17 results about "Regional power" patented technology

The invention discloses an intelligent calculation center comprehensive energy power distribution control system and control method, relates to the technical field of intelligent calculation center energy management, and solves the problems that power distribution is often carried out according to the priority of a power distribution area, and power distribution selection is lack of consideration of the applicable energy type of the area in the prior art. And the power distribution effect of the power distribution area is not good. Generating a regional power utilization priority based on regional data; generating a regional energy priority based on the regional data and the energy data; generating a power distribution scheme based on the regional power utilization priority and the regional energy priority; according to the invention, energy is supplied to each region based on the power distribution scheme, and the region priority and the region energy priority are considered, so that each region has an optimal power distribution scheme during power distribution, and the power distribution effect of the power distribution region is improved.

The application discloses a kind of carbon constraints under regional power system planning collaborative optimization method and system, method includes: the unit product cost of each department technical path of calculation terminal, the technical path occupancy rate is predicted using Logit selection model and inventory replacement model, and terminal energy consumption and carbon emissions are summarized and calculated;Based on terminal energy consumption, power load curve is constructed, and system operation simulation data is obtained by solving power production model;Multi-year power supply and demand side evolution path optimization model with minimum total cost in the whole cycle as the target is constructed, which is decomposed into long-term planning main problem and short-term operation sub-problem using Benders decomposition, under the condition of introducing total carbon, carbon intensity and department carbon emission constraint, the optimal evolution path meeting carbon constraint is output by iteratively solving through investment scheme transmission and feedback optimality cut or feasibility cut between main problem and sub-problem.The application realizes the collaborative optimization of supply and demand sides under carbon constraint.

The invention provides a carbon compensation method based on cross-regional power trade transfer, and relates to the technical field of energy environments. According to the method, through constructing a power transaction carbon emission reduction responsibility accounting model, an emission reduction effort-based carbon emission reduction responsibility allocation model and a carbon compensation cost benefit analysis model, accurate allocation and fair compensation of carbon emission reduction responsibility in cross-regional power transaction are realized. Through accurate responsibility accounting, responsibility allocation considering economic and technical contributions and scientific economical efficiency verification, the defects that in the prior art, responsibility allocation is one-sided, technical investment and economic benefit allocation are difficult to reflect, and economical efficiency verification is lacked are overcome; and a scientific, fair, economical and feasible solution is provided for carbon emission reduction responsibility distribution and compensation in cross-regional power transaction.

The invention discloses a climate change long-term trend assessment method based on periodic data decomposition, and relates to the technical field of climate change assessment, and the method comprises the steps: collecting multi-source climate data, and constructing a multi-source climate data time sequence; performing periodic decomposition, extracting disturbance of long-term trend, periodic fluctuation and short-term random fluctuation components on the long-term trend, optimizing the long-term trend, and generating a long-term change trend of the target climate; and for the influence of the new energy power volatility, the regional power generation efficiency and the power grid load matching degree, generating an influence evaluation result, and generating a visual report. According to the method, the technical problems that the climate trend prediction precision is insufficient and the multi-dimensional influence on the power system is difficult to accurately evaluate in the prior art are solved, and the technical effects of improving the climate trend prediction precision and accurately quantifying the comprehensive disturbance influence on the power system are achieved.

The invention discloses a regional power market subject income optimization decision-making method based on a collaborative intelligent model, and relates to the technical field of power market optimization, and the method comprises the steps: carrying out the preprocessing and feature extraction of original data, and generating a high-quality structured training data set; inputting the high-quality structured training data set into a long-short-term memory network model to carry out price interval prediction, and obtaining an electricity price prediction interval result of each time period in the future; inputting the high-quality structured training data set and a preset quotation level into a random forest model for bidding success probability modeling, and obtaining a bidding success probability value of each capacity section; when it is judged that the market is in a new market, a price interval prediction model and a bidding success probability model are initialized; and constructing a multi-target fitness function by using an electricity price prediction interval result and a bidding success probability value. According to the method, continuous model optimization is realized through feedback learning of market clearing results, so that collaborative optimization of profit maximization and risk control is realized in a dynamic game market environment.

The invention provides a regional power grid voltage early warning method and device based on big data, relates to the field of smart power grids, and solves the problem that errors caused by social changes in historical data are difficult to accurately analyze when a power grid source load end state is predicted according to the historical data. And a time period with self-adaptive change is difficult to set for each region. The method comprises the following steps: planning a plurality of management and control time periods; when the historical data is more, determining the reference power consumption and the reference power generation amount of the next management and control time period based on the date type and the position of the management and control time period; according to the volatility of the historical data, the environmental data, the reference power consumption and the reference power generation, determining the standard power consumption and the standard power generation of the next management and control time period; when the historical data is less, standard electricity consumption and standard generating capacity are determined through the electricity analysis model; an early warning instruction is determined based on the standard electricity consumption and the standard power generation amount, and early warning is given; according to the invention, the accuracy of power grid source-load end data analysis can be improved.

