A regional low-carbon power grid multi-scenario dynamic expansion deduction method and system

Abstract

The present application relates to the technical field of power system planning, and particularly relates to a regional low-carbon power grid multi-scenario dynamic expansion deduction method and system, comprising: modeling steel-electricity coupling demand according to steel electric arc furnace data and regional steel production capacity distribution characteristics to obtain regional power demand fluctuation distribution; identifying power grid stability critical point according to the regional power demand fluctuation distribution; obtaining expansion demand ranking table according to power grid equipment capacity data corresponding to the power grid stability critical point; generating phased power grid capacity expansion path graph according to the expansion demand ranking table, simulating peak load distribution, and obtaining carbon emission intensity index; and generating a low-carbon dynamic expansion planning scheme of steel-electricity coupling according to the carbon emission intensity index and regional energy structure characteristic data. The present application realizes the collaborative optimization of decarburization of the steel industry and expansion of the power system by establishing a dynamic demand model of steel-electricity coupling and based on the identification of the power grid stability critical point and the low-carbon oriented expansion planning.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a method and system for dynamic expansion simulation of regional low-carbon power grids under multiple scenarios. Background Technology

[0002] As a major source of global carbon emissions, the steel industry's carbon emissions have long been a key topic in energy and environmental research. With the global adoption of low-carbon practices, innovations in steel production technology are triggering a chain reaction across the entire industry chain. This not only involves the smelting process itself, but also profoundly affects the supply and demand structure of the power industry due to significant changes in electricity demand. However, current research on decarbonization pathways for the steel industry has obvious limitations and is difficult to meet the practical needs of cross-industry collaborative emission reduction.

[0003] Currently, research on decarbonization in the steel industry largely focuses on carbon reduction pathways within a single industry, neglecting the complex chain reactions across industries. Taking electric arc furnace (EAF) smelting technology as an example, while it can reduce coking coal consumption in steelmaking, it also causes a structural increase in electricity demand. This surge in electricity demand indirectly leads to increased carbon emissions on the power generation side. This transfer emission effect is often overlooked in existing research. This demand change exhibits regional clustering characteristics, and traditional analytical models often simplify it as a uniformly distributed load increase, resulting in systematic biases in the assessment of the actual impact on the power grid. Moreover, in terms of energy security, existing power grid planning methods are ill-equipped to handle non-linearly growing electricity demand. Therefore, accurately predicting and addressing the surge in electricity demand and the pressure on transmission channel expansion caused by the widespread adoption of EAF technology in the steel industry has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0004] To address the above technical problems, this invention provides a method and system for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios.

[0005] In a first aspect, the present invention provides a method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios, the method comprising the following steps:

[0006] Based on pre-acquired data on electric arc furnaces for steelmaking and the regional steel production capacity distribution characteristics, a steel-electricity coupling demand model is performed to obtain the regional electricity demand fluctuation distribution.

[0007] Based on the regional electricity demand fluctuation distribution and historical power grid operation data, hourly load change curves for each region are generated.

[0008] Based on the hourly load change curves of the different regions, the regional power supply reliability gap rate and the regional voltage instability probability are calculated, and the critical point of grid stability is identified.

[0009] Based on the power grid equipment capacity data corresponding to the power grid stability critical point, the priority of power transmission channel expansion in each region is evaluated to obtain an expansion demand ranking table.

[0010] A phased grid capacity expansion path map is generated based on the expansion demand ranking table, and the peak load distribution is simulated through the phased grid capacity expansion path map to obtain the carbon emission intensity index.

[0011] Based on the carbon emission intensity index and the energy structure characteristics of each region, a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling is generated.

[0012] In a further implementation, the step of modeling the steel-electricity coupling demand based on pre-acquired steel electric arc furnace data and regional steel production capacity distribution characteristics to obtain the regional electricity demand fluctuation distribution includes:

[0013] The actual annual power consumption base of electric arc furnaces in each region is calculated based on the pre-acquired data of electric arc furnaces in steel, and the capacity growth rate of electric arc furnaces in each region is obtained based on the actual annual power consumption base of electric arc furnaces and the steel electric arc furnace replacement cycle.

[0014] The basic power demand forecast is calculated based on the electric arc furnace capacity growth rate and power consumption benchmark value, and the basic power demand forecast is coupled and corrected using the regional steel capacity distribution characteristics to obtain the regional electric arc furnace power demand forecast.

[0015] Based on the regional electric arc furnace power demand forecast and the historical power demand data for the same period in each region, the power demand growth rate for each region is obtained.

[0016] The inter-regional demand gradient is calculated based on the degree of difference in the growth rate of electricity demand between adjacent regions. Based on the inter-regional demand gradient and the annual change rate of electricity demand growth in each region, the spatial coupling fluctuation intensity index of inter-regional electricity demand growth is obtained.

[0017] Using regions as the basic unit, the inter-regional demand gradient and spatial coupling fluctuation intensity index are integrated according to regional location to form the regional electricity demand fluctuation distribution.

[0018] In a further implementation, the regional steel production capacity distribution characteristics include the regional steel production capacity density.

[0019] In a further implementation, the step of generating hourly load variation curves for different regions based on the regional electricity demand fluctuation distribution and historical power grid operation data includes:

[0020] Based on the physical connection relationship of the power grid, the inter-regional demand gradient is converted into spatial transfer weights to obtain the power grid spatial coupling weight matrix.

[0021] Extract the typical daily load curve of the power grid during the same period from the historical operation data of the power grid, and map the spatial coupling fluctuation intensity index to each time point of the typical daily load curve of the power grid during the same period to generate the fluctuation intensity time series correction coefficient by region and time period.

[0022] Using the power grid spatial coupling weight matrix as a correction factor, the load of adjacent regions is superimposed on the historical typical daily load curves of the power grid in each region to obtain the spatially coupled corrected load reference curve.

[0023] The fluctuation intensity time series correction coefficient is multiplied point by point with the spatial coupling correction load reference curve to obtain the power load prediction value of each region at each time point;

[0024] The predicted power load values ​​are arranged sequentially according to time to form hourly load change curves for different regions.

[0025] In a further implementation, the step of calculating the regional power supply reliability gap rate and the regional voltage instability probability based on the hourly load change curves of the different regions, and identifying the critical point of grid stability, includes:

[0026] Based on the hourly load change curves of different regions, the power supply equipment capacity boundaries of the corresponding regions are matched from the pre-built power grid equipment parameter database to obtain the upper limit of regional power supply capacity at different times for each region.

[0027] The power supply margin at each time point is calculated based on the upper limit of the regional power supply capacity and the predicted power load value. The proportion of negative margin periods when the power supply margin is less than a preset margin threshold is then calculated to obtain the regional power supply reliability gap rate.

[0028] The load change rate between adjacent time points is extracted from the hourly load change curve of the sub-region, and the voltage deviation rate is calculated based on the load change rate and the load base.

[0029] The probability of regional voltage instability is calculated based on the proportion of voltage instability periods where the voltage deviation rate exceeds a preset deviation threshold.

[0030] The spatiotemporal nodes that simultaneously satisfy the condition that the regional power supply reliability gap rate is greater than a preset gap threshold and the regional voltage instability probability is greater than a preset instability threshold are marked as critical points for grid stability.

