Short-circuit current rapid calculation and suppression method and system based on day-ahead production simulation

Abstract

The application discloses a short-circuit current fast calculation and inhibition method and system based on day-ahead production simulation, and the method comprises the following steps: based on load prediction data, power generation prediction data and various production plan curves, a scenario set covering multiple time sections is generated by using a probabilistic method, and power flow distribution is calculated as a multi-scenario production simulation result; through a preset data mapping and conversion rule library, the production simulation result is converted into simulation data files suitable for electromechanical transient simulation software; by using a parallel computing framework, a short-circuit current calculation module of the electromechanical transient simulation software is called in batches, the maximum short-circuit current of each bus and the corresponding time section and scenario are identified; a suppression measure knowledge base is constructed, a candidate suppression measure combination is generated, a multi-objective optimization algorithm is used to optimize, screen and simulate and check the candidate suppression measures under the condition of safety and stability constraints, and an optimal suppression measure scheme is output. The application couples production simulation and transient simulation, and realizes intelligent decision-making.

Description

Technical Field

[0001] This invention belongs to the field of power system safety and stability control technology, and more specifically, relates to a method and system for rapid calculation and suppression of short-circuit current based on day-ahead production simulation. Background Technology

[0002] The large-scale commissioning of ultra-high-voltage direct current (UHVDC) transmission lines and the dramatic increase in the proportion of intermittent renewable energy sources such as wind and solar power have profoundly changed the power grid's power supply structure, grid configuration, and operational characteristics. This change has resulted in a high degree of uncertainty, volatility, and diversity in grid operation. Against this backdrop, traditional short-circuit current analysis and control methods have reached a bottleneck, exhibiting a severe disconnect between their single control approach based on annual static planning and the multiple operating modes based on real-time dynamic operation. Specifically, the shortcomings of traditional methods are: 1. Static planning cannot adapt to the flexibility requirements of dynamic operation. Traditional short-circuit current calculation and control strategies are mainly geared towards a few typical operating modes under the annual planning level, such as peak currents in summer, winter, wet season, and dry season. The short-circuit current control measures determined accordingly, such as a certain line operating in series year-round or a certain busbar operating separately, aim to cover the few most severe scenarios out of the 8760 hours of operation throughout the year. This model leads to serious resource misallocation and efficiency losses. In particular, under most non-extreme operating modes throughout the year, such as low load in spring and off-peak hours at night, the current-limiting measures that were originally implemented may be unnecessary. This can artificially weaken the grid structure, increase network losses, and reduce power supply reliability and operational economy.

[0003] 2. The analysis results are outdated, lacking the ability to proactively warn of intraday risks. Because traditional methods rely on static calculations, they cannot reflect continuous changes in grid operation patterns the following day or even in the next few hours. This leads to a significant lag in the perception of short-circuit current exceeding limits, often resulting in reactive responses after the risk actually occurs, rather than proactive defense. This greatly compresses decision-making response time and increases the risk to grid operation.

[0004] 3. Control measures are crude, relying on manual experience and lacking systematic optimization. When short-circuit current exceeds the limit or risks are detected, the formulation of suppression measures faces problems such as low efficiency. It is difficult to quickly find feasible solutions from a large number of possible operation combinations in a short period of time. It is difficult to quantify and evaluate the impact of a single measure on multiple objectives such as system power flow, voltage stability, N-1 safety, and economic operation, leading to secondary risks. For new equipment with rapid control capabilities, such as flexible DC transmission, there is a lack of standardized procedures for systematically incorporating them into the short-circuit current suppression strategy library and conducting collaborative optimization.

[0005] Therefore, power grid operation urgently needs a method that can be deeply integrated with day-ahead dispatching plans, can quickly and accurately predict the dynamic changes of short-circuit current in the power grid within the next day, and can automatically and intelligently generate efficient suppression strategies with minimal side effects. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for rapid calculation and suppression of short-circuit current based on day-ahead production simulation. By deeply coupling power system production simulation with electromechanical transient simulation and introducing intelligent decision-making algorithms and advanced control strategies, it achieves panoramic perception, accurate prediction, and active suppression of the next day's grid short-circuit current level.

