Method for reducing power transmission and distribution loss of power grid

Abstract

The invention provides a method for reducing power transmission and distribution loss of a power grid, which belongs to the technical field of power transmission and distribution and comprises the steps of collecting and analyzing historical power consumption data in a region, extracting power time characteristics, acquiring historical weather data related to power consumption requirements and extracting power weather characteristics. Establishing a load prediction model based on the electric quantity time characteristics and the electric quantity weather characteristics; identifying distributed energy resources in the area, classifying and positioning the distributed energy resources, generating a resource distribution heat map, establishing an energy database, and developing a virtual power plant; the virtual power plant outputs a power grid electric energy transmission and distribution strategy according to a predicted load curve output by the load prediction model, performs defect analysis on the power grid electric energy transmission and distribution strategy, deploys an energy storage system and formulates an energy storage scheduling strategy; a final scheduling strategy is obtained by combining an energy storage scheduling strategy and a power grid electric energy transmission and distribution strategy, so that the stability and flexibility of power supply are improved, the utilization rate of renewable energy sources is improved, resource allocation is optimized, and diversified requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy transmission and distribution, and in particular to a method for reducing electric energy transmission and distribution losses in a power grid. Background Art

[0002] With the rapid development of renewable energy and the continuous growth of electricity demand, the rapid development of renewable energy such as wind power and solar energy has made the power supply more unstable. Moreover, with the development of social economy and changes in lifestyle, electricity demand has become diversified and volatile. Traditional power supply methods are difficult to meet new needs.

[0003] Therefore, the present invention provides a method for reducing power transmission and distribution losses in a power grid. Summary of the invention

[0004] The present invention provides a method for reducing power transmission and distribution losses in a power grid. By analyzing historical power consumption data and weather data, a load forecasting model is established, distributed energy resources are identified and located, a resource distribution heat map is generated, and a virtual power plant is developed. The virtual power plant outputs a power grid transmission and distribution strategy based on load forecasting, performs defect analysis, and deploys an energy storage system. Finally, an optimized scheduling strategy is derived by combining energy storage scheduling with power grid strategies to enhance the stability and flexibility of power supply, improve the utilization rate of renewable energy, optimize resource allocation, and meet diverse needs.

[0005] The present invention provides a method for reducing power transmission and distribution losses in a power grid, comprising:

[0006] Step 1: Collect and analyze historical electricity consumption data in the region, extract electricity time characteristics, obtain historical weather data related to electricity demand, extract electricity weather characteristics from the historical weather data, and establish a load forecasting model based on the electricity time characteristics and electricity weather characteristics;

[0007] Step 2: Identify distributed energy resources in the region, classify and locate the distributed energy resources, generate a resource distribution heat map, and then establish an energy database, and develop a virtual power plant based on the energy database;

[0008] Step 3: The virtual power plant outputs a power transmission and distribution strategy for the power grid according to the predicted load curve output by the load forecasting model, performs defect analysis on the power transmission and distribution strategy for the power grid, deploys an energy storage system based on the defect analysis results, and formulates an energy storage scheduling strategy;

[0009] Step 4: Combining the energy storage scheduling strategy with the power grid power transmission and distribution strategy to derive a final scheduling strategy.

[0010] The present invention provides a method for reducing power transmission and distribution losses in a power grid, which is characterized by collecting and analyzing historical power consumption data in a region and extracting time characteristics of power consumption, including:

[0011] Setting data extraction time points of different time scales, extracting corresponding power consumption from historical power consumption data in the region based on the data extraction time points, and drawing power consumption trend graphs of corresponding time scales;

[0012] A value-taking time range is set based on the time scale, and a standard range of applied electricity for each time scale is defined within the value-taking time range. Abnormal points in the electricity consumption trend diagram of each time scale are identified based on the standard range, and the causes of the abnormal points are traced. The time characteristics of the electricity are determined based on the cause tracing results.

[0013] The present invention provides a method for reducing power transmission and distribution losses in a power grid, characterized by obtaining historical weather data related to power demand, extracting electricity quantity weather characteristics from the historical weather data, and establishing a load forecasting model based on the electricity quantity time characteristics and electricity quantity weather characteristics, including:

[0014] Determine weather parameters related to electricity demand based on the electricity-weather correlation table, and obtain historical weather data corresponding to the weather parameters;

[0015] Aligning weather data with historical electricity consumption data in time, creating weather features based on electricity consumption time features, cross-combining the weather features with electricity consumption time features, and then determining electricity consumption weather features from the alignment results;

[0016] A first hysteresis feature is established for the electricity time feature, a second hysteresis feature is set for the electricity weather feature, and a load forecasting model is constructed by integrating the electricity time feature, the electricity weather feature, the first hysteresis feature, and the second hysteresis feature.

[0017] The present invention provides a method for reducing power transmission and distribution losses in a power grid, characterized in that distributed energy resources in a region are identified, and the distributed energy resources are classified and located, a resource distribution heat map is generated, and then an energy database is established, and a virtual power plant is developed based on the energy database, including:

[0018] Classify the distributed energy resources in the region according to the resource-type table to obtain resource classification results, use GIS tools to determine the geographical coordinates of the resource classification results, obtain environmental data in the region, and combine the environmental data with the geographical coordinates to analyze the geographical distribution of the resource classification results and the potential impact of changes in environmental data on each resource classification result;

[0019] Generate a positive resource distribution heat map based on the resource classification results, and generate a negative resource distribution heat map based on the potential impact and resource classification results;

[0020] Performing a difference operation on the positive resource distribution heat map and the negative resource distribution heat map to obtain a resource-intensive area that is not affected by the environment and a resource-intensive area that is affected by the environment, setting regional weights for the resource-intensive area that is not affected by the environment and the resource-intensive area that is affected by the environment, and synthesizing the positive resource distribution heat map and the negative resource distribution heat map based on the regional weights to obtain a resource distribution heat map;

[0021] An energy database is established based on the resource distribution heat map, and a virtual power plant is constructed in combination with the energy database with the design purpose of reducing power transmission and distribution losses in the power grid.

