Method and system for evaluating regional power grid photovoltaic consumption capacity considering meteorological factors

Abstract

This invention presents a method and system for assessing the photovoltaic (PV) absorption capacity of regional power grids, taking meteorological factors into account. It collects multi-source heterogeneous data from the target area; performs spatiotemporal alignment and correlation mapping on the heterogeneous data to generate a standardized input dataset; calculates the total output time-series curve of the regional PV power plant cluster based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset; constructs a time-series production simulation model with multiple operational constraints; clusters typical meteorological scenarios and iteratively calculates the maximum absorption capacity under each scenario to generate dynamic boundaries; analyzes and diagnoses absorption bottlenecks and generates optimization strategies. This invention enables scenario-based and dynamic assessment of PV absorption capacity, accurately identifies bottlenecks and meteorological factors, provides refined guidance for power grid planning, operation scheduling, and distributed PV development, effectively improves the efficiency of new energy absorption, and optimizes the relevance and economy of investment decisions.

Description

Technical Field

[0001] This invention belongs to the field of regional power grid technology, and in particular relates to a method and system for assessing the photovoltaic absorption capacity of regional power grids that takes meteorological factors into account. Background Technology

[0002] In recent years, the penetration rate of photovoltaic (PV) power generation in regional power grids has been continuously increasing. However, the intermittency, volatility, and uncertainty of PV output pose a severe challenge to the real-time balance and long-term planning of the power grid, stemming from the complex spatiotemporal variations of meteorological conditions (such as cloud cover, irradiance, and temperature). Against this backdrop, accurately assessing the PV absorption capacity of regional power grids has become a key task for power grid companies and planning departments. Several existing technologies have attempted to address this issue, but all have limitations to varying degrees.

[0003] 1. Forecasting and assessment techniques based on the fusion of meteorological and power data Existing technologies have constructed high-precision wind and solar power prediction models and high-impact weather forecasting models by integrating meteorological and power data. While these approaches have made progress in short-term forecasting, they remain insufficient in assessing absorption capacity at the long-term planning level, particularly lacking a quantitative description of the dynamic boundaries of absorption capacity under different meteorological scenarios. Although existing technical specifications provide refined forecasting methods, their assessment results are typically presented as a single static capacity value.

[0004] 2. Photovoltaic forecasting and cluster management technologies considering spatiotemporal characteristics For distributed photovoltaic (PV) clusters, existing technical standards propose dynamic grid partitioning methods based on sensitivity to meteorological abrupt changes. While this spatiotemporal coupled modeling improves prediction accuracy, it does not adequately consider the synergistic optimization with grid operation constraints (such as unit ramp-up rates, line transmission capacity, and minimum technical output). It only stays at the level of converting meteorological data into power generation output, lacking deep integration with the physical constraints of grid operation, and is difficult to use directly to assess the actual absorption capacity of the grid.

[0005] 3. Absorption Control Technology in Power Grid Operation and Dispatch At the grid operation level, patent document CN120414688A proposes a control method for real-time improvement of renewable energy utilization, focusing on solving the voltage collapse problem caused by photovoltaic power surges. While such solutions are innovative in real-time control, they mostly focus on local optimization or solving specific problems (such as voltage collapse), lacking a systematic assessment of the overall absorption capacity of the regional power grid.

[0006] In summary, existing technologies have made some progress in meteorological data fusion, photovoltaic power output prediction and real-time control, but the following common problems still exist: (1) Most schemes focus on short-term prediction or local optimization and fail to provide a dynamic evaluation method for the absorption capacity applicable to the long-term planning of the power grid; (2) There is a lack of differentiated analysis of absorption capacity under various typical meteorological scenarios (such as sunny and stable type, cloudy and fluctuating type, and continuous rainy type); (3) The coupling relationship between various constraints of power grid operation (such as minimum technical output of units, line transmission capacity, and ramp rate) and meteorological factors is not fully considered; (4) The evaluation results are often presented as a single static value rather than a dynamic boundary that changes with meteorological conditions, which limits its guiding value in the precise planning and operation of the power grid; (5) The guiding value for planning and operation is limited; (6) The technical solutions are fragmented and lack end-to-end system solutions.

[0007] Therefore, there is an urgent need in this field for a method to assess the photovoltaic absorption capacity of regional power grids that can accurately characterize the dynamic impact of meteorological conditions and deeply couple with the physical constraints of power grid operation. This method should be able to output scenario-based and dynamic assessment results and accurately identify system bottlenecks under different meteorological scenarios, providing more operational decision support for power grid planning and operation. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides an evaluation method capable of precisely characterizing the spatiotemporal dynamics of meteorological conditions and outputting scenario-based, actionable results. This invention aims to answer the core question of "under what weather conditions, how much photovoltaic power can the power grid absorb," providing decision support for the precise planning and efficient operation of the power grid.

[0009] The present invention adopts the following technical solution.

[0010] This invention proposes a method for assessing the photovoltaic absorption capacity of regional power grids that considers meteorological factors, including: S1. Collect multi-source heterogeneous data of the target area, including numerical weather prediction data, power grid topology and equipment parameter data, and power load data; perform spatiotemporal alignment and correlation mapping on the multi-source heterogeneous data to generate a standardized input dataset; S2, based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset, calculates the total output time series curve of the regional photovoltaic power station cluster; S3. Construct a time-series optimization model with the objective of minimizing the total system operating cost and considering grid operation constraints; solve the time-series optimization model based on the standardized input dataset and the total output time-series curve, and output the system curtailment rate; S4. Clustering is performed based on the standardized input dataset to obtain several meteorological scenarios and corresponding scenario data. Under each meteorological scenario, the photovoltaic installed capacity is incremented by a set step size to perform iterative optimization and obtain the maximum absorption capacity under the corresponding meteorological scenario. The regional power grid photovoltaic absorption capacity boundary is obtained based on the maximum absorption capacity of all meteorological scenarios.

[0011] More preferably, in step S2, the total output time series curve of the regional photovoltaic power station cluster is calculated based on the meteorological grid data provided by the numerical weather prediction data in the standardized input dataset; the specific steps include: The horizontal irradiance in the meteorological grid data is converted into the irradiance on the tilted surface of the photovoltaic panel, and the operating temperature of the photovoltaic module is calculated based on the ambient temperature and wind speed. Based on the operating temperature, rated power, efficiency parameters of photovoltaic modules and inverter parameters, the AC output time-series curve of photovoltaic units in each grid is calculated. The AC output time-series curves of all grids are aggregated according to their electrical connection relationships in the power grid to obtain the total output time-series curve of the regional photovoltaic power station cluster.

[0012] 3. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 1, characterized in that: In S3, a time-series optimization model is constructed with the objective of minimizing the total system operating cost and considering grid operation constraints. The model is solved based on a standardized input dataset and the total output time-series curve, outputting the system curtailment rate. Specific steps include: The total operating cost of the system includes fuel costs, start-up and shutdown costs, and curtailment penalty costs. Grid operation constraints include real-time power balance constraints, conventional unit operation constraints, grid security constraints, and system reserve constraints; In real-time power balance constraints, total power generation output equals total load; total power generation output is the sum of the total output time-series curve and the output of conventional units.

[0013] More preferably, in S4, the specific method for obtaining the regional power grid photovoltaic absorption capacity boundary includes: S4.1 Extract key feature variables from meteorological grid data in the standardized input dataset. Based on the key feature variables, use cluster analysis to identify several meteorological scenarios. For each meteorological scenario, calculate the statistical description of the corresponding key feature variables to obtain quantitative feature templates for different meteorological scenarios. S4.2, Based on the quantitative feature templates of different meteorological scenarios, select continuous meteorological time series data of the load corresponding to the quantitative feature template as the input representative data of the meteorological scenario for each meteorological scenario; S4.3 For each meteorological scenario and representative input data, the photovoltaic installed capacity is iteratively optimized with a set step size to obtain the maximum absorption capacity under that meteorological scenario. S4.4 summarizes the maximum absorption capacity under all meteorological scenarios and generates the regional power grid photovoltaic absorption capacity boundary.

