Method, system and device for optimizing configuration of wind, light and storage transmission under cross-regional interconnection and medium

Abstract

The present application relates to a kind of wind light storage and transport configuration optimization method, system, equipment and medium under cross-regional interconnection, the method is based on global latitude grid division wind light candidate site and binds space-time characteristic parameters, generates resource dataset;Then, through geographic partition aggregation, establish regional level wind light combined power generation curve;Innovatively, combine power transmission loss geographic model and multi-objective optimization algorithm, simultaneously solve the capacity configuration scheme of photovoltaic / wind power / energy storage / transmission and the minimum loss scheduling path;Finally, through multi-scenario dynamic simulation, generate the engineering implementation scheme containing geographic coordinates, effectively solve the problems such as transmission efficiency distortion caused by the absence of space constraints in traditional method, scheduling and planning fragmentation, multi-scenario adaptability is insufficient, significantly improve the cross-regional renewable energy consumption efficiency and the operation reliability of engineering scheme.

Description

Technical Field

[0001] This invention relates to the field of power system optimization and control technology, specifically to a method, system, equipment, and medium for optimizing the configuration of wind, solar, energy storage, and transmission under cross-regional interconnection. Background Technology

[0002] With the accelerated global energy transition, the large-scale development of renewable energy sources such as wind and solar power has become a core direction for power system development. In cross-regional interconnection scenarios, coordinating geographically dispersed wind and solar resources with energy storage and transmission systems to achieve multi-regional collaborative optimization is a key challenge for improving the efficiency of clean energy consumption. Current mainstream regional energy planning methods primarily focus on resource optimization within a single grid boundary, and their technical framework has significant limitations: on the one hand, traditional methods often ignore the impact of geographical constraints on transmission losses during the capacity planning stage, using only simplified linear transmission models, leading to distorted assessments of cross-regional transmission efficiency; on the other hand, existing schemes typically treat capacity allocation and dynamic scheduling separately, lacking a full-chain coupling mechanism from resource distribution and grid topology to time-series scheduling, resulting in problems such as rising wind and solar curtailment rates and insufficient energy storage utilization in actual operation. For example, in the preliminary research of the interconnection project between Europe and North Africa, the actual transmission loss deviated significantly from the design value due to insufficient consideration of the loss characteristics and path optimization of the Mediterranean submarine cable, significantly reducing the benefits of cross-regional complementarity.

[0003] Furthermore, existing technologies struggle to quantify the impact of different interconnection models (such as regional standalone and intercontinental interconnection) on system economics and reliability. Most planning tools rely on static assumptions or single-scenario simulations, failing to dynamically simulate the interaction between wind and solar power output fluctuations and inter-regional power flows, resulting in engineering solutions lacking the ability to predict actual operational risks. This disconnect between spatial, temporal, and system dimensions has become a technological bottleneck restricting the high-proportion consumption of renewable energy. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a method, system, equipment, and medium for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection that can couple geographical constraints and connect the entire process of "resource assessment - capacity optimization - dynamic scheduling - multi-scenario verification".

[0005] The objective of this invention is achieved through the following solution:

[0006] In a first aspect, the present invention provides a method for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection, comprising the following steps:

[0007] S1: Based on the acquired global latitude and longitude grid, candidate sites for wind and solar power generation are divided, and the maximum installable capacity and hourly power generation curve are bound to each candidate site to generate a candidate site resource dataset;

[0008] S2: Perform regional aggregation on the candidate point resource dataset, map the candidate points to the regional power grid according to the preset geographical zoning rules, and overlay the power generation curves of candidate points belonging to the same regional power grid to generate a regional wind and solar combined power generation curve.

[0009] S3: Based on the regional wind and solar combined power generation curve, establish a three-objective function including minimizing total investment cost, maximizing wind and solar penetration rate, and minimizing wind and solar curtailment rate. Set capacity constraints and topology constraints and solve decision variables to generate a wind, solar, storage and transmission capacity configuration scheme.

[0010] S4: Calculate the geographical distance between regions based on the transmission capacity data and geographical coordinate data of the wind, solar and energy storage transmission capacity configuration scheme, construct the transmission loss matrix in combination with the transmission line type, and design the optimal path search algorithm for the minimum loss path based on the transmission capacity constraint to generate dynamic scheduling rules with geographical constraints.

[0011] S5: Based on the regional wind-solar combined power generation curve, wind-solar-storage-transmission capacity configuration scheme and dynamic scheduling rules, a simulation environment is configured for different interconnection scenarios. Hourly power balance simulations of a preset duration are performed to update the energy storage status and record the power flow. An executable engineering scheme containing system performance indicators and engineering application indicators is generated. The executable engineering scheme is used to guide the site selection of wind and solar power plants, the configuration of energy storage systems and the construction of inter-regional transmission lines.

[0012] In one embodiment, S1 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0013] S11: Based on the acquired global latitude and longitude grid, the global geographical area is divided into latitude and longitude grids. The surface is divided into equal-area grid units using a preset resolution to generate a set of candidate point geographic coordinates.

[0014] S12: Apply capacity constraints to the geographic coordinate set of candidate points, determine the maximum installable capacity of each grid cell based on the land type database and engineering feasibility parameters, and generate a candidate site set with capacity constraints;

[0015] S13: Bind the power generation curves to the candidate site set, integrate the pre-stored historical meteorological database and equipment performance parameters to generate hourly power generation curves, and generate a candidate point resource dataset.

[0016] In one embodiment, S2 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0017] S21: Geographically partition the candidate point resource dataset, define the topology of the regional power grid based on the pre-stored continental boundary data and power grid jurisdiction data, and generate a regional power grid partitioning scheme;

[0018] S22: Perform site affiliation mapping on the regional power grid division scheme, assign each candidate site to the corresponding regional power grid based on spatial location relationship, and generate a site-region mapping table;

[0019] S23: Perform curve aggregation processing on the site-region mapping table, perform time series overlay calculations on the wind power and photovoltaic power generation curves in the same region, generate regional-level wind power combined generation curves and regional-level photovoltaic combined generation curves, and merge them into a regional-level wind and solar combined generation curve.

[0020] In one embodiment, S3 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0021] S31: The objective function is constructed for the regional wind and solar combined power generation curve. Based on the preset equipment investment cost parameters, the total investment cost calculation model is defined. The wind and solar penetration rate quantification formula is established in combination with the power demand data. The wind and solar curtailment rate calculation model is constructed based on the difference between the power generation curve and the load curve. The total investment cost calculation model, the wind and solar penetration rate quantification formula and the wind and solar curtailment rate calculation model are integrated by weighted summation to construct a three-dimensional optimization objective function, generating a three-dimensional optimization objective that includes minimizing investment cost, maximizing wind and solar penetration rate and minimizing wind and solar curtailment rate.

[0022] S32: Perform constraint injection processing on the three-dimensional optimization objective, set the upper limit constraints of photovoltaic and wind power installation based on the maximum installable capacity of candidate points, set inter-regional transmission capacity constraints based on the power grid topology, integrate the upper limit constraints of installation, transmission capacity constraints and the three-dimensional optimization objective, and generate a wind and solar constrained optimization model with multiple constraints.

[0023] S33: Spatial correlation is performed between the geographic coordinate data of the regional wind-solar combined power generation curve and the inter-regional transmission capacity in the wind-solar constrained optimization model to generate a transmission capacity distribution table with geographic coordinates.

[0024] S34: Solve the wind-solar constrained optimization model and the transmission capacity distribution table, call the multi-objective evolutionary algorithm to optimize four types of decision variables in parallel: photovoltaic installed capacity, wind power installed capacity, energy storage configuration parameters and transmission capacity, and generate a wind-solar-storage-transmission capacity configuration scheme. The wind-solar-storage-transmission capacity configuration scheme is used to output the power station construction scale and inter-regional interconnection planning parameters.

[0025] In one embodiment, S4 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0026] S41: Calculate the distance between geographical coordinates in the wind, solar, storage and transmission capacity configuration scheme, calculate the geographical distance between pairs of nodes in the region based on the spherical distance formula, and generate a geographical distance matrix between regions.

[0027] S42: Perform loss modeling on the geographical distance matrix between regions, query the power loss rate per unit distance for different transmission media based on the preset cable type parameter library, calculate the total cross-regional transmission loss rate in combination with geographical distance, and generate the power transmission loss matrix.

[0028] S43: Perform path search on the transmission loss matrix and the transmission capacity data in the wind-solar-storage transmission capacity configuration scheme, design a dynamic programming algorithm for cross-regional power transmission paths based on the principle of minimizing losses, and generate dynamic scheduling rules with geographical constraints. The dynamic scheduling rules are used to guide the cross-regional power dispatch of the power grid control system.

[0029] In one embodiment, the expression for the dynamic programming algorithm of the cross-regional power transmission path in the wind-solar-storage-transmission configuration optimization method provided by the present invention is as follows:

[0030]

[0031]

[0032]

[0033]

[0034] in, It is the set of paths from the source region to the target region. Let x be the loss rate per unit distance from region x to y. Let x be the geographical distance from region x to y. Let x represent the available transmission capacity from region x to y. R represents the power requirement to be transmitted, and R is the Earth's radius. , Let x and y be the latitudes of the region, respectively. Let x be the latitude difference between region x and region y. Let x be the difference in longitude between region x and region y. This represents the total path loss.

[0035] In one embodiment, S5 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0036] S51: Configure the simulation environment for regional wind-solar combined power generation curves and wind-solar-storage-transmission capacity configuration schemes, set interconnection rule parameters for four scenarios: regional independence, adjacent interconnection, continental interconnection, and global interconnection, and generate a multi-scenario simulation environment configuration set;

[0037] S52: Perform hourly power balance simulation on the multi-scenario simulation environment configuration set, call dynamic scheduling rules according to preset time steps to update energy storage charge status and cross-regional power flow data, and generate a simulation running dataset with timestamps;

[0038] S53: Calculate performance indicators for the time-stamped simulation dataset, extract wind and solar power absorption data to calculate wind and solar power penetration rate, calculate energy storage utilization rate based on energy storage charge and discharge curve, calculate transmission channel load rate based on power flow peak, and integrate the three rate indicators with geographic coordinate data to generate an executable engineering solution with geographic coordinate annotation.