[0022] The present invention provides a method for reducing power transmission and distribution losses of power grids, characterized in that a virtual power plant is constructed in combination with the energy database with the purpose of reducing power transmission and distribution losses of power grids, including:

[0023] Determine the technical architecture of the virtual power plant based on the design purpose;

[0024] Evaluate the power generation potential of each classification result based on the resource distribution heat map. At the same time, perform spatial analysis on the resource distribution heat map and use intelligent algorithms to combine the potential evaluation results with the spatial analysis results to determine the grid layout and resource allocation strategy.

[0025] A virtual power plant is constructed based on the technical framework, grid layout and resource allocation strategy combined with energy database.

[0026] The present invention provides a method for reducing power grid power transmission and distribution losses, characterized in that a virtual power plant outputs a power grid power transmission and distribution strategy according to a predicted load curve output by a load prediction model, performs defect analysis on the power grid power transmission and distribution strategy, deploys an energy storage system based on the defect analysis results, and formulates an energy storage scheduling strategy, including:

[0027] The load forecasting model outputs a forecasted load curve, which is then input into the virtual power plant to derive the power transmission and distribution strategy of the power grid;

[0028] According to the power transmission and distribution strategy of the power grid, the corresponding actual power generation curve is compared with the predicted load curve to determine the deviation between the actual power generation curve and the predicted load curve, and the corresponding prediction error source is identified for each deviation, and the defects of the power transmission and distribution strategy of the power grid in actual operation are analyzed according to the prediction error source to obtain the defect analysis results;

[0029] The type of energy storage system is selected based on the defect analysis results, the layout location of the energy storage system is determined by combining the defect analysis results with the space analysis results, the energy storage scheduling target is set based on the defect analysis results, and an energy storage scheduling strategy is formulated.

[0030] The present invention provides a method for reducing power transmission and distribution losses in a power grid, characterized in that the type of energy storage system is selected based on a defect analysis result, the layout position of the energy storage system is determined by combining the defect analysis result with the spatial analysis result, an energy storage scheduling target is set based on the defect analysis result, and an energy storage scheduling strategy is formulated, including:

[0031] Analyze the loss of power transmission and distribution of the power grid caused by the defect analysis results, and determine the defect points based on the loss conditions combined with the spatial analysis results;

[0032] Combine defect analysis results with resource distribution heat maps to identify load centers in the power grid;

[0033] Energy storage scheduling targets are set for defective points based on the load center, and corresponding energy storage scheduling strategies are formulated according to the energy storage scheduling targets.

[0034] The present invention provides a method for reducing power grid power transmission and distribution losses, characterized in that a final scheduling strategy is obtained by combining the energy storage scheduling strategy with the power grid power transmission and distribution strategy, including:

[0035] Performing a mutual impact analysis on the energy storage dispatching strategy and the electric energy dispatching strategy, and setting mutual constraint conditions based on the mutual impact analysis;

[0036] A final scheduling strategy is derived based on the mutual constraints.

[0037] Compared with the prior art, the beneficial effects of the present application are as follows: through the analysis of historical electricity consumption data and weather data, a load forecasting model is established, distributed energy resources are identified and located, a resource distribution heat map is generated and a virtual power plant is developed. The virtual power plant outputs a grid transmission and distribution strategy based on load forecasts, conducts defect analysis and deploys energy storage systems. Finally, the energy storage scheduling and grid strategy are combined to derive an optimized scheduling strategy to improve the stability and flexibility of power supply, increase the utilization rate of renewable energy, optimize resource allocation, and meet diversified needs.

[0038] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0039] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 It is a flow chart of a method for reducing power transmission and distribution losses in a power grid provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0043] Embodiment 1:

[0044] The embodiment of the present invention provides a method for reducing power transmission and distribution losses of a power grid, such as Figure 1 As shown, including:

[0045] Step 1: Collect and analyze historical electricity consumption data in the region, extract electricity time characteristics, obtain historical weather data related to electricity demand, extract electricity weather characteristics from the historical weather data, and establish a load forecasting model based on the electricity time characteristics and electricity weather characteristics;

[0046] Step 2: Identify distributed energy resources in the region, classify and locate the distributed energy resources, generate a resource distribution heat map, and then establish an energy database, and develop a virtual power plant based on the energy database;

[0047] Step 3: The virtual power plant outputs a power transmission and distribution strategy for the power grid according to the predicted load curve output by the load forecasting model, performs defect analysis on the power transmission and distribution strategy for the power grid, deploys an energy storage system based on the defect analysis results, and formulates an energy storage scheduling strategy;

[0048] Step 4: Combining the energy storage scheduling strategy with the power grid power transmission and distribution strategy to derive a final scheduling strategy.

[0049] In this embodiment, the region refers to a specific geographical area where load forecasting and resource analysis are performed, which may be a city, a county, an industrial park, or any other area with relatively independent electricity demand.

[0050] In this embodiment, historical weather data refers to records of weather conditions that affect the electricity demand in the area during the same time period, and these data usually include factors such as temperature, humidity, precipitation, and wind speed.

[0051] In this embodiment, the load forecasting model is a mathematical model constructed using multiple characteristics (electricity time characteristics, electricity weather characteristics, first lag characteristics, and second lag characteristics) to predict future electricity demand. For example, by establishing a load forecasting model, the peak electricity consumption period can be accurately predicted, so that power generation and energy storage can be scheduled before the peak period. The input of the load forecasting model is the electricity time characteristics, electricity weather characteristics, first lag characteristics, and second lag characteristics, and the output is the predicted load curve and the uncertainty range of the predicted load curve.

[0052] In this embodiment, the historical electricity consumption data is a record of electricity consumption over a period of time in the past, which is usually summarized by hour, day or month. For example, by analyzing the historical electricity consumption data of the past few years, it is found that the electricity consumption in a certain area increases significantly in winter. Based on this, the power generation capacity can be increased before winter to reduce losses caused by insufficient power supply.

