A distributed distribution network planning method, device, equipment and medium

Abstract

The present application relates to the technical field of distribution network planning, and in particular to a planning method, device, equipment and medium for a distributed distribution network. The method includes: the present application can fully understand the current status and possible future operating trends of the distribution network by collecting and analyzing the grid structure sub-data, grid stability sub-data and grid operation sub-data. The transmission strategy formulated based on detailed grid data can fully consider the stability and security requirements of the grid. At the same time, by rationally planning the transmission corridors and transmission methods, the transmission losses in the grid can be reduced, the risk of failures can be reduced, and the overall stability and reliability of the grid can be improved. Combined with the energy category, geographical location and installed capacity information of the energy base, the power generation of each energy category in the future cycle can be accurately predicted, so as to formulate a more scientific and reasonable transmission strategy, optimize the allocation and scheduling of power resources, and improve energy utilization efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network planning, and in particular to a planning method, apparatus, equipment, and medium for a distributed distribution network. Background Art

[0002] With the profound transformation of energy production, the power grid is undergoing unprecedented change. The widespread integration of distributed power sources—particularly renewable energy sources like wind and photovoltaic power—has become a significant trend. This structural change has not only greatly enriched the grid's energy mix but also fundamentally reshaped the topology of traditional distribution networks.

[0003] For energy bases with abundant distributed energy resources, efficiently converting this clean energy into usable electricity, accurately integrating it into the main power grid, and maximizing its utilization through scientific and rational power transmission planning have become critical challenges. However, in reality, some electricity suffers unnecessary losses during transmission due to issues such as improper transmission methods or lengthy transmission routes. This not only weakens the energy base's power generation efficiency but also reduces overall energy utilization. Summary of the Invention

[0004] In order to improve the utilization rate of electric energy generated by energy bases, the present application provides a planning method, apparatus, equipment and medium for a distributed power distribution network.

[0005] In a first aspect, the present application provides a distributed distribution network planning method, which adopts the following technical solutions:

[0006] A distributed distribution network planning method, comprising:

[0007] Acquiring grid data of the distribution network, wherein the grid data includes grid structure sub-data, grid stability sub-data, and grid operation sub-data;

[0008] Obtain the energy type, geographical location, and installed capacity corresponding to the energy base;

[0009] Based on the geographical location of the energy base and the installed capacity, predict the power generation of each energy type in the energy base in a future period;

[0010] Determine a transmission destination corresponding to the power generation;

[0011] Based on the power grid data and the transmission destinations corresponding to the power generation, a transmission corridor and a transmission method corresponding to each transmission destination are determined to obtain a transmission strategy for the energy base in a future period.

[0012] By adopting the above technical solution, by collecting and analyzing grid structure sub-data, grid stability sub-data, and grid operation sub-data, we can fully understand the current status of the distribution network and its possible future operating trends. Based on this detailed grid data, the transmission strategy formulated can fully consider the stability and security requirements of the grid. At the same time, through the rational planning of transmission corridors and transmission methods, it can reduce transmission losses in the grid, lower the risk of failures, and improve the overall stability and reliability of the grid. In combination with the energy type, geographical location, and installed capacity information of energy bases, it is possible to accurately predict the power generation of each energy type in the future cycle, thereby formulating more scientific and reasonable transmission strategies, optimizing the allocation and scheduling of power resources, and improving energy utilization efficiency.

[0013] In one possible implementation, based on the geographical location of the energy base and the installed capacity, predicting the power generation of each energy type in the energy base in a future period includes:

[0014] Obtaining meteorological parameters of the geographic location in a future period, the meteorological parameters including light intensity, wind speed, wind direction, and temperature;

[0015] Determine the meteorological sub-parameters corresponding to each energy category;

[0016] Input the installed sub-capacity and meteorological sub-parameters corresponding to each energy category into the corresponding prediction model;

[0017] The power generation output by each prediction model is obtained to obtain the power generation of each energy type in the energy base in the future period.

[0018] By employing this technical solution, detailed meteorological parameters (such as sunlight intensity, wind speed, wind direction, and temperature) for the energy base's location over the coming period are obtained. Appropriate meteorological sub-parameters are selected as input for different energy types (such as photovoltaic power generation and wind power generation), fully accounting for the direct impact of natural environmental factors on power generation. Combining this data with the energy base's installed sub-capacity and feeding it into a prediction model designed for each energy source's characteristics can significantly improve the accuracy of power generation forecasts. This not only helps grid operators more accurately formulate transmission strategies and scheduling plans, but also provides strong support for investment decisions and operational optimization of energy bases.

[0019] In one possible implementation, determining a transmission destination corresponding to the power generation includes:

[0020] Obtaining a historical transmission destination and a backup transmission destination corresponding to the power generation;

[0021] Predicting the power demand urgency of the historical delivery destination and the backup delivery destination in a future period;

[0022] Based on the power generation amount and the power urgency, at least one destination is selected from the historical transmission destinations and the backup transmission destinations to serve as the transmission destination corresponding to the power generation amount.

[0023] By adopting the above technical solution, by predicting the power urgency of historical transmission destinations and backup transmission destinations in future cycles, the power transmission strategy can be flexibly adjusted. During peak power consumption or emergency situations, power can be transmitted preferentially to destinations with higher power urgency, ensuring stable power supply to key areas or users, thereby improving the reliability and resilience of the entire power system.

