Planning method, device and equipment of distributed power distribution network and medium

Abstract

The invention relates to the technical field of power distribution network planning, in particular to a distributed power distribution network planning method and device, equipment and a medium. The method comprises the steps that by collecting and analyzing power grid structure sub-data, power grid stability sub-data and power grid operation sub-data, the current state and the future possible operation trend of the power distribution network can be comprehensively known, a power transmission strategy is made based on detailed power grid data, the stability and safety requirements of the power grid can be fully considered, and the power transmission efficiency is improved. Meanwhile, by reasonably planning a power transmission corridor and a power transmission mode, the power transmission loss in the power grid can be reduced, the fault risk is reduced, and the overall stability and reliability of the power grid are improved. In combination with the energy category, the geographic position and the installed capacity information of the energy base, the power generation quantity of each energy category in the future period can be accurately predicted, so that a more scientific and reasonable power transmission strategy is formulated, the configuration and scheduling of power resources are optimized, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] 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. Background Art

[0002] With the profound transformation of the energy production pattern, the power grid system is undergoing unprecedented changes, among which the widespread access to distributed power sources, especially wind power generation, photovoltaic power generation and other renewable energy sources, has become a significant trend. This structural change has not only greatly enriched the energy composition of the power grid, but also prompted a fundamental reshaping of the topology of the traditional distribution network.

[0003] For energy bases with abundant distributed energy, how to efficiently convert these clean energies into practically usable electricity, accurately integrate them into the large power grid, and then maximize their utilization through scientific and reasonable power transmission planning has become an important issue that needs to be tackled. However, in reality, some electricity suffers unnecessary losses during transmission due to improper transmission methods or lengthy transmission path designs, which not only weakens the power generation efficiency of the energy base, but also reduces the overall energy utilization rate. 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, device, equipment and medium for a distributed distribution network.

[0005] In a first aspect, the present application provides a distributed distribution network planning method, which adopts the following technical solution: A distributed distribution network planning method, comprising: Acquire power grid data of the distribution network, wherein the power grid data includes power grid structure sub-data, power grid stability sub-data, and power grid operation sub-data; Obtain the energy category, geographical location and installed capacity corresponding to the energy base; Based on the geographical location corresponding to 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, the transmission corridor and the transmission method corresponding to each transmission destination are determined to obtain the transmission strategy of the energy base in the future period.

[0006] By adopting the above technical solutions, by collecting and analyzing the 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 possible future operation 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 the transmission corridors and transmission methods, we can reduce the 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, we can accurately predict the power generation of each energy category in the future cycle, so as to formulate a more scientific and reasonable transmission strategy, optimize the allocation and scheduling of power resources, and improve energy utilization efficiency.

[0007] In a possible implementation, based on the geographical location corresponding to the energy base and the installed capacity, predicting the power generation of each energy type in the energy base in a future period includes: Acquire meteorological parameters of the geographical location in a future period, wherein the meteorological parameters include light intensity, wind speed, wind direction, and temperature; Determine the meteorological sub-parameters corresponding to each energy category; Input the installed sub-capacity and the corresponding 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.

[0008] By adopting the above technical solution, by obtaining detailed meteorological parameters (such as light intensity, wind speed, wind direction and temperature) of the geographical location of the energy base in the future period, and selecting corresponding meteorological sub-parameters as input for different energy categories (such as photovoltaic power generation and wind power generation), the direct impact of natural environmental factors on power generation is fully considered. Combining these data with the installed sub-capacity of the energy base and inputting them into the prediction model designed for various energy characteristics can significantly improve the accuracy of power generation prediction, which not only helps power grid operators to formulate transmission strategies and scheduling plans more accurately, but also provides strong support for investment decisions and operation optimization of energy bases.

[0009] In a possible implementation, determining a transmission destination corresponding to the power generation includes: Obtaining a historical transmission destination and a backup transmission destination corresponding to the power generation; Predicting the electricity 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, 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.

[0010] 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.

