A new energy capacity evaluation method, system, device and medium based on a three-level network

Abstract

This invention discloses a method, system, equipment, and medium for assessing renewable energy capacity based on a three-tiered network. The method includes: acquiring historical power generation data for various renewable energy power generation types over different time periods within a target area, and acquiring structural parameters and operational characteristics data of the three-tiered network within the target area; performing spatiotemporal complementarity analysis on different renewable energy power generation types based on the historical power generation data, and constructing a joint power generation complementarity model based on the analysis results; obtaining the grid's access capacity, the distribution network's absorption capacity, and the microgrid's injectable capacity based on the joint power generation complementarity model, the structural parameters, and operational characteristics data of the three-tiered network within the target area; and assessing the renewable energy capacity based on the access capacity, absorption capacity, and injectable capacity, combined with the interaction effects between different levels of the grid, to obtain the capacity assessment result. This invention addresses the problems of assessment methods being limited to a single power generation type and the poor accuracy of assessment results.

Description

Technical Field

[0001] This invention relates to the field of new energy capacity assessment technology, and in particular to a new energy capacity assessment method, system, equipment and medium based on a three-level network. Background Technology

[0002] With increasing global attention to environmental protection and sustainable energy development, the proportion of new energy sources in the power system is growing. Wind and solar energy, among other new energy sources, are highly favored due to their significant advantages such as being clean and renewable. However, wind and solar power generation is characterized by randomness, fluctuation, and intermittency. These characteristics not only pose challenges to the power balance and stable operation of the power system but also make accurately assessing the generating capacity of new energy sources a key issue in ensuring grid security and efficient utilization of natural resources.

[0003] Currently, in the field of renewable energy capacity assessment, on the one hand, some assessment methods are limited to a single type of power generation, neglecting the complementary characteristics of multiple renewable energy sources when generating power together, making it difficult for the assessment results to reflect the true potential of the combined power generation system. On the other hand, existing assessments do not fully consider the complexity of the power grid hierarchy. For example, the transmission network undertakes long-distance power transmission and must meet power loss and stability constraints; the distribution network is responsible for power distribution, directly affecting the local absorption capacity of renewable energy; and microgrids, as flexible autonomous units, can achieve efficient utilization of renewable energy. Therefore, existing methods often fragment the operational characteristic data of different levels of the power grid, resulting in assessment results that cannot accurately reflect the actual grid's accessibility capacity for renewable energy, thus affecting subsequent renewable energy planning. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a new energy capacity assessment method, system, equipment, and medium based on a three-level network, which solves the problems of existing assessment methods being limited to a single power generation type and having poor accuracy of assessment results.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for assessing the capacity of new energy sources based on a three-level network, comprising: Historical power generation data of various new energy power generation types in different time periods within the target area are obtained, as well as structural parameters and operational characteristics data of the three-level network within the target area; the three-level network includes the transmission network, distribution network, and microgrid. Based on the historical power generation data, a spatiotemporal complementary relationship analysis was conducted on different types of new energy power generation, and a joint power generation complementary model was constructed based on the analysis results. Based on the aforementioned combined generation complementarity model, the structural parameters and operational characteristic data of the transmission network, and in conjunction with the stability constraints of the transmission network, the access capacity of the transmission network is determined. Based on the combined generation complementarity model, the structural parameters and operating characteristic data of the distribution network, and combined with the operating constraints of the distribution network, the local absorption capacity of the distribution network is analyzed to obtain the absorption capacity of the distribution network. Based on the combined generation complementary model, the structural parameters and operating characteristic data of the microgrid, and combined with the grid connection point constraints of the microgrid, the energy storage regulation performance of the microgrid is analyzed to obtain the injectable capacity of the microgrid. Based on the available access capacity, the absorbable capacity, and the injectable capacity, and considering the interaction between power grids at all levels, the capacity of new energy sources is evaluated to obtain the capacity assessment results.

[0007] As a preferred embodiment of the new energy capacity assessment method based on a three-level network described in this invention, the construction of the joint power generation complementarity model includes: The historical power generation data is classified based on the type of new energy power generation to obtain an energy type dataset. Each energy type dataset is then segmented in chronological order to obtain multiple consecutive time series segments. These time series segments are then integrated according to the period category to obtain a continuous periodic basic data sequence. Feature extraction is performed on each time series segment in each of the said periodic basic data sequences to obtain a feature set; Based on the feature set, a feature correlation matrix is ​​constructed that spans energy types and time periods, and based on the feature correlation matrix, periodic correlation relationships are determined. The target area is spatially divided based on geographical location to obtain multiple sub-regions. Based on the spatiotemporal distribution of resources of each energy type in each sub-region, the regional resource field of each energy type in the corresponding sub-region and at the corresponding time is determined. The feature set is split based on the sub-regions to obtain the periodic power generation characteristics of each sub-region. The coupling degree between the periodic power generation characteristics and regional resource fields of different sub-regions and different energy types is processed based on the preset coupling degree function to obtain the coupling degree value. The coupling degree values ​​are filtered based on a preset coupling threshold to obtain a combination of target region types, and the combination of target region types is determined to be a region complementary relationship; Based on the aforementioned periodic correlation and regional complementarity, a joint power generation complementarity model is constructed.

[0008] As a preferred embodiment of the new energy capacity assessment method based on a three-level network described in this invention, the determination of the periodic correlation includes: The feature set is decomposed based on energy type, cycle dimension, and feature dimension to obtain energy cycle feature units; Using the energy cycle characteristic units as the rows and columns of the matrix, an initial correlation matrix is ​​constructed; Based on the mutual information function, the correlation strength of any pair of cells in the initial correlation matrix is ​​processed, and the result of the correlation strength processing is filled into the initial correlation matrix to obtain the feature correlation matrix; the pair of cells is two energy cycle feature cells corresponding to any row and any column in the initial correlation matrix. The feature association matrix is ​​filtered based on a preset mutual information threshold to obtain a preliminary list of association relationships; The preliminary list of relationships is aggregated, and redundancy is removed from the aggregation results to obtain the periodic relationships.

[0009] As a preferred embodiment of the new energy capacity assessment method based on a three-level network described in this invention, the step of constructing a joint power generation complementarity model based on the cycle correlation and the regional complementarity includes: The periodic correlation and the regional complementarity are fused to obtain a multidimensional matrix, and a potential function is constructed based on the multidimensional matrix. Based on the aforementioned potential function, the complementary potential under different time periods, regions, and energy type combinations is processed to obtain a potential value sequence; Using the potential value sequence as constraints, a multi-type joint power generation output function is constructed, and an initial joint model is determined based on the multi-type joint power generation output function; The initial joint model is corrected and updated based on the acquired real-time power generation data to obtain the joint power generation complementary model.

[0010] As a preferred embodiment of the renewable energy capacity assessment method based on a three-level network described in this invention, the process of assessing the renewable energy capacity to obtain the capacity assessment result includes: Based on the power data of the tie lines between the transmission network and the distribution network, and between the distribution network and the microgrid, and the power flow direction under different operating conditions, a power transmission matrix between the three-level networks is constructed. Based on the power transmission matrix, the voltage data of the transmission network, the distribution network, and the nodes of the distribution network are analyzed to obtain the voltage change caused by power transmission. The voltage change is then compared with the rated voltage and the allowable voltage deviation to obtain the voltage influence coefficient. Based on the power transmission matrix and the frequency response data of each level of the power grid, the frequency deviation caused by power transmission is analyzed to obtain the frequency adjustment correlation degree; Based on the voltage influence coefficient and frequency adjustment correlation, the available capacity, the absorbable capacity and the injectable capacity are respectively corrected to obtain the corrected transmission network capacity, distribution network capacity and microgrid capacity; The new energy capacity is evaluated based on the transmission network capacity, the distribution network capacity, and the microgrid capacity to obtain the capacity evaluation results.