[0007] The present invention adopts the following technical solution.

[0008] The first aspect of the present invention provides a method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation, comprising the following steps: Step 1: Based on load forecast data, power generation forecast data, and various production planning curves, a probabilistic method is used to generate a scenario set covering multiple time segments, and the power flow distribution of each time segment under each scenario is calculated as the result of multi-scenario production simulation. Step 2: Through a preset data mapping and conversion rule library, the production simulation results of multiple scenarios are converted into simulation data files suitable for electromechanical transient simulation software; Step 3: Using a parallel computing framework, the short-circuit current calculation module of the electromechanical transient simulation software is called in batches. Based on the simulation data file, all buses are scanned and calculated to identify the maximum short-circuit current of each bus and its corresponding time section and scenario. Step 4: Construct a knowledge base of suppression measures, generate candidate suppression measure combinations, and use a multi-objective optimization algorithm to optimize, screen, and verify the candidate suppression measures under the constraints of safety and stability, and finally output the optimal suppression measure scheme.

[0009] Preferably, in step 1, the load forecast data includes: the input load forecast data for the next day and its error band; the power generation forecast data includes: the new energy power generation forecast data and its probability distribution model based on the historical output deviation fitting; the production plan curves include: the DC transmission plan curve and its adjustment space, the conventional generator set power generation plan curve, and the AC external power receiving plan curve; the power flow distribution includes: generator output, load power, line power flow, bus voltage, and network topology.

[0010] Preferably, in step 1, generating a scene set covering multiple time segments using a probabilistic method includes: Using simulation or sampling methods, an initial scene is generated based on the error band, probability distribution model, and adjustment space. Then, a representative scene set is obtained through scene reduction.

[0011] Preferably, in step 2, the data mapping and transformation rules include the automatic mapping and generation of network topology, power flow data, and fault sets, including: Step 2.1: Establish a standardized network topology mapping table. The parameters of the mapping table include the status code of circuit breakers or disconnectors, bus voltage level, transformer tap position, and the switching status of parallel compensation equipment. Step 2.2: Construct power flow data mapping, fill the generator output and load power into the power flow operation card of the simulation software, and allocate reactive power; Step 2.3: Generate a fault set, including fault type, fault location, and fault duration; Step 2.4: Based on the established data mapping and transformation rule base, generate independent simulation data files for the production simulation results.

[0012] Preferably, step 3 includes: Step 3.1: Using a message passing programming model or a shared memory programming model, write a batch script to call the simulation software to perform parallel short-circuit current calculations on the simulation data file; Step 3.2: The simulation software analyzes all calculation results to find the maximum short-circuit current of each bus and the specific time segment and operating scenario in which it occurs; Step 3.3: Compare the maximum short-circuit current with the rated breaking capacity of the equipment after taking margin into account, and locate all sites and lines where the short-circuit current exceeds the limit.

[0013] Preferably, in step 4, the knowledge base for suppression measures includes traditional grid operation and flexible DC transmission short-circuit current suppression control strategies. The flexible DC transmission short-circuit current suppression control strategies include current source mode, voltage source mode and dynamic impedance adjustment mode. The dynamic impedance adjustment mode adopts an algorithm based on model predictive control.

[0014] Preferably, in step 4, the suppression measures knowledge base integrates equipment ledgers and operating status information, and automatically filters out equipment that has been taken out of operation or is under maintenance.

[0015] Preferably, in step 4, the optimization objectives of the multi-objective optimization algorithm include a safety objective and an economic objective. The safety objective includes minimizing the short-circuit current exceedance, and the economic objective includes minimizing the change in total network loss or minimizing operating costs.

[0016] Preferably, the operating cost includes labor costs estimated based on the number of operating steps and economic losses estimated based on the product of load shedding and real-time electricity price.

[0017] Preferably, in step 4, the safety and stability constraints include: convergence of power flow calculation results, power flow of all lines and transformers not exceeding their short-term current carrying capacity, voltage of all busbars within the allowable range of the operating procedures, N-1 static safety analysis under the expected fault set, and transient stability verification of changes in operating mode.