[0011] In a possible implementation, predicting the electricity urgency of the historical transmission destination and the backup transmission destination in a future period includes: Acquire historical power consumption data corresponding to the historical delivery destination and the standby delivery destination, respectively, wherein the historical power consumption data includes power consumption corresponding to each historical period and historical weather information; Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, predicting the power consumption demand increase rate of the historical transmission destination and the backup transmission destination in the future period; Determine the power supply capacity corresponding to each of the historical delivery destination and the backup delivery destination; Based on the increase rate of electricity demand and the power supply capacity, the electricity urgency corresponding to each of the historical delivery destination and the backup delivery destination is determined.

[0012] By adopting the above technical solution and deeply analyzing historical electricity consumption data and historical weather information, the solution can more accurately predict the increase rate of electricity demand at each delivery destination in the future cycle, so that the energy base can 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 to avoid delivering electricity to areas with relatively abundant power supply and no urgent electricity demand, thereby effectively reducing energy waste. At the same time, it can also ensure that during peak electricity demand or emergency situations, electricity can be quickly and accurately delivered to the areas where it is most needed, avoiding power shortages.

[0013] In a possible implementation, based on the historical power consumption data corresponding to the historical delivery destination and the backup delivery destination, respectively, predicting the power consumption demand increase rate of the historical delivery destination and the backup delivery destination in a future period includes: Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, constructing time series features corresponding to the historical transmission destination and the backup transmission destination respectively; Based on the electricity consumption corresponding to each historical electricity consumption data, the historical electricity demand increase rate corresponding to each historical period is calculated to serve as the demand label corresponding to each historical period; Based on the demand label corresponding to each historical period, the increase rate of electricity demand in the future period is fitted by minimizing the loss function to obtain the increase rate of electricity demand of the historical delivery destination and the backup delivery destination in the future period.

[0014] By adopting the above technical solution and constructing the time series characteristics corresponding to the historical transmission destination and the standby transmission destination, it is possible to capture the law and trend of electricity consumption over time, more accurately reflect the seasonal and cyclical fluctuations in electricity demand, and thus improve the prediction accuracy of the increase rate of electricity demand in the future cycle. At the same time, by using the demand labels of the historical cycle (i.e., the increase rate of historical electricity demand), the increase rate of electricity demand in the future cycle is fitted by minimizing the loss function, which helps to adjust the parameters of the prediction model, reduce the error between the predicted value and the actual value, and further improve the prediction ability and reliability of the model.

[0015] In a 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: 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; Calculate the capacity of the transmission corridor corresponding to each transmission destination based on the power demand of each transmission destination and the power generation; From the initial transmission corridors, a transmission corridor corresponding to the capacity of each transmission corridor is selected 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; 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.

[0016] By adopting the above technical solution, the initial transmission corridor is planned according to the power demand of each transmission destination, and the capacity of the transmission corridor is further accurately calculated according to the power generation and power demand, which can ensure that the design of the transmission corridor not only meets the current power demand, but also leaves a certain margin for possible future growth, thereby improving the transmission efficiency. At the same time, by selecting suitable transmission corridors, the energy loss in the transmission process can be reduced and the transmission reliability can be improved.

[0017] In a second aspect, the present application provides a distributed distribution network planning device, which adopts the following technical solution: A distributed power distribution network planning device, comprising: A first acquisition module is used to acquire power grid data of the distribution network, wherein the power grid data includes power grid structure sub-data, power grid stability sub-data and power grid operation sub-data; The second acquisition module is used to obtain the energy category, 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, used to determine a transmission destination corresponding to the power generation; The module is used to determine the transmission corridor and the 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.

[0018] In a 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 used to: Acquire meteorological parameters of the geographical location in a future period, wherein the meteorological parameters include light intensity, wind speed, wind direction, and temperature; Determine the meteorological sub-parameters corresponding to each energy category; Input the installed sub-capacity and the corresponding 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.

[0019] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: 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 described in the first aspect above.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the distributed distribution network planning method described in the first aspect above.

[0021] In summary, the present application includes the following beneficial technical effects: by collecting and analyzing the grid structure sub-data, grid stability sub-data and grid operation sub-data, the current state of the distribution network and possible future operation trends can be fully understood. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a distributed distribution network planning method provided in an embodiment of the present application; Figure 2 It is a block diagram of a distributed distribution network planning device provided in an embodiment of the present application; Figure 3 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In order to facilitate understanding of the technical solution proposed in this application, several elements 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.