[0039] Secondly, this invention provides a wind-solar-storage-transmission configuration optimization system under cross-regional interconnection, which is configured with the following modules:

[0040] The candidate site resource construction module is used to divide candidate sites for wind and solar power generation based on the acquired global latitude and longitude grid, and bind the maximum installable capacity and hourly power generation curve to each candidate site to generate a candidate site resource dataset.

[0041] The regional power generation curve aggregation module is used to aggregate candidate point resource datasets in a regional manner, map candidate points to regional power grids according to preset geographical zoning rules, and overlay the power generation curves of candidate points belonging to the same regional power grid to generate regional wind and solar combined power generation curves.

[0042] The wind-solar-storage-transmission configuration solution module is used to establish a three-objective function based on the regional wind-solar combined power generation curve, which includes minimizing the total investment cost, maximizing the wind-solar penetration rate, and minimizing the wind curtailment rate. It sets capacity constraints and topology constraints, solves the decision variables, and generates a wind-solar-storage-transmission capacity configuration scheme.

[0043] The power transmission dispatching rule generation module is used to calculate the geographical distance between regions based on the power transmission capacity data and geographical coordinate data of the wind, solar and energy storage transmission capacity configuration scheme, construct the power transmission loss matrix in combination with the power transmission line type, and design the optimal path search algorithm for the minimum loss path based on the power transmission capacity constraint to generate dynamic dispatching rules with geographical constraints.

[0044] The engineering scheme simulation generation module is used to configure simulation environments for different interconnection scenarios based on regional wind and solar power generation curves, wind, solar, energy storage and transmission capacity configuration schemes and dynamic scheduling rules. It performs hourly power balance simulations for a preset duration, updates energy storage status and records power flow, and generates executable engineering schemes containing system performance indicators and engineering application indicators. The executable engineering schemes are used to guide the site selection of wind and solar power plants, the configuration of energy storage systems and the construction of inter-regional transmission lines.

[0045] Thirdly, this application provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the above-mentioned wind-solar-storage-transmission configuration optimization methods under cross-regional interconnection.

[0046] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-mentioned methods for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection.

[0047] In summary, the wind-solar-storage-transmission configuration optimization method provided in this application, based on global latitude and longitude grid candidate site division and resource parameter binding, can achieve refined modeling of the spatiotemporal characteristics of wind and solar resources, thus solving the resource assessment bias caused by insufficient geographical accuracy in traditional methods. Through geographical partition aggregation and regional curve generation, it can achieve accurate characterization of multi-regional collaborative output characteristics, thus overcoming the limitations of single grid boundary optimization. Combining a transmission loss geographical model and a three-objective optimization algorithm, it can achieve the global optimal configuration of photovoltaic / wind power / energy storage / transmission capacity, thus balancing the contradictions between economy, environmental protection and system efficiency. The innovatively designed minimum loss path dynamic programming algorithm can achieve precise control of cross-regional transmission losses, thus solving the efficiency distortion problem caused by traditional linear transmission models. Finally, through multi-scenario dynamic simulation and the fusion of geographical indicators, it can generate optimization schemes that directly guide engineering implementation, thus achieving seamless integration between planning results and actual operational needs. This method connects the entire process of "resource assessment - capacity optimization - dynamic scheduling - scenario verification", which can significantly improve the absorption efficiency of high-proportion renewable energy in cross-regional interconnection scenarios, the operational reliability of engineering solutions and investment benefits, and provide quantifiable decision support for global energy interconnection.

[0048] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0049] Figure 1 A flowchart illustrating a method for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection, as provided in an embodiment of this application;

[0050] Figure 2A flowchart illustrating the generation of geographically constrained dynamic scheduling rules provided in an embodiment of this application;

[0051] Figure 3 This is a schematic diagram of a wind-solar-storage-transmission configuration optimization system under cross-regional interconnection, provided as another embodiment of this application. Detailed Implementation

[0052] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] In one embodiment, such as Figure 1 As shown, a method for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0055] S1: Based on the acquired global latitude and longitude grid, candidate sites for wind and solar power generation are divided, and the maximum installable capacity and hourly power generation curve are bound to each candidate site to generate a candidate site resource dataset.

[0056] Specifically, the system acquires global latitude and longitude grid data, based on a preset geodetic coordinate system. The system then uses digital elevation models, land use type data, and ecological protection boundary data from a geographic information database to progressively filter the acquired latitude and longitude grid. During this filtering process, the system first eliminates grids with terrain features unsuitable for wind and solar power development based on topographic data. Next, it removes grids that do not meet development criteria based on land use type data. Finally, it excludes grids corresponding to ecologically sensitive areas by referring to ecological protection boundary data. Through multiple rounds of data comparison, the system determines the candidate site range for wind and solar power generation. After completing the candidate site division, the system retrieves resource assessment data corresponding to each candidate site. This data, output by the wind and solar resource assessment model, includes the maximum installable capacity parameters and hourly power generation curves for each candidate site.

[0057] Furthermore, the system associates and binds the geographical coordinates of candidate sites with their maximum installable capacity and hourly power generation curves. During the binding process, the system uniformly converts the coordinate data format to ensure that the coordinates of all candidate sites conform to the same data standard. Simultaneously, it calibrates the timestamps of the hourly power generation curves to ensure consistency in the time dimension across all candidate sites. Subsequently, the system performs integrity verification on the bound data, checking for candidate sites with missing parameters or abnormal curves. Problematic data is marked and supplemented or corrected using backup data sources. Finally, all verified candidate site data is integrated according to a preset format to generate a candidate point resource dataset. This dataset stores the geographical coordinates, maximum installable capacity, and hourly power generation curves of the candidate sites.

[0058] S2: Perform regional aggregation on the candidate point resource dataset, map the candidate points to the regional power grid according to the preset geographical zoning rules, and superimpose the power generation curves of candidate points belonging to the same regional power grid to generate regional wind and solar combined power generation curves.

[0059] Specifically, after reading the candidate point resource dataset, the system initiates a regional aggregation process. First, it invokes a preset geographical partitioning rule, which includes administrative boundary data, regional power grid topology data, and electricity market relevance data. Based on the administrative boundary data, the system performs an initial classification of candidate sites. Then, combining this with the regional power grid topology data, it determines the connection relationship between the candidate site's location and the regional power grid, adjusting the initial classification results. Finally, referring to the electricity market relevance data, it groups candidate sites within regions with similar power grid structures and frequent power exchange into the same aggregation unit, ensuring that candidate sites are accurately mapped to their corresponding regional power grids. If the geographical coordinates of a candidate site cross multiple regional power grid boundaries, the system invokes a preset priority rule. This rule uses the electrical distance between the candidate site and each regional power grid as the basis for judgment, assigning the candidate site to the regional power grid with the closest electrical distance to avoid ambiguity in candidate site classification.

[0060] After mapping candidate sites, the system extracts hourly power generation curves from all candidate sites within the same regional power grid. It first performs secondary alignment of the timestamps of each candidate site's power generation curve to ensure that the time units and starting points of all curves are completely consistent. Then, it superimposes the power generation of each candidate site at the same timestamp to obtain the total wind and solar power generation of the regional power grid at each time point. During the calculation process, the system verifies the superposition results in real time. By comparing the superimposed power value with the theoretical maximum output value corresponding to the maximum installable capacity of each candidate site, it determines whether there are any calculation anomalies. If an anomaly is found, the system backtracks to the power generation curve of a single candidate site, checks for data errors, and recalculates. After successful verification, the system arranges the total wind and solar power generation at each time point in chronological order to generate a regional-level combined wind and solar power generation curve. This curve reflects the overall output characteristics of wind and solar resources within the regional power grid.

[0061] S3: Based on the regional wind and solar combined power generation curve, establish a three-objective function that includes minimizing total investment cost, maximizing wind and solar penetration, and minimizing wind and solar curtailment rate. Set capacity constraints and topology constraints and solve the decision variables to generate a wind, solar, storage and transmission capacity configuration scheme.

[0062] Specifically, the system uses regional wind and solar combined power generation curves as core input data, while also invoking power system economic data, load forecast data, and technical specification data to construct three objective functions. In constructing the objective function to minimize total investment cost, the system correlates wind and solar power plant construction cost data, energy storage system procurement cost data, and transmission line construction cost data, transforming cost parameters at each stage into function variables to form a cost calculation expression. In constructing the objective function to maximize wind and solar penetration rate, the system combines regional power grid load forecast data, using the ratio of the region's total annual wind and solar power consumption to the region's total annual electricity demand as the core indicator to establish penetration rate calculation logic. In constructing the objective function to minimize wind and solar curtailment rate, the system uses the ratio of the region's total annual wind and solar curtailment to the region's total annual wind and solar power output as the calculation basis to form a curtailment rate calculation expression.

[0063] Preferably, the system sets capacity constraints and topology constraints. Capacity constraints refer to the existing capacity limits of the regional power grid, the grid connection technical specifications for wind and solar power plants, and the charging and discharging technical parameters of the energy storage system, clarifying the capacity range for each component of wind, solar, and energy storage. Topology constraints are based on the existing topology of the regional power grid, transmission line connection rules, and power grid safety operation standards, limiting the connection methods and transmission capacity of inter-regional transmission lines. The system can use a preset optimization algorithm to solve the three objective functions. In the initial stage of the solution, the system sets an initial value range for the decision variables, which is determined based on historical engineering data and technical specifications. During the iteration process, the system adjusts the decision variables according to the calculation results of each objective function, while simultaneously verifying whether the decision variables meet the capacity and topology constraints. If the constraints are not met, the system corrects the decision variables. When the iteration results meet the preset convergence conditions and all decision variables meet the constraint requirements, the solution process terminates. The system transforms the decision variables obtained from the solution into specific parameters, including the installed capacity of wind and solar power in each region, the power and capacity parameters of energy storage systems, and the transmission capacity parameters of inter-regional transmission lines. These parameters are integrated to generate a wind, solar, energy storage and transmission capacity configuration scheme. After the scheme is generated, the system performs consistency verification on the parameters in the scheme to ensure that the parameters in each link match and there are no logical conflicts.