[0024] In a possible implementation, predicting the power demand urgency of each of the historical delivery destination and the backup delivery destination in a future period includes:

[0025] Acquire historical electricity consumption data corresponding to each of the historical delivery destination and the backup delivery destination, wherein the historical electricity consumption data includes electricity consumption corresponding to each historical period and historical weather information;

[0026] Based on the historical electricity consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, predicting the electricity demand increase rate of the historical transmission destination and the backup transmission destination in a future period;

[0027] Determining the power supply capacity corresponding to each of the historical delivery destination and the backup delivery destination;

[0028] Based on the electricity demand increase rate and the power supply capacity, the electricity urgency corresponding to each of the historical delivery destination and the backup delivery destination is determined.

[0029] By adopting the above technical solution and through in-depth analysis of historical electricity consumption data and historical weather information, the solution can more accurately predict the rate of increase in electricity demand at each delivery destination in the future cycle, allowing the energy base to dispatch and allocate energy in advance according to actual demand changes, ensuring the stability and continuity of power supply. At the same time, by understanding the urgency of electricity demand at each delivery destination, the direction of power generation can be planned more reasonably, avoiding the transmission of electricity to areas with relatively abundant power supply and less urgent electricity demand, thereby effectively reducing energy waste. At the same time, it can also ensure that during peak power consumption or emergency situations, electricity can be quickly and accurately transmitted to the areas most in need, avoiding the occurrence of power shortages.

[0030] In one possible implementation, based on the historical power consumption data corresponding to the historical power delivery destination and the backup power delivery destination, predicting the power demand increase rate of each of the historical power delivery destination and the backup power delivery destination in a future period includes:

[0031] constructing time series features corresponding to the historical delivery destination and the backup delivery destination based on the historical power consumption data corresponding to the historical delivery destination and the backup delivery destination;

[0032] Based on the power consumption corresponding to each historical power consumption data, the historical power demand increase rate corresponding to each historical period is calculated as the demand label corresponding to each historical period;

[0033] Based on the demand label corresponding to each historical period, the electricity demand increase rate in the future period is fitted by minimizing the loss function to obtain the electricity demand increase rate of the historical delivery destination and the backup delivery destination in the future period.

[0034] By employing this technical solution and constructing time series features corresponding to historical and backup delivery destinations, we can capture the patterns and trends of electricity consumption over time, more accurately reflecting seasonal and cyclical fluctuations in electricity demand, thereby improving the accuracy of forecasts of the rate of increase in electricity demand over future cycles. Furthermore, by utilizing demand labels from historical cycles (i.e., historical electricity demand increase rates) and minimizing the loss function to fit the rate of increase in electricity demand over future cycles, we can help adjust the parameters of the forecast model, reduce the error between predicted and actual values, and further enhance the model's forecasting capabilities and reliability.

[0035] In one possible implementation, determining a transmission corridor and a transmission mode corresponding to each transmission destination based on the power grid data and the transmission destination corresponding to the power generation includes:

[0036] Determine the power demand corresponding to each transmission destination;

[0037] Based on the grid data, planning an initial transmission corridor between each transmission destination and the energy base;

[0038] Calculating the capacity of the transmission corridor corresponding to each transmission destination based on the power demand of each transmission destination and the power generation;

[0039] Selecting, from the initial transmission corridors, a transmission corridor corresponding to each transmission corridor capacity as a transmission corridor corresponding to each transmission destination;

[0040] Obtaining the distance between each delivery destination and the energy base;

[0041] Determine the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs;

[0042] Based on the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs, the transmission mode corresponding to each transmission destination is determined.

[0043] By employing this technical solution, initial transmission corridors are planned based on the power demand of each transmission destination. Furthermore, the transmission corridor capacity is accurately calculated based on power generation and demand. This ensures that the transmission corridor design not only meets current power demand but also leaves a margin for possible future growth, thereby improving transmission efficiency. Furthermore, by selecting appropriate transmission corridors, energy losses during transmission can be reduced, improving transmission reliability.

[0044] In a second aspect, the present application provides a distributed distribution network planning device, which adopts the following technical solution:

[0045] A distributed power distribution network planning device, comprising:

[0046] A first acquisition module is used to acquire grid data of the distribution network, wherein the grid data includes grid structure sub-data, grid stability sub-data and grid operation sub-data;

[0047] The second acquisition module is used to obtain the energy type, geographical location and installed capacity corresponding to the energy base;

[0048] a prediction module, configured to predict the power generation of each energy type in the energy base in a future period based on the geographical location corresponding to the energy base and the installed capacity;

[0049] A determination module, configured to determine a delivery destination corresponding to the power generation;

[0050] An obtaining module is used to determine the transmission corridor and transmission mode corresponding to each transmission destination based on the power grid data and the transmission destination corresponding to the power generation, so as to obtain the transmission strategy of the energy base in the future cycle.

[0051] In one possible implementation, when the prediction module predicts the power generation of each energy type in the energy base in a future period based on the geographical location corresponding to the energy base and the installed capacity, it is specifically configured to:

[0052] Obtaining meteorological parameters of the geographic location in a future period, the meteorological parameters including light intensity, wind speed, wind direction, and temperature;

[0053] Determine the meteorological sub-parameters corresponding to each energy category;

[0054] Input the installed sub-capacity and meteorological sub-parameters corresponding to each energy category into the corresponding prediction model;

[0055] The power generation output by each prediction model is obtained to obtain the power generation of each energy type in the energy base in the future period.

[0056] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0057] An electronic device, comprising:

[0058] at least one processor;

[0059] Memory;

[0060] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the distributed distribution network planning method described in the first aspect above.

[0061] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0062] A computer-readable storage medium includes: a computer program stored therein that can be loaded by a processor and execute the distributed distribution network planning method described in the first aspect above.