[0011] As a preferred embodiment of the renewable energy capacity assessment method based on a three-level network described in this invention, the corrected transmission network capacity, distribution network capacity, and microgrid capacity include: In response to the voltage influence coefficient exceeding a preset voltage deviation value or the frequency adjustment correlation exceeding a preset frequency deviation value, the available capacity is corrected based on the voltage influence coefficient and the frequency adjustment correlation to obtain the corrected transmission network capacity. Based on the transmission network capacity, combined with the voltage and frequency constraints of the distribution network itself, and the load characteristic curve of the distribution network, the matching degree between the absorbable capacity and the input power of the transmission network is analyzed, and the absorbable capacity is corrected based on the matching degree analysis results to obtain the corrected distribution network capacity. The injectable capacity is modified based on the distribution network capacity and the microgrid energy storage regulation characteristics to obtain the modified microgrid capacity.

[0012] As a preferred embodiment of the renewable energy capacity assessment method based on a three-level network described in this invention, the assessment of renewable energy capacity based on the transmission network capacity, the distribution network capacity, and the microgrid capacity to obtain capacity assessment results includes: The transmission network capacity, the distribution network capacity, and the microgrid capacity are integrated, and the dynamic process of power transmission, voltage impact, and frequency adjustment between the three networks is simulated based on the integration results. The transmission network capacity, the distribution network capacity, and the microgrid capacity are verified based on the simulation results until the verification results meet the requirements and a verification report is obtained. Based on the combination of transmission network capacity, distribution network capacity and microgrid capacity in the verification report, a simulation analysis of the preset new energy operation scenario is performed to obtain a scenario analysis report; Based on the verification report and the scenario analysis report, the capacity boundary of the three-level network is integrated to obtain a capacity boundary report; The capacity of new energy sources is evaluated based on the capacity boundary report and the scenario analysis report to obtain the capacity evaluation results.

[0013] Secondly, the present invention provides a new energy capacity assessment system based on a three-level network, comprising: The data acquisition module is used to acquire historical power generation data of various new energy power generation types in different time periods within the target area, and to acquire the structural parameters and operating characteristics data of the three-level network within the target area; the three-level network includes the transmission network, the distribution network, and the microgrid. The model building module is used to analyze the spatiotemporal complementarity of different new energy power generation types based on the historical power generation data, and to build a joint power generation complementarity model based on the analysis results. The accessible capacity generation module is used to determine the accessible capacity of the transmission network based on the joint generation complementarity model, the structural parameters and operating characteristic data of the transmission network, and the stability constraints of the transmission network. The absorbable capacity generation module is used to analyze the local absorption performance of the distribution network based on the joint generation complementarity model, the structural parameters and operating characteristic data of the distribution network, and the operating constraints of the distribution network, so as to obtain the absorbable capacity of the distribution network. An injectable capacity generation module is used to analyze the energy storage regulation performance of the microgrid based on the joint generation complementarity model, the structural parameters and operating characteristic data of the microgrid, and the grid connection point constraints of the microgrid, so as to obtain the injectable capacity of the microgrid. The assessment generation module is used to assess the new energy capacity based on the accessible capacity, the absorbable capacity, and the injectable capacity, combined with the interaction between power grids at all levels, to obtain the capacity assessment result.

[0014] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor, when executing the computer-executable instructions, implements the steps of the method.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: By analyzing the spatiotemporal complementarity of different new energy power generation types and constructing a joint power generation complementarity model, this invention can fully explore the complementary advantages of different new energy sources in time and space, achieving complementary advantages and enabling the evaluation results to truly reflect the power generation potential of the joint power generation system. Compared with traditional single-type evaluation methods, the evaluation results are more realistic. Furthermore, this invention combines the characteristics of the transmission network, distribution network, and microgrid in the three-level network to determine the accessible, absorbable, and injectable capacity of new energy sources at each level of the grid. It comprehensively presents the access and utilization of new energy sources at different grid levels, accurately reflecting the accessible capacity of new energy sources in the actual grid. This overcomes the shortcomings of existing methods such as data fragmentation and one-sided evaluation, thus obtaining more accurate new energy capacity evaluation results and providing accurate and reliable data for subsequent new energy planning. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overall process logic of a new energy capacity assessment method based on a three-level network, provided as an embodiment of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Example 1, referring to Figure 1 As one embodiment of the present invention, a method for assessing the capacity of new energy sources based on a three-level network is provided, such as... Figure 1 The specific steps shown are as follows: S100: Acquire historical power generation data of various new energy power generation types in different time periods within the target area, and acquire structural parameters and operational characteristics data of the three-level network within the target area, including the transmission network, distribution network, and microgrid; Specifically, by using equipment such as meteorological monitoring stations and power metering devices, power generation data of new energy sources such as wind, solar and hydropower in the target area are collected in the past 3-5 years at hourly or minute intervals. At the same time, the corresponding meteorological conditions, timestamps and other information are recorded to jointly construct historical power generation data.

[0021] Specifically, the capacity assessment system also utilizes connections with transmission networks, distribution networks, and microgrids to acquire structural parameters and operational characteristics data of the transmission network, distribution network, and microgrid within the target area, including: The structural parameters of the transmission network include line length, conductor type, transformer parameters, etc.; the operating characteristic data of the transmission network include line power flow distribution, node voltage, power loss, etc., which can be collected in real time by the power system dispatch automation system (SCADA) or obtained from historical databases. The structural parameters of the distribution network include feeder length, conductor type, load distribution, and distributed power source access points; the operating characteristic data of the distribution network include voltage deviation at each node, line load rate, and three-phase imbalance, which can be collected by connecting with smart meters and distribution automation equipment. The structural parameters of a microgrid include the capacity, type, charging and discharging power of distributed power sources, and grid connection parameters, such as the grid connection voltage level and access line parameters; the operating characteristic data of a microgrid include the power balance within the microgrid and the charging and discharging status of energy storage.

[0022] S200: Analyze the spatiotemporal complementarity of different new energy power generation types based on historical power generation data, and construct a joint power generation complementarity model based on the analysis results; specifically including the following sub-steps A1~A7: In A1: Historical power generation data is classified based on the type of new energy power generation to obtain an energy type dataset. Each energy type dataset is then segmented in chronological order to obtain multiple consecutive time series segments. These time series segments are then integrated according to the period category to obtain a continuous periodic basic data sequence. Optionally, the capacity assessment system first traverses the database storing historical power generation data, classifying the data into categories such as solar irradiance data for photovoltaic (PV) power, wind power attributes for wind power, and water flow attributes for hydropower. These core attribute data are then assigned to the energy types of PV, wind, and hydropower, forming energy type datasets. Since each dataset includes day (d), month (m), and year (y) time markers, each dataset is labeled as follows: Then, for each labeled energy type dataset, it is segmented according to daily period (data segments from 0:00 to 24:00 each day), monthly period (sequence from the 1st of each month to the number of days in that month), and annual period (complete sequence from January to December of each year) to obtain time series segments. By integrating segments with the same period, such as connecting all daily period segments in chronological order, a continuous basic data sequence of daily, monthly, and yearly periods can be formed.