[0026] The transmission corridor is a strip area within a specified width on both sides of the high-voltage overhead power line, which is used to ensure the normal operation and safety of the power line. The transmission corridor is a three-dimensional space, including the two dimensions of land and net space. The width and range of the corridor are determined according to the technical requirements and safety standards of the power line. The corridor widths are different for lines of different voltage levels.

[0027] Grid structure data refers to a collection of data that describes the connection relationship between the various components of the grid (such as power plants, substations, transmission lines, etc.). This data is the basis for grid planning and operation, and is used to reflect the physical layout and logical structure of the grid. Grid structure data usually includes: the location of power plants and substations, the direction and length of transmission lines, the connection relationship of equipment, and the topological structure of the grid.

[0028] Grid stability data refers to a collection of data that reflects the ability of a grid to maintain a stable operating state when it is operating normally or being disturbed. Grid stability is the cornerstone of safe grid operation, and mainly includes three aspects: static stability, transient stability, and dynamic stability. Grid stability data usually includes: static stability data, transient stability data, and dynamic stability data.

[0029] Grid operation data refers to a collection of data used to monitor and analyze the operation status of the grid. These data reflect the operation status of the grid in real time, including the operation status of equipment, power transmission status, load changes, etc. Grid operation data usually includes: equipment status data, power transmission data, load data, and fault and alarm information.

[0030] The present application embodiment provides a distributed 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, or 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 smart phone, 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: Step S101: Acquire power grid data of the distribution network.

[0031] The power grid data includes power grid structure sub-data, power grid stability sub-data and power grid operation sub-data. Collect power grid data of the distribution network, specifically: Grid structure sub-data: The geographical location and connection relationship of each substation and transmission line in the power grid are obtained through the Geographic Information System (GIS) to construct a grid topology map.

[0032] 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.

[0033] Grid operation sub-data: Real-time acquisition of information such as equipment status, power transmission, load changes, and fault alarms through the grid monitoring system.

[0034] Step S102: Obtain the energy type, geographical location, and installed capacity corresponding to the energy base.

[0035] Among them, it refers to the total rated active power of the generator sets or energy conversion equipment actually installed in the distributed energy system. Specifically, the data of energy bases are obtained, including energy categories (such as wind power, photovoltaics, hydropower, etc.), geographical locations (latitude and longitude), and the installed capacity corresponding to each energy category.

[0036] Step S103: Based on the geographical location and machine capacity corresponding to the energy base, predict the power generation of each energy type in the energy base in the future period.

[0037] 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.

[0038] For each energy category, according to its geographical location (such as climate conditions, light intensity, water resources, etc.) and installed capacity, use a suitable prediction model to predict the power generation in the future cycle. Specifically, for each energy category: obtain the changes in the factors that affect the power generation of the energy category in the future cycle, and calculate the corresponding power generation of the energy category based on the changes in the factors and the installed capacity corresponding to the energy category. Exemplarily, for the photovoltaic power generation energy category, the factor that affects the power generation of the energy category is light intensity. Therefore, obtain a curve of the light intensity change over time at the geographical location in the future cycle, and calculate the corresponding power generation of the photovoltaic power generation energy category in the future cycle based on the installed capacity corresponding to the photovoltaic power generation energy category.

[0039] Step S104: determine the transmission destination corresponding to the power generation amount.

[0040] Receive the location of the energy to be transmitted and the amount of electricity to be transmitted corresponding to each location of the energy to be transmitted, calculate the distance between each location of the energy to be transmitted and the location of the energy base, sort each location of the energy to be transmitted in order of distance from small to large, and according to the sorting and the power generation of each energy category in the future cycle, select the location that can meet the power generation from multiple locations of the energy to be transmitted as the transmission destination. For example, the total power generation of each energy category in the future cycle is 150kwh, the distance between the location of the energy to be transmitted and the location of the energy base and the amount of electricity to be transmitted are 15km and 50kwh, 25km and 60kwh, 27km and 80kwh, 30km and 60kwh, respectively. According to the sorting, the power generation of 150kwh can only be distributed to the locations with a distance of 15km, 25km and 27km, so these three locations are used as the transmission destinations.