[0063] In summary, this application includes the following beneficial technical effects: by collecting and analyzing grid structure sub-data, grid stability sub-data, and grid operation sub-data, it is possible to fully understand the current status of the distribution network and possible future operating trends. The transmission strategy formulated based on detailed grid data can fully consider the stability and security requirements of the grid. At the same time, by rationally planning transmission corridors and transmission methods, it is possible to reduce transmission losses in the grid, reduce the risk of failures, and improve the overall stability and reliability of the grid. Combined with the energy category, geographical location, and installed capacity information of the energy base, it is possible to accurately predict the power generation of each energy category in the future cycle, thereby formulating a more scientific and reasonable transmission strategy, optimizing the allocation and scheduling of power resources, and improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of a distributed distribution network planning method provided in an embodiment of the present application;

[0065] Figure 2 1 is a block diagram of a distributed distribution network planning device provided in an embodiment of the present application;

[0066] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0068] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0069] In order to facilitate understanding of the technical solutions proposed in this application, several elements that will be introduced in the description of this application are first introduced here. It should be understood that the following introduction is only for the convenience of understanding these elements, so as to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0070] A transmission corridor is a strip of land within a specified width on either side of a high-voltage overhead power line, ensuring the normal operation and safety of the line. A transmission corridor is a three-dimensional space encompassing both floor space and clear space. The corridor's width and extent are determined based on the technical requirements and safety standards of the power line. Corridors of different voltage levels correspond to different widths.

[0071] Grid structure data refers to a collection of data describing the connections between various grid components, such as power plants, substations, and transmission lines. This data is fundamental to grid planning and operation, reflecting the physical layout and logical structure of the grid. Grid structure data typically includes the location of power plants and substations, the direction and length of transmission lines, the connectivity of equipment, and the grid topology.

[0072] Grid stability data refers to a collection of data that reflects the grid's ability to maintain a stable operating state during normal operation or when subject to disturbances. Grid stability is the cornerstone of safe grid operation and primarily encompasses three aspects: static stability, transient stability, and dynamic stability. Grid stability data typically includes static stability data, transient stability data, and dynamic stability data.

[0073] Grid operation data refers to a collection of data used to monitor and analyze the grid's operating status. This data provides real-time insights into the grid's operations, including equipment status, power transmission, and load changes. Grid operation data typically includes equipment status data, power transmission data, load data, and fault and alarm information.

[0074] The present application embodiment provides a distributed power distribution network planning method, such as Figure 1 As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S105, wherein:

[0075] Step S101: Acquire grid data of the distribution network.

[0076] The grid data includes grid structure sub-data, grid stability sub-data and grid operation sub-data. Collect grid data of the distribution network, specifically:

[0077] Grid structure sub-data: Use the Geographic Information System (GIS) to obtain the geographical location and connection relationship of each substation and transmission line in the power grid and construct a grid topology map.

[0078] Grid stability sub-data: Obtain relevant data on static stability, transient stability and dynamic stability from the grid stability analysis system, such as stability limit, fault clearing time, etc.

[0079] Grid operation sub-data: Real-time acquisition of equipment status, power transmission, load changes, fault alarms and other information through the grid monitoring system.

[0080] Step S102: Obtain the energy type, geographical location, and installed capacity corresponding to the energy base.

[0081] This refers to the total rated active power of the generators or energy conversion equipment actually installed in the distributed energy system. Specifically, data on energy bases is obtained, including energy type (such as wind power, photovoltaic power, hydropower, etc.), geographic location (latitude and longitude), and the installed capacity corresponding to each energy type.

[0082] Step S103: Based on the geographical location and the capacity of the energy base, the power generation of each energy type in the energy base in the future period is predicted.

[0083] The energy type may be photovoltaic power generation, wind power generation, or hydropower generation, etc. The future period may be one day or the next twelve hours.

[0084] For each energy type, based on its geographical location (such as climate conditions, light intensity, water resources, etc.) and installed capacity, use an appropriate forecasting model to predict the amount of electricity produced in the future cycle. Specifically, for each energy type: obtain the changes in the factors that affect the energy type's electricity generation in the future cycle, and based on the changes in these factors and the installed capacity corresponding to the energy type, calculate the corresponding amount of electricity produced by the energy type. For example, for the photovoltaic power generation energy type, the factor that affects the energy type's electricity generation is light intensity. Therefore, obtain a curve of the light intensity change over time for the geographical location in the future cycle, and based on the installed capacity corresponding to the photovoltaic power generation energy type, calculate the corresponding amount of electricity produced by the photovoltaic power generation energy type in the future cycle.

[0085] Step S104: Determine the transmission destination corresponding to the power generation amount.

[0086] Receive the locations where energy is to be transmitted and the amount of electricity to be transmitted corresponding to each location to be transmitted, calculate the distance between each location to be transmitted and the location of the energy base, sort each location to be transmitted in ascending order of distance, and according to the sorting and the amount of electricity produced by each energy type in the future cycle, select a location that can meet the amount of electricity produced from multiple locations to be transmitted as the transmission destination. For example, the total amount of electricity produced by each energy type in the future cycle is 150 kWh, and the distances between the locations to be transmitted and the location of the energy base and the amount of electricity to be transmitted are 15 km and 50 kWh, 25 km and 60 kWh, 27 km and 80 kWh, and 30 km and 60 kWh, respectively. According to the sorting, the 150 kWh of electricity produced can only be distributed to the locations with distances of 15 km, 25 km, and 27 km, so these three locations are selected as the transmission destinations.

[0087] Step S105: Based on the grid data and the transmission destinations corresponding to the power generation, determine the transmission corridor and transmission method corresponding to each transmission destination to obtain the transmission strategy of the energy base in the future period.