[0023] In some embodiments, taking City A as an example, City A has a photovoltaic power station B, a wind farm C, and a hydropower station D. After traversing the data, the photovoltaic dataset contains hourly power generation data for station B throughout the year, labeled... (d ranges from 1 to 365, m corresponds to January to December, and y represents 2023). The photovoltaic dataset is segmented, and the daily periodic segments are... The monthly cycle is a sequence from March 1st to 31st, and the annual cycle is a sequence from January to December. After integration, the daily, monthly, and annual continuous cycle basic data sequences of photovoltaic power in City A are obtained. The wind power and hydropower datasets are processed in the same way.

[0024] In A2: Feature extraction is performed on each time series segment in the basic data sequence of each period to obtain a feature set; Specifically, features are extracted from time series segments within the basic data sequence for each cycle. For example, for daily cycles, peak power times and power fluctuation variance are extracted; for monthly cycles, average monthly power generation duration and monthly power decay rate are extracted; and for annual cycles, seasonal peak-to-valley differences and annual power generation stability coefficients are extracted. These features are then aggregated into feature sets for each energy type and each cycle.

[0025] In A3: Based on the feature set, construct a feature correlation matrix across energy types and time periods, and determine the periodic correlation based on the feature correlation matrix; specifically including the following sub-steps A31~A35: In A31: The feature set is decomposed based on energy type, cycle dimension, and feature dimension to obtain energy cycle feature units; Specifically, after obtaining the feature set, it is broken down according to energy type + cycle dimension + feature dimension. For example, energy type is divided into photovoltaic, wind power, and hydropower; cycle dimension is divided into daily, monthly, and yearly; and feature dimension is divided into mean, variance, trend, and cycle component. Each combination of energy type + cycle + feature constitutes an energy cycle feature unit. This decomposes all feature units from the feature set into clear analytical particles, making the row and column meanings clear and avoiding confusion when constructing the matrix later.

[0026] In A32: Construct an initial correlation matrix using energy cycle characteristic units as the rows and columns of the matrix; Specifically, the capacity assessment system uses the decomposed energy cycle characteristic units as the rows and columns of a matrix to construct an initial correlation matrix, so that each cell (row i, column j) corresponds to the correlation between cell i and cell j, forming an initial correlation matrix with empty matrix values.

[0027] In A33: Based on the mutual information function, the correlation strength of any pair of cells in the initial correlation matrix is ​​processed, and the result of the correlation strength processing is filled into the initial correlation matrix to obtain the feature correlation matrix; the pair of cells is the two energy cycle feature cells corresponding to any row and any column in the initial correlation matrix. Specifically, the preset mutual information function within the capacity assessment system is as follows: in, These are represented as eigenvalue sequences of two energy cycle characteristic units, Represented as and The joint probability distribution, These are represented as their respective marginal probability distributions; then, for each pair of units in the initial matrix, the mutual information function is used to calculate the correlation strength of each pair. The greater the mutual information, the stronger the correlation between the two units. The mutual information of each pair is then... The calculated values ​​are filled into the corresponding positions in the matrix to obtain the feature correlation matrix.

[0028] In some embodiments, the process continues with City A, using PVD-Mean (the daily average value sequence of photovoltaic power, denoted as...) as the standard. And WFD-Mean (Daily average wind power series, denoted as WFD-Mean) For example, let's assume... The data is (unit (Daily average) The data is (Unit: kW). First, calculate the joint probability. Statistics The frequency of different combinations of values ​​for Y, for example and If it occurs 3 times, and the total sample size is 30 days, then... Then calculate the marginal probabilities ,when It appeared 5 times. ; It appeared 4 times. Finally, substitute the values ​​into the formula to calculate MI; that is... Assuming the calculation is (The larger the value, the stronger the correlation), then fill 0.8 into the PVD-Mean row and... In the cells of the column.

[0029] In A34: The feature association matrix is ​​filtered based on a preset mutual information threshold to obtain a preliminary list of association relationships; Specifically, the capacity assessment system first sets a mutual information threshold (e.g., (Adjusted according to actual data distribution), traverse the feature correlation matrix, filter out unit pairs with MI values ​​greater than or equal to the threshold, and form a preliminary correlation list. For example, if the MI of PVD-Mean and WFD-Mean is 0.8 (≥0.5), then record that PV-Daily Cycle-Mean ↔ Wind Power-Daily Cycle-Mean is strongly correlated; if the MI of PVD-Var (Daily Cycle Variance) and HVM-Trend (Hydropower Monthly Cycle Trend) is 0.3 (<0.5), then remove them.

[0030] In A35: The preliminary list of relationships is aggregated, and redundancy is removed from the aggregated results to obtain periodic relationships.

[0031] Specifically, the capacity assessment system aggregates preliminary correlations based on energy type and cycle dimension. For example, correlations related to photovoltaic (PV) and daily cycles are aggregated into a broad category of PV-daily cycle ↔ wind power-daily cycle. Then, it determines whether there is redundancy in the aggregated relationships (e.g., A→B and B→A are repeated, or A→B and B→C can be merged into a transitive relationship of A→B→C). After removing redundancy, a clear cycle correlation is obtained.

[0032] It should be noted that step A3 above breaks down complex features into clear energy-cycle-feature units, providing a clear framework for subsequent analysis, avoiding data confusion, and using mutual information to capture nonlinear correlations. Compared with linear methods, it is more suitable for the complex fluctuations of new energy sources, and the correlation strength is more accurate. Finally, noise is removed by threshold screening, and macroscopic cycle correlations are aggregated and refined, making the results from microscopic and fragmented to macroscopic and usable.

[0033] In A4: The target area is spatially divided based on geographical location to obtain multiple sub-regions, and based on the spatiotemporal distribution of resources of each energy type in each sub-region, the regional resource field of each energy type in the corresponding sub-region and at the corresponding time is determined; Specifically, the capacity assessment system divides the target area geographically, dividing City A into zones A1, A2, and A3 based on administrative divisions. Within each sub-zone, it statistically analyzes the spatiotemporal distribution of energy resources by type. For photovoltaic power, it calculates hourly / daily / monthly solar intensity and duration; for wind power, it calculates the spatiotemporal variations of wind speed and direction; and for hydropower, it calculates the spatiotemporal data of water flow and level. Using spatial interpolation, given resource data from several sites, it estimates resource conditions at other locations within the region, generating a regional resource field for each energy type at the corresponding area and time.

[0034] In one embodiment, continuing with City A as an example, City A is administratively divided into A1 (urban area), A2 (suburbs), and A3 (mountainous area). Photovoltaic sites B1 and B2 are located in area A1, and hourly solar irradiance was measured in January; site B3 is located in area A2. Using Kriging interpolation, based on the data from B1, B2, and B3, the hourly solar irradiance at other locations in area A1 in January is estimated, generating... (t represents the hour in January, from 1 to 744). Regarding wind power, wind farms C1 and C2 are located in area A3. Wind speed and direction were measured, and interpolation was used to generate [data / values]. This reflects the wind resources in different locations and times within zone A3. Hydropower station D is located in zone A2; data on water flow and level are collected to generate [data / information]. .