[0031] In a further implementation, the step of evaluating the priority of transmission channel expansion in each region based on the grid equipment capacity data corresponding to the grid stability critical point, and obtaining the expansion demand ranking table, includes:

[0032] Extract the rated capacity of power grid equipment and the measured load value at the critical point from the power grid equipment capacity data associated with the power grid stability critical point;

[0033] The capacity margin is calculated based on the rated capacity of the power grid equipment and the measured load value at the critical point. When the capacity margin is less than a preset safety threshold, the substation where the power grid equipment is located is marked as a capacity bottleneck point.

[0034] Calculate the ratio of the actual load to the rated capacity at each capacity bottleneck point to obtain the overload severity value, and multiply the overload severity value by the regional load weighting coefficient to obtain the regional capacity constraint severity score.

[0035] The ratio between the regional capacity constraint severity score and the regional power supply reliability gap rate is calculated to obtain the capacity expansion benefit coefficient. At the same grid stability critical point, the ratio between the load growth rate and the current capacity margin is calculated to obtain the capacity depletion time.

[0036] Based on the capacity depletion time and the capacity expansion benefit coefficient, the capacity expansion urgency index is calculated, and the transmission channels in each region are sorted according to the capacity expansion urgency index to generate a capacity expansion demand ranking table.

[0037] In a further implementation scheme, the regional load weighting coefficient is the proportion of the load within the power supply range of the capacity bottleneck point to the total load of the entire network.

[0038] In a further implementation, the step of generating a phased grid capacity expansion path map based on the expansion demand ranking table, and simulating peak load distribution using the phased grid capacity expansion path map to obtain a carbon emission intensity index includes:

[0039] Based on the expansion demand ranking table, standard expansion templates matching regional geographical conditions and steel load characteristics are retrieved from the power grid capacity planning database to obtain the target capacity expansion plan.

[0040] Based on the phased capacity growth values ​​in the target capacity expansion scheme, a phased power grid capacity expansion path diagram is generated.

[0041] By superimposing the new capacity of each stage in the phased power grid capacity expansion path diagram with the electric arc furnace load growth characteristics, the peak load distribution data of each region after the expansion is simulated.

[0042] Calculate the energy structure change matrix for each stage based on the new capacity and regional energy structure base period data in the phased power grid capacity expansion path diagram.

[0043] Based on the peak load distribution data and the energy structure change matrix at each stage, the carbon emission intensity index of the region is obtained by weighted summation according to the carbon emission coefficient of various power sources.

[0044] In a further implementation scheme, the step of generating a low-carbon dynamic capacity expansion plan for steel-electricity coupling based on the carbon emission intensity index and the energy structure characteristics data of each region includes:

[0045] The emission regions are sorted from high to low according to the carbon emission intensity index to obtain the high emission region sequence. The energy structure characteristic data of the corresponding emission regions are retrieved according to the high emission region sequence.

[0046] Information on the proportion of coal-fired power, the proportion of renewable energy, and reserve regulation capacity are extracted from the energy structure characteristic data to form a regional energy-carbon emission correlation matrix.

[0047] Based on the regional energy-carbon emission correlation matrix and the preset carbon emission intensity control target value, the reduction amount of coal power ratio and the increase amount of renewable energy ratio in each emission region are calculated to obtain the regional low-carbon adjustment amount;

[0048] The low-carbon adjustment amount of the region is mapped to the phased power grid capacity expansion path map, and the new transmission capacity, substation expansion and power access scheme are modified in a time-based manner to form a low-carbon dynamic capacity expansion planning scheme of steel-electricity coupling.

[0049] Secondly, the present invention provides a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation system, the system comprising:

[0050] The coupling modeling module is used to perform steel-electricity coupling demand modeling based on pre-acquired steel electric arc furnace data and regional steel production capacity distribution characteristics, and to obtain the regional electricity demand fluctuation distribution.

[0051] The load analysis module is used to generate hourly load change curves for different regions based on the regional power demand fluctuation distribution and historical power grid operation data.

[0052] The stability analysis module is used to calculate the regional power supply reliability gap rate and the regional voltage instability probability based on the hourly load change curves of the different regions, and to identify the critical point of grid stability.

[0053] The capacity expansion sorting module is used to evaluate the capacity expansion priority of transmission channels in each region based on the power grid equipment capacity data corresponding to the power grid stability critical point, and obtain a capacity expansion demand sorting table.

[0054] The carbon emission analysis module is used to generate a phased grid capacity expansion path map based on the expansion demand ranking table, and to simulate peak load distribution through the phased grid capacity expansion path map to obtain carbon emission intensity indicators.

[0055] The capacity expansion planning module is used to generate a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling based on the carbon emission intensity index and the energy structure characteristics data of each region.

[0056] This invention provides a method and system for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios. The method performs steel-electricity coupling demand modeling based on data from electric arc furnaces in the steel industry and the regional steel production capacity distribution characteristics to obtain the regional electricity demand fluctuation distribution. Based on the regional electricity demand fluctuation distribution and historical power grid operation data, it generates hourly load change curves for each region. Based on the hourly load change curves for each region, it calculates the regional power supply reliability gap rate and the regional voltage instability probability, identifying the critical point for power grid stability. Based on the power grid equipment capacity data corresponding to the critical point for power grid stability, it evaluates the priority of transmission channel expansion in each region, obtaining an expansion demand ranking table. Based on the expansion demand ranking table, it generates a phased power grid capacity expansion path diagram and simulates peak load distribution through the phased power grid capacity expansion path diagram to obtain a carbon emission intensity index. Based on the carbon emission intensity index and energy structure characteristic data of each region, it generates a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling. Compared with existing technologies, this method establishes a dynamic demand model of steel-electricity coupling, accurately captures the regional and time-specific electricity demand characteristics brought about by the popularization of electric arc furnaces, and achieves synergistic optimization of steel industry decarbonization and power system expansion based on grid stability critical point identification and low-carbon-oriented capacity expansion planning. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the process for the multi-scenario dynamic capacity expansion simulation method for regional low-carbon power grids provided in this embodiment of the invention;

[0058] Figure 2 This is a block diagram of a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation system provided in an embodiment of the present invention.

[0059] Explanation of reference numerals in the attached diagram: 101, Coupled Modeling Module; 102, Load Analysis Module; 103, Stability Analysis Module; 104, Capacity Expansion Ranking Module; 105, Carbon Emission Analysis Module; 106, Capacity Expansion Planning Module. Detailed Implementation

[0060] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0061] Figure 1 This is a schematic flowchart of a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation method provided by an embodiment of the present invention. The embodiment of the present invention provides a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation method, which can be used as follows... Figure 1 As shown, the method includes the following steps:

[0062] S1. Based on the pre-acquired data on electric arc furnaces for steelmaking and the regional steel production capacity distribution characteristics, a steel-electricity coupling demand model is performed to obtain the regional electricity demand fluctuation distribution.

[0063] In some implementations, the step of modeling the steel-electricity coupling demand based on pre-acquired electric arc furnace data and regional steel production capacity distribution characteristics to obtain the regional electricity demand fluctuation distribution includes:

[0064] The actual annual power consumption base of electric arc furnaces in each region is calculated based on the pre-acquired data of electric arc furnaces in steel, and the capacity growth rate of electric arc furnaces in each region is obtained based on the actual annual power consumption base of electric arc furnaces and the steel electric arc furnace replacement cycle.