[0088] Specifically, grid planning software is used to plan initial transmission corridors based on grid data and the geographic location of the transmission destination. The capacity requirements for each transmission corridor are calculated based on the predicted power generation and the power demand at the transmission destination. Corridors that meet the capacity requirements are selected from the initial transmission corridors, and optimization is performed based on factors such as terrain, environment, and cost. The distance between the energy base and the transmission destination is determined, and the transmission method (such as HVDC or UHVAC) is determined based on the transmission corridor capacity.

[0089] Furthermore, a transmission strategy for the energy base in the future cycle should be formulated, including the selection of transmission corridors, determination of transmission methods, and allocation of transmission volume.

[0090] The embodiment of the present application provides a planning method for a distributed distribution network. By collecting and analyzing grid structure sub-data, grid stability sub-data, and grid operation sub-data, it is possible to fully understand the current status of the distribution network and possible future operating trends. The transmission strategy formulated based on detailed grid data can fully consider the stability and security requirements of the grid. At the same time, by rationally planning transmission corridors and transmission methods, it can reduce transmission losses in the grid, reduce the risk of failures, and improve the overall stability and reliability of the grid. Combined with the energy category, geographical location, and installed capacity information of the energy base, it is possible to accurately predict the power generation of each energy category in the future cycle, thereby formulating a more scientific and reasonable transmission strategy, optimizing the allocation and scheduling of power resources, and improving energy utilization efficiency.

[0091] In one possible implementation of the embodiment of the present application, in step S103, based on the geographical location and installed capacity of the energy base, the power generation of each energy type in the energy base in the future period is predicted, including:

[0092] Obtain meteorological parameters for the geographic location in the future period, including light intensity, wind speed, wind direction, and temperature;

[0093] Determine the meteorological sub-parameters corresponding to each energy category;

[0094] Input the installed sub-capacity and meteorological sub-parameters corresponding to each energy category into the corresponding prediction model;

[0095] The power generation output by each forecast model is obtained to obtain the power generation of each energy type in the energy base in the future period.

[0096] Among them, one energy category corresponds to a trained prediction model. Each prediction model has been set with input features (meteorological sub-parameters and installed sub-capacity), output targets (power production), model parameters (such as learning rate, number of iterations, number of hidden layer nodes, etc.), etc.

[0097] Specifically, obtain meteorological parameter data for the energy base over the next period. This data can be forecasted based on a meteorological model. Based on the energy type of the energy base (such as wind power, photovoltaic power, or hydropower), determine the meteorological parameters required for each energy type. For example, wind power requires wind speed and direction, while photovoltaic power requires light intensity and temperature.

[0098] Furthermore, based on each energy category, determining the power generation of the energy category in the future period may include: organizing the installed sub-capacity corresponding to the energy category (i.e., the installed capacity of the energy category) and the meteorological sub-parameters in the future period (such as wind speed, light intensity, etc.) into the input format required by the model, and then inputting the organized input data into the corresponding prediction model, executing the prediction process, and obtaining the power generation prediction value output by the prediction model.

[0099] In a possible implementation of the embodiment of the present application, in step S104, determining the transmission destination corresponding to the power generation includes:

[0100] Obtain historical transmission destinations and backup transmission destinations corresponding to power generation;

[0101] Predict the power demand urgency of historical transmission destinations and backup transmission destinations in future periods;

[0102] Based on the power generation amount and the power urgency, at least one destination is selected from the historical transmission destinations and the backup transmission destinations as the transmission destination corresponding to the power generation amount.

[0103] The energy management database records information such as the destinations, amounts delivered, and times of power transmission over a period of time. Backup power transmission destinations are pre-set and can be used as alternatives if the primary power transmission destination is unable to receive power.

[0104] Specifically, electricity consumption data for historical and backup destinations over the past period is collected, including information such as power consumption, peak hours, and load variations. Combined with future weather forecasts, the system predicts the level of power demand urgency for each destination in the coming period. This level of power demand urgency can be measured using a variety of indicators, such as load factor, growth rate, and peak-to-valley differences.

[0105] More specifically, the degree of match between the power demand and distributed energy generation at each delivery destination is assessed. Factors such as the scale and stability of distributed energy generation, as well as losses during transmission, are considered to ensure that the delivered power meets the actual needs of the destination. A comprehensive evaluation is conducted on historical and backup delivery destinations based on the predicted power urgency and power demand match. From the evaluation results, at least one destination is selected as the delivery destination corresponding to the distributed energy generation. The priority order of power delivery is determined based on the power urgency and power demand match of each delivery destination. In times of power shortage, priority is given to destinations with the highest power urgency.

[0106] A possible implementation of the embodiment of the present application, in the above embodiment, predicting the power urgency of each of the historical delivery destination and the backup delivery destination in the future period includes:

[0107] Obtaining historical electricity consumption data corresponding to each of the historical delivery destination and the backup delivery destination, the historical electricity consumption data including the electricity consumption corresponding to each historical period and historical weather information;

[0108] Based on the historical electricity consumption data corresponding to the historical transmission destinations and the backup transmission destinations, predict the increase rate of electricity demand of the historical transmission destinations and the backup transmission destinations in the future period;

[0109] Determine the power supply capacity corresponding to the historical delivery destination and the backup delivery destination;

[0110] Based on the electricity demand increase rate and the power supply capacity, the electricity urgency corresponding to the historical transmission destination and the backup transmission destination is determined.

[0111] Specifically, historical electricity usage data corresponding to each of the historical and backup delivery destinations is obtained. This historical electricity usage data includes electricity usage records for multiple historical periods, as well as weather information (such as temperature, humidity, and rainfall) corresponding to each historical period. This historical electricity usage data is cleaned and organized to ensure its integrity and accuracy.