[0041] Step S105: Based on the power 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.

[0042] Specifically, based on the grid data and the geographical location of the transmission destination, the grid planning software is used to plan the initial transmission corridor. According to the predicted power generation and the power demand of the transmission destination, the capacity demand of each transmission corridor is calculated, and the transmission corridors that meet the capacity demand are selected from the initial transmission corridors, and the terrain, environment, cost and other factors are considered for optimization. The distance between the energy base and the transmission destination is obtained, and the transmission mode (such as high-voltage direct current transmission, ultra-high voltage alternating current transmission, etc.) is determined in combination with the capacity of the transmission corridor.

[0043] Furthermore, a transmission strategy for the energy base in future cycles should be formulated, including the selection of transmission corridors, determination of transmission methods, and allocation of transmission volume.

[0044] The embodiment of the present application provides a planning method for a distributed distribution network. By collecting and analyzing the grid structure sub-data, grid stability sub-data, and grid operation sub-data, it is possible to fully understand the current state of the distribution network and possible future operation 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 the transmission corridors and transmission methods, it is possible to reduce the 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, 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.

[0045] In a possible implementation of the embodiment of the present application, in the above step S103, based on the geographical location and installed capacity corresponding to the energy base, the power generation of each energy type in the energy base in the future period is predicted, including: Obtain meteorological parameters of the geographic location in the future period, 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 the corresponding meteorological sub-parameters corresponding to each energy category into the corresponding prediction model; The power generation output by each forecasting model is obtained to obtain the power generation of each energy type in the energy base in the future period.

[0046] 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.

[0047] Specifically, the meteorological parameter data of the energy base in the future period is obtained. These data can be forecast data based on the meteorological model. According to the energy type of the energy base (such as wind power, photovoltaic power, hydropower, etc.), the meteorological parameters required for each energy type are determined. For example, wind power requires wind speed and wind direction; photovoltaic power requires light intensity, temperature, etc.

[0048] 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.

[0049] In a possible implementation of the embodiment of the present application, in the above step S104, determining the transmission destination corresponding to the power generation includes: Obtain the historical transmission destination and backup transmission destination corresponding to the power generation; Predict the power demand urgency of each of the historical transmission destinations and the backup transmission destinations in the future period; Based on the power generation amount and the power consumption 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.

[0050] The energy management database records the destination, amount, and time of power transmission in the past period of time. The backup transmission destination is pre-set and can be used as a substitute for receiving power when the main transmission destination cannot receive power for some reason.

[0051] Specifically, the electricity consumption data of historical delivery destinations and backup delivery destinations in the past period of time are collected, including information such as electricity consumption, peak hours of electricity consumption, and changes in electricity load. Combined with the weather forecast in the future period, the electricity urgency of each delivery destination in the future period is predicted. Among them, the electricity urgency can be measured by a variety of indicators, such as electricity load rate, electricity growth rate, and peak-to-valley difference in electricity consumption.

[0052] More specifically, evaluate the degree of match between the power demand of each delivery destination and the power generation of distributed energy. Consider factors such as the scale and stability of the power generation of distributed energy and the loss during the transmission process to ensure that the delivered power can meet the actual needs of the destination. Based on the predicted power urgency and the degree of matching of power demand, conduct a comprehensive evaluation of historical delivery destinations and backup delivery destinations. Select at least one destination from the evaluation results as the delivery destination corresponding to the power generation of distributed energy. Determine the priority order of power transmission based on the power urgency of each delivery destination and the degree of matching of power demand. When the power supply is tight, give priority to destinations with high power urgency.

[0053] A possible implementation of the embodiment of the present application, in the above embodiment, predicting the power consumption urgency of the historical delivery destination and the backup delivery destination in the future period includes: Obtaining historical power consumption data corresponding to each of the historical delivery destination and the backup delivery destination, the historical power consumption data including power consumption corresponding to each historical period and historical weather information; Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, predict the increase rate of power demand of the historical transmission destination and the backup transmission destination in the future period; Determine the power supply capacity corresponding to the historical delivery destination and the backup delivery destination; Based on the increase rate of electricity demand and the power supply capacity, the electricity urgency corresponding to each of the historical transmission destination and the backup transmission destination is determined.