[0035] In A5: The feature set is split based on sub-regions to obtain the periodic power generation characteristics of each sub-region, and the coupling degree between the periodic power generation characteristics and regional resource fields of different sub-regions and different energy types is processed based on a preset coupling degree function to obtain the coupling degree value; Specifically, feature sets are split into sub-regions based on geographic location (e.g., city (area), to obtain the periodic power generation characteristics of the corresponding sub-region (e.g. (Mean and variance of the photovoltaic-daily cycle in the region). Then, using a preset coupling degree function, the coupling degree between power generation characteristics (C) and regional resource field (S) in different sub-regions and different energy types is calculated. The coupling degree function is: in, and These represent two different geographical sub-regions; type1 and type2 represent two different types of new energy sources. Represented as a time dimension, used for traversing sequences; Represented as in the region ,time The power generation characteristics of type i; In the region ,time Below, type i, is a regional resource field. Simply put, it computes the region... The characteristics of type 1 power generation and local resource fields, and the region The degree of synergistic matching between the type 2 power generation characteristics and the local resource field. For example... Characteristics of photovoltaic (type 1) power generation in the area (high power output during the day) and The district's solar resource field (strong during the day), and Characteristics of wind power generation in the area (ype2) (high power output at night) and Calculate the coupling degree of the wind resource field in the area (strong at night).

[0036] In A6: Based on a preset coupling threshold, the coupling degree value is filtered to obtain the target region type combination, and the target region type combination is determined as a region complementary relationship; Specifically, the capacity assessment system pre-sets an appropriate coupling threshold (e.g., This indicates a high degree of coupling, thus calculating the coupling degree K value that meets the coupling degree threshold, and obtaining the target region-type combination (such as photovoltaic power in region A1 and wind power in region A3, hydropower in region A2 and wind power in region A3, etc.). These combinations are then identified as regional complementary relationships. This extends from time-cycle complementarity to spatial regional complementarity, finding cross-regional and cross-type power generation-resource synergistic combinations, and clarifying the geographical and type potential of multi-energy complementarity.

[0037] In A7: Based on periodic correlation and regional complementarity, a joint power generation complementarity model is constructed; specifically, it includes the following sub-steps A71~A74: In A71: Periodic correlation and regional complementarity are fused to obtain a multidimensional matrix, and a potential function is constructed based on the multidimensional matrix; Specifically, the capacity assessment system, based on the established cyclical correlations and regional complementarity relationships, integrates energy type, region, and cyclical dimensions to form a multi-dimensional matrix. The matrix rows represent the region-energy type combination, the columns represent the cyclical dimension, and the matrix values ​​represent the correlation / coupling strength (e.g., the combined correlation and coupling value of North China photovoltaic power with its daily cycle and Northwest wind power with its daily cycle). Then, a potential function is constructed based on the multi-dimensional matrix, integrating temporal cyclical complementarity and spatial regional complementarity into a unified potential function, enabling complementary potential to simultaneously reflect when and where complementarity occurs. Specifically, this potential function is: in, Mutual information is represented as a feature correlation matrix across energy types and time periods; This represents the maximum value among all mutual information.

[0038] In A72: Based on the potential function, the complementary potential under different time periods, regions and energy type combinations is processed to obtain a potential value sequence; Specifically, the capacity assessment system iterates through different time periods (t=1 to 8760 hours, representing the whole year), different regional combinations (such as North China-Northwest China, North China-East China), and different energy type combinations (photovoltaic-wind power, wind power-hydropower), substitutes them into the potential function for calculation, and arranges these values ​​in chronological order to obtain a potential value sequence.

[0039] In A73: Using the potential value sequence as a constraint, a multi-type joint generation output function is constructed, and the initial joint model is determined based on the multi-type joint generation output function; Specifically, based on the determined potential value sequence, a multi-type joint generation output function is constructed using this potential value sequence as a constraint: in, Represented as Total combined power generation at all times; Represented as Time, region Complementary coefficients of type (high-potential combinations, initial) (Set height); Represented as a region Single-type power generation capacity of type (e.g., North China photovoltaic power generation) (Actual power generation at any given time); and initialize based on the potential value sequence. High-potential regional-type combinations (such as photovoltaic power in North China and wind power in Northwest China) provide... Higher initial values ​​(e.g., 1.2) encourage the model to initially leverage the advantages of high-potential complementary combinations; lower values ​​(e.g., 0.8) are assigned to combinations with lower potential (e.g., East China Hydropower-South China Photovoltaics). Based on this, the initial joint model is output. Furthermore, based on the initial joint model, the power generation capacity of high-potential combinations can be prioritized, thus initially simulating multi-energy coordinated power generation.

[0040] In A74: The initial joint model is corrected and updated based on the acquired real-time power generation data to obtain a joint power generation complementary model; Specifically, the capacity assessment system incorporates real-time power generation data. Calculation model output Deviation in actual power generation: The gradient descent method is used to correct the complementarity coefficient. Iteration formula: ;in, Represented as the first The model for the next iteration; This is expressed as the learning rate (which controls the correction step size, for example, 0.01). Represented as deviation pair The partial derivatives reflect The direction and extent of the impact of changes on the deviation; after each iteration, the model output is closer to actual power generation. After multiple iterations (e.g., 1000 times), the final model is obtained. ,at this time The values ​​have been adjusted to match the actual power generation effect.

[0041] It should be noted that step S200 above, by exploring the complementary characteristics of different new energy sources in time and space, constructs a joint power generation complementary model, which can more realistically reflect the joint output behavior of multiple energy sources, improve the fit of capacity assessment to the actual power generation process, and overcome the problems of traditional methods ignoring complementarity and having conservative or large biases in assessment results.

[0042] S300: Based on the joint generation complementarity model, the structural parameters and operating characteristics data of the transmission network, and combined with the stability constraints of the transmission network, determine the access capacity of the transmission network; It should be noted that after determining the joint generation complementarity model, the structural parameters and operating characteristic data of the transmission network, the capacity assessment system needs to combine multiple constraints for accurate calculations, since the transmission network undertakes long-distance power transmission and its stable operation is crucial. The access of new energy sources must be carried out on the basis of meeting stability requirements.

[0043] Specifically, the stability constraints of a transmission network include voltage stability constraints, power angle stability constraints, and line thermal stability constraints. Therefore, when determining the available capacity, power flow calculation methods, such as the Newton-Raphson method or the fast decoupling method, are used to inject the power generated by the combined generation complementarity model into the transmission network model. This continuously adjusts the capacity of new energy sources and calculates parameters such as voltage and line power flow at each node of the grid under different capacity limits. When a parameter reaches the stability constraint boundary, the new energy capacity at that point is the available capacity of the transmission network.

[0044] In some embodiments, the combined generation complementarity model constructed in the aforementioned region is integrated into the regional transmission network model, and the Newton-Raphson method is used for power flow calculation. Voltage stability constraints are set as node voltage deviations not exceeding ±5% of the rated voltage, and line thermal stability constraints are set as line currents not exceeding the rated current. Initially, it is assumed that the renewable energy integration capacity is 50MW. Calculations show that some node voltage deviations reach 6%, failing to meet the voltage stability constraints. The integration capacity is gradually reduced. When the integration capacity is 40MW, the voltage deviations of each node are within ±5%, and the line currents do not exceed the rated current. At this point, the usable integration capacity of the transmission network is determined to be 40MW.

[0045] It should be noted that step S300 above combines the combined generation model with the stability conditions of the transmission network to ensure that the access of new energy sources does not jeopardize the safe operation of the large power grid, thereby improving the safety and practicality of capacity assessment at the transmission level.