[0112] Analyze the correlation between historical electricity consumption data and weather information to identify key factors influencing electricity demand. For example, high temperatures may lead to increased air conditioning power consumption, thereby affecting total electricity consumption. Use statistical methods (such as time series analysis and regression analysis) or machine learning models (such as neural networks and random forests) to predict the rate of increase in electricity demand in the future cycle. Based on the prediction results, obtain the predicted rate of increase in electricity demand for the historical and backup transmission destinations in the future cycle.

[0113] Collect information on power supply facilities at historical and backup destinations, including installed capacity of power plants, transmission capacity of power grids, and capacity of energy storage facilities. Evaluate the power supply capacity of each destination, i.e., the maximum amount of electricity that can be provided in the future. Compare the predicted rate of increase in electricity demand with the assessed power supply capacity. If the predicted rate of increase in electricity demand exceeds the growth in power supply capacity or the existing power supply capacity, it indicates a power emergency at that destination.

[0114] The power urgency metric is defined as the supply-demand ratio (i.e., the ratio of predicted power demand to power supply capacity). Based on this metric, the power urgency of historical and backup power delivery destinations over the next period is determined. Power urgency can be categorized into different levels (e.g., low, medium, and high) to facilitate subsequent decision-making and scheduling.

[0115] One possible implementation of the embodiment of the present application, in the above embodiment, predicting the rate of increase in electricity demand for each of the historical delivery destination and the backup delivery destination in a future period based on the historical electricity consumption data corresponding to each of the historical delivery destination and the backup delivery destination, includes:

[0116] Based on the historical electricity consumption data corresponding to the historical transmission destinations and the backup transmission destinations, construct the time series features corresponding to the historical transmission destinations and the backup transmission destinations;

[0117] Based on the power consumption corresponding to each historical power consumption data, the historical power demand increase rate corresponding to each historical period is calculated as the demand label corresponding to each historical period;

[0118] Based on the demand label corresponding to each historical period, the electricity demand increase rate in the future period is fitted by minimizing the loss function to obtain the electricity demand increase rate of the historical delivery destination and the backup delivery destination in the future period.

[0119] Specifically, for each destination, historical electricity consumption data is arranged chronologically to construct a time series. Each point in the time series represents electricity consumption for a historical period. Features are extracted from the time series, including first-order differences (indicating the change in electricity consumption between adjacent periods), second-order differences (indicating the change in the change), moving averages, and seasonal decomposition components (such as trend, seasonality, and residuals). These features help capture trends, periodicity, and randomness in electricity consumption data.

[0120] For each historical period, the ratio of electricity consumption to the previous period is calculated to obtain the historical electricity demand increase rate for that period. The electricity consumption ratio can be selected based on actual conditions. For example, the electricity consumption ratio can be directly calculated by dividing the electricity consumption difference by the electricity consumption of the previous period, or the percentage growth rate can be calculated to obtain the electricity consumption ratio.

[0121] Furthermore, the calculated historical electricity demand increase rate is used as the demand label for each historical period, corresponding to the previously extracted time series features. A deep learning network model is used to predict the electricity demand increase rate for future periods. The deep learning network model outputs the corresponding electricity demand increase rate prediction value based on the learned patterns. The deep learning network model is trained using historical electricity consumption data (including time series features and demand labels). During training, the model learns the patterns of electricity consumption changes in the historical data and attempts to minimize the loss function (such as mean squared error, mean absolute error, etc.) between the predicted and actual values ​​to obtain a trained deep learning network model.

[0122] Furthermore, corresponding power dispatch and transmission plans can be formulated based on the forecast results. When power supply is tight, priority can be given to transmission destinations with high growth rates in demand to ensure stable operation of the power grid and reliable power supply.

[0123] In one possible implementation of the embodiment of the present application, in step S105, based on the grid data and the transmission destinations corresponding to the power generation, determining the transmission corridor and transmission mode corresponding to each transmission destination includes:

[0124] Determine the power demand corresponding to each transmission destination;

[0125] Based on grid data, plan the initial transmission corridor between each transmission destination and energy base;

[0126] Calculate the transmission corridor capacity for each transmission destination based on the power demand and power generation of each transmission destination;

[0127] From the initial transmission corridors, select the transmission corridor corresponding to each transmission corridor capacity as the transmission corridor corresponding to each transmission destination;

[0128] Obtain the distance between each delivery destination and the energy base;

[0129] Determine the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs;

[0130] Based on the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs, the transmission mode corresponding to each transmission destination is determined.

[0131] Specifically, historical electricity demand data for each transmission destination is collected, including information such as historical electricity consumption, peak hours, and load fluctuations. Based on the grid data and the geographic location of the transmission destination, a geographic information system (GIS) or power planning software is used to plan the initial transmission corridor from the energy base to each transmission destination. The required transmission corridor capacity for each transmission destination is calculated based on the predicted electricity demand and power generation.

[0132] Furthermore, the transmission capacity of the initial transmission corridors can be evaluated to ensure they meet or exceed the calculated transmission corridor capacity. Corridors that meet the transmission corridor capacity are screened from the initial transmission corridors. If multiple corridors meet the criteria, additional factors such as construction cost, environmental impact, and maintenance difficulty can be further considered. The final selected transmission corridor is identified and marked as the corresponding transmission corridor for each transmission destination. Specifically, basic data on the transmission corridor is obtained. This data includes the corridor's length, geographic location, topography, climatic conditions, technical parameters of the transmission line, such as conductor type, number of splits, cross-section, resistance, and reactance, and power system structural data, including power source distribution, load distribution, and grid structure. Power system simulation software is used to model the transmission corridor and the power system within it, including power stations, substations, and transmission lines. The transmission capacity of the transmission corridor is quantitatively analyzed using methods such as maximum flow algorithms to determine the corridor's transmission capacity.