[0065] The basic power demand forecast is calculated based on the electric arc furnace capacity growth rate and power consumption benchmark value, and the basic power demand forecast is coupled and corrected using the regional steel capacity distribution characteristics to obtain the regional electric arc furnace power demand forecast.

[0066] Based on the regional electric arc furnace power demand forecast and the historical power demand data for the same period in each region, the power demand growth rate for each region is obtained.

[0067] The inter-regional demand gradient is calculated based on the degree of difference in the growth rate of electricity demand between adjacent regions. Based on the inter-regional demand gradient and the annual change rate of electricity demand growth in each region, the spatial coupling fluctuation intensity index of inter-regional electricity demand growth is obtained.

[0068] Using regions as the basic unit, the inter-regional demand gradient and spatial coupling fluctuation intensity index are integrated according to regional location to form the regional electricity demand fluctuation distribution.

[0069] Specifically, this embodiment collects data on the installed capacity, operating time, and production efficiency of electric arc furnaces in various regions from the steel enterprise's production record system and power monitoring equipment. For example, in a concentrated steel production area, the cumulative operating time of the electric arc furnace within a year is recorded by power monitoring instruments installed on the furnace. The installed capacity of the electric arc furnace is obtained from the enterprise's production management system, which represents the power output of the furnace at full load. At the same time, the actual product output of the furnace within a year is statistically analyzed, and combined with the furnace's rated production efficiency (product output per unit power per unit time), the accuracy of the production efficiency-related data is verified.

[0070] This embodiment calculates the actual annual power consumption base of electric arc furnaces in each region based on pre-acquired data. The actual annual power consumption base is equal to the installed capacity of the electric arc furnace multiplied by its operating time, and then multiplied by the production efficiency correction coefficient. The production efficiency correction coefficient is determined based on the ratio of actual production efficiency to rated production efficiency. For example, if an electric arc furnace in a certain region has an installed capacity of 50 MW, operates for 5000 hours per year, and its actual production efficiency is 90% of its rated production efficiency, then the production efficiency correction coefficient is 0.9. Therefore, the actual annual power consumption base for this electric arc furnace is 50 MW × 5000 hours × 0.9 = In this embodiment, the same method is used to calculate the 225,000 MWh for all electric arc furnaces in the region. Then, the actual annual power consumption base of each electric arc furnace is added together to obtain the actual annual power consumption base of the electric arc furnaces in the region. Next, this embodiment counts the commissioning and decommissioning dates of the electric arc furnaces in the region. Based on the commissioning and decommissioning dates of the electric arc furnaces in the region, the renewal cycle of the steel electric arc furnaces is set. The renewal cycle of the steel electric arc furnaces is the average number of years from commissioning to decommissioning. In this embodiment, the renewal cycle is used as a sliding window to calculate the average annual increase in regional steel production at the end and beginning of the sliding window, thus obtaining the electric arc furnace capacity growth rate.

[0071] The benchmark electricity consumption value refers to the amount of electricity required to produce one unit of steel product under the current production technology and efficiency level. In this embodiment, it can be obtained by statistical analysis of the actual production data of a large number of steel enterprises in the region. For example, if the average electricity consumption per ton of steel produced by steel enterprises in a certain region in the past year is 500 kWh, then the benchmark electricity consumption value for that region is determined to be 500 kWh / ton. In this embodiment, the basic electricity demand forecast value is equal to the sum of the actual annual electricity consumption base of the electric arc furnace multiplied by the electric arc furnace capacity growth rate and 1. The regional steel capacity distribution characteristics include the regional steel capacity scale density, that is, the steel capacity per unit area. If the steel capacity scale density of a certain region is large, it indicates that steel production in that region is concentrated and there will be a scale effect, which will cause the relationship between electricity demand and capacity growth to be not a simple linear relationship. In this embodiment, the scale density correction coefficient is obtained based on the ratio of the difference between the actual capacity scale density of the region and the benchmark capacity scale density to the benchmark capacity scale density. The basic electricity demand forecast value is corrected using the scale density correction coefficient, and the regional electric arc furnace electricity demand forecast value is obtained by coupling the correction. In this embodiment, a region with an average capacity scale density can be selected as the benchmark capacity scale density.

[0072] This embodiment collects electricity demand data for the same period over several years from statistical databases of the power sector or electricity consumption records of enterprises in various regions. It calculates the ratio of the difference between the predicted electricity demand for electric arc furnaces in a region and the historical average electricity demand for the same period to the historical average electricity demand for the same period, thus obtaining the electricity demand growth rate. For any two adjacent regions, this embodiment calculates the absolute value of the difference between the electricity demand growth rates of the two adjacent regions to obtain the inter-regional demand gradient. It also calculates the ratio of the difference between the current year's electricity demand growth rate and the previous year's electricity demand growth rate to the previous year's electricity demand growth rate, thus obtaining the annual change rate of electricity demand growth rate for each region. Then, it multiplies the inter-regional demand gradient with a preset demand gradient weighting coefficient and adds the weighted average of the annual change rate of electricity demand growth rate for each region to obtain the spatial coupling fluctuation intensity index. This embodiment uses the region as the basic unit and integrates the demand gradient and spatial coupling fluctuation intensity index of each region according to geographical location to form the regional electricity demand fluctuation distribution.

[0073] S2. Based on the regional power demand fluctuation distribution and historical power grid operation data, generate hourly load change curves for each region.

[0074] In some implementations, the step of generating hourly load variation curves for different regions based on the regional electricity demand fluctuation distribution and historical power grid operation data includes:

[0075] Based on the physical connection relationship of the power grid, the inter-regional demand gradient is converted into spatial transfer weights to obtain the power grid spatial coupling weight matrix.

[0076] Extract the typical daily load curve of the power grid during the same period from the historical operation data of the power grid, and map the spatial coupling fluctuation intensity index to each time point of the typical daily load curve of the power grid during the same period to generate the fluctuation intensity time series correction coefficient by region and time period.

[0077] Using the power grid spatial coupling weight matrix as a correction factor, the load of adjacent regions is superimposed on the historical typical daily load curves of the power grid in each region to obtain the spatially coupled corrected load reference curve.

[0078] The fluctuation intensity time series correction coefficient is multiplied point by point with the spatial coupling correction load reference curve to obtain the power load prediction value of each region at each time point;

[0079] The predicted power load values ​​are arranged sequentially according to time to form hourly load change curves for different regions.

[0080] Specifically, in this embodiment, each region is abstracted as a node on the power grid geographical connection diagram, and 220 kV and above transmission lines are abstracted as edges connecting these nodes. The capacity of the transmission lines is obtained according to the physical connection relationship of the power grid, and the product of the inter-regional demand gradient and the capacity ratio of the power grid interconnection line between the two regions is used as the transfer weight between the two regions. The larger the transfer weight value, the more significant the mutual influence of the loads of adjacent regions. The capacity ratio of the power grid interconnection line between the two regions is the capacity of the transmission lines between the two regions plus the current total external transmission capacity of the region. In this embodiment, a power grid spatial coupling weight matrix is ​​constructed with regions as rows and columns. Each element in the power grid spatial coupling weight matrix represents the spatial transfer weight between the corresponding two regions. At the same time, typical daily load curve data of the same season and the same type (such as weekdays or holidays) in the past few years are collected from the historical database of the power grid operation and management department to obtain the typical daily load curve of the power grid in the same period in history.