The present application relates to the technical field of power consumption prediction, and particularly relates to a regional power consumption intelligent prediction method based on multi-source heterogeneous data, which comprises the following steps: determining the power consumption prediction influence tendency for the next preset monitoring period based on the power change characteristic value; when it is determined that the next preset monitoring period is a strong influence tendency for power consumption prediction, determining that the power consumption is corrected by the elastic influence weight coefficient, and determining whether the prediction for the power consumption is qualified based on the energy change characteristic value; when it is determined that the prediction for the power consumption is abnormal, correcting the prediction parameter for the power consumption in the next preset monitoring period based on the regional industry change quantitative value, including identifying the main analysis data set, determining whether to increase the model training set or separate the prediction model based on the period quantitative value of the main analysis data, and performing targeted analysis and prediction on the power consumption according to the specific situation of the power elasticity coefficient of the region to be predicted, thereby improving the prediction efficiency of the power consumption in the region.

The application relates to the technical field of power grid data processing, and discloses a regional power consumption prediction method; the regional power consumption prediction method comprises the following steps: S1, first period data collection; S2, period characteristic classification determination; S3, characteristic classification prediction determination; S4, second period data collection; S5, period characteristic classification updating; S6, characteristic classification prediction updating; the application collects the data of the power consumption in a short period in the region, carries out characteristic classification according to the preliminary collection, completes prediction after the classification is finished, and thus can carry out preliminary power consumption prediction and subsequent power consumption operation and maintenance management; in the subsequent power consumption process, the power consumption data is collected again according to a certain period, characteristic updating and prediction updating are carried out, the operation and maintenance strategy can be continuously adjusted according to the real-time change of the power consumption in the region, the real-time performance of the operation and maintenance strategy is ensured, and the power supply of the region brings lower operation cost.

This invention relates to the field of power generation forecasting technology, specifically a regional power generation forecasting system that integrates power grid topology and geographic information. The system includes a topology association construction module that collects node power generation data to generate a power grid association structure; a regional grid division module that receives data and divides the power supply grid structure; a line path mapping module that compares geographic coordinates to generate a line grid path structure; a power generation trend estimation module that establishes a regional power generation change structure; and a power generation forecasting module that determines the distribution and outputs the forecast results. In this invention, a network topology association system is constructed by collecting node data. A refined spatial physical grid is divided based on power supply boundaries. Neighboring spatial sets are selected based on broken line features to establish spatial mapping links for transmission lines. The overall regional demand evolution trend is inferred based on node fluctuation patterns. A deep comparison of topological structural features and spatial geographic attributes is performed to reshape the power flow distribution pattern of the complex power grid system, thereby improving the accuracy of regional forecasting results.

The invention relates to the technical field of electricity consumption prediction, in particular to an intelligent regional electricity consumption prediction method based on multi-source heterogeneous data, which comprises the following steps: determining an electricity consumption prediction influence tendency for a next preset monitoring period based on an electric power change characterization value; when it is determined that the next preset monitoring period is the power consumption prediction strong influence tendency, it is determined that the power consumption is corrected through the elasticity influence weight coefficient, and whether prediction for the power consumption is qualified or not is determined based on the energy change characterization value; and when it is determined that prediction for the electricity consumption is abnormal, correcting prediction parameters for determining the electricity consumption in the next preset monitoring period based on a regional industry change quantized value, including identifying a main analysis data set, determining to add a model training set or separate a prediction model based on a periodic quantized value of the main analysis data, and determining to determine the electricity consumption in the next preset monitoring period. According to the specific condition of the power elastic coefficient of the to-be-predicted region, the power consumption is analyzed and predicted in a targeted manner, and the prediction efficiency of the power consumption in the region is improved.

The invention discloses a cross-regional power transmission section capacity demand assessment method, electronic equipment and system, and relates to the technical field of power transmission planning, and the method comprises the steps: carrying out the sampling solving based on a high-fidelity steady-state mathematical model, and generating a scene set representing the steady-state operation of a plurality of future power systems; learning and mapping internally-associated high-dimensional structure manifolds in all steady-state operation scenes in the scene set through a physical information learning framework; analyzing geometric features of the high-dimensional structure manifold, and generating a power transmission planning index; according to a power transmission planning index, the capacity demand of a target cross-regional power transmission section is evaluated, and for a future power grid in which high-proportion renewable energy sources and flexible resources coexist, high-dimensional and nonlinear structural distortion caused by space-time dimension coordination interaction of multiple resources can be accurately captured and quantified. And evaluating the structural situation of the steady-state power flow of the power grid from a global view.