[0018] A second aspect of the present invention provides a system for rapid calculation and suppression of short-circuit current based on day-ahead production simulation, wherein running the aforementioned method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation includes: The production simulation module is used to generate scenario sets, calculate power flow distribution, and obtain production simulation results for multiple scenarios. The data conversion module is used to generate simulation data files suitable for electromechanical transient simulation software; The parallel computing module is used to call the short-circuit current calculation module of the electromechanical transient simulation software to identify the maximum short-circuit current of each bus and its corresponding time section and scenario; The intelligent decision-making module is used to build a knowledge base of suppression measures, generate candidate suppression measure combinations, optimize and screen them, and perform simulation verification to output the optimal suppression measure scheme.

[0019] Compared with the prior art, the beneficial effects of the present invention include at least the following: 1. This invention realizes dynamic and rapid analysis of short-circuit current. Through data mapping and conversion rule base, it converts general power flow distribution data into special data required for electromechanical transient simulation in real time, realizes deep coupling between production simulation and transient simulation, accurately tracks the short-circuit current level of continuous time section within the day, and solves the challenges brought about by the changing operation mode under new energy and DC access.

[0020] 2. This invention achieves intelligent decision-making through a multi-objective optimization algorithm. Based on a parallel computing framework, it can automatically select hundreds of options in a short time, improving decision-making efficiency and making decisions more scientific and comprehensive, significantly reducing the risk of human error.

[0021] 3. This invention innovatively incorporates the control capabilities of flexible DC transmission equipment into the short-circuit current suppression system, providing a new and more flexible solution for high-proportion new energy power grids and AC / DC hybrid systems.

[0022] 4. This invention improves the automation level of the entire analysis process. From data access, model conversion, batch calculation to result analysis, the entire process is automated, which greatly frees up manpower and ensures the accuracy and repeatability of the results. Attached Figure Description

[0023] Figure 1This is a flowchart of a method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation provided in accordance with an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0025] The core of this invention lies in constructing a closed-loop process of data-driven, simulation verification, and intelligent decision-making to achieve proactive defense against short-circuit current risks throughout the entire process, including early warning, in-process control, and post-event evaluation.

[0026] like Figure 1 As shown, Embodiment 1 of the present invention provides a method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation, comprising the following steps: Step 1: A multi-source data fusion algorithm is used to generate day-ahead production simulation results for multiple scenarios that take into account the uncertainty of new energy sources. Specifically, based on load forecast data, power generation forecast data, and various production plan curves, a probabilistic method is used to generate a set of typical scenarios that cover multiple time segments in the next 24 hours and characterize operational uncertainty. The power flow distribution of each time segment under each scenario is calculated as the multi-scenario production simulation results. [1] In a preferred but non-limiting embodiment of the present invention, step 1 specifically includes: Step 1.1: Integrate high-precision data from 96 time segments of the next day at 15-minute intervals or 24 time segments of the next day at 1-hour intervals, including: load forecast data and its error band, new energy power generation forecast data and its uncertainty model, DC transmission plan curve and its adjustment space, conventional generator set power generation plan curve and inter-regional AC external power receiving plan curve.

[0027] In one exemplary but non-limiting embodiment of the present invention, the error band of the load forecast data is ±5%, the new energy power generation forecast data includes wind power and photovoltaic forecast data, and the DC transmission plan curve adjustment space is ±10% of the maximum output active power P. max [2].

[0028] More preferably, the uncertainty model of the new energy power generation prediction data is a probability distribution model, and the Beta distribution is used to fit the historical output deviation.

[0029] Step 1.2: Using probabilistic methods such as Monte Carlo simulation or Latin hypercube sampling, based on the uncertainty parameters in the high-precision data integrated in Step 1.1, namely the error bands and probability distribution models of various prediction data, as well as the adjustment space of various planning curves, a large number of initial scenarios are generated. Then, a representative set of typical scenarios is obtained through scenario reduction techniques.