[0081] This embodiment maps the calculated spatial coupling fluctuation intensity index of inter-regional electricity demand growth to various time points of the typical daily load curve of the power grid during the same period in history. For example, this embodiment exports 96 load curves (one point every 15 minutes) for the same month, week, and day of each year for the past three years from the EMS system of the power grid dispatching department, as the typical daily load curve of the power grid during the same period in history. The spatial coupling fluctuation intensity index is regarded as the maximum fluctuation capacity within a day. First, the load fluctuation amplitude of 96 points per day in the same period in history is calculated to obtain a historical fluctuation envelope. The spatial coupling fluctuation intensity index is divided into 96 parts according to the shape ratio of the envelope. Each part is the fluctuation intensity time series correction coefficient at a time point. The fluctuation intensity time series correction coefficient obtained in this way has a large value during the peak load period and a small value during the off-peak period, which not only preserves the spatial difference but also fits the intraday time series characteristics. For each region, the spatial... The coupled-corrected load baseline curve is equal to the load value on the typical daily load curve of the power grid in the same period of the past period in that region, plus the load of the adjacent region multiplied by the corresponding spatial transfer weight. This calculation is performed for each region and each time point to obtain the spatial coupled-corrected load baseline curve for each region. At the same time, for each region and each time point, this embodiment multiplies the fluctuation intensity time-series correction coefficient of the region at that time point with the load value on the spatial coupled-corrected load baseline curve to obtain the power load forecast value of each region at each time point. This embodiment arranges the power load forecast values ​​of each region at each time point in chronological order to form the regional hourly load change curve of the region. These curves can intuitively show the power load change of each region at each time point on the future forecast day, providing important load data support for the low-carbon dynamic capacity expansion planning of steel-electricity coupling.

[0082] S3. Calculate the regional power supply reliability gap rate and regional voltage instability probability based on the hourly load change curves of the different regions, and identify the critical point of power grid stability.

[0083] In some implementations, the step of calculating the regional power supply reliability deficit rate and the regional voltage instability probability based on the hourly load change curves of the different regions, and identifying the critical point of grid stability, includes:

[0084] Based on the hourly load change curves of different regions, the power supply equipment capacity boundaries of the corresponding regions are matched from the pre-built power grid equipment parameter database to obtain the upper limit of regional power supply capacity at different times for each region.

[0085] The power supply margin at each time point is calculated based on the upper limit of the regional power supply capacity and the predicted power load value. The proportion of negative margin periods when the power supply margin is less than a preset margin threshold is then calculated to obtain the regional power supply reliability gap rate.

[0086] The load change rate between adjacent time points is extracted from the hourly load change curve of the sub-region, and the voltage deviation rate is calculated based on the load change rate and the load base.

[0087] The probability of regional voltage instability is calculated based on the proportion of voltage instability periods where the voltage deviation rate exceeds a preset deviation threshold.

[0088] The spatiotemporal nodes that simultaneously satisfy the condition that the regional power supply reliability gap rate is greater than a preset gap threshold and the regional voltage instability probability is greater than a preset instability threshold are marked as critical points for grid stability.

[0089] Specifically, this embodiment establishes an equipment parameter database in the power grid production management system. The rated transmission capacity, short-term overload capacity, and thermal stability limit of each transmission line, each main transformer, and each bus are all entered into the database according to region-equipment-time. For each time point in the hourly load change curve of a region, this embodiment searches for the corresponding region's power supply equipment capacity boundary from the power grid equipment parameter database based on the region name and timestamp, and according to the region's power grid topology and equipment configuration. This yields the upper limit of the region's power supply capacity at different times. For example, at a certain time point, the predicted power load of region A is 300 MW. A database query reveals that the main power supply equipment for region A is a transformer with a rated capacity of 400 MVA and a maximum output power of 360 MW, with an allowable load rate of 90%. Therefore, the upper limit of the region's power supply capacity at that time is 360 MW. This matching operation is performed for each region and each time point to obtain the upper limit of the region's power supply capacity at different times.

[0090] Power supply margin refers to the difference between the upper limit of regional power supply capacity and the predicted power load. In this embodiment, the upper limit of regional power supply capacity is subtracted from the predicted power load at a certain time point to obtain the power supply margin at each time point. At the same time, this embodiment sets a preset margin threshold. When the power supply margin is less than the preset margin threshold, it is considered that there is a power supply reliability gap during that period. In this embodiment, the number of time points in all time points where the power supply margin is less than the preset margin threshold is counted and divided by the total number of time points to obtain the proportion of negative margin time points. This proportion of negative margin time points is the regional power supply reliability gap rate, which reflects the degree of inadequacy of power supply reliability in the region over a period of time.

[0091] Next, this embodiment extracts the predicted power load values ​​of two adjacent time points from the hourly load change curve of each region. It calculates the ratio of the predicted power load value of the current time point to the predicted power load value of the previous time point, thus obtaining the load change rate between adjacent time points. Since the voltage deviation rate is related to the load change rate and the load base, generally, the larger the load change rate and the larger the load base, the larger the voltage deviation rate. This embodiment multiplies the load change rate by a conversion factor determined by the system short-circuit capacity to obtain the voltage deviation rate. This conversion factor is pre-set and stored in the database by region. Simultaneously, this embodiment sets a preset deviation threshold for voltage deviation based on the power grid's operating standards and safety requirements. When the voltage deviation rate exceeds this threshold, it is considered that there is a risk of voltage instability during that period, and this period is recorded as a voltage instability period. Then, statistics are compiled across all adjacent time points. The percentage of voltage instability periods is calculated by dividing the number of time periods where the voltage deviation rate exceeds a preset deviation threshold by the total number of adjacent time points. This percentage is the regional voltage instability probability, reflecting the likelihood of voltage instability in the region over a period of time. For each region and each time point, this embodiment checks whether the regional power supply reliability gap rate and the regional voltage instability probability of the region are both greater than the preset threshold. If both conditions are met, the spatiotemporal node is marked as a critical point for grid stability. For example, for region A during a certain time period on a certain day, if the regional power supply reliability gap rate of the region is 12.5% ​​and the regional voltage instability probability is 17.39%, both of which are greater than the preset threshold, then this time period is marked as a critical point for grid stability. These critical points can be used to identify the time and region where the grid faces stability problems, thereby taking corresponding measures for optimization and capacity expansion.

[0092] S4. Evaluate the priority of power transmission channel expansion in each region based on the power grid equipment capacity data corresponding to the power grid stability critical point, and obtain the expansion demand ranking table.

[0093] In some implementations, the step of evaluating the priority of transmission channel expansion in each region based on the grid equipment capacity data corresponding to the grid stability critical point, and obtaining a ranking table of expansion needs, includes:

[0094] Extract the rated capacity of power grid equipment and the measured load value at the critical point from the power grid equipment capacity data associated with the power grid stability critical point;

[0095] The capacity margin is calculated based on the rated capacity of the power grid equipment and the measured load value at the critical point. When the capacity margin is less than a preset safety threshold, the substation where the power grid equipment is located is marked as a capacity bottleneck point.