[0014] More preferably, in S4.1, the specific method for generating the quantization feature template includes: Horizontal irradiance, cloud cover, and ambient temperature were extracted from meteorological grid data in the standardized input dataset as key feature variables. Based on key feature variables, cluster analysis was used to identify several meteorological scenarios; For each meteorological scenario, the statistical description of the corresponding key feature variables is calculated, including the mean, variance, and typical value range of GHI, cloud cover, and temperature data for all samples under that meteorological scenario, to obtain the quantitative feature template for different meteorological scenarios.

[0015] More preferably, in step 4.3, for each meteorological scenario and representative input data, the photovoltaic installed capacity is iteratively optimized with a set step size to obtain the maximum absorption capacity under that meteorological scenario; the specific implementation method is as follows: The photovoltaic installed capacity is assumed to increase with a preset step size. After each increment, the total output time series curve is generated by S2 using representative input data of the meteorological scenario, and the time series optimization model of S3 is run to obtain the system curtailment rate. When the system curtailment rate exceeds the preset threshold, the iteration stops, and the photovoltaic capacity at this time is the maximum absorption capacity under the meteorological scenario.

[0016] More preferably, the method for assessing the photovoltaic absorption capacity of the regional power grid further includes: S5. Analyze the output of step S4, identify the dominant constraints that lead to light curtailment under different meteorological scenarios, and generate corresponding operating strategies based on the dominant constraints.

[0017] More preferably, in S5, the specific implementation method for generating the corresponding running strategy includes: S5.1, during each run of the time-series production simulation model, the time-series data of the system operation status is verified in real time; when the system experiences curtailment, various power grid operation constraints are checked according to the preset priority order, and the constraint that is violated first is determined as the dominant constraint of the curtailment event; the frequency of occurrence of various dominant constraints in all curtailment events under the same meteorological scenario is statistically analyzed, and the constraint type with the highest frequency is identified as the dominant constraint condition under the meteorological scenario. S5.2, based on the dominant constraints under each meteorological scenario, automatically trigger and execute the corresponding enhancement strategy generation process. The type of the enhancement strategy generation process corresponds to the dominant constraints and includes at least: Strategies to address insufficient power balance and regulation capabilities: When the dominant constraint is system power balance or unit regulation capability, generate a set of operating instructions that include adjusting unit standby, calling up fast regulation resources, or initiating demand-side response. Strategies to address network transmission congestion: When the dominant constraint is the thermal stability limit of the transmission line or transformer, generate planning recommendations that include a probabilistic safety assessment of the congesting component and output line capacity expansion or energy storage system configuration to mitigate the congestion based on the assessment results.

[0018] This invention also proposes a regional power grid photovoltaic absorption capacity assessment system that considers meteorological factors, including a dataset construction module, a photovoltaic output time-series modeling module, a time-series production simulation module, and a dynamic absorption capacity boundary calculation module: The dataset construction module collects multi-source heterogeneous data from the target area, including numerical weather prediction data, power grid topology and equipment parameter data, and power load data; it performs spatiotemporal alignment and correlation mapping on the multi-source heterogeneous data to generate a standardized input dataset. The photovoltaic power output time series modeling module calculates the total power output time series curve of the regional photovoltaic power station cluster based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset. The time-series production simulation module constructs a time-series optimization model with the goal of minimizing the total system operating cost and taking into account grid operation constraints; it solves the time-series optimization model based on a standardized input dataset and the total output time-series curve, and outputs the system curtailment rate. The dynamic absorption capacity boundary calculation module performs clustering based on the standardized input dataset to obtain several meteorological scenarios and corresponding scenario data. Under each meteorological scenario, the photovoltaic installed capacity is incremented by a set step size to perform iterative optimization and obtain the maximum absorption capacity under the corresponding meteorological scenario. The regional power grid photovoltaic absorption capacity boundary is obtained based on the maximum absorption capacity of all meteorological scenarios.

[0019] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0020] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention integrates high-resolution meteorological data with power grid operation information to construct dynamic absorption capacity boundaries under typical scenarios such as sunny, cloudy, and rainy weather. This enables power grid dispatching departments to intuitively grasp the real variation range of absorption capacity under different weather conditions, thereby avoiding excessive restriction of photovoltaic output on sunny days and reserving adjustment space in advance during fluctuating weather, significantly reducing resource waste and operational risks.

[0022] 2. This invention establishes for the first time a closed-loop coupled model of meteorological conditions, photovoltaic output, and power grid physical constraints. It transforms meteorological fluctuations such as cloud movement and irradiance jumps into quantitative impacts on the minimum technical output of generating units, ramp-up rates, and line thermal stability limits. It can accurately assess the risk of curtailment and equipment overload during critical periods such as midday photovoltaic power generation or sudden changes in cloud shadows. It achieves in-depth collaborative analysis of meteorological factors and hard constraints of power grid operation, significantly improving the technical credibility of the assessment results.

[0023] 3. This invention introduces meteorological-grid coupling sensitivity analysis based on simulation. It uses statistical methods to quantify the marginal contribution of constraints such as unit regulation capacity, line transmission capacity, and voltage level to the curtailment rate under different meteorological scenarios. It can accurately identify "what kind of weather triggers what kind of bottleneck" and give a ranking of the degree of impact, completely getting rid of the traditional binary judgment of "whether it can be absorbed". It provides a quantitative basis for investment priorities for subsequent flexibility upgrades, line capacity expansion or energy storage configuration.

[0024] 4. The dynamic absorption capacity boundary, meteorological sensitivity bottleneck report, and zonal development guidance map output by this invention can be directly embedded into the power grid planning, dispatching, and distributed photovoltaic management process: planners can determine the power grid reinforcement sequence based on meteorological zonal differences, dispatchers can formulate unit start-up, shutdown, and standby strategies in advance for forecast scenarios, and county management departments can guide the orderly access of rooftop photovoltaics according to the green, yellow, and red three-level standards, realizing the upgrade from "one-size-fits-all" capacity restriction to "grid-based" precise management and control, and fully releasing the potential of new energy absorption. Attached Figure Description

[0025] Figure 1 This is a flowchart of the method for assessing the photovoltaic absorption capacity of regional power grids considering meteorological factors according to the present invention. Figure 2 This is a flowchart illustrating a specific embodiment of the present invention. Detailed Implementation

[0026] 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.

[0027] The present invention proposes the following technical solution: like Figure 1 As shown, this invention proposes a method for assessing the photovoltaic absorption capacity of regional power grids that takes meteorological factors into account: S1. Collect multi-source heterogeneous data of the target area, including numerical weather prediction data, power grid topology and equipment parameter data, and power load data; perform spatiotemporal alignment and correlation mapping on the multi-source heterogeneous data to generate a standardized input dataset; S2, based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset, calculates the total output time series curve of the regional photovoltaic power station cluster; The horizontal irradiance in the meteorological grid data is converted into the irradiance on the tilted surface of the photovoltaic panel, and the operating temperature of the photovoltaic module is calculated based on the ambient temperature and wind speed. Based on the operating temperature, rated power, efficiency parameters of photovoltaic modules and inverter parameters, the AC output time-series curve of photovoltaic units in each grid is calculated. The AC output time-series curves of all grids are aggregated according to their electrical connection relationships in the power grid to obtain the total output time-series curve of the regional photovoltaic power station cluster.