[0030] In one exemplary but non-limiting embodiment of the present invention, 1000 initial scenes are generated, and then reduced to a set of 20 typical scenes by K-means clustering.

[0031] The typical scenario set covers more than 95% of the probability distributions, effectively characterizing the uncertainties brought about by new energy fluctuations and load changes.

[0032] Step 1.3 involves conducting production simulations under safety constraints. Specifically, algorithms such as Mixed Integer Programming (MIP) or Optimal Power Flow (OPF) are used to perform refined production simulation calculations for each typical scenario generated in Step 1.2. While ensuring power balance, transient stability constraints are also embedded. Time-domain simulation is used to verify the voltage stability of key nodes, ensuring that the production simulation results strictly meet the requirements of the "Guidelines for the Safety and Stability of Power Systems". Finally, the power flow distribution of the entire network at each time segment under each scenario is obtained. The power flow distribution includes generator output, load power, line power flow, bus voltage, and network topology.

[0033] Understandably, this step is the foundation for all subsequent analyses. Through multi-source data fusion algorithms, a set of typical scenarios with high precision and multiple time sections is generated that can cover the uncertainties of future operation.

[0034] Step 2: Construct an automated data conversion engine for electromechanical transient simulation. Through the data conversion engine, standardized data is automatically converted to generate electromechanical transient simulation data for software such as PSASP and BPA. Specifically, the multi-scenario production simulation results obtained in Step 1 are automatically converted into simulation data files suitable for batch calculation of electromechanical transient simulation software through the preset data mapping and conversion rule library that supports standard formats in the automated data conversion engine [3]. The data mapping and conversion rules include the automatic mapping and generation of network topology, power flow data and fault sets.

[0035] In a preferred but non-limiting embodiment of the present invention, step 2 specifically includes: Step 2.1: Construct network topology mapping rules in the data mapping and transformation rule base. Specifically, establish a standardized network topology mapping table that includes parameters such as circuit breaker or disconnector status codes, bus voltage levels, transformer tap positions, and parallel compensation equipment switching status.

[0036] In one exemplary but non-limiting embodiment of the invention, the switch state code indicates that 0 represents closed and 1 represents open.

[0037] More preferably, the automated data conversion engine supports cross-platform data format conversion, supports the IEC61970-301 standard, and can parse general information models such as CIM / XML[4], i.e. standardized data, and automatically convert and generate specific format data files required by mainstream simulation software such as PSASP and BPA, such as .dat, .bse, i.e. electromechanical transient simulation data.

[0038] Step 2.2: Construct the power flow data mapping in the data mapping and transformation rule base. Specifically, accurately fill the generator output and load power of each section output in Step 1.3 into the power flow operation card of the simulation software, and automatically allocate reactive power to ensure the accuracy of the initial power flow.

[0039] Step 2.3 generates a fault set in the data mapping and transformation rule base. Specifically, based on the "Technical Specification for Relay Protection of Power Systems" and the characteristics of the power grid structure, a fault template is automatically constructed, including the fault type, fault location, and fault duration, forming a typical fault set to prepare for subsequent batch calculations.

[0040] In one exemplary but non-limiting embodiment of the present invention, the fault types include three-phase short circuit and single-phase grounding, and the fault locations include all busbars and the beginning and end of critical lines.

[0041] Step 2.4: Based on the data mapping and transformation rule base established in Steps 2.1-2.3, automatically generate independent simulation data files for the production simulation results in Step 1.3, laying the foundation for large-scale parallel computing.

[0042] Understandably, this step is a key bridge connecting planning analysis and safety verification. By developing a standardized data mapping and transformation rule base and an automated data transformation engine, seamless integration from production simulation results to various electromechanical transient simulation software is achieved. [5] Step 3: Quickly scan the short-circuit current of the entire network based on parallel computing. Specifically, a parallel computing framework is used to call the short-circuit current calculation module of the electromechanical transient simulation software described in Step 2 in batches. Based on the batch simulation data file generated in Step 2, the short-circuit current of all buses in the entire network is scanned and calculated to identify the maximum short-circuit current of each bus and its corresponding time section [6] and scenario.