[0096] Calculate the ratio of the actual load to the rated capacity at each capacity bottleneck point to obtain the overload severity value, and multiply the overload severity value by the regional load weighting coefficient to obtain the regional capacity constraint severity score.

[0097] The ratio between the regional capacity constraint severity score and the regional power supply reliability gap rate is calculated to obtain the capacity expansion benefit coefficient. At the same grid stability critical point, the ratio between the load growth rate and the current capacity margin is calculated to obtain the capacity depletion time.

[0098] Based on the capacity depletion time and the capacity expansion benefit coefficient, the capacity expansion urgency index is calculated, and the transmission channels in each region are sorted according to the capacity expansion urgency index to generate a capacity expansion demand ranking table.

[0099] Specifically, in this embodiment, the power grid stability critical point is associated with specific power grid equipment. For example, a certain time point in region A is marked as the power grid stability critical point. Through the power grid topology and equipment connection relationships, the power grid equipment associated with this critical point can be determined, such as a transformer or a transmission line. Power grid equipment has clearly defined rated capacity parameters before being put into use. In this embodiment, the rated capacity of the determined power grid equipment is extracted, and at the time point corresponding to the power grid stability critical point, the power grid equipment associated with the critical point is extracted from the actual load values ​​of each power grid device recorded in real time by the power grid monitoring system. The measured load value of the equipment at that time point is calculated, and then the difference between the rated capacity of the power grid equipment and the measured load value at the critical point is calculated to obtain the capacity margin. At the same time, this embodiment sets a preset safety threshold for the capacity margin based on the safety standards and experience data of power grid operation. When the capacity margin is less than this preset safety threshold, it indicates that the power grid equipment is close to its capacity limit at the critical point and there is a significant operational risk. If the calculated capacity margin is less than the preset safety threshold, the substation where the power grid equipment is located is marked as a capacity bottleneck point, and the ratio of the actual load to the rated capacity of the capacity bottleneck point is calculated to obtain the overload degree value.

[0100] This embodiment statistically analyzes the load values ​​within the power supply range of the capacity bottleneck point and the total load value of the entire network, and calculates the proportion of the sum of all loads within the power supply range of the capacity bottleneck point to the total load of the entire network, obtaining a regional load weighting coefficient. This regional load weighting coefficient reflects the extent of the bottleneck point's impact on overall power supply security. This embodiment multiplies the overload degree value by the regional load weighting coefficient to obtain a regional capacity constraint severity score. The higher the regional capacity constraint severity score, the more severe the bottleneck point is, indicating both heavy load and a large impact area, requiring priority attention. This embodiment calculates the ratio between the regional capacity constraint severity score and the regional power supply reliability gap rate to obtain the capacity expansion benefit coefficient. Simultaneously, this embodiment analyzes historical load data to calculate the load growth rate of the region corresponding to the same grid stability critical point over a period of time. For example, this embodiment statistically analyzes the weekly load growth rate of region A over the past month. The load value is calculated, the weekly load increase is calculated, and then divided by the time interval (week) to obtain the load growth rate. Next, this embodiment calculates the ratio of the load growth rate to the current capacity margin to obtain the capacity depletion time. The capacity depletion time is used to predict the duration that the current margin can support. In this embodiment, the capacity expansion urgency index comprehensively considers the capacity depletion time and the capacity expansion benefit coefficient. The capacity expansion urgency index is equal to the capacity expansion benefit coefficient multiplied by the reciprocal of the capacity depletion time. This embodiment sorts the capacity expansion urgency indices calculated for all regions and sorts all transmission channels in descending order to form a capacity expansion demand ranking table. The capacity expansion demand ranking table can include attributes such as the name of the transmission channel, the capacity bottleneck point it belongs to, and the capacity expansion urgency index. This capacity expansion demand ranking table can reflect the urgency of the transmission channel capacity expansion in each region and provide a basis for subsequent capacity expansion planning.

[0101] S5. Generate a phased grid capacity expansion path map based on the expansion demand ranking table, and simulate peak load distribution through the phased grid capacity expansion path map to obtain carbon emission intensity index.

[0102] In some implementations, the step of generating a phased grid capacity expansion path map based on the expansion demand ranking table, and simulating peak load distribution using the phased grid capacity expansion path map to obtain a carbon emission intensity index includes:

[0103] Based on the expansion demand ranking table, standard expansion templates matching regional geographical conditions and steel load characteristics are retrieved from the power grid capacity planning database to obtain the target capacity expansion plan.

[0104] Based on the phased capacity growth values ​​in the target capacity expansion scheme, a phased power grid capacity expansion path diagram is generated.

[0105] By superimposing the new capacity of each stage in the phased power grid capacity expansion path diagram with the electric arc furnace load growth characteristics, the peak load distribution data of each region after the expansion is simulated.

[0106] Calculate the energy structure change matrix for each stage based on the new capacity and regional energy structure base period data in the phased power grid capacity expansion path diagram.

[0107] Based on the peak load distribution data and the energy structure change matrix at each stage, the carbon emission intensity index of the region is obtained by weighted summation according to the carbon emission coefficient of various power sources.

[0108] Specifically, this embodiment determines the priority order of transmission channel expansion in each region based on the expansion demand ranking table. If the expansion demand ranking table shows that region A has the highest expansion urgency index, it indicates that the transmission channel expansion demand in region A is the most urgent. In this embodiment, multiple standard expansion templates are stored in advance for each transmission channel in the power grid capacity planning database. These templates are classified according to different regional geographical conditions and steel load characteristics. Based on the geographical conditions and steel load characteristics of region A, the database is searched to select the standard expansion template that matches it. This template specifies the transformer capacity, transmission line specifications and quantity, etc. that should be added at different stages. This embodiment combines the retrieved standard expansion template with the actual situation of region A to obtain the target capacity expansion plan. It should be noted that this embodiment can fine-tune the transformer capacity and transmission line quantity in the standard expansion template according to the specific power demand and power network layout of region A to form the final target capacity expansion plan. The target capacity expansion plan can reflect the power grid equipment and its parameters that need to be added at each stage. This embodiment extracts the power grid equipment capacity value that needs to be added at each stage from the target capacity expansion plan and sets a certain time node for each stage.

[0109] This embodiment uses time as the horizontal axis and the required increase in grid equipment capacity for each stage extracted from the target capacity expansion plan as the vertical axis. The capacity increase value and time node for each stage are marked on the graph, forming a phased grid capacity expansion path diagram. For example, in the phased grid capacity expansion path diagram, starting from the current time point, an upward curve is drawn according to the time node and capacity increase value of the first stage, representing the increase in grid capacity. Then, curves for the second and third stages are drawn sequentially, visually showing the change in grid capacity over time. The height of each step represents the newly added capacity in that stage. Simultaneously, this embodiment collects historical load growth data for electric arc furnaces. Based on the historical load growth data of electric arc furnaces, the growth patterns and trends of the historical load growth data of electric arc furnaces are analyzed to obtain the load growth characteristics of electric arc furnaces. For example, by analyzing the electricity consumption records of electric arc furnaces in steel enterprises in region A, it was found that their load grows linearly when the production scale expands, and the load fluctuates greatly during peak electricity consumption periods (daytime). According to the phased grid capacity expansion path diagram, the newly added grid capacity in each phase is combined with the load growth characteristics of electric arc furnaces. In the simulation process, this embodiment considers the fluctuation of electric arc furnace load during peak periods. Combining the distribution of newly added capacity and the power supply range, the peak load distribution data of each region over a period of time after the expansion is calculated.