[0133] More specifically, the capacity of transmission corridors is compared with the power demand of the transmission destinations to identify those whose capacity can meet or exceed power demand. For example, assume there are three transmission corridors (A, B, and C) with capacities of 500MW, 800MW, and 300MW, respectively, and two transmission destinations (D and E) with power demands of 400MW and 600MW, respectively. Comparing the capacity and demand reveals that corridors A and B can meet the demand of D, while corridor B can meet the demand of E.

[0134] If there are multiple transmission corridors that meet the capacity requirements, the construction cost is further considered to select the transmission corridor. The construction cost of each corridor can be evaluated, including land acquisition, materials, construction and other costs. Taking into account the capacity requirements and construction costs, the most suitable transmission corridor is determined for each transmission destination. For example, for destination D, although corridors A and B both meet the capacity requirements, if the construction cost of corridor A is lower, corridor A is selected. For destination E, only corridor B meets the capacity requirements, and assuming its construction cost is within an acceptable range, corridor B is selected. Mark each transmission destination with its corresponding transmission corridor for subsequent design, construction and operation. Example marking: Destination D: Transmission Corridor A Destination E: Transmission Corridor B

[0135] Furthermore, the actual distance between each transmission destination and the energy base can be obtained using GIS or measurement tools. Distance and capacity can be categorized into different ranges or levels based on distance and transmission corridor capacity. For example, distance can be categorized into short, medium, and long distances, and capacity can be categorized into small, medium, and large capacities.

[0136] Different transmission methods (such as HVDC and UHVAC) are suitable for different distances and capacity ranges. By properly selecting a transmission method, transmission costs can be further reduced, efficiency improved, and environmental impact minimized. Based on the distance and capacity range or level, the appropriate transmission method for each transmission destination is determined. Specifically, for short-distance, small- and medium-capacity transmission: Low-voltage AC transmission: For very short distances (such as within cities or industrial parks) and smaller power demands, low-voltage AC transmission can be an economical and efficient option. This method generally does not require complex transmission equipment or large amounts of land, and is relatively inexpensive.

[0137] Medium-voltage AC transmission: When transmission distances are slightly longer or capacity requirements increase, medium-voltage AC transmission may be more appropriate. It can effectively transmit power within a certain range while maintaining high efficiency and low cost.

[0138] For medium- and long-distance, high-capacity power transmission: High-voltage AC transmission: As transmission distances increase and capacity demands expand, high-voltage AC transmission has become the mainstream choice. High-voltage transmission reduces current losses during transmission, improves transmission efficiency, and is relatively easy to implement over long distances.

[0139] Ultra-high voltage AC transmission: For longer transmission distances and greater capacity requirements, ultra-high voltage AC transmission is the preferred option. It enables long-distance transmission of electricity with extremely low losses and is particularly suitable for interconnecting large power grids.

[0140] For extremely long-distance, high-capacity power transmission: High-voltage direct current (HVDC): HVDC offers significant advantages when transmission distances are extremely long, such as across borders or via submarine cables. HVDC transmission reduces losses over long distances and easily interconnects different power grids through converter stations. Furthermore, HVDC transmission reduces electromagnetic radiation and interference with communication lines.

[0141] Ultra-high voltage direct current (UHVDC): For ultra-large capacity, long-distance transmission projects, UHVDC technology offers a more efficient and economical solution. UHVDC technology has been widely adopted worldwide and has become a key technology for achieving a global energy internet.

[0142] The above embodiment introduces a distributed distribution network planning method from the perspective of method flow, and the following embodiment introduces a distributed distribution network planning device from the perspective of virtual modules or virtual units. Please refer to the following embodiments for details.

[0143] See also Figure 2 The distributed distribution network planning device 20 may specifically include: a first acquisition module 201, a second acquisition module 202, a prediction module 203, a determination module 204, and an acquisition module 205. Specifically:

[0144] A distributed distribution network planning device 20, comprising:

[0145] The first acquisition module 201 is used to acquire grid data of the distribution network, where the grid data includes grid structure sub-data, grid stability sub-data, and grid operation sub-data;

[0146] The second acquisition module 202 is used to obtain the energy type, geographical location and installed capacity corresponding to the energy base;

[0147] A prediction module 203 is configured to predict the power generation of each energy type in the energy base in a future period based on the geographical location and installed capacity of the energy base;

[0148] A determination module 204 is used to determine a transmission destination corresponding to the power generation;

[0149] The obtaining module 205 is used to determine the transmission corridor and transmission mode corresponding to each transmission destination based on the power grid data and the transmission destination corresponding to the power generation, so as to obtain the transmission strategy of the energy base in the future period.

[0150] In one possible implementation of the embodiment of the present application, the prediction module 203 is configured to, when predicting the power generation of each energy type in the energy base in a future period based on the geographical location and installed capacity of the energy base, specifically:

[0151] Obtain meteorological parameters for the geographic location in the future period, including light intensity, wind speed, wind direction, and temperature;

[0152] Determine the meteorological sub-parameters corresponding to each energy category;

[0153] Input the installed sub-capacity and meteorological sub-parameters corresponding to each energy category into the corresponding prediction model;

[0154] The power generation output by each forecast model is obtained to obtain the power generation of each energy type in the energy base in the future period.

[0155] In one possible implementation of the embodiment of the present application, the determination module 204 is configured to, when determining the delivery destination corresponding to the power generation, specifically:

[0156] Obtain historical transmission destinations and backup transmission destinations corresponding to power generation;

[0157] Predict the power demand urgency of historical transmission destinations and backup transmission destinations in future periods;

[0158] Based on the power generation amount and the power urgency, at least one destination is selected from the historical transmission destinations and the backup transmission destinations as the transmission destination corresponding to the power generation amount.