[0046] S400: Based on the joint generation complementarity model, the structural parameters and operating characteristics of the distribution network, and combined with the operating constraints of the distribution network, the local absorption capacity of the distribution network is analyzed to obtain the absorption capacity of the distribution network. It should be noted that after determining the combined generation complementarity model, the structural parameters and operating characteristics of the distribution network, the capacity assessment system needs to analyze various constraints in order to reasonably plan the access capacity of new energy in the distribution network, since the distribution network is directly responsible for power distribution and its operating status directly affects the local consumption of new energy.

[0047] Specifically, distribution network operation constraints include voltage quality constraints, line capacity constraints, and three-phase imbalance constraints. That is, when analyzing the local absorption of renewable energy generation within the distribution network, based on the combined generation complementarity model, the renewable energy generation power is injected into the distribution network model. Power flow calculations are performed using the forward-backward substitution method, and combined with the aforementioned operational constraints, optimization algorithms are used to adjust the renewable energy access capacity and location to maximize the local absorption capacity of the distribution network, thus obtaining the distribution network's absorbable capacity.

[0048] In one embodiment, power from a combined generation complementary model is injected into the distribution network of the region. The distribution network voltage quality constraints require a voltage deviation within ±7%, and the line capacity constraints are a maximum allowable current of 200A for a given feeder and a three-phase imbalance of no more than 15%. Based on the combined generation complementary model, power flow calculations are performed using the forward-backward substitution method, considering line load factor, voltage quality, and three-phase imbalance constraints. Through particle swarm optimization, the calculated renewable energy absorption capacity of the city's distribution network under current operating conditions is 100MW.

[0049] It should be noted that the above step S400 focuses on the local absorption capacity of the distribution network, takes into account operating constraints such as voltage quality and load rate, and conducts a detailed assessment of the potential for new energy to be connected to the distribution network, which helps to optimize the layout of distributed power sources and reduce wind and solar curtailment.

[0050] S500: Based on the joint generation complementary model, the structural parameters and operating characteristics data of the microgrid, and combined with the grid connection point constraints of the microgrid, the energy storage regulation performance of the microgrid is analyzed to obtain the injectable capacity of the microgrid. It should be noted that after determining the combined generation complementarity model, the structural parameters and operating characteristics of the microgrid, the connection between the microgrid and the main grid, as well as the regulation effect of the energy storage system on new energy sources, are key factors affecting the injectable capacity of new energy. Therefore, it is necessary to comprehensively consider various factors such as the microgrid grid connection point constraints in order to reasonably determine its injectable capacity.

[0051] Specifically, microgrid grid connection constraints include voltage amplitude and frequency constraints, power factor constraints, etc. When analyzing the energy storage regulation performance, factors such as the charging and discharging power, capacity limitations, and charging and discharging efficiency of the energy storage system are considered. Based on the output power of the combined generation complementarity model and the load demand within the microgrid, a charging and discharging strategy for the energy storage system is formulated. An optimization algorithm is then used to solve for the maximum power that the microgrid can inject into the main grid under the condition of satisfying the grid connection constraints, i.e., the injectable capacity of the microgrid.

[0052] In one embodiment, taking a microgrid in the region as an example, it is known that the grid connection point voltage amplitude is required to be within ±10% of the rated voltage, the frequency is required to be 50±0.5Hz, and the power factor is required to be between 0.9 and 1. The energy storage system in the microgrid has a capacity of 100 kWh and a rated charging and discharging power of 20 kW. Based on the output power of the combined generation complementarity model and the load demand, an energy storage charging and discharging strategy is formulated. When the renewable energy generation power is greater than the load demand, the energy storage system charges; otherwise, it discharges. Through optimization algorithm calculation, under all constraints, the maximum power that the microgrid can inject into the main grid is 15 MW, and the injectable capacity of the microgrid is determined to be 15 MW.

[0053] It should be noted that step S500 above quantifies the microgrid's support capacity for the main grid by introducing the regulation capacity and grid connection point constraints of the microgrid energy storage system, making the capacity assessment more closely reflect the actual operation of the microgrid, which is characterized by both autonomy and interaction, and improving the accuracy and applicability of the assessment.

[0054] S600: Based on the available access capacity, absorbable capacity, and injectable capacity, and considering the interaction between power grids at different levels, the capacity of new energy sources is assessed to obtain the capacity assessment results; specifically, it includes the following sub-steps B1~B5: In B1: Based on the power data of the tie lines between the transmission network and the distribution network, and between the distribution network and the microgrid, as well as the power flow direction under different operating conditions, a power transmission matrix between the three-level networks is constructed. Optionally, the capacity assessment system first uses the acquired power data of the tie lines from the transmission network to the distribution network and from the distribution network to the microgrid to statistically analyze the power flow under different operating conditions and construct a power transmission matrix between the three levels of networks. Specifically, the power transmission matrix uses... Indicated, where i represents the sending-end power grid. Power transmission network Distribution network Microgrids..., but because the three-tier network is transmission-distribution... Micro, therefore actually Matrix elements Represented as from the power grid arrive The average transmission power. For example, the power during summer peak (condition 1) and winter off-peak (condition 2) is statistically analyzed: Operating Condition 1 (Summer Peak): Power Transmission Network Distribution network Distribution network → microgrid ; Operating Condition 2 (Winter Off-Peak): Power Transmission Network Distribution network Distribution network microgrids Averaging these across different operating conditions, a power transfer matrix is ​​constructed (simplified to averaging across two operating conditions): in, The diagonal being 0 indicates that the internal circulation of the power grid is not considered. By quantifying the power flow between the three levels of the power grid using matrices, the abstract power interaction is transformed into a computable numerical relationship, laying the foundation for subsequent voltage and frequency analysis.

[0055] In B2: Based on the power transmission matrix, the voltage data of the transmission network, distribution network and the node voltage data of the distribution network are analyzed to obtain the voltage change caused by power transmission, and the voltage change is compared with the rated voltage and the allowable voltage deviation to obtain the voltage influence coefficient; Optionally, the capacity assessment system utilizes node voltage monitoring data from transmission, distribution, and microgrids (such as the rated voltage of a node in the distribution network). (Based on actual monitoring of voltage changes with power transmission), and combined with circuit theory (Ohm's law), the voltage change during power transmission is calculated. The calculation formula is as follows: in, Expressed as the equivalent resistance of the tie line (e.g., the resistance of the transmission-distribution network tie line). Distribution network - microgrid , This is expressed as the rated voltage (the transmission network may be...). Distribution network microgrid ; Optionally, after calculating the voltage variation of each tie line, compare it with the rated voltage. and allowable voltage deviation (such as allowable deviation of distribution network) ,Right now The voltage influence coefficient is obtained. : like This indicates that the voltage deviation exceeds the allowable range and the capacity needs to be corrected. like If so, then it is within the permissible range.

[0056] It should be noted that by quantifying the impact of power transmission on voltage, voltage constraints are transformed into calculable coefficients. When adjusting capacity in the future, it is clear how much power transmission will cause voltage exceedance, making capacity assessment more aligned with voltage safety.

[0057] In B3: Based on the power transfer matrix and frequency response data of each level of the power grid, the frequency deviation caused by power transfer is analyzed to obtain the frequency adjustment correlation degree; Optionally, the capacity assessment system analyzes the frequency deviation caused by power transmission based on the collected frequency response data of various levels of the power grid. Based on the frequency response characteristic formula ;in, This is expressed as a change in power transmission (e.g., the transmission network sending more power to the distribution network). , Represented as the system frequency regulation coefficient (such as that of the transmission network). , indicating frequency change Adjustments are needed (Power). Then, the correlation between power interaction and frequency adjustment, i.e., frequency deviation, is calculated. Deviation from allowable frequency (For example, the ratio of the allowable frequency deviation of the power grid to ±0.2Hz): ;like This indicates that the frequency deviation exceeds the allowable range and the capacity needs to be corrected.