[0064] S4: Calculate the geographical distance between regions based on the transmission capacity data and geographical coordinate data of the wind, solar and energy storage transmission capacity configuration scheme, construct the transmission loss matrix in combination with the transmission line type, and design the optimal path search algorithm for the minimum loss path based on the transmission capacity constraint to generate dynamic scheduling rules with geographical constraints.

[0065] Specifically, the system extracts transmission capacity data from the wind-solar-storage transmission capacity configuration scheme, and simultaneously retrieves the geographical coordinate data of each regional power grid. This geographical coordinate data represents the coordinates of the load centers of the regional power grids. The system calculates the geographical distance between different regional power grids using a coordinate calculation algorithm. During the calculation process, the system performs projection transformation on the coordinate data to ensure the accuracy of the distance calculation results. Subsequently, the system calls the transmission line type database, which stores loss characteristic data for different types of transmission lines. Based on the geographical environment data and transmission capacity requirements between regions, the system determines the applicable transmission line type for each region. If the regions are connected on land and the distance falls within a preset range, the onshore transmission line type is selected; if the regions are connected across waterways, the submarine transmission line type is selected.

[0066] Furthermore, based on the selected transmission line type, the system invokes the corresponding loss calculation logic, substituting the geographical distance and transmission capacity data between regions into the loss calculation formula to obtain the transmission loss rate per unit power between each region. Using the regional power grid as the rows and columns of a matrix, the loss rate is filled into the corresponding positions to construct a transmission loss matrix. Regions without direct transmission conditions are marked with preset invalid values, and the region's own position is marked as zero. After completing the matrix construction, the system designs an optimal path search algorithm based on transmission capacity constraints. The algorithm treats the regional power grid as nodes and the transmission lines as weighted edges with capacity constraints. The edge weight is the product of the inter-regional transmission loss rate and the geographical distance, and the edge capacity constraint is the transmission capacity data. The system initializes the algorithm parameters, sets the initial states of the starting and ending regions, and during iteration, calculates the path loss and remaining capacity between each node. The path with the minimum loss and satisfying the capacity constraint is selected as the current optimal path. If the current path capacity is insufficient, the system splits the transmission power and re-searches for a suboptimal path for the remaining power until all power finds a feasible path or no feasible path is determined.

[0067] Based on the path search results and combined with the regional power supply and demand data processing logic, the system formulates dynamic dispatch rules. The rules include regional supply and demand judgment logic, in which the system calculates the supply and demand difference by comparing the regional wind and solar power output, load demand and base load power output to determine whether the region is a supply region or a demand region; it includes power transmission path selection logic, which prioritizes the optimal path to transmit power between supply and demand regions; and it includes energy storage coordinated adjustment logic, in which the system adjusts the charging and discharging state of energy storage to supplement or absorb power when the transmission capacity is insufficient or surplus, ultimately forming dynamic dispatch rules with geographical constraints.

[0068] S5: Based on the regional wind-solar combined power generation curve, wind-solar-storage-transmission capacity configuration scheme and dynamic scheduling rules, a simulation environment is configured for different interconnection scenarios. Hourly power balance simulations of a preset duration are performed to update the energy storage status and record the power flow. An executable engineering scheme containing system performance indicators and engineering application indicators is generated. The executable engineering scheme is used to guide the site selection of wind and solar power plants, the configuration of energy storage systems and the construction of inter-regional transmission lines.

[0069] Specifically, the system initiates a multi-scenario simulation environment configuration process based on regional-level wind-solar combined power generation curves, wind-solar-storage-transmission capacity configuration schemes, and dynamic scheduling rules. The system calls a preset scenario parameter library, which contains the interconnection range definitions, transmission condition constraints, and simulation boundary conditions for different interconnection scenarios. The system configures simulation environments for scenarios such as regional independence, adjacent interconnection, continental interconnection, and global interconnection according to the scenario parameters. Under each scenario, the system loads the corresponding scenario's transmission capacity data, loss matrix data, and constraint condition data to ensure that the simulation environment is consistent with the scenario settings.

[0070] After the simulation starts, the system performs hourly power balance simulation with a preset time unit. At the beginning of each time period, the system reads the regional wind and solar combined generation curve data, regional load data, and baseload power output data for that time period, and calculates the power supply and demand difference for each region. Then, it calls the dynamic scheduling rules to determine the power transmission direction and transmission volume for each region based on the supply and demand difference. Combining the transmission loss matrix and the optimal path search algorithm, it allocates inter-regional transmission power. At the same time, the system updates the state of charge of the energy storage system and adjusts the energy storage charging and discharging power according to the current power supply and demand situation to ensure that the state of charge is within the preset safety range. During the simulation, the system records the power flow data for each time period in real time, including inter-regional transmission power, transmission loss power, energy storage charging and discharging power, and wind and solar curtailment power.

[0071] After the simulation, the system performs statistical analysis on the recorded data for all time periods, extracting system performance indicators and engineering application indicators. System performance indicators include total investment cost, wind and solar penetration rate, wind and solar curtailment rate, and average transmission loss rate. The system calculates the total investment cost by summarizing cost data from each stage, calculates the wind and solar penetration rate by statistically analyzing the annual wind and solar consumption and total electricity demand, calculates the wind and solar curtailment rate by statistically analyzing the total amount of wind and solar curtailment and total wind and solar power output, and calculates the average transmission loss rate by summarizing the total transmission loss and total transmission power. Engineering application indicators include the site selection basis for wind and solar power stations, energy storage system configuration parameters, and transmission line construction requirements. The system determines the site selection basis based on the resource data and grid connection conditions of candidate sites, determines the energy storage configuration parameters based on regional load fluctuations and power regulation needs, and determines the transmission line construction requirements based on path search results and geographical environment data. The system integrates system performance indicators with engineering application indicators to form an executable engineering plan. The plan specifies the site selection scope of wind and solar power stations, the power and capacity parameters of energy storage systems, and the route and technical specifications of inter-regional transmission lines, which are used to guide the site selection of wind and solar power stations, the configuration of energy storage systems, and the construction of inter-regional transmission lines.

[0072] In summary, the wind-solar-storage-transmission configuration optimization method provided in this application, based on global latitude and longitude grid candidate site division and resource parameter binding, can achieve refined modeling of the spatiotemporal characteristics of wind and solar resources, thus solving the resource assessment bias caused by insufficient geographical accuracy in traditional methods. Through geographical partition aggregation and regional curve generation, it can achieve accurate characterization of multi-regional collaborative output characteristics, thus overcoming the limitations of single grid boundary optimization. Combining a transmission loss geographical model and a three-objective optimization algorithm, it can achieve the global optimal configuration of photovoltaic / wind power / energy storage / transmission capacity, thus balancing the contradictions between economy, environmental protection and system efficiency. The innovatively designed minimum loss path dynamic programming algorithm can achieve precise control of cross-regional transmission losses, thus solving the efficiency distortion problem caused by traditional linear transmission models. Finally, through multi-scenario dynamic simulation and the fusion of geographical indicators, it can generate optimization schemes that directly guide engineering implementation, thus achieving seamless integration between planning results and actual operational needs. This method connects the entire process of "resource assessment - capacity optimization - dynamic scheduling - scenario verification", which can significantly improve the absorption efficiency of high-proportion renewable energy in cross-regional interconnection scenarios, the operational reliability of engineering solutions and investment benefits, and provide quantifiable decision support for global energy interconnection.

[0073] In one embodiment, S1 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0074] S11: Based on the acquired global latitude and longitude grid, the global geographical area is divided into latitude and longitude grids. The surface is divided into equal-area grid units using a preset resolution to generate a set of candidate point geographic coordinates.

[0075] Specifically, the system first connects to the global geographic data service interface to obtain global latitude and longitude baseline data. This data is based on a preset geodetic coordinate system and covers the entire latitude and longitude range of the Earth. The system retrieves preset resolution parameters from its built-in parameter library. The determination of these parameters needs to consider the spatial accuracy required for wind and solar resource assessment and the computational efficiency of subsequent data processing. After confirming the grid division rules corresponding to this resolution through parameter matching logic, the system initiates the global surface grid division process. During the division process, the system first imports the latitude and longitude data into the geographic projection processing module and uses a geographic projection correction algorithm to perform planar transformation on the Earth's curved surface data. This algorithm needs to perform projection calculations regionally to avoid grid cell area deviations caused by curvature differences in different latitude regions, ensuring that the area of ​​each grid cell is consistent in the planar coordinate system after transformation. After the projection transformation is completed, the system starts the grid cell division program, dividing the planarized global geographic area into equal-area grid cells according to the preset resolution.

[0076] After grid division, the system performs coordinate extraction, selecting the geometric center of each grid cell as its representative coordinates according to preset rules. Even if the grid cell boundary crosses a polygonal geographical area, the geometric center coordinates are still used as the unique identifier. The system then standardizes the extracted coordinates, converting all coordinates to decimal latitude and longitude format in a preset geodetic coordinate system. Simultaneously, it performs coordinate validity checks, filtering out and deleting invalid coordinates that exceed the actual geographical range of the Earth. After validity checks, the system initiates a uniqueness check process, calculating and comparing coordinate hash values ​​to identify duplicate coordinates caused by projection correction or grid division algorithm errors. Duplicate coordinates are merged, retaining one as the valid coordinate. Finally, the system sorts all valid coordinates by latitude and longitude values, assigns a unique identifier ID to each coordinate, and associates and stores the unique identifier ID with the corresponding latitude and longitude coordinates to generate a candidate point geographic coordinate set.

[0077] S12: Apply capacity constraints to the candidate point geographic coordinate set, determine the maximum installable capacity of each grid cell based on the land type database and engineering feasibility parameters, and generate a candidate site set with capacity constraints.