[0054] Specifically, the historical power consumption data corresponding to the historical delivery destination and the backup delivery destination are obtained. The historical power consumption data includes power consumption records of multiple historical periods and weather information corresponding to each historical period (such as temperature, humidity, rainfall, etc.). The historical power consumption data is cleaned and sorted to ensure the integrity and accuracy of the data.

[0055] Analyze the correlation between historical electricity consumption data and weather information to identify key factors that affect electricity demand. For example, hot weather may lead to increased air conditioning electricity consumption, thereby affecting total electricity consumption. Use statistical methods (such as time series analysis, regression analysis) or machine learning models (such as neural networks, random forests) to predict the rate of increase in electricity demand in future cycles. Based on the prediction results, obtain the predicted value of the rate of increase in electricity demand for historical transmission destinations and backup transmission destinations in future cycles.

[0056] Collect information on power supply facilities at historical and backup delivery destinations, including installed capacity of power plants, transmission capacity of power grids, capacity of energy storage facilities, etc. Evaluate the power supply capacity of each delivery destination, i.e., the maximum amount of power that can be provided in the future period. Compare the predicted rate of increase in power demand with the assessed power supply capacity. If the predicted rate of increase in power demand exceeds the growth in power supply capacity or the existing power supply capacity, it indicates that there is a power emergency at the delivery destination.

[0057] The power urgency index is defined as the supply-demand ratio (i.e., the ratio of predicted power demand to power supply capacity). Based on the power urgency index, the power urgency of the historical delivery destination and the backup delivery destination in the future cycle is determined. The power urgency can be divided into different levels (such as low, medium, and high) to facilitate subsequent decision-making and scheduling.

[0058] A possible implementation of the embodiment of the present application, in the above embodiment, based on the historical power consumption data corresponding to the historical delivery destination and the backup delivery destination, predicting the power demand increase rate of each of the historical delivery destination and the backup delivery destination in the future period includes: Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, the time series features corresponding to the historical transmission destination and the backup transmission destination are constructed; Based on the electricity consumption corresponding to each historical electricity consumption data, the historical electricity demand increase rate corresponding to each historical period is calculated to serve as the demand label corresponding to each historical period; Based on the demand label corresponding to each historical period, the increase rate of electricity demand in the future period is fitted by minimizing the loss function to obtain the increase rate of electricity demand of the historical delivery destination and the backup delivery destination in the future period.

[0059] Specifically, for each transmission destination, its historical electricity consumption data is arranged in chronological order to construct a time series. Each point in the time series represents the electricity consumption of a historical period. The features in the time series are extracted, including the first-order difference of electricity consumption (indicating the change in electricity consumption in adjacent periods), the second-order difference (indicating the change in the change), the moving average, and the seasonal decomposition components (such as trend, seasonality, residuals). The above features help capture the trend, periodicity, and randomness in electricity consumption data.

[0060] For each historical cycle, the power consumption ratio between the historical cycle and the previous cycle is calculated to obtain the historical power demand increase rate of the cycle. The power consumption ratio can be selected according to the actual situation. For example, the power consumption difference can be directly used as the power consumption ratio divided by the power consumption of the previous cycle, or the percentage growth rate can be calculated as the power consumption ratio.

[0061] Furthermore, the calculated historical electricity demand increase rate is used as the demand label corresponding to each historical period, corresponding to the time series features extracted previously. The deep learning network model is used to predict the electricity demand increase rate in the future period, and the deep learning network model outputs the corresponding electricity demand increase rate prediction value according to the learned rules. Among them, the historical electricity consumption data (including time series features and demand labels) is used as training data to train the deep learning network model. During the training process, the model will learn the law of electricity consumption changes in historical data, and try to minimize the loss function (such as mean square error, mean absolute error, etc.) between the predicted value and the actual value to obtain a trained deep learning network model.