[0043] This step aims to efficiently and comprehensively identify potential risk points. In a preferred but non-limiting embodiment of the present invention, step 3 specifically includes: Step 3.1: Using high-performance computing (HPC) resources or parallel computing frameworks, write batch processing scripts to call simulation software and perform parallel short-circuit current calculations on the massive simulation data files generated in step 2.4.

[0044] In one exemplary but non-limiting embodiment of the present invention, the parallel computing framework employs a message passing interface (MPI) or open multi-processing (OpenMP) programming model, and the simulation software invoked includes the SWI module of PSASP and the SWNT program of BPA.

[0045] Step 3.2: After the parallel short-circuit current calculation is completed, the simulation software automatically analyzes all the calculation results to find the maximum short-circuit current of each bus and the specific time section and operating scenario in which it occurs.

[0046] Step 3.3 compares the maximum short-circuit current with the rated breaking capacity of the equipment after taking necessary margins, quickly locating all sites and lines with excessive short-circuit currents and identifying potential risks.

[0047] Compared to the hours-long calculation time of traditional manual operations, the calculation time for locating sites and lines with excessive short-circuit currents in this invention is reduced to minutes, improving efficiency by more than 20 times.

[0048] Step 4: Intelligent analysis of short-circuit current suppression measures and making optimal decisions. Specifically, a knowledge base of suppression measures is constructed, candidate suppression measure combinations are generated, and a multi-objective optimization algorithm is used to optimize and screen the candidate suppression measures under the constraints of safety and stability. The optimal suppression measure scheme that meets the short-circuit current limit requirements and has the least impact on system operation is selected.

[0049] This step is the core of intelligent decision-making, and its optimization and screening process is a typical constrained multi-objective optimization problem. In a preferred but non-limiting embodiment of the present invention, step 4 specifically includes: Step 4.1: For the sites and lines with excessive short-circuit current identified in Step 3.3, construct a knowledge base of suppression measures that includes traditional power grid operation and flexible DC transmission short-circuit current suppression and control strategies. Based on the knowledge base of suppression measures, automatically generate a set of candidate suppression measures.

[0050] More preferably, the flexible DC transmission short-circuit current suppression control strategy includes a current source mode, a voltage source mode, and a dynamic impedance adjustment mode; the dynamic impedance adjustment mode adopts a model predictive control (MPC) based algorithm, with a prediction time domain of 3-5 power frequency cycles.

[0051] More preferably, the suppression measures knowledge base integrates equipment ledgers and operating status information, which can automatically filter out equipment that has been taken out of operation or is under maintenance, ensuring that the generated candidate suppression measures are executable.

[0052] Step 4.2: Establish a multi-objective optimization model, set multiple optimization objectives and corresponding objective functions, and set safety and stability constraints.

[0053] More preferably, the optimization objectives include safety objectives and economic objectives. Specifically, the safety objective is to minimize the extent of short-circuit current exceeding the standard, even to minimize the short-circuit current value of the bus exceeding the standard; the economic objective is to minimize the change in total system network loss or to minimize operating costs. It is worth noting that those skilled in the art can also set other index conditions as safety and economic objectives as needed, and based on the spirit of this invention, all of them fall within the protection scope of this invention.

[0054] More preferably, the operating cost includes labor costs estimated based on the number of operating steps and economic losses estimated based on the product of load shedding and real-time electricity price.

[0055] More preferably, the safety and stability constraints are hard constraints. Any candidate solution must satisfy all safety and stability constraints at the same time; otherwise, it will be regarded as an infeasible solution and eliminated. The safety and stability constraints include: convergence of power flow calculation results, power flow of all lines and transformers not exceeding their short-term current carrying capacity, all bus voltages within the allowable range of the operating procedures, passing the N-1 static safety analysis under the typical fault set in step 2.3 (i.e., N-1 static safety constraints), and performing transient stability verification on major mode changes to ensure system stability (i.e., transient stability constraints).