[0078] Specifically, the system reads the geographic coordinate set of candidate points from the database and establishes a connection with the land type database. This database stores global land type classification data, including land use type and land area parameters corresponding to each geographic coordinate. Further, the system performs layer-by-layer matching between the candidate point geographic coordinates and the coordinate data in the land type database. First, it narrows the coordinate search range through approximate regional matching, and then determines the land type corresponding to each grid cell through precise coordinate comparison. Simultaneously, it calculates the actual usable area of ​​that grid cell, deducting the area of ​​the land type that is unsuitable for wind and solar development. After the actual usable area is calculated, the system disconnects from the land type database and connects instead to the engineering feasibility parameter database, which stores various engineering constraint parameters required for wind and solar development.

[0079] Furthermore, the system correlates the candidate points' geographical coordinates with data from the engineering feasibility parameter database, extracting topographic slope data, altitude data, distance data from existing power grid connection points, and ecological protection attribute data for each grid cell. Then, a feasibility assessment program is initiated. This program sequentially verifies whether each data point meets the requirements for wind and solar development according to preset logic. If any data point exceeds the engineering limitations, the system marks the grid cell as "undevelopable" and excludes it from the candidate site pool. If all data points meet the engineering limitations, the system marks the grid cell as "developable" and includes it in subsequent calculations.

[0080] Preferably, the system performs maximum installable capacity calculations on all "developable" grid cells. It calls the built-in capacity calculation model, inputting the actual usable area of ​​the grid cell and a preset installed capacity density parameter per unit area. This parameter needs to differentiate between wind farms and photovoltaic power plants according to the engineering standards for wind and solar power plant construction. The system then calculates the maximum installable capacity for each "developable" grid cell. After the capacity calculation is complete, the system initiates a rationality check, comparing the capacity with the conventional installed capacity of similar land units, identifying abnormal capacity values, and performing backtracking corrections for abnormal values. The actual usable area is recalculated, or the installed capacity density parameter per unit area is adjusted until the capacity value conforms to the conventional range. Finally, the system integrates the geographic coordinates, unique identifier ID, and corresponding maximum installable capacity of the "developable" grid cells to generate a candidate site set with capacity constraints.

[0081] S13: Bind the power generation curves to the candidate site set, integrate the pre-stored historical meteorological database and equipment performance parameters to generate hourly power generation curves, and generate a candidate point resource dataset.

[0082] Specifically, the system reads a set of candidate stations with capacity constraints from a dedicated data catalog and connects to a pre-stored historical meteorological database. This database stores hourly meteorological data for geographic coordinates worldwide, including parameters such as wind speed, solar irradiance, ambient temperature, and wind direction, with the data spanning multiple complete years. The system initiates a coordinate matching program to accurately associate the latitude and longitude coordinates of the candidate stations with the coordinates in the historical meteorological database. During the association process, coordinate offset correction is performed to ensure the consistency between the candidate station coordinates and the meteorological data coordinates. Subsequently, the system extracts the corresponding hourly meteorological data according to the development type of the candidate station (wind farm or photovoltaic power station).

[0083] During data extraction, the system continuously checks the timestamp continuity of meteorological data. If missing periods are found, the system uses a meteorological data interpolation algorithm, selecting an appropriate interpolation method based on the length of the missing period, and supplementing the missing periods with concurrent meteorological data from adjacent coordinates to ensure the extracted meteorological data is complete and without gaps. After the meteorological data extraction is complete, the system disconnects from the historical meteorological database and instead calls the equipment performance parameter library. This database stores the technical performance parameters of different types of wind turbines and photovoltaic modules, including the power curves of wind turbines and the efficiency curves of photovoltaic modules. The system selects the corresponding equipment performance parameters from the equipment performance parameter library according to the development type of the candidate sites, as the basis for calculating the power generation curve. The system then starts the output calculation model, substituting the extracted hourly meteorological data into the model: for wind farm candidate sites, the model calculates the wind turbine output power for each hour based on wind speed data and wind turbine power curves; for photovoltaic power station candidate sites, the model calculates the photovoltaic module output power for each hour based on solar irradiance, ambient temperature data, and photovoltaic module efficiency curves, thus obtaining the hourly power generation curve for each candidate site.

[0084] In one embodiment, S2 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0085] S21: Geographically partition the candidate point resource dataset, define the topology of the regional power grid based on the pre-stored continental boundary data and power grid jurisdiction data, and generate a regional power grid partitioning scheme.

[0086] Specifically, the system reads the candidate point resource dataset and extracts the geographic coordinates of the candidate sites as the positioning benchmark for geographic partitioning. The system establishes a connection with a pre-stored geographic data warehouse, retrieving continental boundary data and power grid jurisdiction data from the warehouse. The continental boundary data contains vector boundary coordinate sequences for all major continents globally, while the power grid jurisdiction data includes administrative jurisdiction boundaries of different levels of power grids, power supply area boundaries, and existing power grid facility distribution data. The system imports both types of data into the same data processing environment and performs coordinate benchmark unification operations, ensuring that both types of data and the candidate point resource dataset use the same geodetic coordinate system, eliminating potential partitioning biases caused by coordinate benchmark differences.

[0087] Furthermore, the system uses continental boundary data as its foundational framework, dividing the globe into continental-level geographical blocks. Within each continental block, it further subdivides into regional-level power grid units based on the hierarchical relationship of power grid jurisdiction data. During this subdivision process, the system performs overlap and gap detection on continental boundaries and power grid jurisdiction boundaries. If overlapping areas exist, the final boundary is determined according to the rule of prioritizing power grid jurisdiction data; if gap areas exist, they are assigned to adjacent power grid jurisdictions. The system extracts the coordinates of existing substations and transmission line nodes within each regional power grid unit, determines the connection relationships between these nodes, clarifies the input and output port locations of the regional power grid, and forms a topology document containing regional power grid boundary coordinates, internal node distribution, and node connection relationships. The system integrates the topology document with the regional power grid boundary data, supplements it with a unique regional power grid identifier ID and geographical range description information, and generates a regional power grid division scheme. The scheme clearly defines the identification information, spatial boundary coordinates, and topological connection relationships of each regional power grid.

[0088] S22: Perform site affiliation mapping on the regional power grid division scheme, assign each candidate site to the corresponding regional power grid based on spatial location relationships, and generate a site-region mapping table.

[0089] Specifically, before performing site affiliation mapping, the system calls upon the regional power grid division scheme and candidate site resource dataset. The regional power grid division scheme includes the spatial boundaries and identification information of each regional power grid, while the candidate site resource dataset includes the latitude and longitude coordinates and identification information of each candidate site. The system imports the latitude and longitude coordinates of the candidate sites and the spatial boundary data in the regional power grid division scheme into a spatial location analysis tool for spatial location matching calculation. Preferably, when handling cases where candidate sites are located near regional power grid boundaries, the system can adopt a distance-based determination principle. When the distance between the latitude and longitude coordinates of a candidate site and the boundaries of two or more regional power grids is within a set fuzzy range, the candidate site is assigned according to the power grid load carrying capacity rules. The power grid load carrying capacity data comes from the historical operation statistics report of the regional power grid.

[0090] The system performs a matching operation on each candidate site one by one to ensure that each candidate site is uniquely assigned to a regional power grid, avoiding duplicate assignments or sites without a designated affiliation. After matching is completed, the system integrates the candidate site identification information with the corresponding regional power grid identification information and generates a site-region mapping table according to a standardized data format. The mapping table contains two columns of core information: the unique identifier of the candidate site and the unique identifier of its regional power grid, which can be directly used for subsequent aggregation processing of power generation curves.

[0091] S23: Perform curve aggregation processing on the site-region mapping table, perform time series overlay calculations on the wind power and photovoltaic power generation curves in the same region, generate regional-level wind power combined generation curves and regional-level photovoltaic combined generation curves, and merge them into a regional-level wind and solar combined generation curve.

[0092] Specifically, the system reads the site-region mapping table from the associated database and the hourly wind power generation curve and hourly photovoltaic power generation curve of each candidate site in the candidate point resource dataset. Based on the unique regional grid identifier ID in the site-region mapping table, the wind power generation curves of candidate sites belonging to the same regional grid are grouped into one group, and the photovoltaic power generation curves are grouped into another group, forming a curve grouping set based on the regional grid. For example, the system extracts the timestamp information of each group of curves, converts the timestamps of all curves into a preset standard time format, checks the number of timestamps and the time interval of all curves in the same group, and if there are curves with missing timestamps, the missing time intervals are filled in based on the output data of adjacent time periods of the curve. The system performs time-by-time output superposition on the wind power curve groups of the same regional grid. For each timestamp, the system sums the output values ​​of all wind power curves in the group corresponding to that timestamp to obtain the total wind power output value of the regional grid at that timestamp. The total wind power output values ​​are arranged in the order of timestamps to generate a regional-level wind power combined generation curve.

[0093] Using the same method, the photovoltaic curves of the same regional power grid are superimposed time-by-time to generate a regional photovoltaic combined power generation curve. The regional wind power combined power generation curve and the regional photovoltaic combined power generation curve of the same regional power grid are matched according to the timestamp. The total wind power output value and the total photovoltaic output value of each timestamp are summed to obtain the total wind and solar power output value of the regional power grid at that timestamp. The total wind and solar power output values ​​are arranged in the order of timestamp to generate a regional wind and solar combined power generation curve.

[0094] In one embodiment, S3 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0095] S31: The objective function is constructed for the regional wind and solar combined power generation curve. Based on the preset equipment investment cost parameters, a total investment cost calculation model is defined. A quantitative formula for wind and solar penetration rate is established in combination with power demand data. A wind and solar curtailment rate calculation model is constructed based on the difference between the power generation curve and the load curve. By weighted summation and integration of the total investment cost calculation model, the wind and solar penetration rate quantitative formula and the wind and solar curtailment rate calculation model, a three-dimensional optimization objective function is constructed to generate a three-dimensional optimization objective that includes minimizing investment cost, maximizing wind and solar penetration rate and minimizing wind and solar curtailment rate.