[0062] Furthermore, corresponding power dispatching and transmission plans can be formulated based on the forecast results. When power supply is tight, priority can be given to transmission destinations with higher rates of power demand increase to ensure the stable operation of the power grid and the reliability of power supply.

[0063] A possible implementation of the embodiment of the present application is that in the above step S105, based on the power grid data and the transmission destination corresponding to the power generation, the transmission corridor and the transmission mode corresponding to each transmission destination are determined, including: Determine the power demand corresponding to each transmission destination; Based on the grid data, plan the initial transmission corridor between each transmission destination and the energy base; Calculate the transmission corridor capacity for each transmission destination based on the power demand and power generation of each transmission destination; From the initial transmission corridors, select the transmission corridor corresponding to the capacity of each transmission corridor as the transmission corridor corresponding to each transmission destination; Obtain 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; 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.

[0064] Specifically, the historical power demand data of each transmission destination is collected, including historical power consumption, peak power consumption hours, power load changes, etc. Based on the grid data and the geographical location of the transmission destination, the initial transmission corridor from the energy base to each transmission destination is planned using a geographic information system (GIS) or power planning software, and the transmission corridor capacity required for each transmission destination is calculated based on the predicted power demand and power generation.

[0065] Furthermore, the transmission capacity of the initial transmission corridors can be evaluated to ensure that they can meet or exceed the calculated transmission corridor capacity. Corridors that meet the transmission corridor capacity are selected from the initial transmission corridors. If multiple corridors meet the conditions, other factors such as construction cost, environmental impact, maintenance difficulty, etc. can be further considered. The final selected transmission corridor is determined and marked as the transmission corridor corresponding to each transmission destination. Specifically, the basic data of the transmission corridor is obtained, wherein the basic data of the transmission corridor includes the length, geographical location, topography, climatic conditions, technical parameters of the transmission line including conductor model, number of split roots, cross section, resistance, reactance, etc., and power system structure data including power distribution, load distribution, and grid structure. The transmission corridor and the power system in which it is located are modeled using power system simulation software, including power stations, substations, transmission lines, etc. According to the maximum flow algorithm, etc., the transmission capacity of the transmission corridor is quantitatively analyzed to obtain the transmission capacity of the transmission corridor.

[0066] More specifically, the capacity of the transmission corridor is compared with the power demand of the transmission destination, and those transmission corridors whose capacity can meet or exceed the power demand are screened out. For example, suppose 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. By comparing the capacity and demand, it is found that corridors A and B can meet the demand of D, and corridor B can meet the demand of E.

[0067] 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. Considering the capacity requirements and construction costs, the most suitable transmission corridor is determined for each transmission destination. For example, for destination D, although both corridors A and B 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 that 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.

[0068] Furthermore, the actual distance between each transmission destination and the energy base can be obtained using GIS or measurement tools. According to the distance and transmission corridor capacity, the distance and capacity can be divided into different ranges or levels. For example, the distance can be divided into short distance, medium distance and long distance, and the capacity can be divided into small capacity, medium capacity and large capacity.

[0069] Since different transmission methods (such as high-voltage direct current transmission, ultra-high-voltage alternating current transmission, etc.) are suitable for different distances and capacity ranges. By reasonably selecting the transmission method, the transmission cost can be further reduced, the transmission efficiency can be improved, and the impact on the environment can be reduced. Based on the range or level of distance and capacity, determine the transmission method corresponding to each transmission destination. 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 may be an economical and efficient choice. This method usually does not require complex transmission equipment and a large amount of land, and the cost is relatively low.

[0070] Medium voltage AC transmission: When the transmission distance is slightly longer or the capacity demand increases, medium voltage AC transmission may be more suitable. It can effectively transmit electric energy within a certain range while maintaining high efficiency and low cost.

[0071] For medium- and long-distance, large-capacity power transmission: High-voltage AC transmission: With the increase in transmission distance and the expansion of capacity demand, high-voltage AC transmission has become the mainstream choice. High-voltage transmission can reduce the loss of current during the transmission process, improve transmission efficiency, and it is relatively easy to achieve long-distance transmission.