[0056] Specifically, the N-1 static safety constraints include: the new operating mode after implementing suppression measures must be able to withstand any single component in a typical fault concentration [7], such as the system power flow being redistributed after any line or transformer is disconnected without causing overload of other components or bus voltage exceeding limits. This is a key constraint to ensure that the system has the necessary redundancy.

[0057] The transient stability constraints include: for significant changes in operating mode, such as the shutdown of large power transmission lines or the opening of major cross-sections, transient stability checks are required. After the measures are implemented, the generator power angle difference should remain stable after a large disturbance such as a three-phase short-circuit fault, and voltage dips and recovery during dynamic processes must meet the requirements of the "Guidelines for the Safety and Stability of Power Systems".

[0058] Step 4.3: Improved constrained multi-objective optimization algorithms, such as the constrained version of NSGA-II and the MOEA / D algorithm, are used to optimize, screen, and verify the suppression measures through simulation.

[0059] More preferably, the evaluation function of the optimization algorithm integrates an automated safety verification process to verify whether each candidate suppression measure meets the safety and stability constraints of step 4.2. Through the safety verification process, only suppression measures that successfully pass all safety checks will be assigned an objective function value and participate in the subsequent Pareto optimal solution selection.

[0060] More preferably, under the premise of ensuring that the power flow distribution of the system is reasonable, the static voltage is stable, the transient power angle is stable and the standards such as the "Guidelines for the Safety and Stability of Power Systems" are met after the implementation of the suppression measures, simulation software is used to perform integrated verification calculations of short-circuit current and safety and stability for each candidate suppression measure combination.

[0061] Step 4.4: Based on the optimization screening and simulation verification results, the optimal Pareto solution set obtained by optimization is sorted using the entropy weight-TOPSIS comprehensive evaluation method to determine and finally output the optimal suppression measure scheme.

[0062] Understandably, the optimal suppression measure scheme not only ensures that the short-circuit current does not exceed the limit, but also that it has passed comprehensive safety and stability verification and possesses high operational reliability. Furthermore, the report generating the optimal suppression measure scheme will clearly list the power flow, voltage, N-1, and other safety indicators under that scheme, providing dispatchers with sufficient confidence for decision-making.

[0063] The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation of the present invention realizes the leap from "static analysis" to "dynamic early warning" and from "manual decision-making" to "intelligent optimization" of short-circuit current, and significantly improves the safe and stable operation level of the new power system.

[0064] Embodiment 2 of the present invention provides a system for rapid calculation and suppression of short-circuit current based on day-ahead production simulation, which runs the method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation described in Embodiment 1, including: The production simulation module is used to generate scenario sets, calculate power flow distribution, and obtain production simulation results for multiple scenarios. The data conversion module is used to generate simulation data files suitable for electromechanical transient simulation software; The parallel computing module is used to call the short-circuit current calculation module of the electromechanical transient simulation software to identify the maximum short-circuit current of each bus and its corresponding time section and scenario; Intelligent decision-making module. Used to build a knowledge base of suppression measures, generate candidate suppression measure combinations, perform optimization screening and simulation verification, and output the optimal suppression measure scheme.

[0065] The present invention will now be described in more detail with reference to the accompanying drawings and specific embodiments. An application example of the present invention involves the development and deployment of a software system that can implement the method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation as described in Embodiment 1, and can interact with the existing platform of the dispatch center. Taking a provincial power grid dispatch center as an example, the software system of the present invention is deployed, and the basic implementation process and results are as follows.

[0066] Before formally deploying the software system, the environment and interfaces must first be configured, including: Configure the hardware environment by deploying the system on a high-performance server cluster in the scheduling center, equipped with multi-core CPUs and large memory to support parallel computing.

[0067] The software environment is configured with Linux as the operating system. The core computing module is coupled with the commercial software PSASP / BPA through an API interface, and the optimization algorithm is implemented using Python / Matlab.

[0068] Configure the data interface and use the OPC-UA protocol or Restful API to interact with the SCADA system, load forecasting system, and new energy power forecasting system. The data update frequency is ≤1 second to ensure the real-time nature of the input data.