[0053] In this embodiment, distributed energy resources are small-scale energy production facilities deployed on the user side or close to the user, such as solar panels, wind turbines, etc. For example, installing distributed solar panels in residential areas can meet electricity demand nearby and reduce losses caused by long-distance transmission.

[0054] In this embodiment, the resource classification result is the classification result of different distributed energy resources in the region based on the resource-type table, usually including the type, distribution and potential assessment of the resources. For example, the energy resources in a certain area are divided into high, medium and low potential types, and high-potential resources are developed first to improve energy utilization efficiency and reduce transmission and distribution losses. Positioning refers to using GIS tools to clarify the distribution location of each resource classification result.

[0055] In this embodiment, the resource distribution heat map is a resource distribution map generated after comprehensive consideration of positive and negative impacts, showing the resource potential and environmental adaptability of each region. For example, based on the resource distribution heat map, power companies can decide where to build new power generation facilities to ensure efficient power generation and low losses.

[0056] In this embodiment, the energy database is a database that stores information on distributed energy resources in a region, including resource types, distribution conditions, environmental data, etc. For example, an energy database is established to record information on all distributed energy resources in a region to facilitate subsequent analysis and decision-making, thereby reducing power transmission and distribution losses in the power grid.

[0057] In this embodiment, the construction of the virtual power plant is based on the energy database, and a coordinated energy production and consumption system is formed by integrating distributed energy resources. For example, by building a virtual power plant, multiple small solar and wind power generation facilities are integrated to optimize power output and reduce transmission and distribution losses caused by power fluctuations.

[0058] In this embodiment, the process of building a virtual power plant is to determine the goals and needs of the virtual power plant, including power generation capacity, load demand and market demand, evaluate the power generation potential of different regions according to the resource distribution heat map, identify available distributed energy resources, integrate resource data, environmental data and power grid data into the energy database for subsequent analysis and decision-making, use GIS tools to perform spatial analysis on resource distribution, identify resource-intensive areas and risk areas, apply intelligent algorithms to optimize resource scheduling strategies, ensure efficient use of resources, design power grid layout based on analysis results, determine the location of power transmission lines and substations, integrate various components (resource management, scheduling system, communication system, etc.) into a unified platform, and conduct system testing to obtain a virtual power plant; assuming that in a city, through the implementation of the technical framework, real-time monitoring of a sudden increase in load in a certain area, and the power generation potential assessment shows that there is a high-potential photovoltaic power station near the area. Using the potential assessment results and spatial analysis results, the scheduling system decides to immediately dispatch electricity from the photovoltaic power station instead of mobilizing electricity from traditional power plants far away from the area. The power grid layout strategy ensures that the transmission line between the photovoltaic power station and the load center is as short as possible, thereby effectively reducing transmission losses.

[0059] In this embodiment, the predicted load curve is generated based on historical data and prediction algorithms (such as time series analysis, machine learning, etc.), and represents the expected power load demanded by the power grid in a certain period of time in the future. For example, assuming that the load forecast for a city in summer shows that the load at 3 pm is expected to be 500MW, while the load at 8 pm is expected to be 800MW.

[0060] In this embodiment, the power transmission and distribution strategy of the power grid refers to a plan for optimizing power dispatch and distribution under specific load and power generation conditions to achieve efficient and safe power grid operation. For example, during peak load periods, the strategy may be to prioritize renewable energy (such as wind power and solar energy) and enable energy storage systems when necessary.

[0061] In this embodiment, the analysis process refers to the systematic steps from data collection, deviation calculation, to error source identification and defect analysis. For example, first collect historical load data and meteorological data, calculate the deviation, then identify the impact of weather changes on load forecasting, and finally perform defect analysis.

[0062] In this embodiment, the defect analysis results refer to the deficiencies in the power transmission and distribution strategy of the power grid discovered during the analysis process, as well as possible directions for improvement. For example, if high losses are found due to insufficient power generation in a certain period of time, it can be concluded that the load forecasting model and scheduling strategy need to be improved.

[0063] In this embodiment, the energy storage scheduling strategy refers to how to schedule the charging and discharging of the energy storage system in different time periods to achieve the set goals, for example, charging when there is excess renewable energy generation and discharging during peak load periods to balance the load and improve the efficiency of grid operation.

[0064] In this embodiment, the energy storage system can be charged when electricity demand is low (such as at night or when there is excess renewable energy generation) and discharged when electricity demand is high (such as during peak load periods), thereby balancing the supply and demand relationship of the power grid, optimizing the use of electricity, monitoring the deviation between the actual load and the predicted load curve, identifying the source of prediction error, and analyzing the defects of the power grid's transmission and distribution strategy. Based on the defect analysis results, the appropriate type of energy storage system is selected, and the layout location of the energy storage system is determined in combination with the spatial analysis results to deploy the energy storage system.

[0065] In this embodiment, the final dispatching strategy refers to a comprehensive dispatching plan determined after considering mutual influences and mutual constraints to achieve efficient operation of the power grid and optimal allocation of resources. For example, the comprehensive dispatching plan is: charging during low load periods (such as at night) to utilize low electricity prices and excess electricity from renewable energy, discharging during peak load periods (such as 5 pm to 8 pm) to support the needs of load centers, and timely dispatching traditional power generation sources when renewable energy generation is insufficient to ensure the balance of supply and demand in the power grid. By analyzing the mutual influence between the energy storage dispatching strategy and the electric energy dispatching strategy, mutual constraints are identified and set. On this basis, these constraints are comprehensively considered to form the final dispatching strategy.

[0066] The working principle and beneficial effects of the above technical solution are: through the analysis of historical electricity consumption data and weather data, a load forecasting model is established, distributed energy resources are identified and located, a resource distribution heat map is generated and a virtual power plant is developed. The virtual power plant outputs a grid transmission and distribution strategy based on load forecasts, conducts defect analysis and deploys energy storage systems. Finally, the energy storage scheduling and grid strategy are combined to derive an optimized scheduling strategy to improve the stability and flexibility of power supply, increase the utilization rate of renewable energy, optimize resource allocation, and meet diversified needs.