[0072] Ultra-high voltage AC transmission: Ultra-high voltage AC transmission is a better choice for longer transmission distances and larger capacity requirements. It can achieve long-distance transmission of electric energy with extremely low losses and is particularly suitable for interconnection between large power grids.

[0073] For extremely long distances and large capacity transmission: High-voltage direct current (HVDC): When the transmission distance is extremely long (such as cross-border transmission or submarine cable transmission), high-voltage direct current has significant advantages. DC transmission has low losses when transmitted over long distances, and can easily achieve interconnection between different power grids through converter stations. In addition, DC transmission can also reduce electromagnetic radiation and interference with communication lines.

[0074] UHV DC transmission: For large-capacity and long-distance transmission projects, UHV DC transmission technology can provide a more efficient and economical solution. UHV DC transmission technology has been widely used around the world and has become an important technical means to realize the global energy Internet.

[0075] The above-mentioned 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 a virtual module or a virtual unit. For details, please refer to the following embodiment.

[0076] 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: A distributed power distribution network planning device 20, comprising: A first acquisition module 201 is used to acquire power grid data of the distribution network, where the power grid data includes power grid structure sub-data, power grid stability sub-data and power grid operation sub-data; The second acquisition module 202 is used to acquire the energy type, geographical location and installed capacity corresponding to the energy base; A prediction module 203, used 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 corresponding to the energy base; A determination module 204 is used to determine a transmission destination corresponding to the power generation; The 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.

[0077] In a possible implementation of the embodiment of the present application, the prediction module 203 is specifically used to predict the power generation of each energy type in the energy base in the future period based on the geographical location and installed capacity corresponding to the energy base: Obtain meteorological parameters of the geographic location in the future period, 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 the corresponding meteorological sub-parameters corresponding to each energy category into the corresponding prediction model; The power generation output by each forecasting model is obtained to obtain the power generation of each energy type in the energy base in the future period.

[0078] In a possible implementation of the embodiment of the present application, when determining the transmission destination corresponding to the power generation, the determination module 204 is specifically configured to: Obtain the historical transmission destination and backup transmission destination corresponding to the power generation; Predict the power demand urgency of each of the historical transmission destinations and the backup transmission destinations in the future period; Based on the power generation amount and the power consumption 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.

[0079] In a possible implementation of the embodiment of the present application, when the determination module 204 predicts the power consumption urgency of the historical delivery destination and the backup delivery destination in the future period, it is specifically used to: Obtaining historical power consumption data corresponding to each of the historical delivery destination and the backup delivery destination, the historical power consumption data including power consumption corresponding to each historical period and historical weather information; Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, predict the increase rate of power demand of the historical transmission destination and the backup transmission destination in the future period; Determine the power supply capacity corresponding to the historical delivery destination and the backup delivery destination; Based on the increase rate of electricity demand and the power supply capacity, the electricity urgency corresponding to each of the historical transmission destination and the backup transmission destination is determined.

[0080] In a possible implementation of the embodiment of the present application, when the determination module 204 predicts the increase rate of electricity demand of the historical delivery destination and the backup delivery destination in the future period based on the historical electricity consumption data corresponding to the historical delivery destination and the backup delivery destination, it is specifically used to: Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, the time series features corresponding to the historical transmission destination and the backup transmission destination are constructed; Based on the electricity consumption corresponding to each historical electricity consumption data, the historical electricity demand increase rate corresponding to each historical period is calculated to serve as the demand label corresponding to each historical period; Based on the demand label corresponding to each historical period, the increase rate of electricity demand in the future period is fitted by minimizing the loss function to obtain the increase rate of electricity demand of the historical delivery destination and the backup delivery destination in the future period.

[0081] In a possible implementation of the embodiment of the present application, when the obtaining module 205 determines the transmission corridor and the 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 used to: Determine the power demand corresponding to each transmission destination; Based on the grid data, plan the initial transmission corridor between each transmission destination and the energy base; Calculate the transmission corridor capacity for each transmission destination based on the power demand and power generation of each transmission destination; From the initial transmission corridors, select the transmission corridor corresponding to the capacity of each transmission corridor as the transmission corridor corresponding to each transmission destination; Obtain 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; 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.