[0028] S3. Construct a time-series optimization model with the objective of minimizing the total system operating cost and considering grid operation constraints; solve the time-series optimization model based on the standardized input dataset and the total output time-series curve, and output the system curtailment rate; The total operating cost of the system includes fuel costs, start-up and shutdown costs, and curtailment penalty costs. Grid operation constraints include real-time power balance constraints, conventional unit operation constraints, grid security constraints, and system reserve constraints; In real-time power balance constraints, total power generation output equals total load; total power generation output is the sum of the total output time-series curve and the output of conventional units.

[0029] S4. Clustering is performed based on the standardized input dataset to obtain several meteorological scenarios and corresponding scenario data. Under each meteorological scenario, the photovoltaic installed capacity is incremented by a set step size to perform iterative optimization and obtain the maximum absorption capacity under the corresponding meteorological scenario. The regional power grid photovoltaic absorption capacity boundary is obtained based on the maximum absorption capacity of all meteorological scenarios.

[0030] S4.1 Extract key feature variables from meteorological grid data in the standardized input dataset. Based on the key feature variables, use cluster analysis to identify several meteorological scenarios. For each meteorological scenario, calculate the statistical description of the corresponding key feature variables to obtain quantitative feature templates for different meteorological scenarios. Specific methods for generating quantized feature templates include: Horizontal irradiance, cloud cover, and ambient temperature were extracted from meteorological grid data in the standardized input dataset as key feature variables. Based on key feature variables, cluster analysis was used to identify several meteorological scenarios; For each meteorological scenario, the statistical description of the corresponding key feature variables is calculated, including the mean, variance, and typical value range of GHI, cloud cover, and temperature data for all samples under that meteorological scenario, to obtain the quantitative feature template for different meteorological scenarios.

[0031] S4.2, Based on the quantitative feature templates of different meteorological scenarios, select continuous meteorological time series data of the load corresponding to the quantitative feature template as the input representative data of the meteorological scenario for each meteorological scenario; S4.3 For each meteorological scenario and representative input data, the photovoltaic installed capacity is iteratively optimized with a set step size to obtain the maximum absorption capacity under that meteorological scenario. The photovoltaic installed capacity is assumed to increase with a preset step size. After each increment, the total output time series curve is generated by S2 using representative input data of the meteorological scenario, and the time series optimization model of S3 is run to obtain the system curtailment rate. When the system curtailment rate exceeds the preset threshold, the iteration stops, and the photovoltaic capacity at this time is the maximum absorption capacity under the meteorological scenario.

[0032] S4.4 summarizes the maximum absorption capacity under all meteorological scenarios and generates the regional power grid photovoltaic absorption capacity boundary.

[0033] The method for assessing the photovoltaic absorption capacity of the regional power grid also includes: S5. Analyze the output of step S4, identify the dominant constraints that lead to light curtailment under different meteorological scenarios, and generate corresponding operating strategies based on the dominant constraints.

[0034] S5.1, during each run of the time-series production simulation model, the time-series data of the system operation status is verified in real time; when the system experiences curtailment, various power grid operation constraints are checked according to the preset priority order, and the constraint that is violated first is determined as the dominant constraint of the curtailment event; the frequency of occurrence of various dominant constraints in all curtailment events under the same meteorological scenario is statistically analyzed, and the constraint type with the highest frequency is identified as the dominant constraint condition under the meteorological scenario. S5.2, based on the dominant constraints under each meteorological scenario, automatically trigger and execute the corresponding enhancement strategy generation process. The type of the enhancement strategy generation process corresponds to the dominant constraints and includes at least: Strategies to address insufficient power balance and regulation capabilities: When the dominant constraint is system power balance or unit regulation capability, generate a set of operating instructions that include adjusting unit standby, calling up fast regulation resources, or initiating demand-side response. Strategies to address network transmission congestion: When the dominant constraint is the thermal stability limit of the transmission line or transformer, generate planning recommendations that include a probabilistic safety assessment of the congesting component and output line capacity expansion or energy storage system configuration to mitigate the congestion based on the assessment results.

[0035] This invention also proposes a regional power grid photovoltaic absorption capacity assessment system that considers meteorological factors, including a dataset construction module, a photovoltaic output time-series modeling module, a time-series production simulation module, and a dynamic absorption capacity boundary calculation module: The dataset construction module collects multi-source heterogeneous data from the target area, including numerical weather prediction data, power grid topology and equipment parameter data, and power load data; it performs spatiotemporal alignment and correlation mapping on the multi-source heterogeneous data to generate a standardized input dataset. The photovoltaic power output time series modeling module calculates the total power output time series curve of the regional photovoltaic power station cluster based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset. The time-series production simulation module constructs a time-series optimization model with the goal of minimizing the total system operating cost and taking into account grid operation constraints; it solves the time-series optimization model based on a standardized input dataset and the total output time-series curve, and outputs the system curtailment rate. The dynamic absorption capacity boundary calculation module performs clustering based on the standardized input dataset to obtain several meteorological scenarios and corresponding scenario data. Under each meteorological scenario, the photovoltaic installed capacity is incremented by a set step size to perform iterative optimization and obtain the maximum absorption capacity under the corresponding meteorological scenario. The regional power grid photovoltaic absorption capacity boundary is obtained based on the maximum absorption capacity of all meteorological scenarios.

[0036] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0037] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0038] Example 1 This invention employs a systematic evaluation process that couples meteorological science and power system analysis, and proposes a method for evaluating the photovoltaic absorption capacity of regional power grids that considers meteorological factors. Its core technical solution specifically includes the following steps (such as...). Figure 2 ): Step S1: Data Acquisition and Fusion Processing. This step is the foundational data preparation stage of the method. The specific operations are as follows: Data Acquisition: Acquire high spatiotemporal resolution (e.g., 15-minute temporal resolution, 1km × 1km spatial resolution) numerical weather forecast data for the target area. Key meteorological variables include, but are not limited to: total horizontal irradiance, direct normal irradiance, ambient temperature, wind speed, cloud cover, and relative humidity. Simultaneously, acquire power grid topology data, equipment parameters including generator (e.g., thermal power, hydropower) parameters, impedance and capacity parameters of transmission lines and transformers, and historical and predicted power load data.

[0039] As a preferred embodiment of the present invention, taking a provincial power grid as an example, the process of obtaining the predicted power load data includes: Using a time-series forecasting network such as LSTM, the input includes 5 years of historical load data from 96 points, real-time measurements of 3290 10kV busbars, numerical weather prediction data (spatial resolution 1km) and meteorological grid data from across the province, as well as holiday tags and maintenance plans for large users (referring to the shutdown and maintenance schedules reported to the power grid by high-energy-consuming enterprises such as steel, chemical, and data centers) to distinguish load characteristics under different social activity modes. Automatic retraining occurs daily at 2:30 AM, automatically adding the previous day's new data to the training set and retraining the model to adapt to the latest load change patterns. The rolling window of 1800 days means that when training the model, not all historical data is used, but only the data from the most recent 1800 days. This mechanism aims to continuously adapt the prediction model to the latest load change trends by updating the training set. Output the total load and 220kV busbar load curves for the province over the next 365 days at 15-minute intervals; Through actual testing, the annual average MAPE is 1.9%, with a maximum monthly MAPE of 2.4%, which meets the accuracy requirements for engineering applications.

[0040] Fusion processing: The above multi-source heterogeneous data are correlated and mapped under a unified spatiotemporal benchmark to generate a spatiotemporally aligned standardized input dataset.

[0041] Step S2: Perform time-series modeling of regional photovoltaic power output based on the standardized input dataset. This step aims to transform the meteorological grid data in the standardized input dataset into a photovoltaic power generation curve usable by the power grid. The specific operation is as follows: Based on the meteorological grid data obtained in step S1, grid-by-grid calculations are performed using a photovoltaic power generation physical model (preferably implemented through an open-source toolkit).

