perform power flow calculation based on the power grid operation mode data. For power flow convergence data, check whether the tie-line power target and the critical section power target have been achieved. If not, call the power flow intelligent adjustment model based on electricity market constraints, set the tie-line power and critical section power as targets, adjust the operation mode data, and call the power flow calculation program again to perform power flow calculation until the tie-line power and critical section power reach the target, or until the target is still not reached after reaching the upper limit of adjustment times. For non-convergent power flow data, the power flow convergence adjustment model is invoked to bring the non-convergent power flow data to convergence. Then, the power flow intelligent adjustment model based on electricity market constraints is invoked to adjust the tie line power and key section power targets.

23. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The security and market-based collaborative verification of the target power grid operation mode includes: Safety and electricity market verifications are performed on the converged grid operation mode data that meet the target in terms of tie line power and critical section power, in order to determine the safety constraints and electricity market constraints of the target grid operation mode. The safety verification includes: setting an N-1 power flow verification range, performing N-1 power flow fault scanning calculations on the power grid operation mode data of each set of target power grid operation modes that meet the requirements, and verifying whether the line power and cross-sectional power exceed the limits; The electricity market verification includes: performing market compliance verification on the power grid operation mode data of each target power grid operation mode after completing the N-1 security verification; The market compliance verification includes at least one of the following: checking the contracted electricity volume completion rate, verifying the deviation between unit output and market clearing, and assessing the consistency of unit node electricity prices.

24. The intelligent generation and verification system for power grid operation modes according to claim 23, characterized in that, The check of the contract electricity completion rate includes: calculating the ratio β1 of the actual transaction electricity to the electricity agreed in the market transaction contract based on the operating time of the target power grid operation mode that has completed the N-1 verification in the actual market transaction; The deviation between the verified unit output and the market clearing includes: comparing the output of the generator unit in the target grid operation mode that has completed the N-1 safety verification with the output of the actual generator unit in the corresponding market clearing data, and calculating its output deviation β2. The unit node price consistency assessment includes: calculating whether the generator price for the target grid operation mode that has completed the N-1 safety verification and participates in the actual electricity market transaction is consistent with the generator price data, and calculating the deviation β3 between the generator price data and the generator price data.

25. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The congestion management and rescheduling simulation includes: determining whether line congestion has occurred based on the line power upper limit threshold; if congestion has occurred, performing rescheduling simulation calculations to confirm that the congestion has been eliminated. The line blockage includes: the absolute value of the active power flow of the transmission line or transformer exceeds the safety limit, causing the power to be unable to be transmitted along the planned path; The rescheduling simulation includes: readjusting the power grid operation mode data of the target power grid and performing power flow calculations to determine that the active power of the blocked lines or transformers has been reduced to within the safe limit.

26. The intelligent generation and verification system for power grid operation modes according to claim 14, characterized in that, The step of selecting the optimal target power grid operation mode from the target power grid operation modes includes: Calculate the deviation value β of the generated target power grid operation mode. Let the contract power completion rate coefficient be f1, the unit output and market clearing deviation coefficient be f2, and the unit node price consistency coefficient be f3. Then β = f1×β1 + f2×β2 + f3×β3, f1 + f2 + f3 = 1. The target power grid is sorted by the size of β according to the target power grid operation mode, and the target power grid operation mode with the smallest β is the optimal mode.

27. A computer device, characterized in that, include: One or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described in any one of claims 1-13 is implemented.

28. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method as described in any one of claims 1-13.

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