This invention discloses a method for refined simulation calculation of carbon emissions from regional power systems, belonging to the field of simulation calculation technology. The method includes the following steps: collecting regional power system data; initially calculating carbon emissions from the generation side, grid side, and user side; determining the carbon emission accounting boundary of the regional power system based on regional geographic information data; obtaining distributed storage data using distributed storage technology; extracting features from the topological data to obtain regional topological feature data; constructing a simulation calculation model by combining the distributed storage data with a neural network algorithm; outputting refined simulation data for carbon emissions from the regional power system; and correcting the refined simulation data by integrating environmental data and a random forest algorithm, finally outputting the corrected refined simulation data for carbon emissions from the regional power system. This invention achieves refined simulation calculation of carbon emissions from regional power systems throughout their entire lifecycle and across multiple dimensions.

The invention relates to an economic structure intelligent monitoring method and system based on power utilization big data, and the method comprises the following steps: S1, collecting and preprocessing multi-source data of a regional power system; s2, extracting multi-dimensional power features based on the standardized user power consumption behavior data; s3, constructing an industry classification and economic structure recognition model, obtaining the industry affiliation and confidence distribution of each user based on the multi-dimensional power feature vector set, and calculating the power consumption ratio of each industry according to the power consumption of different types of users in the region to form an industrial power consumption structure matrix; s4, according to the industrial power consumption structure matrix, establishing an industrial power consumption-output value regression model in combination with historical economic statistical data; s5, by solving the industry electric quantity-output value regression model, the relative output value proportion of each industry in the region is obtained; and S6, mapping a model solving result into a GIS (Geographic Information System). According to the invention, quasi-real-time intelligent monitoring of the economic structure is realized, and the sensing speed and the response capability of the economic operation situation are greatly accelerated.

The invention provides an extreme weather uncertainty-oriented regional energy power configuration optimization method and system, and the method comprises the steps: firstly determining a regional historical typical extreme scene based on historical extreme weather and synchronous power data; then, constructing a power gap optimization upper-layer model considering load uncertainty disturbance and new energy fluctuation, and obtaining a power gap value of the region in a planning period; finally, on the premise that the regional power supply safety requirement is met, the minimum economic cost and the minimum carbon emission serve as targets, and an energy power low-carbon optimization configuration lower-layer model is constructed; and a regional energy power configuration optimization scheme is obtained by solving the energy power low-carbon optimization configuration lower-layer model. By considering the power supply and demand conditions under the influence of the extreme weather, the safety guarantee and low-carbon optimal configuration of the regional energy power system under the extreme weather condition are realized, the capability of the power system for coping with regional extreme events is improved, and the structural optimization of a regional power supply and the improvement of the cleanliness of a power grid are promoted.

The invention relates to the technical field of data processing and analysis of an energy power system, in particular to a method and a system for calculating a full-life-cycle average carbon footprint factor of a regional power system. The invention discloses a method and a system for calculating a full-life-cycle average carbon footprint factor of a regional electric power system. The method comprises the following steps: constructing a calculation model of an electric power average carbon footprint factor CFP; carbon emissions of different power generation types in the whole life cycle are calculated, and the total amount of carbon emissions in the whole life cycle of power transmission and distribution is calculated; and finally, according to the central station acquisition data and the accounting models in the steps S1-S3, calculating the whole life cycle average carbon footprint factor CFP of the regional power system of the to-be-calculated region. Compared with the prior art, a product carbon footprint management system is constructed for supporting, and different subjects can conveniently check the carbon footprints generated by power production and consumption.

The invention discloses a regional power load prediction method, system and device and a medium, and the method comprises the steps: obtaining the information of a region where a power system is located, and carrying out the dividing and clustering grouping of the power system based on the region information; obtaining an electrical load sequence of each clustering group, and carrying out relevance calculation in combination with environmental factors to obtain a relevance degree; constructing an electrical load prediction model based on the correlation degree, inputting the environmental factor prediction sequence into the electrical load prediction model to carry out grouped electrical load prediction, and obtaining grouped electrical load data corresponding to the to-be-predicted time; and accumulating the grouped electrical load data to obtain regional power system load prediction data corresponding to the to-be-predicted time. According to the method, the influence of various environmental factors on the electrical load is comprehensively considered, the data volume of prediction calculation is effectively reduced through clustering grouping, the prediction efficiency is improved, and the problems of insufficient consideration of multiple factors, complex data processing and low prediction precision in a traditional method are solved.

The application discloses a regional power load prediction method and device and a medium, relates to the technical field of smart power grids, and solves the technical problem that in the prior art, global modeling is usually adopted in prediction processing, the load characteristic differences of different types of regions are ignored, and a large error exists between the predicted result and the actual result; geographical information data, historical data and real-time influence data in a target region are acquired; the target region is divided into a plurality of sub-regions according to the geographical information data, and type regions are obtained; characteristic scores of the plurality of type regions are analyzed based on the historical data; the power load of the plurality of type regions is predicted by using a load prediction model according to the real-time influence data and the characteristic scores, and predicted loads are obtained; prediction errors are analyzed based on the predicted loads and actual loads; and the load prediction model is updated and optimized according to the prediction errors; the load prediction model can adapt to the characteristics of different regions, and the prediction accuracy is improved.