[0058] In B4: Based on the voltage influence coefficient and frequency adjustment correlation, the connectable capacity, absorbable capacity, and injectable capacity are respectively corrected to obtain the corrected transmission network capacity, distribution network capacity, and microgrid capacity; specifically, this includes the following sub-steps B41~B43: In B41: In response to the voltage influence coefficient exceeding the preset voltage deviation value or the frequency adjustment correlation degree exceeding the preset frequency deviation value, the available capacity is corrected based on the voltage influence coefficient and the frequency adjustment correlation degree to obtain the corrected transmission network capacity. Optionally, the capacity assessment system first uses the calculated voltage influence coefficient. Correlation with frequency adjustment A first threshold (i.e., the maximum allowable voltage deviation, such as 5% of the rated voltage) is set for the voltage influence coefficient, and a second threshold (i.e., the maximum allowable frequency deviation, such as 0.5Hz) is set for the frequency adjustment correlation. When the voltage influence coefficient exceeds the first threshold or the frequency adjustment correlation exceeds the second threshold, a correction function constructed from the voltage influence coefficient and the frequency adjustment correlation is used. in, This represents the initially determined grid capacity that can be connected (e.g., the original assessment was 200MW). Expressed as the percentage of voltage deviation (e.g., voltage deviation) Allowable deviation If so, the percentage is 0.6%; Expressed as the percentage of frequency deviation (e.g., frequency deviation) Allowable deviation (0.75%), ultimately yielding the corrected transmission network capacity. It should be noted that when the difference ratio exceeds 1, This avoids negative capacity. The impact of voltage and frequency exceeding limits is directly quantified and incorporated into capacity correction, preventing the transmission network from being overloaded with new energy sources, which could lead to voltage / frequency collapse and ensure the stability of the upper layers of the power grid.

[0059] In B42: Based on the transmission network capacity, combined with the voltage and frequency constraints of the distribution network itself, and the load characteristic curve of the distribution network, the matching degree between the absorbable capacity and the input power of the transmission network is analyzed, and the absorbable capacity is corrected based on the matching degree analysis results to obtain the corrected distribution network capacity. Optionally, the capacity assessment system uses the obtained corrected transmission network capacity. As the power input of the distribution network, it is combined with the voltage and frequency constraints of the distribution network itself (such as the allowable voltage deviation of the distribution network). Frequency deviation and the load characteristic curve of the distribution network. (The load power that the distribution network can carry under different voltages and frequencies). Analyze the load-bearing capacity of the distribution network. The degree of matching between the power input to the transmission network and the distribution network voltage; if the power input to the transmission network (i.e., the corrected transmission network capacity) causes the distribution network voltage to... If the frequency exceeds the stable range, the distribution network capacity is corrected using the following formula: ;in, These represent the voltage and frequency (e.g., rated voltage) required for stable operation of the distribution network. (Frequency 50Hz); These represent the actual calculated voltage and frequency of the distribution network under the input power of the transmission network (if the input power is too high, the calculated voltage will be lower). ,frequency The final corrected distribution network capacity is obtained. By combining the load characteristics and voltage / frequency constraints of the distribution network, the capacity adjustment of the distribution network takes into account both the input from the upper layer and its own carrying capacity, thus avoiding the problem that the transmission network can transmit but the distribution network cannot receive.

[0060] In B43: The injectable capacity is corrected based on the distribution network capacity and the microgrid energy storage regulation characteristics to obtain the corrected microgrid capacity; Optionally, the capacity assessment system uses the obtained corrected distribution network capacity. For reference, the power interaction between the microgrid and the distribution network is considered, as well as the energy storage regulation characteristics of the microgrid. (Energy storage charging and discharging power capability), calculate the injectable capacity of the microgrid. The ability to mitigate power fluctuations in the distribution network. The correction formula is as follows: in, This represents the current regulation capacity of microgrid energy storage (such as energy storage charging capacity). ,put Comprehensive adjustment capability ), This is expressed as the maximum regulation capacity of energy storage (e.g., rated charge / discharge power of 30MW). Incorporating the energy storage regulation characteristics of the microgrid into capacity correction allows the microgrid capacity to be considered not only in terms of its own injection capacity but also in its ability to mitigate power fluctuations in the distribution network.

[0061] It should be noted that in this embodiment of the invention, from the transmission network (upper layer) to the distribution network (middle layer) and then to the microgrid (lower layer), the capacity correction of each layer takes the correction result of the upper layer as input, while taking into account its own constraints, so that each layer can contain the influence of the upper layer and ensure the capacity adaptation of the entire power grid.

[0062] In B5: The capacity of new energy sources is assessed based on the transmission network capacity, distribution network capacity, and microgrid capacity to obtain the capacity assessment results; specifically, this includes the following sub-steps B51~B54: In B51: The capacity of the transmission network, distribution network, and microgrid are integrated, and the dynamic process of power transmission, voltage impact, and frequency adjustment between the three networks is simulated based on the integration results. The capacity of the transmission network, distribution network, and microgrid is verified based on the simulation results until the verification results meet the requirements and a verification report is obtained. Optionally, the capacity assessment system first adjusts the transmission network capacity. Distribution network capacity and microgrid capacity The simulation is integrated and a simplified transient simulation model (or power system simulation tool, such as DlgSILENT) is used to simulate the dynamic process of power transmission, voltage fluctuation, and frequency response in a three-level power grid. During the simulation, the voltage and voltage stability margin of key busbars in the transmission network, the frequency deviation and over-limit duration in the distribution network area, and the power impact and fluctuation peak at the microgrid connection point are monitored.

[0063] Furthermore, for monitoring the voltage and voltage stability margin of key busbars in the transmission network, the voltage stability margin formula is adopted: in, This is expressed as the rated voltage. This represents the lowest voltage measured in the simulation. Furthermore, the safe voltage margin during dynamic processes is measured; for frequency deviation and over-limit duration in the distribution network area, frequency fluctuations at the transformer substation level are statistically analyzed, and the over-limit duration is also statistically analyzed. That is, the frequency exceeds The cumulative time in Hz; for the power surge and fluctuation peak at the microgrid grid connection point, monitor the maximum value of sudden changes in energy storage charging and discharging power. If the indicator exceeds the standard, such as... or If the timeout period is less than a few seconds, the corrected capacity is returned; otherwise, a verification report is output. This allows for the simulation of dynamic interactions within a three-tiered power grid, extending static capacity correction to dynamic process verification and avoiding the problem of static calculations failing dynamically.

[0064] In B52: Based on the combination of transmission network capacity, distribution network capacity and microgrid capacity in the verification report, the preset new energy operation scenario is simulated and analyzed to obtain the scenario analysis report; Optionally, based on the capacity combinations that passed the verification in the previous step, typical operating scenarios can be selected from the preset new energy operation scenarios for further simulation. For example, in time-series power flow simulation, voltage, frequency, and power transmission data can be simulated hourly over 24 hours (e.g., off-peak at 0:00 and full photovoltaic power generation at 12:00); in risk entropy quantification, a risk entropy model can be used. ,in This is expressed as the probability of voltage / frequency exceeding limits to quantify scenario risk. In the energy storage regulation limit analysis, the regulation limits of microgrid energy storage in the scenario are examined (e.g., whether the energy storage discharge depth exceeds 80% in scenario 1). The final output is a scenario analysis report, including the risk level of each scenario, energy storage limits, and voltage / frequency fluctuation curves. This achieves breakthroughs beyond single-condition limitations, covering complex scenarios actually faced by the power grid, and clearly defining the feasible capacity boundaries under different scenarios.