[0067] Embodiment 2:

[0068] The embodiment of the present invention provides a method for reducing power transmission and distribution losses in a power grid, collecting and analyzing historical power consumption data in a region, and extracting time characteristics of power consumption, including:

[0069] Setting data extraction time points of different time scales, extracting corresponding power consumption from historical power consumption data in the region based on the data extraction time points, and drawing power consumption trend graphs of corresponding time scales;

[0070] A value-taking time range is set based on the time scale, and a standard range of applied electricity for each time scale is defined within the value-taking time range. Abnormal points in the electricity consumption trend diagram of each time scale are identified based on the standard range, and the causes of the abnormal points are traced. The time characteristics of the electricity are determined based on the cause tracing results.

[0071] In this embodiment, the time scale refers to the time period for data analysis, which can be hours, days, weeks, months, etc. For example, hourly scale: analyze the peak and trough electricity consumption within a certain day, identify the electricity demand during peak hours, facilitate the scheduling of power generation and energy storage resources, and reduce the burden on transmission lines; weekly scale: analyze the electricity consumption pattern every week, identify the difference in electricity consumption between weekends and weekdays, optimize the power generation plan, and reduce unnecessary transmission and distribution losses.

[0072] In this embodiment, the corresponding value time range is set according to the selected time scale, for example, the hour scale can be set to 24 hours on a certain day (for example, 0:00 to 23:59 on January 1, 2024).

[0073] In this embodiment, the data extraction time point is the specific moment when electricity consumption data is extracted on a specific time scale, such as a specific period of time every day. For example, daily extraction: extract electricity consumption data during the morning peak (such as 7-9 o'clock) and evening peak (such as 18-20 o'clock) every day, analyze the peak power consumption, formulate corresponding scheduling strategies, and reduce transmission and distribution losses during peak periods.

[0074] In this embodiment, the electricity consumption trend chart uses a visualization tool to display the electricity consumption data extracted at different time scales, showing the trend of electricity consumption over time. For example, a monthly electricity consumption trend chart is drawn: if an abnormal increase in electricity consumption in a certain month is found, it may be due to equipment failure or a sudden increase in demand, and timely measures are taken to optimize power distribution and reduce losses.

[0075] In this embodiment, the standard range is the normal power consumption range set for each time scale, which is usually based on the average value and standard deviation of historical data. For example, the standard range is set: by calculating the average value and standard deviation of historical data, a normal power consumption range is set (for example, within ±2 standard deviations). If the power consumption on a certain day exceeds this range, it indicates that there may be equipment loss or failure, and maintenance measures are taken.

[0076] In this embodiment, an abnormal point is a point in the electricity consumption trend chart that exceeds the standard range, which may indicate abnormal electricity consumption or failure. For example, abnormal points are identified: in the daily electricity consumption trend chart, it is found that the electricity consumption on a certain day is abnormally high. After analyzing the cause, it may be found that it is caused by aging equipment in a certain area. Timely replacement of equipment can reduce losses.

[0077] In this embodiment, the electricity time feature is a feature extracted from the cause tracing of the abnormal point, such as peak and valley electricity consumption, seasonal changes, etc. For example, the electricity time feature is extracted: after analyzing the electricity time feature, it is found that the electricity consumption in certain periods fluctuates greatly, and the energy storage system is deployed in a targeted manner to balance the load and reduce losses.

[0078] The working principle and beneficial effects of the above technical solution are: by setting data extraction time points of different time scales, extract the corresponding power consumption from the historical power consumption data, and draw a power consumption trend chart. Define the standard range to identify abnormal points, trace their causes, and finally determine the time characteristics of power consumption, which helps to deeply understand the power consumption behavior and fluctuation rules, provide a basis for subsequent load forecasting, accurately identify abnormal fluctuations, and improve the flexibility of power dispatching.

[0079] Embodiment 3:

[0080] The embodiment of the present invention provides a method for reducing power transmission and distribution losses in a power grid, obtaining historical weather data related to power demand, extracting power weather characteristics from the historical weather data, and establishing a load forecasting model based on the power time characteristics and power weather characteristics, including:

[0081] Determine weather parameters related to electricity demand based on the electricity-weather correlation table, and obtain historical weather data corresponding to the weather parameters;

[0082] Aligning weather data with historical electricity consumption data in time, creating weather features based on electricity consumption time features, cross-combining the weather features with electricity consumption time features, and then determining electricity consumption weather features from the alignment results;

[0083] A first hysteresis feature is established for the electricity time feature, a second hysteresis feature is set for the electricity weather feature, and a load forecasting model is constructed by integrating the electricity time feature, the electricity weather feature, the first hysteresis feature, and the second hysteresis feature.

[0084] In this embodiment, the electricity-weather correlation table is a table containing the relationship between electricity consumption and weather parameters, which is usually obtained through statistical analysis. The table may include data such as electricity consumption, temperature, humidity, precipitation, etc. For example, through analysis, it is found that for every 1°C increase in temperature, the summer electricity demand increases by about 3%. Using this relationship, power generation and energy storage can be scheduled in advance when high temperature weather is predicted, reducing losses caused by insufficient power supply.

[0085] In this embodiment, weather parameters are meteorological factors that affect electricity demand, such as temperature, humidity, wind speed, precipitation, etc. For example, in summer, rising temperatures will lead to increased use of air conditioners, thereby increasing electricity demand. By monitoring temperature changes, peak electricity demand can be effectively predicted and grid scheduling can be optimized.

[0086] In this embodiment, the power demand is the amount of electricity demanded by users in a specific time period, usually expressed in kilowatt-hours (kWh). For example, during the morning peak hours (7-9 am) on weekdays, the power demand is usually high. By analyzing historical power consumption data, peak hours can be identified, and power generation and energy storage resources can be prepared in advance to reduce the risk of power supply shortages.

[0087] In this embodiment, weather features are features extracted from weather data, typically including average temperature, humidity changes, precipitation frequency, etc. For example, extracted weather features such as "average temperature for three consecutive days" can help predict the upcoming peak power consumption period, thereby optimizing power grid scheduling.