[0096] Specifically, the system reads the regional wind-solar combined power generation curves and extracts the hourly wind power output (P_{w,k,t}) and hourly photovoltaic power output for each region from the curves. (k is the region identifier, t is the timestamp), and simultaneously retrieve preset data from the equipment cost parameter database, including the unit construction cost of wind power. Unit construction cost of photovoltaic power Energy storage system power unit cost Unit cost of energy storage system capacity Construction cost per unit length of transmission lines In constructing the total investment cost calculation model, the system associates the costs of various equipment with their corresponding capacity variables, considers cost allocation throughout the entire project lifecycle, and constructs the model formula:

[0097]

[0098] in, The total installed wind power capacity is the sum of the installed wind power capacity in all regions. , The total installed photovoltaic capacity is the sum of the installed photovoltaic capacity in all regions. , The total power of the energy storage system is the sum of the energy storage power of all regions. , The total capacity of the energy storage system is the sum of the energy storage capacities of all regions. , The total length of the transmission lines is the sum of the line lengths between all regions. , The average transmission capacity of the transmission lines is the mean of the line capacity across all regions. , This represents the total number of lines. The system uses this formula to integrate the dispersed equipment costs and capacity variables, forming a unified cost calculation logic.

[0099] Furthermore, the system connects to the electricity demand database to extract hourly electricity demand for each region. Construct a quantitative formula for wind and solar penetration rate:

[0100]

[0101]

[0102] in, Let be the total power of wind and solar power curtailed in region k at time t. For wind curtailment power, The numerator is the total wind and solar power actually consumed in all regions and all time periods, and the denominator is the total electricity demand in all regions and all time periods. The system uses this formula to quantify the contribution of clean energy to the load supply.

[0103] Preferably, in the construction of the wind and solar curtailment rate calculation model, the system constructs the following model expression with the ratio of total curtailed power to total output as the core:

[0104]

[0105] In this model expression, the numerator is the total amount of wind and solar power curtailment across all regions and time periods, and the denominator is the total wind and solar power output across all regions and time periods. The system uses this formula to evaluate the utilization efficiency of wind and solar resources. After the three models are built individually, the system retrieves the cost weights from the engineering priority parameter library. Penetration weight Weight of curtailment rate By integrating the three-dimensional objectives through linear weighting, the dimensions of the outputs of each model are unified before integration, ultimately forming a three-dimensional optimization objective, expressed as:

[0106]

[0107] in, To ensure consistency among the three objectives, the total investment cost is standardized.

[0108] S32: Perform constraint injection processing on the three-dimensional optimization objective, set the upper limit constraints of photovoltaic and wind power installation based on the maximum installable capacity of candidate points, set inter-regional transmission capacity constraints based on the power grid topology, integrate the upper limit constraints of installation, transmission capacity constraints and the three-dimensional optimization objective, and generate a wind and solar constrained optimization model with multiple constraints.

[0109] Specifically, the system extracts the maximum installable capacity of each candidate point from the candidate point resource dataset. Let i be the candidate point identifier. Statistics are compiled by region k to obtain the total exploitable wind power capacity of region k. Total exploitable photovoltaic capacity :

[0110]

[0111]

[0112] in, Let k be the set of candidate wind power sites. Given the set of photovoltaic candidate sites within region k, we construct the upper limit constraint formula for installed capacity based on this set:

[0113]

[0114] in, Let k be the decision variable for wind power installed capacity in region k. Let k be the decision variable for the photovoltaic installed capacity of a region. The constraints are clear: the installed capacity of wind and solar power in each region must not exceed the total local developable capacity and cannot be negative.

[0115] The system simultaneously reads power grid topology data and extracts technical parameters of inter-regional transmission lines, including line current carrying capacity. Rated voltage Power factor Wherein, the line current carrying capacity represents the maximum allowable current of line mn. The system derives the maximum transmission power of the line based on the following formula and constructs the transmission capacity constraint formula:

[0116]

[0117]

[0118]

[0119] in, Let mn be the actual transmission power decision variable for line mn at time t. Let mn be the maximum allowable transmission power of the line. The constraint ensures that the transmission power of the line does not exceed the limit and is not negative.

[0120] Furthermore, the system maps the aforementioned installed capacity limit constraints, transmission capacity constraints, and three-dimensional optimization objectives to establish decision variables. The system establishes the correspondence between the objective function and the constraints. Subsequently, it performs constraint logic verification, checking for constraint conflicts by substituting typical decision variable values. For conflicting items, parameters are adjusted according to grid safety priority. After verification, the system integrates the objective function with all constraints to generate a wind and solar constrained optimization model with multiple constraints. The model document includes the objective function expression, constraint list, decision variable definitions, and parameter value sources.

[0121] S33: Spatial correlation is performed between the geographic coordinate data of the regional wind-solar combined power generation curve and the inter-regional transmission capacity in the wind-solar constrained optimization model to generate a transmission capacity distribution table with geographic coordinates.

[0122] Specifically, the system reads the geographic coordinate data attached to the regional wind-solar combined power generation curve, including the geometric center coordinates of the power grid in each region. With boundary coordinate set ,in, The number of boundary vertices in region k is used, and inter-regional power transmission capacity data is extracted from the wind-solar constrained optimization model. , This represents the maximum transmission capacity of line mn.

[0123] To establish the correlation between geographical coordinates and transmission capacity, the system constructs a line coordinate mapping formula to determine the starting coordinates of line mn (center of region m). ) and endpoint coordinates (center of region n) And calculate the spatial length of the line as an auxiliary parameter:

[0124]

[0125] in, Let mn be the planar spatial length of line mn. These are the x and y coordinates of the center of region m, respectively. These are the x-coordinate and y-coordinate of the center of region n, respectively. This length is used for subsequent engineering construction planning and also serves as a key field in the power transmission capacity distribution table.

[0126] Furthermore, based on the above formula results, the system constructs a "region identifier-coordinates-capacity" association table. Each record in the table includes the starting region identifier m and the starting coordinates. End point area identifier n, End point coordinates Line length Maximum transmission capacity The system then performs a spatial rationality check, verifying the coordinates of the starting point of the route using an algorithm that determines if a point is within a polygon. Does it fall within the boundary of region m? End point coordinates Whether the coordinates fall within the boundary of region n. If the coordinates exceed the boundary, the system corrects the center coordinates to the nearest position within the region based on the boundary vertex coordinates. The correction formula is:

[0127]

[0128] in, This is the reverse compensation value for distances exceeding the limit. After verification and correction, the system sorts the data in ascending order by the starting area identifier m, adds the line technology type field, and generates a transmission capacity distribution table with geographical coordinates. The table is stored in a structured data format to ensure that the coordinate-capacity relationship can be directly accessed in subsequent solution processes.

[0129] S34: Solve the wind-solar constrained optimization model and the transmission capacity distribution table, call the multi-objective evolutionary algorithm to optimize four types of decision variables in parallel: photovoltaic installed capacity, wind power installed capacity, energy storage configuration parameters and transmission capacity, and generate a wind-solar-storage-transmission capacity configuration scheme. The wind-solar-storage-transmission capacity configuration scheme is used to output the power station construction scale and inter-regional interconnection planning parameters.

[0130] Specifically, the system loads a wind-solar constrained optimization model and a transmission capacity distribution table, identifying four types of decision variables to be optimized: regional photovoltaic installed capacity. Regional wind power installed capacity Regional-level energy storage configuration parameters Inter-regional power transmission capacity The system invokes a multi-objective evolutionary algorithm to construct the algorithm's fitness function. This function is based on a three-dimensional optimization objective and constraints, and the formula is as follows:

[0131]

[0132] in, This is a reference total investment cost based on industry benchmark engineering data. For reference, wind and solar penetration rate For reference purposes, wind and solar curtailment rates, , , These are the standardized indicators: cost, penetration rate, and curtailment rate. The weighting coefficients are consistent with those in step S31. The smaller the fitness function value, the better the solution.

[0133] Furthermore, the system sets the algorithm parameters: population size N, number of iterations G, and crossover probability. Probability of mutation The system iteratively optimizes four types of decision variables using a parallel computing framework. In each iteration, the system first calculates the fitness function values ​​of individuals in each population, and then verifies whether the individuals meet the constraints, for example... Individuals that do not meet the constraints are penalized to ensure that only those that meet the constraints are selected for the next generation.

[0134] Simultaneously, the system can access the transmission capacity distribution table and correct the transmission capacity variables during the iteration process. If the optimized capacity of a certain line mn exceeds its engineering feasibility range calculated based on coordinates, the system will adjust the capacity based on the line length. Adjustment After the system has iterated a preset number of times G, it selects the Pareto optimal solution set from the final population. By calculating the crowding distance between each solution, it selects the solution with the most uniform distribution and the smallest fitness function value as the optimal solution.

[0135] The system categorizes and organizes the decision variables in the optimal solution by region and equipment type. The regional wind and solar installed capacity section lists the regional wind power installed capacity for each region. Regional photovoltaic installed capacity The energy storage configuration section lists the regional-level energy storage configuration parameters for each area. and The power transmission planning section lists the mn values ​​for each line. With the corresponding The coordinates are used to generate a wind, solar, storage and transmission capacity configuration scheme. The scheme includes the calculation basis for each parameter, which is used to directly output the power station construction scale and inter-regional interconnection planning parameters.

[0136] In one embodiment, such as Figure 2 As shown, step S4 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0137] S41: Calculate the distance between geographical coordinates in the wind, solar, storage and transmission capacity configuration scheme, calculate the geographical distance between pairs of nodes in the region based on the spherical distance formula, and generate a geographical distance matrix between regions.

[0138] Specifically, the system first reads the wind-solar-storage-transmission capacity configuration scheme and extracts the geographical coordinate data of each regional power grid node in the scheme. This data includes the unique identifier x and latitude of each regional node. With longitude The system preprocesses the extracted coordinate data and checks for outliers, such as latitude exceeding the limit. Longitude exceeds The coordinates are used to correct abnormal coordinates by calling the regional power grid boundary data and adjusting them to the geometric center of the corresponding region, ensuring that the coordinates of all nodes conform to the actual geographical range.