[0110] Simultaneously, this embodiment collects base-period energy structure data for Region A before capacity expansion. This base-period energy structure data can include information such as the installed capacity, power generation, and energy consumption of various power sources (e.g., coal-fired power plants, gas-fired power plants, and renewable energy generation). Based on the phased grid capacity expansion path diagram, the energy type corresponding to the newly added grid capacity in each phase is determined. Using the base-period energy structure data as a basis, the newly added energy installed capacity in each phase is calculated, resulting in the changes in energy structure at each stage. In the energy structure change matrix, rows represent different energy types (coal-fired, gas-fired, and renewable energy, etc.), and columns represent different stages (e.g., the first stage). (e.g., Phase I, Phase II, Phase III, etc.) The values ​​in the matrix represent the changes in installed capacity of the energy type in each phase. In this embodiment, based on peak load distribution data and the energy structure change matrix of each phase, combined with the grid operation mode, the power generation of each energy type in each phase is calculated. The power generation of each energy type in each phase is multiplied by its corresponding unit carbon emission coefficient to obtain the carbon emission of each energy type. Then, the carbon emissions of each energy type are added together to obtain the total carbon emission of that phase. Finally, the total carbon emission is divided by the total power generation of that phase to obtain the carbon emission intensity index of that phase, thus forming a sequence of carbon emission intensity indices for the region in each phase.

[0111] S6. Based on the carbon emission intensity index and the energy structure characteristics data of each region, generate a low-carbon dynamic expansion planning scheme for steel-electricity coupling.

[0112] In some embodiments, the step of generating a low-carbon dynamic capacity expansion plan for steel-electricity coupling based on the carbon emission intensity index and regional energy structure characteristics data includes:

[0113] The emission regions are sorted from high to low according to the carbon emission intensity index to obtain the high emission region sequence. The energy structure characteristic data of the corresponding emission regions are retrieved according to the high emission region sequence.

[0114] Information on the proportion of coal-fired power, the proportion of renewable energy, and reserve regulation capacity are extracted from the energy structure characteristic data to form a regional energy-carbon emission correlation matrix.

[0115] Based on the regional energy-carbon emission correlation matrix and the preset carbon emission intensity control target value, the reduction amount of coal power ratio and the increase amount of renewable energy ratio in each emission region are calculated to obtain the regional low-carbon adjustment amount;

[0116] The low-carbon adjustment amount of the region is mapped to the phased power grid capacity expansion path map, and the new transmission capacity, substation expansion and power access scheme are modified in a time-based manner to form a low-carbon dynamic capacity expansion planning scheme of steel-electricity coupling.

[0117] Specifically, this embodiment sorts the emission regions from high to low according to the carbon emission intensity index, thus obtaining a high emission region sequence. For each region in the high emission region sequence, this embodiment retrieves the energy structure characteristic data of the corresponding emission region from the power grid planning database. The energy structure characteristic data may include information such as energy type, source, and consumption. This embodiment extracts the coal-fired power ratio (i.e., the percentage of coal-fired power installed capacity or power generation in the total installed capacity or power generation of the region), the renewable energy ratio (the proportion of various renewable energy installed capacity in the total installed capacity), and reserve regulation capacity information (the percentage of regulation capacity that gas turbines, energy storage, and demand-side response can provide in relation to peak load) from the retrieved energy structure characteristic data. The coal-fired power ratio reflects... This reflects the proportion of coal-fired power generation in the energy supply within the region; the proportion of renewable energy reflects the status of renewable energy generation such as solar, wind, and hydropower in the energy structure; and the reserve regulation capacity is the capacity that is not used under normal circumstances to ensure the stable operation of the power system, but can be quickly put into operation when the power system experiences faults or load fluctuations. In this embodiment, the extracted information on the proportion of coal-fired power generation, the proportion of renewable energy, and the reserve regulation capacity of each region are integrated to form a regional energy-carbon emission correlation matrix. The row vectors of this regional energy-carbon emission correlation matrix represent different emission regions, and the column vectors are the proportion of coal-fired power generation, the proportion of renewable energy, and the proportion of reserve regulation capacity, respectively. This matrix can intuitively present the intrinsic relationship between the energy structure and carbon emissions of each region.

[0118] This embodiment calculates the regional low-carbon adjustment amount based on the regional energy-carbon emission correlation matrix and a pre-set carbon emission intensity control target value. Specifically, for each high-emission region, this embodiment calculates the difference between the existing coal-fired power generation ratio and the target coal-fired power generation ratio to obtain the coal-fired power generation reduction amount. The target coal-fired power generation ratio is obtained by dividing the carbon emission intensity control target value by the coal-fired power carbon emission coefficient, representing the maximum share of coal-fired power generation that can be retained while meeting the control target. Simultaneously, this embodiment replaces the reduced coal-fired power generation with an equivalent amount of renewable energy generation, achieving simultaneous completion of power balance and carbon emission reduction. Therefore, the reduction in the proportion of coal-fired power is taken as the increase in the proportion of renewable energy. If the reserve regulation capacity is insufficient to fully accommodate the increase in renewable energy, the increase in renewable energy will be reduced proportionally, and the shortfall will be converted into gas peak shaving or energy storage to ensure system dispatchability. In this embodiment, the incremental reduction in the proportion of coal-fired power and the incremental increase in the proportion of renewable energy required in each region are taken as the regional low-carbon adjustment amount. The calculated regional low-carbon adjustment amount is mapped to the phased grid capacity expansion path diagram, and the new transmission capacity, substation expansion and power access scheme are adjusted according to time period. The coal-fired power reduction plan in the low-carbon adjustment amount is marked on the path diagram. The corresponding unit shutdown periods; according to the increase in renewable energy, the scale of new transmission capacity, substation expansion plans, and renewable energy power access sequence are adjusted at each stage. Through this time-based correction process, the final output is a steel-electricity coupled dynamic expansion planning scheme that meets both the grid expansion needs and the low-carbon development goals. It should be noted that this embodiment maps the regional low-carbon adjustment amount to the path diagram, which can clarify the impact of the low-carbon development goals of each region on grid construction at different stages. Based on the mapping results, the new transmission capacity, substation expansion, and power access scheme in the phased grid capacity expansion path diagram are corrected in a time-based manner. For example, for regions where the proportion of coal power needs to be significantly reduced and the proportion of renewable energy needs to be increased, renewable energy power access is prioritized in the corresponding time period, and the new transmission capacity is adjusted to meet the transmission needs of renewable energy. For regions where steel enterprises have concentrated electricity consumption and high requirements for power supply reliability, the substation expansion plan and backup power configuration are reasonably adjusted. After the above processing, a steel-electricity coupled low-carbon dynamic expansion planning scheme is finally formed. This scheme comprehensively considers the electricity demand of steel enterprises, the carbon emission situation of each region, and the development plan of the grid, and realizes the low-carbon dynamic expansion of the steel-electricity system.