[0069] After configuring the environment and interfaces, the software system automatically executes the following process: Daily scheduled trigger: The system automatically starts at 14:00 every day to obtain all forecast and plan data for the next day.

[0070] Step 1: Call the production simulation module to generate power flow results for 96 points under 20 typical scenarios.

[0071] Step 2: The data conversion engine works automatically to generate 20 × 96 = 1920 simulation data files in PSASP format.

[0072] Step 3: The parallel computing module starts and completes the short-circuit current calculation for all 1920 sections within 30 minutes.

[0073] Step 4: The system identified that the short-circuit current of 500kV substation S exceeded the limit at 13:15 the following day (during peak photovoltaic power generation). Subsequently, the intelligent decision-making module screened from 325 candidate solutions within 5 minutes. During the screening process, each solution automatically underwent a three-layer filtering process: 'short-circuit current verification → power flow convergence and equipment safety verification → N-1 static safety scan'. For solutions involving changes in the main grid structure, transient stability time-domain simulation was also performed. The final optimal solution selected was: 'Enable the current source mode of VSC-HVDC project F and open the 500kV bus 5022 switch of station G', which not only reduced the short-circuit current of station S to 52kA (the safety limit is 63kA), but also verified that it could simultaneously meet the following conditions: 1. The power flow distribution is reasonable, and there is no equipment overload; 2. The voltage of the entire network is within the acceptable range; 3. When an N-1 fault occurs on any 500kV line, the system can remain stable without any new overload or voltage exceeding the limit; 4. The transient stability calculation shows that the power angle swing curve has good damping.

[0074] Finally, the system automatically generates reports and operational suggestions, which are then pushed to relevant dispatchers via the scheduling console, providing direct decision support for the next day's planning.

[0075] It is worth noting that the present invention is not limited to the specific embodiments described above. Various modifications and variations made by those skilled in the art based on the same core idea within the scope of the present invention, such as using different multi-objective optimization algorithms like MOEA / D or adapting to other types of simulation software like PSSE, should all be included within the protection scope of the present invention.

[0076] Compared with the prior art, the beneficial effects of the present invention include at least the following: 1. This invention realizes dynamic and rapid analysis of short-circuit current. Through data mapping and conversion rule base, it converts general power flow distribution data into special data required for electromechanical transient simulation in real time, realizes deep coupling between production simulation and transient simulation, accurately tracks the short-circuit current level of continuous time section within the day, and solves the challenges brought about by the changing operation mode under new energy and DC access.

[0077] 2. This invention achieves intelligent decision-making through a multi-objective optimization algorithm. Based on a parallel computing framework, it can automatically select hundreds of options in a short time, improving decision-making efficiency and making decisions more scientific and comprehensive, significantly reducing the risk of human error.

[0078] 3. This invention innovatively incorporates the control capabilities of flexible DC transmission equipment into the short-circuit current suppression system, providing a new and more flexible solution for high-proportion new energy power grids and AC / DC hybrid systems.

[0079] 4. This invention improves the automation level of the entire analysis process. From data access, model conversion, batch calculation to result analysis, the entire process is automated, which greatly frees up manpower and ensures the accuracy and repeatability of the results.

[0080] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation, characterized in that, Includes the following steps: Step 1: Based on load forecast data, power generation forecast data, and various production planning curves, a probabilistic method is used to generate a scenario set covering multiple time segments, and the power flow distribution of each time segment under each scenario is calculated as the result of multi-scenario production simulation. Step 2: Through a preset data mapping and conversion rule library, the production simulation results of multiple scenarios are converted into simulation data files suitable for electromechanical transient simulation software; Step 3: Using a parallel computing framework, the short-circuit current calculation module of the electromechanical transient simulation software is called in batches. Based on the simulation data file, all buses are scanned and calculated to identify the maximum short-circuit current of each bus and its corresponding time section and scenario. Step 4: Construct a knowledge base of suppression measures, generate candidate suppression measure combinations, and use a multi-objective optimization algorithm to optimize, screen, and verify the candidate suppression measures under the constraints of safety and stability, and finally output the optimal suppression measure scheme.

2. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 1, characterized in that: In step 1, the load forecast data includes: the input load forecast data for the next day and its error band; the power generation forecast data includes: the new energy power generation forecast data and its probability distribution model based on the historical output deviation fitting; the production plan curves include: the DC transmission plan curve and its adjustment space, the conventional generator set power generation plan curve, and the AC external power receiving plan curve; the power flow distribution includes: generator output, load power, line power flow, bus voltage and network topology.

3. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 2, characterized in that: In step 1, a probabilistic method is used to generate a scene set covering multiple time segments, including: Using simulation or sampling methods, an initial scene is generated based on the error band, probability distribution model, and adjustment space. Then, a representative scene set is obtained through scene reduction.

4. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 2, characterized in that: In step 2, the data mapping and transformation rules include the automatic mapping and generation of network topology, power flow data, and fault sets, including: Step 2.1: Establish a standardized network topology mapping table. The parameters of the mapping table include the status code of circuit breakers or disconnectors, bus voltage level, transformer tap position, and the switching status of parallel compensation equipment. Step 2.2: Construct power flow data mapping, fill the generator output and load power into the power flow operation card of the simulation software, and allocate reactive power; Step 2.3: Generate a fault set, including fault type, fault location, and fault duration; Step 2.4: Based on the established data mapping and transformation rule base, generate independent simulation data files for the production simulation results.

5. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 1, characterized in that: Step 3 includes: Step 3.1: Using a message passing programming model or a shared memory programming model, write a batch script to call the simulation software to perform parallel short-circuit current calculations on the simulation data file; Step 3.2: The simulation software analyzes all calculation results to find the maximum short-circuit current of each bus and the specific time segment and operating scenario in which it occurs; Step 3.3: Compare the maximum short-circuit current with the rated breaking capacity of the equipment after taking margin into account, and locate all sites and lines where the short-circuit current exceeds the limit.

6. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 1, characterized in that: In step 4, the knowledge base for suppression measures includes traditional grid operation and flexible DC transmission short-circuit current suppression control strategies. The flexible DC transmission short-circuit current suppression control strategies include current source mode, voltage source mode and dynamic impedance adjustment mode. The dynamic impedance adjustment mode adopts an algorithm based on model predictive control.

7. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 1, characterized in that: In step 4, the suppression measures knowledge base integrates equipment ledgers and operating status information, automatically filtering out equipment that has been taken out of operation or is under maintenance.

8. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 1, characterized in that: In step 4, the optimization objectives of the multi-objective optimization algorithm include safety objectives and economic objectives. The safety objective includes minimizing the short-circuit current exceedance, and the economic objective includes minimizing the change in total network loss or minimizing operating costs.

9. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 8, characterized in that: The operating costs include labor costs estimated based on the number of operating steps and economic losses estimated based on the product of load shedding and real-time electricity price.

10. The method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation according to claim 1, characterized in that: In step 4, the safety and stability constraints include: convergence of power flow calculation results, power flow of all lines and transformers not exceeding their short-term current carrying capacity, voltage of all busbars within the allowable range of the operating procedures, N-1 static safety analysis under the expected fault set, and transient stability verification for changes in operating mode.

11. A system for rapid calculation and suppression of short-circuit current based on day-ahead production simulation, running the method for rapid calculation and suppression of short-circuit current based on day-ahead production simulation as described in any one of claims 1-10, characterized in that, include: The production simulation module is used to generate scenario sets, calculate power flow distribution, and obtain production simulation results for multiple scenarios. The data conversion module is used to generate simulation data files suitable for electromechanical transient simulation software; The parallel computing module is used to call the short-circuit current calculation module of the electromechanical transient simulation software to identify the maximum short-circuit current of each bus and its corresponding time section and scenario; The intelligent decision-making module is used to build a knowledge base of suppression measures, generate candidate suppression measure combinations, optimize and screen them, and perform simulation verification to output the optimal suppression measure scheme.

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