[0139] After preprocessing, the system calculates the geographical distance between pairs of nodes in the region based on the spherical distance formula. This formula is suitable for large-scale distance calculations across regions and can avoid the errors of the planar distance formula in high-latitude regions. The formula is as follows:

[0140]

[0141] in, Let x be the geographical distance from region x to region y. The average radius of the Earth , Let x and y be the latitudes of the region, respectively. , Let x and y be the latitudes of the region, respectively. Let x be the latitude difference between region x and region y. Let x be the longitude difference between region x and region y. The system iterates through all region node combinations according to the "pairwise traversal" principle. Substitute into the formula one by one to calculate ,like ,but After the calculation is complete, the system uses the region node identifiers as the rows and columns of the matrix, and... Fill in the corresponding locations to construct a geographic distance matrix between regions.

[0142] S42: Perform loss modeling on the geographical distance matrix between regions, query the power loss rate per unit distance for different transmission media based on the preset cable type parameter library, calculate the total cross-regional transmission loss rate in combination with geographical distance, and generate the power transmission loss matrix.

[0143] Specifically, the system calls a preset cable type parameter library, which stores core parameters for three types of transmission media, including power loss rate per unit distance. The system is applicable to various geographical environments and distance adaptation thresholds. Transmission media include terrestrial high-voltage AC cables (HVAC), terrestrial high-voltage DC cables (HVDC), and submarine high-voltage DC cables (HVDC). The system first queries the geographic information database to determine the terrain type between regions x and y, classifying the area as land or sea; then it considers the geographical distance between region x and region y. Match the transmission medium according to the rules: the terrain is land and When the distance is less than the adaptation threshold, select land-based HVAC; the terrain is land and When the distance is not less than the matching threshold, select the terrestrial HVDC; when the terrain is ocean, select the seabed HVDC. After matching, the system extracts the corresponding transmission medium. , The physical meaning is the power loss ratio when a unit of power is transmitted per unit length of this type of cable.

[0144] Furthermore, the system calculates the total transmission loss rate across regions based on linear computational logic. Since the loss rate exhibits an approximately linear relationship with distance in short- to medium-distance transmission scenarios in engineering practice, the formula for the total loss rate is:

[0145]

[0146] If there is no feasible transmission path between regions x and y, such as in uninhabited areas with no construction access, then Set as When x and y are in the same region, Set it to 0. The system iterates through all region node combinations, substituting each into the formula for calculation. Then, using the region identifiers as the rows and columns of the matrix, Fill in the corresponding positions to construct the transmission loss matrix.

[0147] S43: Perform path search on the transmission loss matrix and the transmission capacity data in the wind-solar-storage transmission capacity configuration scheme, design a dynamic programming algorithm for cross-regional power transmission paths based on the principle of minimizing losses, and generate dynamic scheduling rules with geographical constraints. The dynamic scheduling rules are used to guide the cross-regional power dispatch of the power grid control system.

[0148] Specifically, the system loads the transmission loss matrix and the transmission capacity data from the wind-solar-storage transmission capacity configuration scheme. The transmission capacity data includes the available transmission capacity from region x to y. , The maximum allowable transmission power of the line is defined, along with the source region S, the target region D, and the required transmission power. The system designs a dynamic programming algorithm for cross-regional power transmission paths. The objective function of the algorithm is:

[0149]

[0150] in, Let S be the set of paths from source region S to target region D, where each path consists of consecutive pairs of region nodes (x, y). Loss rate per unit distance Let x be the geographical distance from region x to y. The objective function is to minimize the total path loss. The algorithm constraints are:

[0151]

[0152] in, Let x represent the available transmission capacity from region x to y. Given the required power transmission capacity, constraints ensure that the total available capacity of the path meets the power transmission demand. The algorithm also includes a formula for calculating the total path loss rate:

[0153]

[0154] in, Let be the total loss rate of path p, used to evaluate the overall loss level of the path. The system starts from the source region S and progressively traverses adjacent region nodes, calculating the loss and capacity of each path segment, filtering paths that meet the constraints, and selecting the path with the smallest objective function value as the optimal path; if the capacity of the optimal path is insufficient, the capacity of the second-best path is added until the total capacity meets the requirements. Based on the optimal path results, the system generates dynamic scheduling rules with geographical constraints. The rules include power demand matching, path priority, anomaly adjustment, and geographical constraint adaptation. Finally, the rules are stored as an executable logic instruction set to guide the cross-regional power dispatch of the power grid control system.

[0155] In one embodiment, S5 of the wind-solar-storage-transmission configuration optimization method under cross-regional interconnection provided by the present invention specifically includes the following steps:

[0156] S51: Configure the simulation environment for regional wind-solar combined power generation curves and wind-solar-storage-transmission capacity configuration schemes, set interconnection rule parameters for four scenarios: regional independence, adjacent interconnection, continental interconnection, and global interconnection, and generate a multi-scenario simulation environment configuration set.

[0157] Specifically, the system reads the regional wind-solar combined power generation curves, extracts the hourly wind power output sequence and hourly photovoltaic power output sequence for each region from the curves, and stores the two types of sequences associative by region identifier to form a basic dataset of regional wind and solar power output. Simultaneously, the system reads the wind-solar-storage-transmission capacity configuration scheme, extracts the energy storage power parameters, energy storage capacity parameters, and maximum capacity parameters of transmission lines between regions for each region in the scheme, and categorizes these parameters according to the dimensions of "region-equipment type" and "line start and end regions" to form a basic dataset of equipment parameters. Based on these two basic datasets, the system initiates the multi-scenario simulation environment configuration process.

[0158] Furthermore, the system defines an interconnectivity parameter to quantify the degree of openness of power transmission capacity between regions under different scenarios. The interconnectivity parameter is the ratio of the actual available power transmission capacity between regions to the maximum power transmission capacity. The system sets the value rules for the interconnectivity parameter for four preset scenarios: In the independent region scenario, the interconnectivity parameter between all regions is set to zero, meaning there is no power exchange between regions, and the power supply and demand of each region depends entirely on local wind and solar power output and energy storage regulation; In the adjacent interconnection scenario, the system first determines the list of adjacent regions for each region through geographic coordinate matching, and only the interconnectivity parameter between adjacent regions is set to one, while the interconnectivity parameter between non-adjacent regions is set to zero, allowing adjacent regions to call up all maximum power transmission capacity for power exchange; In the continental interconnection scenario, the system divides all regions into different continental groups based on pre-stored continental boundary data, setting the interconnectivity parameter between all regions within the same continental group to one, and setting the interconnectivity parameter between regions across continental groups to zero, allowing regions within a continental group to freely conduct cross-regional power transmission; In the global interconnection scenario, the interconnectivity parameter between all regions is set to one, allowing any region to call up the maximum power transmission capacity for power exchange.

[0159] Preferably, the system configures corresponding simulation boundary conditions for each scenario, including the calling path for hourly load data of the region, the initial state of charge of the energy storage system, the selection of the loss calculation model for transmission lines, and the setting of the simulation duration. The system integrates the interconnectivity parameters and boundary condition parameters of the four scenarios with the basic dataset to form a complete configuration parameter set for each scenario. The system performs logical verification on each configuration parameter set, checking whether the interconnectivity parameter values ​​conform to the scenario rules, whether the equipment parameters are complete, and whether the boundary conditions are clear. After the verification is passed, the configuration parameter sets of all scenarios are sorted by scenario identifier to generate a multi-scenario simulation environment configuration set.

[0160] S52: Perform hourly power balance simulation on the multi-scenario simulation environment configuration set, and update the energy storage charge state and cross-regional power flow data by calling dynamic scheduling rules according to the preset time step, and generate a simulation operation dataset with timestamps.

[0161] Specifically, the system loads a multi-scenario simulation environment configuration set from the simulation data directory, reads the configuration parameter group corresponding to each scenario, and determines the simulation time step. The time step is set to the hour level to maintain consistency with the time resolution of the regional wind and solar combined power generation curve, ensuring that the time dimension of the simulation data matches. The system executes hourly power balance simulations sequentially according to the scenarios, with each scenario's simulation covering the preset full duration. After the simulation begins, the system calls the configuration parameter group for the current scenario at each time step, reading the wind and solar power output data and load data for each region at that time step. The system calculates the initial supply-demand difference for each region. The initial supply-demand difference is the region's wind and solar power output value minus the load value at that time step. A positive initial supply-demand difference indicates that the region has a power surplus at that time step, while a negative initial supply-demand difference indicates that the region has a power deficit at that time step.

[0162] For regions with power surplus, the system invokes dynamic scheduling rules to select the minimum-loss transmission path from that region to each power deficit region based on the transmission loss matrix and inter-regional interconnectivity parameters. Transmission power is then allocated according to path priority, with the allocated transmission power not exceeding the region's surplus power and the actual available capacity of the corresponding transmission line. For regions with power deficit, the system receives transmission power from surplus regions according to dynamic scheduling rules, with the received transmission power not exceeding the region's deficit power and the actual available capacity of the corresponding transmission line.

[0163] Simultaneously, the system updates the state of charge (SOC) of energy storage systems in each region. This update is based on the energy storage charging and discharging power at that time step. If a region has a power surplus and there is still remaining power after transmission power allocation, the system controls the energy storage system to charge, with the charging power not exceeding the maximum charging power and the remaining surplus power. If a region has a power deficit that is not filled after receiving transmission power, the system controls the energy storage system to discharge, with the discharging power not exceeding the maximum discharging power and the remaining deficit power. The system calculates the SOC of the energy storage system at the next time step based on the charging and discharging power, charging and discharging efficiency, and energy storage capacity.

[0164] The system records the state of charge (SOC) data of energy storage in each region at each time step, including the current SOC value, charging power value, and discharging power value. It also records the power flow data of transmission lines between regions, including line transmission power value and line loss power value. The system adds corresponding timestamps and scenario identifiers to all recorded data to ensure data traceability to specific time steps and scenarios. After the simulation of all time steps is completed, the system integrates the recorded data of all time steps under that scenario in timestamp order to form a subset of simulation operation data for that scenario. The system repeats the above process for all scenarios, generating a subset of simulation operation data for all scenarios. Finally, all subsets are categorized according to scenario identifiers to generate a timestamped simulation operation dataset.