[0088] In this embodiment, the cross-combination is to combine the electricity time characteristics with the weather characteristics to form a new feature, which is usually used in the load forecasting model. For example, the "temperature" and "electricity consumption" are cross-combined to generate the "ratio of temperature to electricity consumption" feature, which helps the model better understand the impact of weather on electricity consumption, thereby improving the prediction accuracy and reducing energy waste caused by incorrect predictions.

[0089] In this embodiment, the first lag feature refers to moving the time series data backward by one time unit (such as one hour or one day) to capture the impact of historical data on current data. For example, using the previous day's electricity consumption as the first lag feature can help the model identify electricity consumption trends and reduce prediction errors caused by emergencies, thereby optimizing power grid scheduling.

[0090] In this embodiment, the second lag feature refers to moving the time series data backward by two time units to capture the impact within a longer time range. For example, using the electricity consumption of the previous two days as the second lag feature can better identify changes in electricity consumption patterns, help power companies prepare for power generation in advance, and reduce power transmission and distribution losses.

[0091] The working principle and beneficial effects of the above technical solution are: by analyzing the correlation between electricity volume and weather, determining key weather parameters and obtaining their historical data, aligning weather data with historical electricity consumption data, creating weather characteristics, and cross-combining them with electricity volume time characteristics, extracting electricity volume weather characteristics, setting lag characteristics for electricity volume time characteristics and electricity volume weather characteristics respectively, and comprehensively constructing a load forecasting model, thereby improving the accuracy of the forecast, helping to effectively integrate renewable resources such as wind and solar energy and enhance system stability.

[0092] Embodiment 4:

[0093] The embodiment of the present invention provides a method for reducing power transmission and distribution losses of a power grid, identifying distributed energy resources in a region, classifying and locating the distributed energy resources, generating a resource distribution heat map and then establishing an energy database, and developing a virtual power plant based on the energy database, including:

[0094] Classify the distributed energy resources in the region according to the resource-type table to obtain resource classification results, use GIS tools to determine the geographical coordinates of the resource classification results, obtain environmental data in the region, and combine the environmental data with the geographical coordinates to analyze the geographical distribution of the resource classification results and the potential impact of changes in environmental data on each resource classification result;

[0095] Generate a positive resource distribution heat map based on the resource classification results, and generate a negative resource distribution heat map based on the potential impact and resource classification results;

[0096] Performing a difference operation on the positive resource distribution heat map and the negative resource distribution heat map to obtain a resource-intensive area that is not affected by the environment and a resource-intensive area that is affected by the environment, setting regional weights for the resource-intensive area that is not affected by the environment and the resource-intensive area that is affected by the environment, and synthesizing the positive resource distribution heat map and the negative resource distribution heat map based on the regional weights to obtain a resource distribution heat map;

[0097] An energy database is established based on the resource distribution heat map, and a virtual power plant is constructed in combination with the energy database with the design purpose of reducing power transmission and distribution losses in the power grid.

[0098] In this embodiment, the resource-type table is a table that lists different types of distributed energy resources (such as solar energy, wind energy, biomass energy, etc.) and their characteristics. For example, by analyzing the resource-type table, it is found that a certain area has abundant solar energy resources and is suitable for building a photovoltaic power station, thereby reducing dependence on traditional power grids and reducing transmission and distribution losses.

[0099] In this embodiment, the GIS tool is a geographic information system tool that is used to analyze and visualize geographic data to help determine the geographic distribution of resources. For example, the GIS tool is used to draw a distributed energy resource distribution map in a region, identify the best installation location, and reduce energy losses caused by improper location.

[0100] In this embodiment, the environmental data is environmental factor data related to resource distribution, such as climate, topography, land use, etc. For example, by analyzing the climate data of a certain area, it is found that the wind speed is suitable for wind energy development. Combined with the resource distribution, the wind farm is rationally arranged to reduce transmission losses.

[0101] In this embodiment, the potential impact is the impact of environmental changes on the resource classification results, such as the impact of climate change, land use change, etc. on energy resource development. For example, studies have shown that rising temperatures may affect the efficiency of solar power generation. Identifying these impacts in advance can optimize resource allocation and reduce potential energy losses.

[0102] In this embodiment, the active resource distribution heat map is a heat map representing resource-rich and suitable development areas, and is usually generated based on resource classification results. For example, the generated active resource distribution heat map shows that a certain area is rich in solar energy resources, guides investment and development, and reduces losses caused by improper resource allocation.

[0103] In this embodiment, the negative resource distribution heat map is a heat map representing areas that are rich in resources but affected by the environment. For example, the negative resource distribution heat map shows that the development potential of wind energy resources in a certain area is affected by high humidity, helping decision makers avoid investing in this area and reduce risks.

[0104] In this embodiment, the resource-intensive and environmentally unaffected area is an area that is rich in resources and not affected by environmental factors, and is suitable for development and investment. For example, the area around a city is identified as a resource-intensive and environmentally unaffected area, and the construction of distributed energy facilities in this area is encouraged to reduce transmission and distribution losses.

[0105] In this embodiment, the resource-intensive environmentally affected area is an area that is rich in resources but affected by environmental factors, and development needs to be cautious. For example, a certain area is rich in wind resources, but frequent strong winds affect the power generation efficiency, and development needs to be carefully planned to avoid losses.

[0106] In this embodiment, the regional weight is a weight set according to the potential of the resource and the environmental impact, and is used to optimize the synthesis of the resource distribution map. For example, a higher weight is set for resource-intensive areas that are not affected by the environment to ensure the priority of resource development and reduce the burden on the power grid.

[0107] In this embodiment, the synthesis process is to superimpose the positive resource distribution heat map with the negative resource distribution heat map to generate a comprehensive resource distribution map. For example, the synthesized resource distribution map shows the best development area, helping power companies to maximize resource utilization while reducing transmission and distribution losses.