[0159] In a possible implementation of the embodiment of the present application, the determination module 204 is specifically configured to:

[0160] Obtaining historical electricity consumption data corresponding to each of the historical delivery destination and the backup delivery destination, the historical electricity consumption data including the electricity consumption corresponding to each historical period and historical weather information;

[0161] Based on the historical electricity consumption data corresponding to the historical transmission destinations and the backup transmission destinations, predict the increase rate of electricity demand of the historical transmission destinations and the backup transmission destinations in the future period;

[0162] Determine the power supply capacity corresponding to the historical delivery destination and the backup delivery destination;

[0163] Based on the electricity demand increase rate and the power supply capacity, the electricity urgency corresponding to the historical transmission destination and the backup transmission destination is determined.

[0164] In one possible implementation of the embodiment of the present application, the determination module 204, when predicting the electricity demand increase rate of each of the historical delivery destination and the backup delivery destination in a future period based on the historical electricity consumption data corresponding to each of the historical delivery destination and the backup delivery destination, is specifically configured to:

[0165] Based on the historical electricity consumption data corresponding to the historical transmission destinations and the backup transmission destinations, construct the time series features corresponding to the historical transmission destinations and the backup transmission destinations;

[0166] Based on the power consumption corresponding to each historical power consumption data, the historical power demand increase rate corresponding to each historical period is calculated as the demand label corresponding to each historical period;

[0167] Based on the demand label corresponding to each historical period, the electricity demand increase rate in the future period is fitted by minimizing the loss function to obtain the electricity demand increase rate of the historical delivery destination and the backup delivery destination in the future period.

[0168] In one possible implementation of the embodiment of the present application, when the obtaining module 205 determines the transmission corridor and transmission mode corresponding to each transmission destination based on the power grid data and the transmission destination corresponding to the power generation, it is specifically configured to:

[0169] Determine the power demand corresponding to each transmission destination;

[0170] Based on grid data, plan the initial transmission corridor between each transmission destination and energy base;

[0171] Calculate the transmission corridor capacity for each transmission destination based on the power demand and power generation of each transmission destination;

[0172] From the initial transmission corridors, select the transmission corridor corresponding to each transmission corridor capacity as the transmission corridor corresponding to each transmission destination;

[0173] Obtain the distance between each delivery destination and the energy base;

[0174] Determine the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs;

[0175] Based on the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs, the transmission mode corresponding to each transmission destination is determined.

[0176] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0177] See also Figure 3 , the embodiment of the present application also introduces an electronic device from the perspective of a physical device, such as Figure 3 As shown, Figure 3 The electronic device 30 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 30 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 30 does not constitute a limitation on the embodiments of the present application.

[0178] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0179] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0180] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0181] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0182] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0183] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0184] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified 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 of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0185] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A distributed distribution network planning method, characterized in that: include: Acquiring grid data of the distribution network, wherein the grid data includes grid structure sub-data, grid stability sub-data, and grid operation sub-data; Obtain the energy type, geographical location, and installed capacity corresponding to the energy base; Based on the geographical location of the energy base and the installed capacity, predict the power generation of each energy type in the energy base in a future period; Determine a transmission destination corresponding to the power generation; Based on the power grid data and the transmission destinations corresponding to the power generation, determining the transmission corridor and transmission mode corresponding to each transmission destination to obtain the power transmission strategy of the energy base in the future period; The determining of a transmission destination corresponding to the power generation includes: Obtaining the locations of electric energy to be transmitted and the amount of electric energy to be transmitted corresponding to each location of electric energy to be transmitted; Calculating the distance between each location where electric energy is to be delivered and the geographical location of the energy base; Sort each location to be transmitted electric energy according to distance from smallest to largest to obtain a sorting result; According to the ranking result and the power generation of each energy type in the future period, a location that can meet the power generation requirements is selected from the plurality of locations to be transmitted as a transmission destination; The determining of the transmission destination corresponding to the power generation further includes: Obtaining a historical transmission destination and a backup transmission destination corresponding to the power generation; Predicting the power demand urgency of the historical delivery destination and the backup delivery destination in a future period; Based on the power generation amount and the power urgency, selecting at least one destination from the historical transmission destinations and the backup transmission destinations as the transmission destination corresponding to the power generation amount; The predicting of the electricity demand urgency of the historical transmission destination and the backup transmission destination in the future period includes: Acquire historical electricity consumption data corresponding to each of the historical delivery destination and the backup delivery destination, wherein the historical electricity consumption data includes electricity consumption corresponding to each historical period and historical weather information; Based on the historical electricity consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, predicting the electricity demand increase rate of the historical transmission destination and the backup transmission destination in a future period; Determining the power supply capacity corresponding to each of the historical delivery destination and the backup delivery destination; determining the power urgency corresponding to each of the historical delivery destination and the backup delivery destination based on the power demand increase rate and the power supply capacity; The determining, based on the power grid data and the transmission destinations corresponding to the power generation, a transmission corridor and a transmission mode corresponding to each transmission destination, includes: Determine the power demand corresponding to each transmission destination; Based on the grid data, planning an initial transmission corridor between each transmission destination and the energy base; Calculating the capacity of the transmission corridor corresponding to each transmission destination based on the power demand of each transmission destination and the power generation; Selecting, from the initial transmission corridors, a transmission corridor corresponding to each transmission corridor capacity as a transmission corridor corresponding to each transmission destination; Obtaining the distance between each delivery destination and the energy base; Determine the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs; Determine the transmission mode corresponding to each transmission destination based on the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs; The step of selecting, from the initial transmission corridors, a transmission corridor corresponding to the capacity of each transmission corridor as a transmission corridor corresponding to each transmission destination includes: Selecting transmission corridors that meet the transmission corridor capacity from the initial transmission corridors; If there are multiple transmission corridors that meet the transmission corridor capacity, the transmission corridors corresponding to the transmission destination will be further screened based on construction cost, environmental impact and maintenance difficulty.