[0165] S53: Calculate performance indicators for the time-stamped simulation dataset, extract wind and solar power absorption data to calculate wind and solar power penetration rate, calculate energy storage utilization rate based on energy storage charge and discharge curve, calculate transmission channel load rate based on power flow peak, and integrate the three rate indicators with geographic coordinate data to generate an executable engineering solution with geographic coordinate annotation.

[0166] Specifically, the system loads a timestamped simulation dataset, extracts complete data for each scenario according to scenario identifiers, and filters out key data items needed to calculate performance indicators from the data, including hourly actual grid-connected power data of wind and solar power in each region, hourly charging and discharging power data of energy storage systems in each region, and hourly transmission power data of each transmission line. The system calculates the wind and solar penetration rate, which is the ratio of the region's annual actual wind and solar power consumption to the region's annual total load electricity consumption. The system first accumulates the hourly actual grid-connected power of wind and solar power in each region to obtain the region's annual actual wind and solar power consumption; then it accumulates the hourly load data of each region to obtain the region's annual total load electricity consumption; finally, it divides the region's annual actual wind and solar power consumption by the region's annual total load electricity consumption to obtain the wind and solar penetration rate for each region.

[0167] Furthermore, the system calculates the energy storage utilization rate, which is the ratio of the total annual charge / discharge of the regional energy storage system to the product of the system's rated capacity and the total simulation duration. The system accumulates the hourly charging and discharging power of each regional energy storage system, and then sums the results to obtain the total annual charge / discharge of the regional energy storage system. The system also calculates the product of the system's rated capacity and the total simulation duration to obtain the theoretical maximum charge / discharge capacity. Finally, the system divides the total annual charge / discharge of the regional energy storage system by the theoretical maximum charge / discharge capacity to obtain the energy storage utilization rate for each region.

[0168] Furthermore, the system calculates the transmission channel load factor, which is the ratio of the maximum annual transmission power of a transmission line to its maximum design capacity. The system filters the hourly transmission power data for each transmission line, extracting the maximum value as the line's maximum annual transmission power; it also reads the maximum design capacity of the line from the wind-solar-storage-transmission capacity configuration scheme; finally, it divides the line's maximum annual transmission power by its maximum design capacity to obtain the transmission channel load factor for each transmission line.

[0169] Preferably, the system reads pre-stored geographic coordinate data, including the geometric center latitude and longitude coordinates of each region and the start and end points latitude and longitude coordinates of each transmission line. The system correlates the wind and solar penetration rate and energy storage utilization rate of each region with the geometric center latitude and longitude coordinates of that region, forming a regional performance index-geographic coordinate correspondence table; it also correlates the transmission channel load rate of each transmission line with the start and end points latitude and longitude coordinates of that line, forming a line performance index-geographic coordinate correspondence table. The system integrates these two types of correspondence tables, combining regional wind and solar combined power generation curves and wind-solar-storage-transmission capacity configuration schemes to generate an executable engineering scheme. The scheme clearly defines wind and solar power plant site selection recommendations based on the coordinates of high wind and solar penetration areas, recommendations for adjusting the configuration of energy storage systems in each region based on optimized energy storage utilization rates, and recommendations for the construction and expansion of inter-regional transmission lines based on transmission channel load rates. All recommendations are accompanied by corresponding geographic coordinate data and performance index data, ensuring that the scheme can be directly used to guide actual engineering construction.

[0170] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0171] Based on the same inventive concept, this application also provides a system for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection, for implementing the aforementioned method for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the system for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection provided below can be found in the limitations of the method for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection described above, and will not be repeated here.

[0172] Preferably, such as Figure 3 As shown, the present invention provides a wind-solar-storage-transmission configuration optimization system 600 under cross-regional interconnection, which is configured with the following modules:

[0173] The candidate site resource construction module 610 is used to divide candidate sites for wind and solar power generation based on the acquired global latitude and longitude grid, and bind the maximum installable capacity and hourly power generation curve to each candidate site to generate a candidate site resource dataset.

[0174] The regional power generation curve aggregation module 620 is used to perform regional aggregation of candidate point resource datasets, map candidate points to regional power grids according to preset geographical zoning rules, and superimpose the power generation curves of candidate points belonging to the same regional power grid to generate regional wind and solar combined power generation curves.

[0175] The wind-solar-storage-transmission configuration solution module 630 is used to establish a three-objective function based on the regional wind-solar combined power generation curve, which includes minimizing the total investment cost, maximizing the wind-solar penetration rate, and minimizing the wind curtailment rate. It sets capacity constraints and topology constraints, solves the decision variables, and generates a wind-solar-storage-transmission capacity configuration scheme.

[0176] The power transmission dispatching rule generation module 640 is used to calculate the geographical distance between regions based on the power transmission capacity data and geographical coordinate data of the wind, solar and energy storage transmission capacity configuration scheme, construct the power transmission loss matrix in combination with the power transmission line type, and design the optimal path search algorithm for the minimum loss path based on the power transmission capacity constraint to generate dynamic dispatching rules with geographical constraints.

[0177] The engineering scheme simulation generation module 650 is used to configure simulation environments for different interconnection scenarios based on regional wind and solar combined power generation curves, wind, solar, energy storage and transmission capacity configuration schemes and dynamic scheduling rules. It performs hourly power balance simulations for a preset duration, updates energy storage status and records power flow, and generates executable engineering schemes containing system performance indicators and engineering application indicators. The executable engineering schemes are used to guide the site selection of wind and solar power plants, the configuration of energy storage systems and the construction of inter-regional transmission lines.

[0178] Preferably, the candidate point resource construction module 610 provided in this application is configured with the following units:

[0179] Latitude and longitude grid division unit is used to divide the global geographical area into latitude and longitude grids based on the acquired global latitude and longitude grid. It divides the earth's surface into equal-area grid units with a preset resolution and generates a set of candidate point geographic coordinates.

[0180] Capacity-constrained site cells are used to impose capacity constraints on the geographic coordinate set of candidate points. Based on the land type database and engineering feasibility parameters, the maximum installable capacity of each grid cell is determined, and a candidate site set with capacity constraints is generated.

[0181] The power generation curve binding unit is used to bind power generation curves to the candidate site set, integrate the pre-stored historical meteorological database and equipment performance parameters to generate hourly power generation curves, and generate candidate point resource datasets.

[0182] Preferably, the regional power generation curve aggregation module 620 provided in this application is configured with the following units:

[0183] The geographic partitioning unit is used to geographically partition the candidate point resource dataset, define the topology of the regional power grid based on the pre-stored continental boundary data and power grid jurisdiction data, and generate a regional power grid partitioning scheme.

[0184] The site affiliation mapping unit is used to perform site affiliation mapping on the regional power grid division scheme. Based on the spatial location relationship, each candidate site is assigned to the corresponding regional power grid, and a site-region mapping table is generated.

[0185] The regional curve aggregation unit is used to perform curve aggregation processing on the site-region mapping table. It performs time series superposition calculations on the wind power and photovoltaic power generation curves in the same region to generate regional-level wind power combined generation curves and regional-level photovoltaic combined generation curves, and then merges them into a regional-level wind and solar combined generation curve.

[0186] Preferably, the wind-solar-storage-transmission configuration solution module 630 provided in this application is configured with the following units:

[0187] The three-dimensional target construction unit is used to construct the objective function for the regional wind and solar power generation curve. It defines the total investment cost calculation model based on the preset equipment investment cost parameters, establishes a quantitative formula for wind and solar penetration rate based on electricity demand data, and constructs a wind and solar curtailment rate calculation model based on the difference between the power generation curve and the load curve. Through weighted summation and integration, it generates a three-dimensional optimization target that includes minimizing investment cost, maximizing wind and solar penetration rate, and minimizing wind and solar curtailment rate.

[0188] The constraint model injection unit is used to perform constraint injection processing on the three-dimensional optimization objective. It sets the upper limit constraint of photovoltaic and wind power installation based on the maximum installable capacity of candidate points, sets inter-regional transmission capacity constraint conditions based on the power grid topology, and generates a wind and solar constraint optimization model with multiple constraints after integration.

[0189] The coordinate capacity association unit is used to spatially associate the geographic coordinate data of the regional wind-solar combined power generation curve with the inter-regional transmission capacity in the wind-solar constrained optimization model, and generate a transmission capacity distribution table with geographic coordinates.

[0190] The multi-objective solution unit is used to solve the wind-solar constrained optimization model and the transmission capacity distribution table. It calls the multi-objective evolutionary algorithm to optimize four types of decision variables in parallel: photovoltaic installed capacity, wind power installed capacity, energy storage configuration parameters and transmission capacity, and generates a wind-solar-storage-transmission capacity configuration scheme. The wind-solar-storage-transmission capacity configuration scheme is used to output the power station construction scale and inter-regional interconnection planning parameters.

[0191] Preferably, the power transmission dispatching rule generation module 640 provided in this application is configured with the following units:

[0192] The regional distance calculation unit is used to calculate the distance of geographical coordinate data in the wind, solar and energy storage capacity configuration scheme. It calculates the geographical distance between pairs of nodes between regions based on the spherical distance formula and generates a geographical distance matrix between regions.

[0193] The power transmission loss modeling unit is used to model the loss of the geographical distance matrix between regions. It queries the power loss rate per unit distance for different transmission media based on the preset cable type parameter library, calculates the total cross-regional transmission loss rate based on the geographical distance, and generates the power transmission loss matrix.

[0194] The scheduling path search unit is used to perform path search on the transmission capacity data in the transmission loss matrix and the wind-solar-storage transmission capacity configuration scheme. It designs a dynamic programming algorithm for cross-regional power transmission paths based on the principle of minimizing losses and generates dynamic scheduling rules with geographical constraints. These dynamic scheduling rules are used to guide the cross-regional power dispatching of the power grid control system.