[0042] The calculation process includes: converting the horizontal irradiance in the meteorological grid data into the irradiance on the tilted surface of the photovoltaic panel; calculating the operating temperature of the photovoltaic module considering ambient temperature and wind speed; and finally, calculating the AC output time-series curve of the photovoltaic unit in each grid based on the rated power, efficiency parameters and inverter parameters of the module.

[0043] Specifically, the calculation process for the AC power output timing of the photovoltaic unit within each grid is as follows: 1. Calculation of total irradiance on inclined surface Direct irradiance on an inclined surface: equal to the normal direct irradiance multiplied by the cosine of the solar incidence angle; Tilt surface diffuse irradiance: equal to horizontal surface diffuse irradiance multiplied by [(1 + cosine of photovoltaic panel tilt angle) / 2 + total horizontal surface irradiance divided by the square root of 1000 × cube of the sine of half the photovoltaic panel tilt angle]; The cosine of the solar incidence angle is equal to the cosine of the solar zenith angle × the cosine of the photovoltaic panel tilt angle + the sine of the solar zenith angle × the sine of the photovoltaic panel tilt angle × the cosine of (solar azimuth angle - photovoltaic panel azimuth angle). Total irradiance on an inclined surface: equals the direct irradiance on the inclined surface plus the diffuse irradiance on the inclined surface plus (the product of the two-sided factor, ground reflectivity, and total irradiance on the horizontal surface). 2. Battery junction temperature calculation Battery junction temperature: equal to ambient temperature + (total irradiance on the inclined surface / 800) × (battery operating temperature under standard test conditions - 20) / 0.8 × wind speed correction for convective heat transfer. 3. DC power Basic DC power: equals rated DC power × (total irradiance on inclined surface / 1000) × [1 + maximum power temperature coefficient × (cell junction temperature - 25)]; Correction for bifacial photovoltaic systems: Multiply the base DC power by the bifacial gain factor; 4. Inverter AC power AC power: Take the smaller of the product of DC power and inverter efficiency and the inverter's rated power, and then multiply it by the inverter's dynamic load limiting factor. 5. From “mono-component” to “1km grid” Number of components = product of effective roof area, installation density, and orientation weight; Grid AC output: equal to the product of the number of components and the inverter AC power divided by 1000; 6. Accuracy Verification The measured AC power output data of a distributed photovoltaic power station with a total capacity of 600 MW from July to December 2023 were selected as the baseline true value and compared with the power output prediction value generated by the model of this invention based on 1 km grid data aggregation.

[0044] The verification results show that the model's prediction error is 3.2% during peak photovoltaic power generation periods. This level of accuracy meets the highest national accuracy requirements for photovoltaic power generation prediction.

[0045] Steps 1–6 above have been encapsulated into a Python module pvlib_cma_1km.py. After each cluster node automatically reads the CMA-RUCNetCDF, it completes the calculation of 40,000 grid points in a 200×200km area in 8 seconds, and can directly output the inverter AC power curve for subsequent time-series production simulation.

[0046] The AC output time-series curves of all grids are aggregated and summed step by step according to the grid topology path of "geographic location → electrical node → collector line → main transformer → transmission section" based on a pre-established connection table between photovoltaic power station coordinates and grid electrical nodes, to obtain the total output time-series curve P_pv(t) of the entire photovoltaic power station cluster in the region. This curve includes the fluctuations and intermittencies caused by meteorological factors such as cloud movement and temperature effects.

[0047] Step S3: Construct a time-series optimization model with the goal of minimizing system operating costs and considering grid operation constraints. Solve the model based on the grid and load data in the standardized input dataset and the total output time-series curve P_pv(t), and output the system curtailment rate.

[0048] This step involves building the core simulation engine for evaluating the grid's absorption capacity. The specific operation is as follows: Establish a time-series optimization model with the objective function of minimizing the total system operating cost (including fuel cost, start-up and shutdown cost, and curtailment penalty cost). This model runs at preset time steps (e.g., 15 minutes or 1 hour) to simulate the grid's operation throughout the year or a representative period (e.g., 7-15 consecutive days). The final curtailment rate of the system is then obtained.

[0049] The constraints of the model must be comprehensive and accurate, including: 1. Real-time power balance constraint of the system: The total power generation output (total output time series curve P_pv(t) + conventional unit output) must be equal to the total load.

[0050] 2. Conventional unit operation constraints: including the upper and lower limits of unit output, ramp rate, minimum start-up and shutdown time, and minimum stable output (this constraint is one of the key bottlenecks leading to daytime curtailment of solar power).

[0051] 3. Power grid safety constraints: thermal stability power transmission limit constraints of transmission lines and transformers to prevent reverse power flow overload.

[0052] 4. System standby constraints: Meet the specified positive and negative spinning reserve capacity requirements to cope with sudden fluctuations in photovoltaic output and load.

[0053] The model is solved using mixed-integer linear programming or linear programming algorithms to efficiently handle discrete decisions such as unit start-up and shutdown.

[0054] Step S4: Calculate the dynamic absorption capacity boundary based on the standardized input dataset and the system light rejection rate.

[0055] This step is the core of the invention, aiming to extend the evaluation result from a single numerical value to a dynamic range. Specifically, it includes the following sub-steps: S4.1: Definition of Typical Meteorological Scenarios: Horizontal irradiance, cloud cover, and ambient temperature from the meteorological grid data in the standardized input dataset are extracted as key feature variables. Cluster analysis methods (such as K-means clustering or hierarchical clustering) are used to identify and define several representative typical meteorological scenarios. These scenarios include at least "sunny and hot summer type," "cloudy and fluctuating spring and autumn type," and "continuous cloudy and rainy winter type." For each scenario generated by clustering, the statistical description of its key meteorological features is calculated (e.g., a quantitative summary of the mean, variance, and typical value range of variables such as GHI, cloud cover, and temperature for all samples under that scenario), forming a quantitative feature template for that scenario.

[0056] S4.2: Selection of representative meteorological data for each defined typical meteorological scenario: Select a segment (e.g., 7-15 consecutive days) of continuous meteorological time series data from historical data that can fully characterize the features of the scenario, and use it as the input representative for the scenario.

[0057] The selection method is as follows: For each defined typical meteorological scenario, a continuous meteorological time series data segment (e.g., 7-15 days) is selected from historical data as the simulation input representative for that scenario. The selection method is as follows: for the feature template of each scenario, the continuous time period that best matches its statistical characteristics is screened from the historical database. Specifically, this can be achieved by calculating the Mahalanobis distance between the candidate time period and the scenario template in the multidimensional feature space, and ensuring that the daily average value and volatility of its key indicators (e.g., GHI, cloud cover) deviate from the scenario template by less than a preset threshold (e.g., 5%), and that it can reflect the typical process of the scenario (e.g., "cloudy and fluctuating type" should include sufficient high-frequency cloud cover change events).

[0058] S4.3: Iterative Calculation of Single-Scenario Absorption Capacity: For each typical meteorological scenario and representative input data, perform iterative optimization: 1. Set the initial photovoltaic installed capacity C0.

[0059] 2. Increase photovoltaic capacity in increments of ΔC (e.g., 50MW).

[0060] 3. After each increment, use representative meteorological data of the scenario to drive step S2, generate a new P_pv(t), and run the time-series production simulation model of step S3 to obtain the system curtailment rate CUR.

[0061] 4. When the system curtailment rate calculated by simulation exceeds the preset threshold η (e.g., 3%), the iteration stops. The photovoltaic capacity C_max at this point is the maximum absorption capacity under this meteorological scenario.