[0065] In one embodiment, taking Scenario 1: Full power generation of new energy sources + peak load (full power generation of photovoltaic power at noon, while residential electricity consumption is at its peak) as an example: Record the voltage and frequency every hour from 12:00 to 14:00. It was found that the distribution network voltage was lowest at 13:00 (9.7kV), with a frequency of 50.3Hz; calculate the probability of voltage exceeding limits. (i.e., voltage below 9.3kV for 20% of the time), frequency over-limit probability Then the risk entropy (The higher the value, the higher the risk); the microgrid energy storage discharge depth is 75% (not exceeding 80%, safe); this indicates that the risk level of scenario 1 is medium risk, the energy storage regulation has a margin, and voltage fluctuations need to be monitored.

[0066] In B53: The capacity boundary of the three-level network is integrated based on the verification report and the scenario analysis report to obtain the capacity boundary report; Optionally, when integrating the capacity boundaries of a Level 3 network by combining the verification report and the scenario analysis report, the capacity boundaries are integrated in three steps, including security boundary quantification, economic boundary balancing, and capacity range integration. Specifically, for safety boundary quantification, the minimum voltage stability frequency exceeding the threshold value is extracted for each scenario: in, , , All are safety coefficients, greater than 0 and less than 1, derived from the scenario risk; the higher the risk, the smaller the coefficient.

[0067] Specifically, for economic boundary equilibrium, a network loss cost model is introduced. ,in, Represented as current, Represented as resistance, time, In conjunction with new energy subsidies and energy storage investment, the economically optimal capacity is calculated. .

[0068] Specifically, for capacity range consolidation, the absolute safety capacity is consolidated. and economically optimal capacity The capacity range is obtained. This process clearly defines the minimum safety margin and the recommended maximum capacity. The final result is a capacity boundary report, including the safety minimum, the economic maximum, and the boundary calculation formula.

[0069] In B54: The capacity of new energy sources is assessed based on the capacity boundary report and scenario analysis report to obtain the capacity assessment results; Optionally, based on the obtained capacity boundary report and scenario analysis report, the renewable energy capacity can be assessed, and feasible supporting recommendations can be proposed. For example, grid transformation recommendations: if the risk of extreme weather scenarios is high, it is recommended to reinforce the transmission network interconnection lines (e.g., invest 1 million to increase safety capacity by 5%); for renewable energy full-load scenarios, formulate a strategy for prioritizing charging of microgrid energy storage and time-sharing discharge of distribution network energy storage, using the following formula to guide dispatch: in, It is represented as a coordination coefficient (range 0.8-1.2), used to adjust the power interaction threshold between the distribution network and the microgrid, and needs to be set according to the grid stability requirements; This represents the charging and discharging power of the stored energy at time t. A positive result indicates that the stored energy is discharging, while a negative result indicates that the stored energy is charging. This represents the net power of the microgrid at time t (such as the net power after combining renewable energy generation and load consumption within the microgrid; if positive, it can be understood that the microgrid has surplus power that can be considered for energy storage or external transmission; if negative, it needs to draw power from external sources). This represents the absorbable capacity of the distribution network at time t (reflecting the distribution network's ability to support the power of the microgrid).

[0070] when When the power generation of new energy sources in the microgrid exceeds the load demand and the excess power exceeds the threshold of the distribution network's absorption capacity, the energy storage system starts the discharge mode.

[0071] when When the microgrid's power surplus or deficit is within the distribution network's absorption capacity, the energy storage system remains stationary.

[0072] when When the microgrid load demand exceeds the power generation capacity of new energy sources, and the power deficit exceeds the carrying capacity threshold of the distribution network, the energy storage system will start charging mode.

[0073] Operating warning threshold: Extract voltage over-limit warning values ​​from scenario analysis. Frequency exceeding the limit warning value Hz is embedded in the scheduling system. Finally, capacity boundaries, scenario risks, and supporting recommendations are integrated to form the final assessment report.

[0074] It should be noted that step S600 above comprehensively considers the capacity and interactive effects of power grids at all levels, making the assessment results more comprehensive and accurate in reflecting the actual available capacity of new energy in the entire power system, and providing a scientific basis for new energy planning and power grid construction.

[0075] Example 2: This example provides a new energy capacity assessment system based on a three-level network, including: The data acquisition module is used to acquire historical power generation data of various new energy power generation types in different time periods within the target area, and to acquire structural parameters and operational characteristics data of the three-level network within the target area; the three-level network includes the transmission network, distribution network, and microgrid; The model building module is used to analyze the spatiotemporal complementarity of different new energy power generation types based on historical power generation data, and to build a joint power generation complementarity model based on the analysis results. The accessibility capacity generation module is used to determine the accessibility capacity of the transmission network based on the joint generation complementarity model, the structural parameters and operating characteristic data of the transmission network, and the stability constraints of the transmission network. The absorbable capacity generation module is used to analyze the local absorption performance of the distribution network based on the joint generation complementarity model, the structural parameters and operating characteristic data of the distribution network, and the operating constraints of the distribution network, so as to obtain the absorbable capacity of the distribution network. The injectable capacity generation module is used to analyze the energy storage regulation performance of the microgrid based on the joint generation complementarity model, the structural parameters and operating characteristic data of the microgrid, and the grid connection point constraints of the microgrid, so as to obtain the injectable capacity of the microgrid. The assessment generation module is used to assess the capacity of new energy sources based on the available capacity, absorbable capacity, and injectable capacity, combined with the interaction between power grids at all levels, to obtain the capacity assessment results.

[0076] It should be noted that the technical solution of the new energy capacity assessment system based on a three-level network is based on the same concept as the technical solution of the new energy capacity assessment method based on a three-level network described above. For details not described in detail in the technical solution of the new energy capacity assessment system based on a three-level network in this embodiment, please refer to the description of the technical solution of the new energy capacity assessment method based on a three-level network described above.

[0077] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0078] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a new energy capacity assessment method based on a three-tier network. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0079] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0080] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the present invention.

Claims

1. A method for assessing the capacity of new energy sources based on a three-level network, characterized in that, include: Historical power generation data of various new energy power generation types in different time periods within the target area are obtained, as well as structural parameters and operational characteristics data of the three-level network within the target area; the three-level network includes the transmission network, distribution network, and microgrid. Based on the historical power generation data, a spatiotemporal complementary relationship analysis was conducted on different types of new energy power generation, and a joint power generation complementary model was constructed based on the analysis results. Based on the aforementioned combined generation complementarity model, the structural parameters and operational characteristic data of the transmission network, and in conjunction with the stability constraints of the transmission network, the access capacity of the transmission network is determined. Based on the combined generation complementarity model, the structural parameters and operating characteristic data of the distribution network, and combined with the operating constraints of the distribution network, the local absorption capacity of the distribution network is analyzed to obtain the absorption capacity of the distribution network. Based on the combined generation complementary model, the structural parameters and operating characteristic data of the microgrid, and combined with the grid connection point constraints of the microgrid, the energy storage regulation performance of the microgrid is analyzed to obtain the injectable capacity of the microgrid. Based on the available access capacity, the absorbable capacity, and the injectable capacity, and considering the interaction between power grids at all levels, the capacity of new energy sources is evaluated to obtain the capacity assessment results.