[0108] The working principle and beneficial effects of the above technical solution are: by classifying the distributed energy resources in the region, using GIS tools to determine their geographic coordinates, and combining environmental data to analyze resource distribution and potential impacts. Generate positive and negative resource distribution heat maps, superimpose to identify resource areas affected by the environment and those not affected, set regional weights, synthesize the heat maps, establish resource distribution heat maps, and build an energy database based on them, and finally design a virtual power plant to reduce power grid transmission and distribution losses, optimize power transmission and distribution paths, and improve the overall efficiency of the system.

[0109] Embodiment 5:

[0110] The embodiment of the present invention provides a method for reducing power transmission and distribution losses in a power grid, which is designed for the purpose of reducing power transmission and distribution losses in the power grid, and constructs a virtual power plant in combination with the energy database, including:

[0111] Determine the technical architecture of the virtual power plant based on the design purpose;

[0112] Evaluate the power generation potential of each classification result based on the resource distribution heat map. At the same time, perform spatial analysis on the resource distribution heat map and use intelligent algorithms to combine the potential evaluation results with the spatial analysis results to determine the grid layout and resource allocation strategy.

[0113] A virtual power plant is constructed based on the technical framework, grid layout and resource allocation strategy combined with energy database.

[0114] In this embodiment, the technical framework is the overall framework of the virtual power plant, which usually includes the following components: resource management system: used to manage and monitor distributed energy resources (such as solar energy, wind energy, energy storage, etc.); data acquisition and monitoring: real-time collection of resource operation data and environmental data through sensors and monitoring equipment; scheduling and optimization algorithm: use intelligent algorithms (such as genetic algorithms, particle swarm optimization, etc.) to optimize resource scheduling and improve power generation efficiency; communication and control system: realize communication between various devices to ensure system coordination and control; user interface: provide users with a visual interface to facilitate monitoring and management of virtual power plants. For example, the system can quickly mobilize the energy storage system to release energy during peak load, reduce dependence on traditional power generation, and reduce transmission losses.

[0115] In this embodiment, power generation potential refers to the maximum power generation capacity of each distributed energy resource in a specific area, which is usually affected by the following factors: resource type: different types of resources (such as solar energy, wind energy) have different power generation potentials; environmental conditions: climate, topography, and land use affect the actual power generation capacity of resources; technical efficiency: the conversion efficiency of different technologies (such as photovoltaic panels, wind turbines, etc.).

[0116] In this embodiment, the potential assessment result is a quantitative analysis of the power generation potential of each resource based on the resource distribution heat map and environmental data. The corresponding steps are: using GIS tools to analyze the resource distribution heat map, extracting the potential power generation capacity of each resource, using mathematical models (such as PVWatts, WindPRO, etc.) to calculate the power generation potential under different environmental conditions, and integrating the power generation potential of various resources to form a comprehensive assessment result.

[0117] In this embodiment, the spatial analysis results are the results of the spatial characteristics and potential impacts of resource distribution obtained through GIS analysis, which include: resource density analysis: identifying resource concentrated areas and sparse areas, risk assessment: assessing the impact of environmental risks on resource development, and suitability analysis: determining the suitability of resource development in different areas.

[0118] In this embodiment, the grid layout and resource allocation strategy is a strategy formulated based on power generation potential and spatial analysis results, mainly including: determining the structure and connection method of the grid to ensure efficient transmission and distribution of electricity, reasonably allocating different types of distributed energy according to resource potential and risk assessment results, and formulating real-time scheduling strategies to optimize the matching of power supply and demand.

[0119] The working principle and beneficial effects of the above technical solution are: determine the technical framework of the virtual power plant according to the design purpose, use the resource distribution heat map to evaluate the power generation potential of various resources, and conduct spatial analysis, use intelligent algorithms to integrate potential evaluation and spatial analysis results, optimize the grid layout and resource allocation strategy, combine the energy database, build an efficient virtual power plant, so as to improve the flexibility and stability of power supply, and reduce the impact of renewable energy fluctuations on the power grid through reasonable allocation of resources.

[0120] Embodiment 6:

[0121] The embodiment of the present invention provides a method for reducing power grid power transmission and distribution losses, wherein a virtual power plant outputs a power grid power transmission and distribution strategy according to a predicted load curve output by a load prediction model, performs defect analysis on the power grid power transmission and distribution strategy, deploys an energy storage system based on the defect analysis results, and formulates an energy storage scheduling strategy, including:

[0122] The load forecasting model outputs a forecasted load curve, which is then input into the virtual power plant to derive the power transmission and distribution strategy of the power grid;

[0123] According to the power transmission and distribution strategy of the power grid, the corresponding actual power generation curve is compared with the predicted load curve to determine the deviation between the actual power generation curve and the predicted load curve, and the corresponding prediction error source is identified for each deviation, and the defects of the power transmission and distribution strategy of the power grid in actual operation are analyzed according to the prediction error source to obtain the defect analysis results;

[0124] The type of energy storage system is selected based on the defect analysis results, the layout location of the energy storage system is determined by combining the defect analysis results with the space analysis results, the energy storage scheduling target is set based on the defect analysis results, and an energy storage scheduling strategy is formulated.

[0125] In this embodiment, the actual power generation curve refers to the power output generated by the actual power generation equipment of the power grid during a specific time period, reflecting the real-time situation of power generation. For example, during the same time period, the actual power generation data shows that the power generation at 3 pm is 450MW and the power generation at 8 pm is 750MW.

[0126] In this embodiment, the deviation refers to the difference between the actual power generation curve and the predicted load curve, which is usually expressed as an error in load forecasting. For example, if the predicted load is 500MW and the actual power generation is 450MW, the deviation is -50MW, indicating insufficient power generation.

[0127] In this embodiment, the source of prediction error refers to various factors that cause deviations between the predicted load and the actual load, which may include weather changes, equipment failures, changes in user behavior, etc. For example, if the weather forecast is inaccurate, resulting in a higher temperature than predicted, thereby increasing the air conditioning load, this may be the source of the prediction error.