[0062] As a further optional implementation, step S4.3a is also included: meteorological-power grid coupling sensitivity analysis (optional but important): Based on the extensive simulation data generated by S4.3, analysis of variance (ANOVA) or the calculation of Pearson correlation coefficients is used to quantify the statistical correlation between the fluctuations of key meteorological variables (such as GHI volatility and cloud cover) and operational indicators such as system curtailment rate and critical line load factor. This identifies the meteorologically sensitive factors that have the most significant impact on the grid's absorption capacity, thereby improving the scientific rigor, robustness, and forward-looking nature of grid planning and operation decisions. The specific operation is as follows: ① Sample preparation: For each typical meteorological scenario, 30 segments of 7-day historical data were extracted, totaling 210 days; 14 photovoltaic capacity were scanned at a step size of 1MW to obtain 210×14=2940 sets of (meteorological-operational) samples.

[0063] ② Meteorological factors: GHI volatility σ = 15-minute standard deviation of daily average GHI ÷ daily average GHI; Cloud cover jump f = Number of times in 1 hour that the daily cloud cover change is greater than 30%.

[0064] ③ Operational indicators: System curtailment rate CUR (%) and critical line maximum load rate Lmax (%).

[0065] ④ Quantitative test: Use univariate linear regression CUR=a·σ+b and CUR=c·f+d to calculate the Pearson correlation coefficient r between the independent variables σ and f and the dependent variable CUR; when |r|>0.5 and the two-tailed t-test p<0.05, the factor is judged to be “significant”; and sorted in descending order of |r|, the top k factors with cumulative explained variance ≥80% are taken as the “most significant meteorological sensitive factors”.

[0066] S4.4: Dynamic Boundary Generation: The calculation results {C_max,i} of all typical meteorological scenarios are summarized, showing the upper and lower limits and variation patterns of the power grid's absorption capacity.

[0067] Step S5: Bottleneck Diagnosis and Enhancement Strategy Generation This step involves in-depth analysis of the simulation results to output specific and actionable suggestions. The specific steps are as follows: Bottleneck Diagnosis: Analyze the output of step S4. For each 15-minute point, check the four hard constraints sequentially according to the scheduling procedure: ① System power balance; ② Minimum technical output of generating units; ③ Line thermal stability limit; ④ Spinning reserve. The first constraint to exceed the limit is recorded as the "dominant constraint" for that sample. Statistically analyze all limit-exceeding events within the same weather scenario; the constraint with the highest percentage is the "dominant constraint condition" for that scenario. (For example, in clear weather, it is the minimum technical output limit of thermal power units; in cloudy weather, it is line congestion; and in fluctuating weather, it is insufficient reserve).

[0068] Based on the aforementioned dominant constraints, an enhancement strategy is generated: 1. Probabilistic Congestion Management and Planning: For sub-regions where grid absorption capacity declines sharply under specific scenarios (such as "cloudy and fluctuating" conditions), targeted grid strengthening recommendations are proposed, such as upgrading key lines, installing series compensation devices, or planning and constructing energy storage systems to mitigate fluctuations. Simultaneously, areas with high absorption capacity under multiple scenarios are designated as priority photovoltaic development areas.

[0069] As a preferred embodiment of the present invention, firstly, the "line congestion" type over-limit events in 2940 samples are sampled 1000 times using Monte Carlo sampling to establish a probability distribution map of line load rate >95%; grid points with a probability >20% and co-occurrence of multiple scenarios are automatically marked as "high congestion risk areas"; then, PSASP is called to calculate N-1 thermal stability verification. If more than 80% of the fault set still causes the load to >110%, a "line capacity expansion" instruction is output. If the over-limit is only short-term, the energy storage optimization model is called, with "100MW / 200MWh, 2C" as the initial scale, and linear programming is used to minimize the sum of annual abandoned electricity and equipment present value, and finally the economically optimal power-capacity configuration is given.

[0070] The construction and solution of the energy storage optimization model will not be elaborated here; existing models and solution methods can be used directly.

[0071] 2. Output a differentiated set of operating strategies: Based on the determined dominant constraints, Python is used to automatically scan the scheduling rule library and generate "if-then" rules: if the cloud cover fluctuation frequency f>12 and σ>0.4 in the next 24 hours, the system is classified as "multi-cloud fluctuation scenario". The rule engine automatically writes the following into the day-ahead scheduling plan: reserve an additional 3% spinning reserve for gas turbine units, start demand response of 120MW, and prohibit backfeeding of 220kV section >80%. The rule file is output in CIM / XML format and pushed to the intelligent scheduling platform to realize automatic generation and closed-loop distribution of strategies.

[0072] For example, when the forecast is for a "sunny and hot" scenario, the model simultaneously imports the "1km×15min temperature-electricity elasticity coefficient" library from the provincial company's marketing department: when the forecast Ta.max ≥ 35℃ and lasts for more than 3 hours, the model automatically adds the air conditioning cooling increment ΔL = α·(Ta.max) to the day-ahead load curve. 34)·Pop, where α = 0.85MW℃ - ¹(100,000 population), Pop is the grid population density; if the minimum technical output of thermal power units is greater than the minimum system load + ΔL after this ΔL overlaps with the peak photovoltaic power generation period, it is determined that there is a double squeeze of "high temperature load increase + full photovoltaic power generation", and the gas turbine units are pre-triggered to be deeply adjusted to 25%Pn or 100MW-level energy storage is called for charging to ensure that there is no curtailment of solar power; when the forecast is a "cloudy and fluctuating" scenario, additional rapid backup capacity is reserved and the demand-side response procedure is initiated.

[0073] Through the above complete technical solution, the present invention realizes dynamic, accurate and scenario-based assessment of the photovoltaic absorption capacity of regional power grids.

[0074] This invention fundamentally solves the technical problem of existing assessment results being static, singular, and lacking adaptability to meteorological scenarios. It abandons the traditional approach of seeking a single absorption capacity and pioneers the concept and construction method of a "dynamic absorption capacity boundary." This method first uses historical meteorological data and cluster analysis to objectively define several "typical meteorological scenarios" (such as "sunny and stable," "cloudy and fluctuating," and "continuous rainy"). Then, for each meteorological scenario, a complete iterative time-series production simulation is independently executed: starting from the initial photovoltaic capacity, the installed capacity is gradually increased, and the simulation is driven by representative meteorological data for that scenario until the system curtailment rate exceeds a preset threshold, thus obtaining the maximum absorption capacity under that specific scenario. Finally, the calculation results of all scenarios are integrated and plotted into a graph that clearly shows the absorption capacity changing with the weather. The "Regional Photovoltaic Dynamic Absorption Capacity Boundary Map" (i.e., absorption capacity envelope) reflects weather types and seasonal variations. This innovation elevates the assessment conclusion from a static statement like "the grid can absorb 15GW of photovoltaic power" to a dynamic, scenario-based statement like "it can absorb 18GW under clear weather conditions, but only 14GW under cloudy and fluctuating weather conditions." This provides grid dispatching departments with unprecedentedly refined decision-making support, enabling them to predict the absorption capacity for the next few days or even the current day based on weather forecasts and formulate differentiated operating methods, thereby maximizing the absorption of new energy sources while ensuring safety.

[0075] This invention also addresses the technical problems of existing technologies in diagnosing bottlenecks and accurately attributing causes. Based on generating a large amount of process data through iterative simulation, this invention introduces a meteorological-grid coupling sensitivity analysis step. This technique employs mathematical statistics to deeply mine simulation data, quantifying the statistical correlation between fluctuations in key meteorological variables (such as irradiance fluctuation rate and cloud cover) and key system operating indicators (such as curtailment rate, critical line load rate, and unit regulation depth). This invention can accurately identify the most significant "meteorological sensitive factors" affecting grid absorption capacity and the specific "weak links in the grid" they trigger. This invention upgrades bottleneck diagnosis from "empirical speculation" to "data-driven" approaches, explicitly answering questions such as "curtailment in this area is mainly caused by line congestion, and this congestion is strongly correlated with the rapid movement of cloud clusters under cloudy weather." This allows power grid companies to make precise investment decisions, directly using limited funds to solve the most critical bottlenecks (e.g., installing energy storage on sensitive lines to cope with power fluctuations), rather than blindly expanding general capacity, thereby significantly improving investment efficiency and the effectiveness of absorption capacity enhancement.