2. The renewable energy capacity assessment method based on a three-level network as described in claim 1, characterized in that, The construction of the joint power generation complementarity model includes: The historical power generation data is classified based on the type of new energy power generation to obtain an energy type dataset. Each energy type dataset is then segmented in chronological order to obtain multiple consecutive time series segments. These time series segments are then integrated according to the period category to obtain a continuous periodic basic data sequence. Feature extraction is performed on each time series segment in each of the said periodic basic data sequences to obtain a feature set; Based on the feature set, a feature correlation matrix is ​​constructed that spans energy types and time periods, and based on the feature correlation matrix, periodic correlation relationships are determined. The target area is spatially divided based on geographical location to obtain multiple sub-regions. Based on the spatiotemporal distribution of resources of each energy type in each sub-region, the regional resource field of each energy type in the corresponding sub-region and at the corresponding time is determined. The feature set is split based on the sub-regions to obtain the periodic power generation characteristics of each sub-region. The coupling degree between the periodic power generation characteristics and regional resource fields of different sub-regions and different energy types is processed based on the preset coupling degree function to obtain the coupling degree value. The coupling degree values ​​are filtered based on a preset coupling threshold to obtain a combination of target region types, and the combination of target region types is determined to be a region complementary relationship; Based on the aforementioned periodic correlation and regional complementarity, a joint power generation complementarity model is constructed.

3. The renewable energy capacity assessment method based on a three-level network as described in claim 2, characterized in that, The determination of the periodic correlation includes: The feature set is decomposed based on energy type, cycle dimension, and feature dimension to obtain energy cycle feature units; Using the energy cycle characteristic units as the rows and columns of the matrix, an initial correlation matrix is ​​constructed; Based on the mutual information function, the correlation strength of any pair of cells in the initial correlation matrix is ​​processed, and the result of the correlation strength processing is filled into the initial correlation matrix to obtain the feature correlation matrix; the pair of cells is two energy cycle feature cells corresponding to any row and any column in the initial correlation matrix. The feature association matrix is ​​filtered based on a preset mutual information threshold to obtain a preliminary list of association relationships; The preliminary list of relationships is aggregated, and redundancy is removed from the aggregation results to obtain the periodic relationships.

4. The renewable energy capacity assessment method based on a three-level network as described in claim 2, characterized in that, The construction of the joint power generation complementarity model based on the cycle correlation and the regional complementarity includes: The periodic correlation and the regional complementarity are fused to obtain a multidimensional matrix, and a potential function is constructed based on the multidimensional matrix. Based on the aforementioned potential function, the complementary potential under different time periods, regions, and energy type combinations is processed to obtain a potential value sequence; Using the potential value sequence as constraints, a multi-type joint power generation output function is constructed, and an initial joint model is determined based on the multi-type joint power generation output function; The initial joint model is corrected and updated based on the acquired real-time power generation data to obtain the joint power generation complementary model.

5. The renewable energy capacity assessment method based on a three-level network as described in claim 4, characterized in that, The capacity assessment results obtained from evaluating the new energy capacity include: Based on the power data of the tie lines between the transmission network and the distribution network, and between the distribution network and the microgrid, and the power flow direction under different operating conditions, a power transmission matrix between the three-level networks is constructed. Based on the power transmission matrix, the voltage data of the transmission network, the distribution network, and the nodes of the distribution network are analyzed to obtain the voltage change caused by power transmission. The voltage change is then compared with the rated voltage and the allowable voltage deviation to obtain the voltage influence coefficient. Based on the power transmission matrix and the frequency response data of each level of the power grid, the frequency deviation caused by power transmission is analyzed to obtain the frequency adjustment correlation degree; Based on the voltage influence coefficient and frequency adjustment correlation, the available capacity, the absorbable capacity and the injectable capacity are respectively corrected to obtain the corrected transmission network capacity, distribution network capacity and microgrid capacity; The new energy capacity is evaluated based on the transmission network capacity, the distribution network capacity, and the microgrid capacity to obtain the capacity evaluation results.

6. The renewable energy capacity assessment method based on a three-level network as described in claim 5, characterized in that, The revised transmission network capacity, distribution network capacity, and microgrid capacity include: In response to the voltage influence coefficient exceeding a preset voltage deviation value or the frequency adjustment correlation exceeding a preset frequency deviation value, the available capacity is corrected based on the voltage influence coefficient and the frequency adjustment correlation to obtain the corrected transmission network capacity. Based on the transmission network capacity, combined with the voltage and frequency constraints of the distribution network itself, and the load characteristic curve of the distribution network, the matching degree between the absorbable capacity and the input power of the transmission network is analyzed, and the absorbable capacity is corrected based on the matching degree analysis results to obtain the corrected distribution network capacity. The injectable capacity is modified based on the distribution network capacity and the microgrid energy storage regulation characteristics to obtain the modified microgrid capacity.

7. The renewable energy capacity assessment method based on a three-level network as described in claim 5, characterized in that, The assessment of the new energy capacity based on the transmission network capacity, the distribution network capacity, and the microgrid capacity to obtain the capacity assessment results includes: The transmission network capacity, the distribution network capacity, and the microgrid capacity are integrated, and the dynamic process of power transmission, voltage impact, and frequency adjustment between the three networks is simulated based on the integration results. The transmission network capacity, the distribution network capacity, and the microgrid capacity are verified based on the simulation results until the verification results meet the requirements and a verification report is obtained. Based on the combination of transmission network capacity, distribution network capacity and microgrid capacity in the verification report, a simulation analysis of the preset new energy operation scenario is performed to obtain a scenario analysis report; Based on the verification report and the scenario analysis report, the capacity boundary of the three-level network is integrated to obtain a capacity boundary report; The capacity of new energy sources is evaluated based on the capacity boundary report and the scenario analysis report to obtain the capacity evaluation results.

8. A renewable energy capacity assessment system based on a three-level network, employing the renewable energy capacity assessment method based on a three-level network as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire historical power generation data of various new energy power generation types in different time periods within the target area, and to acquire the structural parameters and operating characteristics data of the three-level network within the target area; the three-level network includes the transmission network, the distribution network, and the microgrid. The model building module is used to analyze the spatiotemporal complementarity of different new energy power generation types based on the historical power generation data, and to build a joint power generation complementarity model based on the analysis results. The accessible capacity generation module is used to determine the accessible capacity of the transmission network based on the joint generation complementarity model, the structural parameters and operating characteristic data of the transmission network, and the stability constraints of the transmission network. The absorbable capacity generation module is used to analyze the local absorption performance of the distribution network based on the joint generation complementarity model, the structural parameters and operating characteristic data of the distribution network, and the operating constraints of the distribution network, so as to obtain the absorbable capacity of the distribution network. An injectable capacity generation module is used to analyze the energy storage regulation performance of the microgrid based on the joint generation complementarity model, the structural parameters and operating characteristic data of the microgrid, and the grid connection point constraints of the microgrid, so as to obtain the injectable capacity of the microgrid. The assessment generation module is used to assess the new energy capacity based on the accessible capacity, the absorbable capacity, and the injectable capacity, combined with the interaction between power grids at all levels, to obtain the capacity assessment result.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the new energy capacity assessment method based on a three-level network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the new energy capacity assessment method based on a three-level network as described in any one of claims 1 to 7.

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