[0128] In this embodiment, the energy storage system type refers to an energy storage device used to balance the difference between power generation and load. Common types include lithium batteries, pumped storage, flywheel energy storage, etc. For example, a lithium battery energy storage system is selected because of its fast response speed and is suitable for coping with short-term load fluctuations.

[0129] In this embodiment, the layout location refers to the installation location of the energy storage system in the power grid, which is usually selected to be close to the load center or power generation source. For example, the energy storage system is arranged near the city center to reduce transmission losses and improve response speed.

[0130] In this embodiment, the energy storage scheduling target refers to a specific goal achieved by the energy storage system, such as reducing peak load, improving the utilization rate of renewable energy, etc. For example, the target is set to release energy storage during peak hours to reduce peak load and avoid overloading the power grid.

[0131] The working principle and beneficial effects of the above technical solution are: monitoring the deviation between the actual load and the predicted load curve, identifying the source of prediction error, and analyzing the defects of the power grid's transmission and distribution strategy. Based on the defect analysis results, the appropriate type of energy storage system is selected, and the layout location of the energy storage system is determined in combination with the spatial analysis results. The energy storage scheduling target is set and the corresponding energy storage scheduling strategy is formulated to optimize the operation of the power grid, improve the reliability of power supply, and enable the power grid to flexibly respond to demand fluctuations and meet diverse electricity needs.

[0132] Embodiment 7:

[0133] The embodiment of the present invention provides a method for reducing power transmission and distribution losses in a power grid, selecting a type of energy storage system based on a defect analysis result, determining a layout location of the energy storage system in combination with the defect analysis result and the spatial analysis result, setting an energy storage scheduling target based on the defect analysis result, and formulating an energy storage scheduling strategy, including:

[0134] Analyze the loss of power transmission and distribution of the power grid caused by the defect analysis results, and determine the defect points based on the loss conditions combined with the spatial analysis results;

[0135] Combine defect analysis results with resource distribution heat maps to identify load centers in the power grid;

[0136] Energy storage scheduling targets are set for defective points based on the load center, and corresponding energy storage scheduling strategies are formulated according to the energy storage scheduling targets.

[0137] In this embodiment, the loss situation refers to the power loss caused by various reasons for power transmission and distribution (such as line impedance, equipment efficiency, etc.) during the operation of the power grid. For example, assuming that in a power grid, the loss rate of the transmission line is 5%, if the total power generation of the power grid in a certain period of time is 1000MW, the loss is 50MW, and the actual amount of electricity delivered to users is 950MW.

[0138] In this embodiment, a defect point refers to a specific location or link in the operation of the power grid that exhibits inefficiency, excessive losses, or is unable to meet load demand. For example, if insufficient load often occurs near a substation and the transmission loss in the area is higher than the average level, then the substation can be regarded as a defect point.

[0139] In this embodiment, the process of identifying the load center is to analyze the load distribution of each area in the power grid and determine the area with the most concentrated power demand and the greatest impact on the power grid. The corresponding process is: collect load data and power consumption of each area, use heat map tools to visualize the load data, and display the areas with dense loads. Through heat map analysis, it is found that the load demand of a certain commercial area is significantly higher than that of the surrounding areas. This area is the load center.

[0140] The working principle and beneficial effects of the above technical solution are: analyzing the losses of power grid transmission and distribution, determining defect points based on spatial analysis results, combining defect analysis results with resource distribution heat maps, identifying load centers in the power grid, and setting energy storage scheduling targets for defect points based on load centers. In addition, corresponding energy storage scheduling strategies are formulated to optimize power grid operation and reduce losses, which helps to better allocate resources and improve the stability of power supply.

[0141] Embodiment 8:

[0142] The embodiment of the present invention provides a method for reducing power grid power transmission and distribution losses, combining the energy storage scheduling strategy with the power grid power transmission and distribution strategy to derive a final scheduling strategy, including:

[0143] Performing a mutual impact analysis on the energy storage dispatching strategy and the electric energy dispatching strategy, and setting mutual constraint conditions based on the mutual impact analysis;

[0144] A final scheduling strategy is derived based on the mutual constraints.

[0145] In this embodiment, mutual impact analysis refers to the evaluation of the interaction between the energy storage scheduling strategy and the electric energy scheduling strategy to determine how they affect each other's performance and effects. For example, the energy storage scheduling strategy: discharge during peak load periods to support load demand, the electric energy scheduling strategy: prioritize renewable energy generation, mutual impact: if the energy storage system discharges during peak periods, it may reduce dependence on traditional power generation sources, thereby reducing power generation costs; conversely, if the electric energy scheduling strategy fails to effectively utilize renewable energy, the charging efficiency of the energy storage system may be reduced.

[0146] In this embodiment, mutual constraints refer to the restrictions and conditions that need to be followed between energy storage scheduling and electric energy scheduling when formulating a scheduling strategy to ensure coordination and optimization between the two, such as energy storage charging and discharging restrictions: the charging and discharging capabilities of the energy storage system (such as a maximum charging power of 200MW and a maximum discharging power of 150MW), power supply and demand balance: the minimum load demand that the power grid must meet during peak load periods (such as the load demand must be ensured to be 800MW), and renewable energy generation restrictions: the instability of renewable energy generation may result in lower-than-expected power generation during certain periods.

[0147] The working principle and beneficial effects of the above technical solution are: by analyzing the mutual influence between the energy storage scheduling strategy and the electric energy scheduling strategy, identifying and setting mutual constraints, and on this basis, comprehensively considering these constraints to form a final scheduling strategy to achieve optimal allocation and efficient operation of power grid resources, enhance the power grid's ability to adapt to fluctuations in renewable energy, and ensure reliable power supply.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reducing power transmission and distribution losses in a power grid, characterized in that: include: Step 1: Collect and analyze historical electricity consumption data in the region, extract electricity time characteristics, obtain historical weather data related to electricity demand, extract electricity weather characteristics from the historical weather data, and establish a load forecasting model based on the electricity time characteristics and electricity weather characteristics; Step 2: Identify distributed energy resources in the region, classify and locate the distributed energy resources, generate a resource distribution heat map, and then establish an energy database, and develop a virtual power plant based on the energy database; Step 3: The virtual power plant outputs a power transmission and distribution strategy for the power grid according to the predicted load curve output by the load forecasting model, performs defect analysis on the power transmission and distribution strategy for the power grid, deploys an energy storage system based on the defect analysis results, and formulates an energy storage scheduling strategy; Step 4: Combining the energy storage scheduling strategy with the power grid power transmission and distribution strategy to derive a final scheduling strategy.