2. The distributed distribution network planning method according to claim 1, characterized in that: The predicting of the power generation of each energy type in the energy base in a future period based on the geographical location corresponding to the energy base and the installed capacity includes: Obtaining meteorological parameters of the geographic location in a future period, the meteorological parameters including light intensity, wind speed, wind direction, and temperature; Determine the meteorological sub-parameters corresponding to each energy category; Input the installed sub-capacity and meteorological sub-parameters corresponding to each energy category into the corresponding prediction model; The power generation output by each prediction model is obtained to obtain the power generation of each energy type in the energy base in the future period.

3. The distributed distribution network planning method according to claim 1, characterized in that: The predicting, based on the historical electricity consumption data corresponding to the historical transmission destination and the backup transmission destination, the electricity demand increase rate of each of the historical transmission destination and the backup transmission destination in a future period includes: constructing time series features corresponding to the historical delivery destination and the backup delivery destination based on the historical power consumption data corresponding to the historical delivery destination and the backup delivery destination; Based on the power consumption corresponding to each historical power consumption data, the historical power demand increase rate corresponding to each historical period is calculated as the demand label corresponding to each historical period; Based on the demand label corresponding to each historical period, the electricity demand increase rate in the future period is fitted by minimizing the loss function to obtain the electricity demand increase rate of the historical delivery destination and the backup delivery destination in the future period.

4. A distributed distribution network planning device, characterized in that: include: A first acquisition module is used to acquire grid data of the distribution network, wherein the grid data includes grid structure sub-data, grid stability sub-data and grid operation sub-data; The second acquisition module is used to obtain the energy type, geographical location and installed capacity corresponding to the energy base; a prediction module, configured to predict the power generation of each energy type in the energy base in a future period based on the geographical location corresponding to the energy base and the installed capacity; A determination module, configured to determine a delivery destination corresponding to the power generation; an obtaining module, configured to determine, based on the grid data and the transmission destinations corresponding to the power generation, a transmission corridor and a transmission mode corresponding to each transmission destination, so as to obtain a transmission strategy for the energy base in a future period; The determining module is used to determine the transmission destination corresponding to the power generation, including: Obtaining the locations of electric energy to be transmitted and the amount of electric energy to be transmitted corresponding to each location of electric energy to be transmitted; Calculating the distance between each location where electric energy is to be delivered and the geographical location of the energy base; Sort each location to be transmitted electric energy according to distance from smallest to largest to obtain a sorting result; According to the ranking result and the power generation of each energy type in the future period, a location that can meet the power generation requirements is selected from the plurality of locations to be transmitted as a transmission destination; The determining module is used to determine the transmission destination corresponding to the power generation, and further includes: Obtaining a historical transmission destination and a backup transmission destination corresponding to the power generation; Predicting the power demand urgency of the historical delivery destination and the backup delivery destination in a future period; Based on the power generation amount and the power urgency, selecting at least one destination from the historical transmission destinations and the backup transmission destinations as the transmission destination corresponding to the power generation amount; The determining module is used to predict the electricity urgency of each of the historical delivery destination and the backup delivery destination in a future period, including: Acquire historical electricity consumption data corresponding to each of the historical delivery destination and the backup delivery destination, wherein the historical electricity consumption data includes electricity consumption corresponding to each historical period and historical weather information; Based on the historical electricity consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, predicting the electricity demand increase rate of the historical transmission destination and the backup transmission destination in a future period; Determining the power supply capacity corresponding to each of the historical delivery destination and the backup delivery destination; determining the power urgency corresponding to each of the historical delivery destination and the backup delivery destination based on the power demand increase rate and the power supply capacity; The obtaining module is configured to determine a transmission corridor and a transmission mode corresponding to each transmission destination based on the power grid data and the transmission destination corresponding to the power generation, including: Determine the power demand corresponding to each transmission destination; Based on the grid data, planning an initial transmission corridor between each transmission destination and the energy base; Calculating the capacity of the transmission corridor corresponding to each transmission destination based on the power demand of each transmission destination and the power generation; Selecting, from the initial transmission corridors, a transmission corridor corresponding to each transmission corridor capacity as a transmission corridor corresponding to each transmission destination; Obtaining the distance between each delivery destination and the energy base; Determine the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs; Determine the transmission mode corresponding to each transmission destination based on the distance range to which each distance belongs and the capacity range to which each transmission corridor capacity belongs; The step of selecting, from the initial transmission corridors, a transmission corridor corresponding to the capacity of each transmission corridor as a transmission corridor corresponding to each transmission destination includes: Selecting transmission corridors that meet the transmission corridor capacity from the initial transmission corridors; If there are multiple transmission corridors that meet the transmission corridor capacity, the transmission corridors corresponding to the transmission destination will be further screened based on construction cost, environmental impact and maintenance difficulty.

5. The distributed power distribution network planning device according to claim 4, characterized in that: The prediction module is specifically configured to: Obtaining meteorological parameters of the geographic location in a future period, the meteorological parameters including light intensity, wind speed, wind direction, and temperature; Determine the meteorological sub-parameters corresponding to each energy category; Input the installed sub-capacity and meteorological sub-parameters corresponding to each energy category into the corresponding prediction model; The power generation output by each prediction model is obtained to obtain the power generation of each energy type in the energy base in the future period.

6. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the distributed distribution network planning method according to any one of claims 1 to 3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the distributed distribution network planning method according to any one of claims 1 to 3.

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