[0076] Example 2: Provincial Power Grid Annual Planning Assessment Based on Dynamic Absorption Capacity Boundary 1. Application Scenarios and Objectives A provincial power grid company plans to significantly increase its photovoltaic (PV) installed capacity over the next three years, but the grid's absorption capacity is uncertain. Traditional assessment methods assign a single annual absorption capacity of 15GW. This invention aims to provide a more refined and dynamic assessment result to guide annual power generation planning and grid investment planning.

[0077] 2. Specific Implementation Process Step S1: Data Acquisition and Fusion Processing Meteorological data: Numerical weather forecast reanalysis data for the past 5 years were collected for the province with a temporal resolution of 1 hour and a spatial resolution of 3 km × 3 km, including GHI (total horizontal irradiance), DNI (direct normal irradiance), ambient temperature, wind speed, and cloud cover.

[0078] Power Grid Data: Obtain the topology of the 220kV and above power grid in the province, detailed parameters of the main thermal power units (50 units in total) (minimum technical output is 40%-50% of rated capacity, ramp rate), and current carrying capacity of the main transmission lines.

[0079] Load data: Obtain historical hourly load data for the corresponding 5 years.

[0080] Integration Processing: Using a digital twin platform, the entire province is divided into 1km×1km grids, each grid is associated with its meteorological data, and a mapping relationship is established with the power grid assets below, such as substations and power lines.

[0081] Step S2: Regional photovoltaic power output time series modeling Assuming that each grid is equipped with a photovoltaic power station of a certain capacity, using the PGLIB toolkit and inputting meteorological data from each grid, the typical characteristics of high and stable output in summer, drastic fluctuations in output in spring and autumn due to cloudy weather, and the lowest output level in winter are clearly shown.

[0082] Step S3: Construct a time-series production simulation model A full-year simulation model with a 15-minute timeframe was constructed. The objective function was to minimize the total system operating cost, including coal consumption cost of thermal power plants, start-up and shutdown costs, and a curtailment penalty cost of up to 800 yuan / MWh. Strict constraints were set, with particular emphasis on the key variable of minimum technical output of thermal power units, which totals approximately 35% of the system's maximum load. This is the potential core bottleneck leading to daytime curtailment.

[0083] Step S4: Calculation of dynamic absorption capacity boundary S4.1: Definition of Typical Meteorological Scenarios: K-means clustering analysis was performed on 5 years of hourly meteorological data (mainly characterized by GHI and cloud cover) to identify 4 typical scenarios: Scenario A (Summer Sunny and Stable): High GHI, low cloud cover, high temperature.

[0084] Scenario B (Spring and Autumn Cloudy Fluctuation): Moderate GHI, with drastic changes in cloud cover, resulting in minute-level fluctuations in GHI exceeding 70%.

[0085] Scenario C (Winter with few clouds and low temperature): Low GHI, low cloud cover, and low ambient temperature (which is beneficial for improving the efficiency of photovoltaic modules, but the total energy is low).

[0086] Scenario D (continuous rainy weather): extremely low GHI, high cloud cover, and photovoltaic output close to zero.

[0087] S4.2 & S4.3: Iterative Calculation: For scenarios A, B, and C (scenario D has no grid integration issue), representative 14-day meteorological data are selected for iterative simulation. Variables: Preset photovoltaic installed capacity, starting from 10GW and increasing in 1GW increments. Observed variable: System curtailment rate.

[0088] S4.4: Generating Dynamic Boundaries: Simulation results show that: Scenario A (Sunny Summer): When the photovoltaic capacity reaches 18GW, solar power generation surges at midday. Even with thermal power plants operating at their minimum technical output, they cannot fully absorb the power, resulting in a curtailment rate exceeding the threshold. Bottleneck: Minimum technical output of thermal power plants.

[0089] Scenario B (Spring and Autumn Fluctuations): When the photovoltaic capacity reaches only 14GW, although the total energy is not high, its drastic fluctuations make it impossible for conventional units to keep up with the ramp-up schedule, and the power prediction deviations caused by the fluctuations result in insufficient reserve capacity, leading to curtailment. Bottleneck: Unit ramp-up rate and system reserve.

[0090] Scenario C (Winter): The absorption capacity can reach 16GW, mainly limited by the output level of the photovoltaic power itself. The photovoltaic absorption capacity of the provincial power grid is not fixed at 15GW, but dynamically changes between 14GW (spring and autumn fluctuation bottleneck) and 18GW (summer bottleneck).

[0091] 3. Advantages of this invention More scientific planning: Power grid companies recognize that if photovoltaic (PV) capacity exceeds 14GW, curtailment will inevitably occur in the spring and autumn. This explains why the traditionally assessed 15GW resulted in severe curtailment in actual operation. Therefore, planning targets should be closer to the "lower limit" of 14GW.

[0092] More precise investment: To address the 14GW bottleneck, the focus of investment should not be on continuing to expand photovoltaic power, but rather on improving system flexibility, such as making flexible modifications to thermal power units (reducing their minimum technical output variables), or configuring energy storage to mitigate fluctuations.

[0093] More economical operation: On sunny summer days (Scenario A), the dispatcher can arrange for thermal power units to operate at minimum output in advance to maximize the absorption of photovoltaic power; on cloudy spring and autumn days (Scenario B), it is necessary to start gas-fired units with rapid start-stop or demand-side response to cope with fluctuations.

[0094] Example 3: Refined Assessment and Solutions for Localized Blockage 1. Application Scenarios and Objectives A certain region has abundant photovoltaic resources, but after centralized grid connection, some transmission lines frequently experience heavy load alarms during midday. It is necessary to determine the precise absorption limit of this local area and find the optimal solution.

[0095] 2. Specific Implementation Process Steps S1-S2: Focus on this region and build a more refined model (grid accuracy increased to 500m). Accurately simulate the output of the photovoltaic cluster in this region and identify differences in its output characteristics compared to the provincial average.

[0096] Step S3: In the production simulation model, focus on the power P_line(t) of lines L1 and L2, and use their thermal stability limit as the key constraint.

[0097] The reason for selecting L1 and L2 as the key lines of focus is as follows: ① For two consecutive years, the load factor has ranked in the top 2% of the entire grid. The maximum transmission power of L1 is 478MW with a load factor of 97%, while L2 is 452MW with a load factor of 96%, both approaching the 500MW thermal stability limit. ② As the only channel for centralized photovoltaic grid connection, all 1.4GW of installed capacity in the region is transmitted through L1 and L2. During the photovoltaic power generation period (09:00–16:00), the reverse power flow accounts for as high as 78%. Once curtailment occurs, these two lines have the highest probability of overload. ③ Statistics from the 2022–2023 dispatch logs show that 87% of the heavy load alarms caused by photovoltaic fluctuations occurred in L1 and L2. Among them, the frequency of power flow rise of more than 10% within 15 minutes caused by cloud shadow flicker reached 42 times / year, significantly higher than other lines (<5 times / year). Therefore, using L1 and L2 as key lines for power-meteorological coupling monitoring can directly identify the regional absorption bottleneck and avoid the computational redundancy caused by traversing the entire grid.

[0098] Step S4: Calculation of dynamic absorption capacity boundary and sensitivity analysis Cluster analysis revealed that the region is mainly affected by two scenarios: "clear skies" and "rapid cloud passage".

[0099] Iterative simulation revealed: Sunny scenario: The regional absorption capacity is 800MW. When this capacity is reached, line L1 will continuously exceed 95% load rate at noon.