2. A method for reducing power transmission and distribution losses in a power grid according to claim 1, characterized in that: Collect and analyze historical electricity consumption data in the region and extract time characteristics of electricity consumption, including: Setting data extraction time points of different time scales, extracting corresponding power consumption from historical power consumption data in the region based on the data extraction time points, and drawing power consumption trend graphs of corresponding time scales; A value-taking time range is set based on the time scale, and a standard range of applied electricity for each time scale is defined within the value-taking time range. Abnormal points in the electricity consumption trend diagram of each time scale are identified based on the standard range, and the causes of the abnormal points are traced. The time characteristics of the electricity are determined based on the cause tracing results.

3. A method for reducing power transmission and distribution losses in a power grid according to claim 2, characterized in that: Acquire historical weather data related to electricity demand, extract electricity weather characteristics from the historical weather data, and establish a load forecasting model based on the electricity time characteristics and electricity weather characteristics, including: Determine weather parameters related to electricity demand based on the electricity-weather correlation table, and obtain historical weather data corresponding to the weather parameters; Aligning weather data with historical electricity consumption data in time, creating weather features based on electricity consumption time features, cross-combining the weather features with electricity consumption time features, and then determining electricity consumption weather features from the alignment results; A first hysteresis feature is established for the electricity time feature, a second hysteresis feature is set for the electricity weather feature, and a load forecasting model is constructed by integrating the electricity time feature, the electricity weather feature, the first hysteresis feature, and the second hysteresis feature.

4. A method for reducing power transmission and distribution losses in a power grid according to claim 1, characterized in that: Identify distributed energy resources in the region, classify and locate the distributed energy resources, generate a resource distribution heat map, and then establish an energy database. Develop a virtual power plant based on the energy database, including: Classify the distributed energy resources in the region according to the resource-type table to obtain resource classification results, use GIS tools to determine the geographical coordinates of the resource classification results, obtain environmental data in the region, and combine the environmental data with the geographical coordinates to analyze the geographical distribution of the resource classification results and the potential impact of changes in environmental data on each resource classification result; Generate a positive resource distribution heat map based on the resource classification results, and generate a negative resource distribution heat map based on the potential impact and resource classification results; Performing a difference operation on the positive resource distribution heat map and the negative resource distribution heat map to obtain a resource-intensive area that is not affected by the environment and a resource-intensive area that is affected by the environment, setting regional weights for the resource-intensive area that is not affected by the environment and the resource-intensive area that is affected by the environment, and synthesizing the positive resource distribution heat map and the negative resource distribution heat map based on the regional weights to obtain a resource distribution heat map; An energy database is established based on the resource distribution heat map, and a virtual power plant is constructed in combination with the energy database with the design purpose of reducing power transmission and distribution losses in the power grid.

5. A method for reducing power transmission and distribution losses in a power grid according to claim 4, characterized in that: The design purpose is to reduce the power transmission and distribution loss of the power grid, and a virtual power plant is constructed in combination with the energy database, including: Determine the technical architecture of the virtual power plant based on the design purpose; Evaluate the power generation potential of each classification result based on the resource distribution heat map. At the same time, perform spatial analysis on the resource distribution heat map and use intelligent algorithms to combine the potential evaluation results with the spatial analysis results to determine the grid layout and resource allocation strategy. A virtual power plant is constructed based on the technical framework, grid layout and resource allocation strategy combined with energy database.

6. A method for reducing power transmission and distribution losses in a power grid according to claim 5, characterized in that: The virtual power plant outputs a power grid power transmission and distribution strategy based on the predicted load curve output by the load forecasting model, performs defect analysis on the power grid power transmission and distribution strategy, deploys an energy storage system based on the defect analysis results, and formulates an energy storage scheduling strategy, including: The load forecasting model outputs a forecasted load curve, which is then input into the virtual power plant to derive the power transmission and distribution strategy of the power grid; According to the power transmission and distribution strategy of the power grid, the corresponding actual power generation curve is compared with the predicted load curve to determine the deviation between the actual power generation curve and the predicted load curve, and the corresponding prediction error source is identified for each deviation, and the defects of the power transmission and distribution strategy of the power grid in actual operation are analyzed according to the prediction error source to obtain the defect analysis results; The type of energy storage system is selected based on the defect analysis results, the layout location of the energy storage system is determined by combining the defect analysis results with the space analysis results, the energy storage scheduling target is set based on the defect analysis results, and an energy storage scheduling strategy is formulated.

7. A method for reducing power transmission and distribution losses in a power grid according to claim 6, characterized in that: The type of energy storage system is selected based on the defect analysis results, the layout location of the energy storage system is determined in combination with the defect analysis results and the spatial analysis results, the energy storage scheduling target is set based on the defect analysis results, and an energy storage scheduling strategy is formulated, including: Analyze the loss of power transmission and distribution of the power grid caused by the defect analysis results, and determine the defect points based on the loss conditions combined with the spatial analysis results; Combine defect analysis results with resource distribution heat maps to identify load centers in the power grid; Energy storage scheduling targets are set for defective points based on the load center, and corresponding energy storage scheduling strategies are formulated according to the energy storage scheduling targets.

8. The method for reducing power transmission and distribution losses of a power grid according to claim 1, characterized in that: The final dispatching strategy is derived by combining the energy storage dispatching strategy with the power grid power transmission and distribution strategy, including: Performing a mutual impact analysis on the energy storage dispatching strategy and the electric energy dispatching strategy, and setting mutual constraint conditions based on the mutual impact analysis; A final scheduling strategy is derived based on the mutual constraints.

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