[0119] This invention provides a method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios. The method performs steel-electricity coupling demand modeling based on data from electric arc furnaces in the steel industry and the regional steel production capacity distribution characteristics to obtain the regional electricity demand fluctuation distribution. Based on the regional electricity demand fluctuation distribution and historical power grid operation data, it generates hourly load change curves for each region. Based on the hourly load change curves for each region, it calculates the regional power supply reliability gap rate and the regional voltage instability probability to identify the critical point of power grid stability. Based on the power grid equipment capacity data corresponding to the critical point of power grid stability, it evaluates the priority of transmission channel expansion in each region to obtain an expansion demand ranking table. Based on the expansion demand ranking table, it generates a phased power grid capacity expansion path diagram and simulates peak load distribution through the phased power grid capacity expansion path diagram to obtain a carbon emission intensity index. Based on the carbon emission intensity index and energy structure characteristic data of each region, it generates a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling. Compared with existing technologies, this method establishes a dynamic demand model that couples steel and electricity, accurately captures the regional and time-specific electricity demand characteristics brought about by the popularization of electric arc furnaces, and achieves synergistic optimization of steel industry decarbonization and power system expansion based on grid stability critical point identification and low-carbon-oriented capacity expansion planning. This ensures power supply reliability while suppressing the rebound of carbon emissions on the power side caused by steel decarbonization, and can simultaneously meet the requirements of grid safe and stable operation and low-carbon development.

[0120] It should be noted that the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0121] In one embodiment, such as Figure 2 As shown, this embodiment of the invention provides a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation system, the system comprising:

[0122] The coupling modeling module 101 is used to perform steel-electricity coupling demand modeling based on pre-acquired steel electric arc furnace data and regional steel production capacity distribution characteristics, so as to obtain the regional electricity demand fluctuation distribution.

[0123] The load analysis module 102 is used to generate hourly load change curves for different regions based on the regional power demand fluctuation distribution and historical power grid operation data.

[0124] Stability analysis module 103 is used to calculate the regional power supply reliability gap rate and regional voltage instability probability based on the hourly load change curve of the sub-region, and identify the critical point of grid stability.

[0125] The capacity expansion sorting module 104 is used to evaluate the capacity expansion priority of each region's transmission channel based on the power grid equipment capacity data corresponding to the power grid stability critical point, and obtain a capacity expansion demand sorting table.

[0126] The carbon emission analysis module 105 is used to generate a phased grid capacity expansion path map based on the expansion demand ranking table, and to simulate peak load distribution through the phased grid capacity expansion path map to obtain carbon emission intensity indicators.

[0127] The capacity expansion planning module 106 is used to generate a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling based on the carbon emission intensity index and the energy structure characteristics data of each region.

[0128] Specific limitations regarding a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation system can be found in the above-described limitations regarding a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation method, and will not be repeated here. Those skilled in the art will recognize that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] This invention provides a regional low-carbon power grid multi-scenario dynamic capacity expansion simulation system. The system's coupled modeling module models the steel-electricity coupled demand based on data from electric arc furnaces in the steel industry and the regional steel production capacity distribution characteristics, obtaining the regional electricity demand fluctuation distribution. The load analysis module generates hourly load change curves for each region based on the regional electricity demand fluctuation distribution and historical power grid operation data. The stability analysis module calculates the regional power supply reliability gap rate and the regional voltage instability probability based on the hourly load change curves for each region, identifying the critical point for power grid stability. The capacity expansion ranking module evaluates the expansion priority of transmission channels in each region based on the power grid equipment capacity data corresponding to the critical point for power grid stability, obtaining a capacity expansion demand ranking table. The carbon emission analysis module generates a phased power grid capacity expansion path diagram based on the capacity expansion demand ranking table and simulates peak load distribution through the phased power grid capacity expansion path diagram to obtain carbon emission intensity indicators. The capacity expansion planning module generates a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling based on the carbon emission intensity indicators and the energy structure characteristics data of each region. Compared with existing technologies, this system establishes a dynamic demand model of steel-electricity coupling, accurately captures the regional and time-specific electricity demand characteristics brought about by the popularization of electric arc furnaces, and achieves synergistic optimization of steel industry decarbonization and power system expansion based on grid stability critical point identification and low-carbon-oriented capacity expansion planning. This ensures power supply reliability while suppressing the rebound of carbon emissions on the power side caused by steel decarbonization, and can simultaneously meet the requirements of grid safe and stable operation and low-carbon development.

[0130] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios, characterized in that, Includes the following steps: Based on the pre-acquired data on electric arc furnaces for steelmaking and the regional steel production capacity distribution characteristics, a steel-electricity coupling demand model is performed to obtain the regional electricity demand fluctuation distribution. Based on the regional electricity demand fluctuation distribution and historical power grid operation data, hourly load change curves for each region are generated. Based on the hourly load change curves of the different regions, the regional power supply reliability gap rate and the regional voltage instability probability are calculated, and the critical point of grid stability is identified. Based on the power grid equipment capacity data corresponding to the power grid stability critical point, the priority of power transmission channel expansion in each region is evaluated to obtain an expansion demand ranking table. A phased grid capacity expansion path map is generated based on the expansion demand ranking table, and the peak load distribution is simulated through the phased grid capacity expansion path map to obtain the carbon emission intensity index. Based on the carbon emission intensity index and the energy structure characteristics of each region, a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling is generated. The steps of generating a phased grid capacity expansion path map based on the expansion demand ranking table, and simulating peak load distribution using the phased grid capacity expansion path map to obtain the carbon emission intensity index include: Based on the expansion demand ranking table, standard expansion templates matching regional geographical conditions and steel load characteristics are retrieved from the power grid capacity planning database to obtain the target capacity expansion plan. Based on the phased capacity growth values ​​in the target capacity expansion scheme, a phased power grid capacity expansion path diagram is generated. By superimposing the new capacity of each stage in the phased power grid capacity expansion path diagram with the electric arc furnace load growth characteristics, the peak load distribution data of each region after the expansion is simulated. Calculate the energy structure change matrix for each stage based on the new capacity and regional energy structure base period data in the phased power grid capacity expansion path diagram. Based on the peak load distribution data and the energy structure change matrix at each stage, the carbon emission intensity index of the region is obtained by weighted summation according to the carbon emission coefficient of various power sources.

2. The method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios as described in claim 1, characterized in that, The step of modeling the steel-electricity coupling demand based on pre-acquired electric arc furnace data and regional steel production capacity distribution characteristics to obtain the regional electricity demand fluctuation distribution includes: The actual annual power consumption base of electric arc furnaces in each region is calculated based on the pre-acquired data of electric arc furnaces in steel, and the capacity growth rate of electric arc furnaces in each region is obtained based on the actual annual power consumption base of electric arc furnaces and the steel electric arc furnace replacement cycle. The basic power demand forecast is calculated based on the electric arc furnace capacity growth rate and power consumption benchmark value, and the basic power demand forecast is coupled and corrected using the regional steel capacity distribution characteristics to obtain the regional electric arc furnace power demand forecast. Based on the regional electric arc furnace power demand forecast and the historical power demand data for the same period in each region, the power demand growth rate for each region is obtained. The inter-regional demand gradient is calculated based on the degree of difference in the growth rate of electricity demand between adjacent regions. Based on the inter-regional demand gradient and the annual change rate of electricity demand growth in each region, the spatial coupling fluctuation intensity index of inter-regional electricity demand growth is obtained. Using regions as the basic unit, the inter-regional demand gradient and spatial coupling fluctuation intensity index are integrated according to regional location to form the regional electricity demand fluctuation distribution.