[0100] In a scenario with rapid cloud cover: the regional absorption capacity drops sharply to 600MW. This is because cloud movement causes a spatiotemporal complementary effect in the region's photovoltaic output, which in turn triggers a momentary superposition of power flows between lines L1 and L2 at a specific time, resulting in a short-term overload.

[0101] S4.3a: Meteorological-Power Grid Coupling Sensitivity Analysis: Through analysis of variance, it was quantitatively demonstrated that the spatial distribution gradient of cloud cover and wind speed (affecting cloud movement speed) are the most sensitive meteorological factors for power line congestion in this region. This is consistent with engineering experience, but this invention is the first to provide a quantitative correlation index.

[0102] 3. Advantages of this invention The bottleneck was located with extreme precision: not only were the critical pathways L1 and L2 identified, but it was also discovered that under the specific meteorological condition of "rapid cloud passage," the absorption capacity would decrease by an additional 30%. This is a dynamic risk that traditional steady-state power flow analysis simply cannot detect.

[0103] Optimal solution: The traditional approach would be to directly propose expanding lines L1 and L2, which would be very costly.

[0104] Based on the analysis of this invention, a superior probabilistic congestion management and planning scheme is proposed: a 100MW / 200MWh energy storage system (variables: energy storage power and capacity) is configured at key nodes in the region. Through optimized control, charging is performed when the power lines approach their limits at midday, and power is rapidly absorbed when cloud cover causes momentary congestion. This approach can stabilize the region's absorption capacity at over 750MW at a cost far lower than power line modifications, while simultaneously smoothing out photovoltaic output.

[0105] Improving the utilization rate of power grid assets: By configuring flexible energy storage resources to cope with short-term congestion under specific weather scenarios, the "hard upgrade" of transmission assets is avoided, which greatly improves the utilization efficiency and investment benefits of the existing power grid.

[0106] Example 4: County-level Site Selection and Capacity Determination Guidance for Distributed Photovoltaic Development 1. Application Scenarios and Objectives A county plans to promote rooftop distributed photovoltaic (PV) systems on a large scale, but is concerned that unregulated development could lead to voltage overruns and reverse overloads in the power distribution network. A scientific method is needed to delineate priority development areas and restricted development areas.

[0107] 2. Specific Implementation Process Steps S1-S2: Using digital twin technology, construct a high-precision county-level model containing information on 10kV distribution network lines, transformer substations, and building rooftops. Meteorological data is collected using a 1km grid.

[0108] Step S3: First, construct a time-series production simulation model of the distribution network. An OPF framework is established with a 15-minute time step and 96×365 points per year. The objective function is to minimize the sum of curtailment penalty and network loss cost. The constraint set includes: node power balance, branch power flow upper limit, transformer rated capacity, voltage 0.93–1.07 pu, reverse overload limit, and inverter reactive power limit. Both photovoltaic and load data are read from the 1km aggregation curve output in step S2 and automatically injected into the corresponding nodes according to the "inverter-feeder-transformer area" GIS mapping.

[0109] Secondly, after each OPF rolling solution, all node voltages V_node(t) and distribution transformer load rates Load_transformer(t)=S_flow(t) / S_rated are automatically extracted and recorded to form two time series matrices for subsequent voltage overrun and overload statistics.

[0110] Finally, perform an intraday extreme value scan on the voltage and load matrix: if V_node(t) is >1.07pu or <0.93pu for 4 consecutive time periods, then mark the node as a "voltage risk node"; if the peak value of Load_transformer(t) is >80% and the annual cumulative value is >100h, then mark the transformer area as a "heavy load warning transformer area" and output it to step S5 to generate a differentiated access strategy.

[0111] Step S4: Calculation of dynamic absorption capacity boundary At the county level, the results of meteorological scene clustering are more localized, such as "sunny in the east of the county vs. cloudy in the west of the county".

[0112] By iteratively increasing the distributed photovoltaic installed capacity (variable) of different regions (e.g., divided by townships) and running time-series power flow calculations at the distribution network level, we obtain: Area A: Regardless of weather conditions, its power distribution network voltage remains within acceptable limits, with a absorption capacity of up to 50MW. It is designated as a "Green Priority Development Zone".

[0113] Zone B: Under clear conditions, when the photovoltaic capacity exceeds 20MW, the voltage at the end of the line will exceed the limit of 1.07pu at midday. This zone is marked as a "Yellow Restricted Development Zone".

[0114] Area C: Under fluctuating conditions, when the photovoltaic capacity exceeds 10MW, the reverse load rate of its main distribution transformer exceeds 80%, posing an overheating risk. This area is marked as a "Red Warning Zone".

[0115] Step S5: Generate a differentiation strategy Output a "County-level Distributed Photovoltaic Development Guidance Map" with each area clearly marked in green, yellow, and red.

[0116] For "yellow restricted development zones", it is recommended that any new photovoltaic installations must be equipped with intelligent inverters with "voltage adaptive control function" to suppress voltage rise by automatically absorbing reactive power.

[0117] For "red alert zones", it is recommended to postpone large-scale grid connection or to first upgrade the capacity of key transformers.

[0118] 3. Advantages of this invention From "one-size-fits-all" to "refined" management: County governments and power grid companies no longer set a uniform upper limit for access capacity across the entire county. Instead, they have implemented grid-based and differentiated management, guiding investment to areas with good power grid conditions and mitigating safety risks from the source.

[0119] Unleashing development potential: The enormous potential (50MW) of Area A was accurately identified, avoiding the "throwing the baby out with the bathwater" phenomenon of limiting the development of the entire county due to concerns about Area C, and effectively promoting the healthy development of new energy.

[0120] Guiding the formulation of technical standards: It clearly requires the installation of smart inverters in certain areas, promotes the application of advanced technologies, and improves the intelligence level and proactive management capabilities of the power distribution network.

[0121] Based on the above description of the embodiments, the present invention has the following beneficial effects compared to the prior art: 1. Innovation in assessment dimensions: From "static values" to "dynamic boundaries", it reveals the essence of absorption capacity changing with meteorological conditions and answers the core questions of "when, where, and under what weather conditions can it absorb so much".

[0122] 2. Innovation in diagnostic depth: By using "meteorological-power grid coupling sensitivity analysis", the operational bottleneck is quantitatively correlated with specific meteorological causes, upgrading bottleneck diagnosis from "experience-driven" to "data-driven".

[0123] 3. Innovation in application value: The output results such as "dynamic boundary map", "blockage sensitivity report" and "development guidance map" can directly and accurately guide power grid planning, operation mode arrangement, flexible resource allocation and distributed photovoltaic development strategy, realizing a perfect combination of theoretical research and engineering practice, and ultimately achieving the triple goal of improving absorption capacity, ensuring power grid security and optimizing investment decisions.

[0124] Example 5 This invention also proposes a regional power grid photovoltaic absorption capacity assessment system that considers meteorological factors, including a dataset construction module, a photovoltaic output time-series modeling module, a time-series production simulation module, and a dynamic absorption capacity boundary calculation module: The dataset construction module collects multi-source heterogeneous data from the target area, including numerical weather prediction data, power grid topology and equipment parameter data, and power load data; it performs spatiotemporal alignment and correlation mapping on the multi-source heterogeneous data to generate a standardized input dataset. The photovoltaic power output time series modeling module calculates the total power output time series curve of the regional photovoltaic power station cluster based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset. The time-series production simulation module constructs a time-series optimization model with the goal of minimizing the total system operating cost and taking into account grid operation constraints; it solves the time-series optimization model based on a standardized input dataset and the total output time-series curve, and outputs the system curtailment rate. The dynamic absorption capacity boundary calculation module performs clustering based on the standardized input dataset to obtain several meteorological scenarios and corresponding scenario data. Under each meteorological scenario, the photovoltaic installed capacity is incremented by a set step size to perform iterative optimization and obtain the maximum absorption capacity under the corresponding meteorological scenario. The regional power grid photovoltaic absorption capacity boundary is obtained based on the maximum absorption capacity of all meteorological scenarios.