[0082] Those skilled in the art can 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.

[0083] 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, such as through a bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 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.

[0084] The processor 301 may 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 devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may 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.

[0085] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0086] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices 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 compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0087] The memory 303 is used to store the 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 contents shown in the above method embodiment.

[0088] The electronic devices include but are not limited to: mobile phones, laptop computers, 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 bring any limitation to the functions and scope of use of the embodiments of the present application.

[0089] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0090] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence 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 a part of the steps in the flowchart 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 part of the sub-steps or stages of other steps.

[0091] The above are only some 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: Acquire power grid data of the distribution network, wherein the power grid data includes power grid structure sub-data, power grid stability sub-data, and power grid operation sub-data; Obtain the energy category, geographical location and installed capacity corresponding to the energy base; Based on the geographical location corresponding to 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, the transmission corridor and the transmission method corresponding to each transmission destination are determined to obtain the transmission strategy of the energy base in the future period.

2. The distributed distribution network planning method according to claim 1, characterized in that: The predicting of the amount of electricity produced by 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: Acquire meteorological parameters of the geographical location in a future period, wherein the meteorological parameters include light intensity, wind speed, wind direction, and temperature; Determine the meteorological sub-parameters corresponding to each energy category; Input the installed sub-capacity and the corresponding 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 determining of the transmission destination corresponding to the power generation includes: Obtaining a historical transmission destination and a backup transmission destination corresponding to the power generation; Predicting the electricity urgency of the historical delivery destination and the backup delivery destination in future periods; 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.

4. The distributed distribution network planning method according to claim 3, characterized in that: The predicting of the electricity consumption urgency of the historical transmission destination and the backup transmission destination in the future period includes: Acquire historical power consumption data corresponding to the historical delivery destination and the standby delivery destination, respectively, wherein the historical power consumption data includes power consumption corresponding to each historical period and historical weather information; Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, predicting the power consumption demand increase rate of the historical transmission destination and the backup transmission destination in the future period; Determine the power supply capacity corresponding to each of the historical delivery destination and the backup delivery destination; Based on the increase rate of electricity demand and the power supply capacity, the electricity urgency corresponding to each of the historical delivery destination and the backup delivery destination is determined.

5. The distributed distribution network planning method according to claim 4, characterized in that: The predicting the increase rate of electricity demand of each of the historical transportation destination and the backup transportation destination in a future period based on the historical electricity consumption data corresponding to each of the historical transportation destination and the backup transportation destination includes: Based on the historical power consumption data corresponding to the historical transmission destination and the backup transmission destination, respectively, constructing time series features corresponding to the historical transmission destination and the backup transmission destination respectively; Based on the electricity consumption corresponding to each historical electricity consumption data, the historical electricity demand increase rate corresponding to each historical period is calculated to serve as the demand label corresponding to each historical period; Based on the demand label corresponding to each historical period, the increase rate of electricity demand in the future period is fitted by minimizing the loss function to obtain the increase rate of electricity demand of the historical delivery destination and the backup delivery destination in the future period.

6. The distributed distribution network planning method according to any one of claims 1 to 5, characterized in that: 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, comprises: 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; Calculate the capacity of the transmission corridor corresponding to each transmission destination based on the power demand of each transmission destination and the power generation; From the initial transmission corridors, a transmission corridor corresponding to the capacity of each transmission corridor is selected 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; 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.

7. A distributed distribution network planning device, characterized in that: include: A first acquisition module is used to acquire power grid data of the distribution network, wherein the power grid data includes power grid structure sub-data, power grid stability sub-data and power grid operation sub-data; The second acquisition module is used to obtain the energy category, 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, used to determine a transmission destination corresponding to the power generation; The module is used to determine the transmission corridor and the 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.

8. The distributed power distribution network planning device according to claim 7, characterized in that: The prediction module is specifically used 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: Acquire meteorological parameters of the geographical location in a future period, wherein the meteorological parameters include light intensity, wind speed, wind direction, and temperature; Determine the meteorological sub-parameters corresponding to each energy category; Input the installed sub-capacity and the corresponding 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.

9. 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 6.

10. 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 6.

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