3. The method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios as described in claim 2, characterized in that: The regional steel production capacity distribution characteristics include the regional steel production capacity density.

4. The method for dynamic expansion simulation of regional low-carbon power grids under multiple scenarios as described in claim 2, characterized in that, The step of generating hourly load variation curves for different regions based on the regional electricity demand fluctuation distribution and historical power grid operation data includes: Based on the physical connection relationship of the power grid, the inter-regional demand gradient is converted into spatial transfer weights to obtain the power grid spatial coupling weight matrix. Extract the typical daily load curve of the power grid during the same period from the historical operation data of the power grid, and map the spatial coupling fluctuation intensity index to each time point of the typical daily load curve of the power grid during the same period to generate the fluctuation intensity time series correction coefficient by region and time period. Using the power grid spatial coupling weight matrix as a correction factor, the load of adjacent regions is superimposed on the historical typical daily load curves of the power grid in each region to obtain the spatially coupled corrected load reference curve. The fluctuation intensity time series correction coefficient is multiplied point by point with the spatial coupling correction load reference curve to obtain the power load prediction value of each region at each time point; The predicted power load values ​​are arranged sequentially according to time to form hourly load change curves for different regions.

5. The method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios as described in claim 4, characterized in that, The step of calculating the regional power supply reliability deficit rate and regional voltage instability probability based on the hourly load change curves of the different regions, and identifying the critical point of grid stability, includes: Based on the hourly load change curves of different regions, the power supply equipment capacity boundaries of the corresponding regions are matched from the pre-built power grid equipment parameter database to obtain the upper limit of regional power supply capacity at different times for each region. The power supply margin at each time point is calculated based on the upper limit of the regional power supply capacity and the predicted power load value. The proportion of negative margin periods when the power supply margin is less than a preset margin threshold is then calculated to obtain the regional power supply reliability gap rate. The load change rate between adjacent time points is extracted from the hourly load change curve of the sub-region, and the voltage deviation rate is calculated based on the load change rate and the load base. The probability of regional voltage instability is calculated based on the proportion of voltage instability periods where the voltage deviation rate exceeds a preset deviation threshold. The spatiotemporal nodes that simultaneously satisfy the condition that the regional power supply reliability gap rate is greater than a preset gap threshold and the regional voltage instability probability is greater than a preset instability threshold are marked as critical points for grid stability.

6. The method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios as described in claim 1, characterized in that, The step of evaluating the priority of power transmission channel expansion in each region based on the power grid equipment capacity data corresponding to the power grid stability critical point, and obtaining the expansion demand ranking table, includes: Extract the rated capacity of power grid equipment and the measured load value at the critical point from the power grid equipment capacity data associated with the power grid stability critical point; The capacity margin is calculated based on the rated capacity of the power grid equipment and the measured load value at the critical point. When the capacity margin is less than a preset safety threshold, the substation where the power grid equipment is located is marked as a capacity bottleneck point. Calculate the ratio of the actual load to the rated capacity at each capacity bottleneck point to obtain the overload severity value, and multiply the overload severity value by the regional load weighting coefficient to obtain the regional capacity constraint severity score. The ratio between the regional capacity constraint severity score and the regional power supply reliability gap rate is calculated to obtain the capacity expansion benefit coefficient. At the same grid stability critical point, the ratio between the load growth rate and the current capacity margin is calculated to obtain the capacity depletion time. Based on the capacity depletion time and the capacity expansion benefit coefficient, the capacity expansion urgency index is calculated, and the transmission channels in each region are sorted according to the capacity expansion urgency index to generate a capacity expansion demand ranking table.

7. The method for dynamic expansion simulation of regional low-carbon power grids under multiple scenarios as described in claim 6, characterized in that: The regional load weighting coefficient is the proportion of the load within the power supply range of the capacity bottleneck point to the total load of the entire network.

8. The method for dynamic capacity expansion simulation of regional low-carbon power grids under multiple scenarios as described in claim 1, characterized in that, The steps for generating a low-carbon dynamic capacity expansion plan for steel-electricity coupling based on the carbon emission intensity index and the energy structure characteristics data of each region include: The emission regions are sorted from high to low according to the carbon emission intensity index to obtain the high emission region sequence. The energy structure characteristic data of the corresponding emission regions are retrieved according to the high emission region sequence. Information on the proportion of coal-fired power, the proportion of renewable energy, and reserve regulation capacity are extracted from the energy structure characteristic data to form a regional energy-carbon emission correlation matrix. Based on the regional energy-carbon emission correlation matrix and the preset carbon emission intensity control target value, the reduction amount of coal power ratio and the increase amount of renewable energy ratio in each emission region are calculated to obtain the regional low-carbon adjustment amount; The low-carbon adjustment amount of the region is mapped to the phased power grid capacity expansion path map, and the new transmission capacity, substation expansion and power access scheme are modified in a time-based manner to form a low-carbon dynamic capacity expansion planning scheme of steel-electricity coupling.

9. A regional low-carbon power grid multi-scenario dynamic capacity expansion simulation system, characterized in that, The system includes: The coupling modeling module is used to perform steel-electricity coupling demand modeling based on pre-acquired steel electric arc furnace data and regional steel production capacity distribution characteristics, and to obtain the regional electricity demand fluctuation distribution. The load analysis module is used to generate hourly load change curves for different regions based on the regional power demand fluctuation distribution and historical power grid operation data. The stability analysis module is used to calculate the regional power supply reliability gap rate and the regional voltage instability probability based on the hourly load change curves of the different regions, and to identify the critical point of grid stability. The capacity expansion sorting module is used to evaluate the capacity expansion priority of transmission channels in each region based on the power grid equipment capacity data corresponding to the power grid stability critical point, and obtain a capacity expansion demand sorting table. The carbon emission analysis module is used to generate a phased grid capacity expansion path map based on the expansion demand ranking table, and to simulate peak load distribution through the phased grid capacity expansion path map to obtain carbon emission intensity indicators. The capacity expansion planning module is used to generate a low-carbon dynamic capacity expansion planning scheme for steel-electricity coupling based on the carbon emission intensity index and the energy structure characteristics data of each region. The steps of generating a phased grid capacity expansion path map based on the expansion demand ranking table, and simulating peak load distribution using the phased grid capacity expansion path map to obtain the carbon emission intensity index include: Based on the expansion demand ranking table, standard expansion templates matching regional geographical conditions and steel load characteristics are retrieved from the power grid capacity planning database to obtain the target capacity expansion plan. Based on the phased capacity growth values ​​in the target capacity expansion scheme, a phased power grid capacity expansion path diagram is generated. By superimposing the new capacity of each stage in the phased power grid capacity expansion path diagram with the electric arc furnace load growth characteristics, the peak load distribution data of each region after the expansion is simulated. Calculate the energy structure change matrix for each stage based on the new capacity and regional energy structure base period data in the phased power grid capacity expansion path diagram. Based on the peak load distribution data and the energy structure change matrix at each stage, the carbon emission intensity index of the region is obtained by weighted summation according to the carbon emission coefficient of various power sources.

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