[0195] Preferably, the engineering scheme simulation generation module 650 provided in this application is configured with the following units:

[0196] The multi-scenario simulation configuration unit is used to configure the simulation environment for regional wind-solar combined power generation curves and wind-solar-storage-transmission capacity configuration schemes. It sets the interconnection rule parameters for four scenarios: regional independence, adjacent interconnection, continental interconnection, and global interconnection, and generates a multi-scenario simulation environment configuration set.

[0197] The hourly power simulation unit is used to perform hourly power balance simulation on a multi-scenario simulation environment configuration set. It calls dynamic scheduling rules to update the energy storage charge status and cross-regional power flow data according to a preset time step, and generates a simulation operation dataset with timestamps.

[0198] The performance index and scheme generation unit is used to calculate performance indexes on the simulation operation dataset with timestamps, extract wind and solar power absorption data to calculate wind and solar power penetration rate, calculate energy storage utilization rate based on energy storage charge and discharge curves, calculate transmission channel load rate based on power flow peak, and integrate the three rate indicators with geographic coordinate data to generate an executable engineering scheme with geographic coordinate annotations.

[0199] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for optimizing the configuration of wind, solar, storage and transmission under cross-regional interconnection.

[0200] In one embodiment, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection.

[0201] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0202] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0203] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the configuration of wind, solar, storage, and transmission under cross-regional interconnection, characterized in that, Includes the following steps: S1: Based on the acquired global latitude and longitude grid, candidate sites for wind and solar power generation are divided, and the maximum installable capacity and hourly power generation curve are bound to each candidate site to generate a candidate site resource dataset; S2: Perform regional aggregation on the candidate point resource dataset, map the candidate points to the regional power grid according to the preset geographical zoning rules, and superimpose the power generation curves of candidate points belonging to the same regional power grid to generate a regional wind and solar combined power generation curve. S3: Based on the regional wind-solar combined power generation curve, establish a three-objective function including minimizing total investment cost, maximizing wind and solar penetration rate, and minimizing wind and solar curtailment rate. Set capacity constraints and topology constraints and solve the decision variables to generate a wind-solar-storage-transmission capacity configuration scheme. S4: Calculate the geographical distance between regions based on the transmission capacity data and geographical coordinate data of the wind-solar-storage transmission capacity configuration scheme, construct the transmission loss matrix in combination with the transmission line type, and design the optimal path search algorithm for the minimum loss path based on the transmission capacity constraint to generate dynamic scheduling rules with geographical constraints. S5: Based on the regional wind-solar combined power generation curve, the wind-solar-storage-transmission capacity configuration scheme, and the dynamic scheduling rules, configure simulation environments for different interconnection scenarios, perform hourly power balance simulations for a preset duration, update the energy storage status and record the power flow, and generate an executable engineering scheme containing system performance indicators and engineering application indicators. The executable engineering scheme is used to guide the site selection of wind and solar power plants, the configuration of energy storage systems, and the construction of inter-regional transmission lines.

2. The method according to claim 1, characterized in that, S1 includes: S11: Based on the acquired global latitude and longitude grid, the global geographical area is divided into latitude and longitude grids. The surface is divided into equal-area grid units using a preset resolution to generate a set of candidate point geographic coordinates. S12: Apply capacity constraints to the geographic coordinate set of the candidate points, determine the maximum installable capacity of each grid cell based on the land type database and engineering feasibility parameters, and generate a candidate site set with capacity constraints; S13: Bind the power generation curves to the candidate site set, integrate the pre-stored historical meteorological database and equipment performance parameters to generate hourly power generation curves, and generate a candidate point resource dataset.

3. The method according to claim 1, characterized in that, S2 includes: S21: Geographically partition the candidate point resource dataset, define the topology of the regional power grid based on the pre-stored continental boundary data and power grid jurisdiction data, and generate a regional power grid partitioning scheme; S22: Perform site affiliation mapping on the regional power grid division scheme, and assign each candidate site to the corresponding regional power grid based on spatial location relationship, and generate a site-region mapping table; S23: Perform curve aggregation processing on the site-region mapping table, perform time series superposition calculation on the wind power and photovoltaic power generation curves in the same region respectively, generate regional wind power combined power generation curve and regional photovoltaic combined power generation curve, and merge them into regional wind and solar combined power generation curve.

4. The method according to claim 1, characterized in that, S3 includes: S31: The regional wind and solar combined power generation curve is processed to construct an objective function. Based on the preset equipment investment cost parameters, a total investment cost calculation model is defined. Combined with the power demand data, a wind and solar penetration rate quantification formula is established. Based on the difference between the power generation curve and the load curve, a wind and solar curtailment rate calculation model is constructed. By weighted summation and integration of the total investment cost calculation model, the wind and solar penetration rate quantification formula, and the wind and solar curtailment rate calculation model, a three-dimensional optimization objective function is constructed to generate a three-dimensional optimization objective that includes minimizing investment cost, maximizing wind and solar penetration rate, and minimizing wind and solar curtailment rate. S32: Perform constraint injection processing on the three-dimensional optimization objective, set the upper limit constraints of photovoltaic and wind power installation based on the maximum installable capacity of candidate points, set inter-regional transmission capacity constraints based on the power grid topology, integrate the upper limit constraints of installation, transmission capacity constraints and the three-dimensional optimization objective, and generate a wind and solar constrained optimization model with multiple constraints. S33: Spatial correlation is performed between the geographic coordinate data of the regional wind-solar combined power generation curve and the inter-regional transmission capacity in the wind-solar constrained optimization model to generate a transmission capacity distribution table with geographic coordinates. S34: Solve the wind-solar constrained optimization model and the power transmission capacity distribution table, and call the multi-objective evolutionary algorithm to optimize four types of decision variables in parallel: photovoltaic installed capacity, wind power installed capacity, energy storage configuration parameters and power transmission capacity, to generate a wind-solar-storage-transmission capacity configuration scheme. The wind-solar-storage-transmission capacity configuration scheme is used to output the power station construction scale and inter-regional interconnection planning parameters.

5. The method according to claim 1, characterized in that, S4 includes: S41: Perform distance calculation on the geographical coordinate data in the wind-solar-storage-transmission capacity configuration scheme, calculate the geographical distance between pairs of nodes between regions based on the spherical distance formula, and generate a geographical distance matrix between regions. S42: Perform loss modeling on the geographical distance matrix between the regions, query the power loss rate per unit distance for different transmission media according to the preset cable type parameter library, calculate the total cross-regional transmission loss rate in combination with the geographical distance, and generate a power transmission loss matrix. S43: Perform path search on the transmission loss matrix and the transmission capacity data in the wind-solar-storage transmission capacity configuration scheme, design a dynamic programming algorithm for cross-regional power transmission paths based on the principle of minimizing losses, and generate dynamic scheduling rules with geographical constraints. The dynamic scheduling rules are used to guide the cross-regional power dispatch of the power grid control system.

6. The method according to claim 5, characterized in that, The expression for the dynamic programming algorithm for cross-regional power transmission paths is: in, It is the set of paths from the source region to the target region. Let x be the loss rate per unit distance from region x to y. Let x be the geographical distance from region x to y. Let x represent the available transmission capacity from region x to y. R represents the power requirement to be transmitted, and R is the Earth's radius. , Let x and y be the latitudes of the region, respectively. Let x be the latitude difference between region x and region y. Let x be the difference in longitude between region x and region y. This represents the total path loss.

7. The method according to any one of claims 1-6, characterized in that, S5 includes: S51: Configure the simulation environment for the regional wind-solar combined power generation curve and the wind-solar-storage-transmission capacity configuration scheme, set the interconnection rule parameters for four scenarios: regional independence, adjacent interconnection, continental interconnection and global interconnection, and generate a multi-scenario simulation environment configuration set; S52: Perform hourly power balance simulation on the multi-scenario simulation environment configuration set, and update the energy storage charge state and cross-regional power flow data by calling the dynamic scheduling rules according to the preset time step, and generate a simulation running dataset with timestamps. S53: Calculate performance indicators for the timestamped simulation dataset, extract wind and solar power absorption data to calculate wind and solar power penetration rate, calculate energy storage utilization rate based on energy storage charge and discharge curve, calculate transmission channel load rate based on power flow peak, and integrate the three rate indicators with geographic coordinate data to generate an executable engineering solution with geographic coordinate annotation.

8. A wind-solar-storage-transmission configuration optimization system under cross-regional interconnection, characterized in that, The system includes: The candidate site resource construction module is used to divide candidate sites for wind and solar power generation based on the acquired global latitude and longitude grid, and bind the maximum installable capacity and hourly power generation curve to each candidate site to generate a candidate site resource dataset. The regional power generation curve aggregation module is used to perform regional aggregation on the candidate point resource dataset, map the candidate points to the regional power grid according to the preset geographical zoning rules, and perform superposition calculation on the power generation curves of candidate points belonging to the same regional power grid to generate a regional wind and solar combined power generation curve. The wind-solar-storage-transmission configuration solution module is used to establish a three-objective function based on the regional wind-solar combined power generation curve, which includes minimizing total investment cost, maximizing wind and solar penetration rate, and minimizing wind and solar curtailment rate. It sets capacity constraints and topology constraints, solves decision variables, and generates a wind-solar-storage-transmission capacity configuration scheme. The power transmission dispatching rule generation module is used to calculate the geographical distance between regions based on the power transmission capacity data and geographical coordinate data of the wind, solar and energy storage power transmission capacity configuration scheme, construct the power transmission loss matrix in combination with the power transmission line type, and design the optimal path search algorithm for the minimum loss path based on the power transmission capacity constraint to generate dynamic dispatching rules with geographical constraints. The engineering scheme simulation generation module is used to configure simulation environments for different interconnection scenarios based on the regional wind-solar combined power generation curve, the wind-solar-storage-transmission capacity configuration scheme, and the dynamic scheduling rules. It performs hourly power balance simulations for a preset duration, updates the energy storage status and records the power flow, and generates executable engineering schemes containing system performance indicators and engineering application indicators. The executable engineering schemes are used to guide the site selection of wind and solar power plants, the configuration of energy storage systems, and the construction of inter-regional transmission lines.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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