[0125] Example 6 The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0126] Example 7 The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0127] 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.

[0128] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0129] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0130] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0131] 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 evaluating the photovoltaic (PV) power consumption capacity of a regional power grid considering meteorological factors, characterized in that, include: S1 collects multi-source heterogeneous data of the target area, including numerical weather prediction data, power grid topology and equipment parameter data, and power load data; Spatiotemporal alignment and correlation mapping are performed on multi-source heterogeneous data to generate a standardized input dataset; S2, based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset, calculates the total output time series curve of the regional photovoltaic power station cluster; S3. Construct a time-series optimization model with the objective of minimizing the total system operating cost and considering grid operation constraints; solve the time-series optimization model based on the standardized input dataset and the total output time-series curve, and output the system curtailment rate; S4. Clustering is performed based on the standardized input dataset to obtain several meteorological scenarios and corresponding scenario data. Under each meteorological scenario, the photovoltaic installed capacity is incremented by a set step size to perform iterative optimization and obtain the maximum absorption capacity under the corresponding meteorological scenario. The regional power grid photovoltaic absorption capacity boundary is obtained based on the maximum absorption capacity of all meteorological scenarios.

2. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 1, characterized in that: In S2, the total output time series curve of the regional photovoltaic power station cluster is calculated based on the meteorological grid data provided by the numerical weather prediction data in the standardized input dataset. The specific steps include: The horizontal irradiance in the meteorological grid data is converted into the irradiance on the tilted surface of the photovoltaic panel, and the operating temperature of the photovoltaic module is calculated based on the ambient temperature and wind speed. Based on the operating temperature, rated power, efficiency parameters of photovoltaic modules and inverter parameters, the AC output time-series curve of photovoltaic units in each grid is calculated. The AC output time-series curves of all grids are aggregated according to their electrical connection relationships in the power grid to obtain the total output time-series curve of the regional photovoltaic power station cluster.

3. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 1, characterized in that: In S3, a time-series optimization model is constructed with the goal of minimizing the total system operating cost and taking into account the constraints of power grid operation. The timing optimization model is solved based on the standardized input dataset and the total output time-series curve, outputting the system curtailment rate; the specific steps include: The total operating cost of the system includes fuel costs, start-up and shutdown costs, and curtailment penalty costs. Grid operation constraints include real-time power balance constraints, conventional unit operation constraints, grid security constraints, and system reserve constraints; In real-time power balance constraints, total power generation output equals total load; total power generation output is the sum of the total output time-series curve and the output of conventional units.

4. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 1, characterized in that: In S4, the specific methods for obtaining the boundary of the regional power grid's photovoltaic absorption capacity include: S4.1 Extract key feature variables from meteorological grid data in the standardized input dataset. Based on the key feature variables, use cluster analysis to identify several meteorological scenarios. For each meteorological scenario, calculate the statistical description of the corresponding key feature variables to obtain quantitative feature templates for different meteorological scenarios. S4.2, Based on the quantitative feature templates of different meteorological scenarios, select continuous meteorological time series data of the load corresponding to the quantitative feature template as the input representative data of the meteorological scenario for each meteorological scenario; S4.3 For each meteorological scenario and representative input data, the photovoltaic installed capacity is iteratively optimized with a set step size to obtain the maximum absorption capacity under that meteorological scenario. S4.4 summarizes the maximum absorption capacity under all meteorological scenarios and generates the regional power grid photovoltaic absorption capacity boundary.

5. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 4, characterized in that: In S4.1, the specific methods for generating quantized feature templates include: Horizontal irradiance, cloud cover, and ambient temperature were extracted from meteorological grid data in the standardized input dataset as key feature variables. Based on key feature variables, cluster analysis was used to identify several meteorological scenarios; For each meteorological scenario, the statistical description of the corresponding key feature variables is calculated, including the mean, variance, and typical value range of GHI, cloud cover, and temperature data for all samples under that meteorological scenario, to obtain the quantitative feature template for different meteorological scenarios.

6. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 4, characterized in that: In step 4.3, for each meteorological scenario and representative input data, the photovoltaic installed capacity is iteratively optimized with a set step size to obtain the maximum absorption capacity under that meteorological scenario; the specific implementation method is as follows: The photovoltaic installed capacity is assumed to increase with a preset step size. After each increment, the total output time series curve is generated by S2 using representative input data of the meteorological scenario, and the time series optimization model of S3 is run to obtain the system curtailment rate. When the system curtailment rate exceeds the preset threshold, the iteration stops, and the photovoltaic capacity at this time is the maximum absorption capacity under the meteorological scenario.

7. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 1, characterized in that: The method for assessing the photovoltaic absorption capacity of the regional power grid also includes: S5. Analyze the output of step S4, identify the dominant constraints that lead to light curtailment under different meteorological scenarios, and generate corresponding operating strategies based on the dominant constraints.

8. The method for assessing the photovoltaic absorption capacity of a regional power grid considering meteorological factors according to claim 7, characterized in that: In S5, the specific implementation methods for generating the corresponding running strategy include: S5.1, during each run of the time-series production simulation model, the time-series data of the system operation status is verified in real time; when the system experiences curtailment, various power grid operation constraints are checked according to the preset priority order, and the constraint that is violated first is determined as the dominant constraint of the curtailment event; the frequency of occurrence of various dominant constraints in all curtailment events under the same meteorological scenario is statistically analyzed, and the constraint type with the highest frequency is identified as the dominant constraint condition under the meteorological scenario. S5.2, based on the dominant constraints under each meteorological scenario, automatically trigger and execute the corresponding enhancement strategy generation process. The type of the enhancement strategy generation process corresponds to the dominant constraints and includes at least: Strategies to address insufficient power balance and regulation capabilities: When the dominant constraint is system power balance or unit regulation capability, generate a set of operating instructions that include adjusting unit standby, calling up fast regulation resources, or initiating demand-side response. Strategies to address network transmission congestion: When the dominant constraint is the thermal stability limit of the transmission line or transformer, generate planning recommendations that include a probabilistic safety assessment of the congesting component and output line capacity expansion or energy storage system configuration to mitigate the congestion based on the assessment results.

9. A regional power grid photovoltaic absorption capacity assessment system considering meteorological factors, utilizing the method described in any one of claims 1-8, comprising a dataset construction module, a photovoltaic output time-series modeling module, a time-series production simulation module, and a dynamic absorption capacity boundary calculation module, characterized in that: The dataset construction module collects multi-source heterogeneous data from the target area, including numerical weather prediction data, power grid topology and equipment parameter data, and power load data; it performs spatiotemporal alignment and correlation mapping on the multi-source heterogeneous data to generate a standardized input dataset. The photovoltaic power output time series modeling module calculates the total power output time series curve of the regional photovoltaic power station cluster based on meteorological grid data provided by numerical weather prediction data in the standardized input dataset. The time-series production simulation module constructs a time-series optimization model with the goal of minimizing the total system operating cost and taking into account grid operation constraints; it solves the time-series optimization model based on a standardized input dataset and the total output time-series curve, and outputs the system curtailment rate. The dynamic absorption capacity boundary calculation module performs clustering based on the standardized input dataset to obtain several meteorological scenarios and corresponding scenario data. Under each meteorological scenario, the photovoltaic installed capacity is incremented by a set step size to perform iterative optimization and obtain the maximum absorption capacity under the corresponding meteorological scenario. The regional power grid photovoltaic absorption capacity boundary is obtained based on the maximum absorption capacity of all meteorological scenarios.

10. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-8.

11. A computer readable storage medium having stored thereon a computer program, characterized in that When executed by a processor, the program implements the steps of the method according to any one of claims 1-8.

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