44 results about "Regional power" patented technology

The invention relates to the technical field of power prediction, and discloses a regional power prediction system and method based on artificial intelligence, and the method comprises the steps: collecting historical power data in a target region, carrying out the outlier analysis of the historical power data, and constructing a power data prediction sequence; classifying the power data prediction sequence to obtain an operation data prediction sequence and an associated data prediction sequence; calculating a power prediction value of the target area according to the operation data prediction sequence and the associated data prediction sequence; according to the historical meteorological information of the position of the target area, establishing historical meteorological change curves of different time periods, and determining a meteorological electric power influence coefficient of the target area; and compensating the electric power predicted value based on the meteorological electric power influence coefficient to obtain a target electric power predicted value of the target region, so that the regional electric power can be accurately predicted, the utilization efficiency of electric power resources is improved, electric power waste is avoided, and a scientific basis is provided for electric power resource management and allocation.

The invention relates to the technical field of electric power, and provides a power load prediction model establishment method and system based on regional characteristics and meteorology, and the method comprises the steps: S1, building an influence index system based on regional characteristics and meteorological data; s2, acquiring a historical sample data set based on the influence index system; s3, constructing a machine learning model based on a random forest algorithm, and performing model parameter optimization through the historical sample data set to obtain a power load prediction model; s4, evaluating the power load prediction model; and S5, predicting the power load through the power load prediction model and the influence index system. Multi-dimensional attribute variables of regional power consumption building parameters, outdoor meteorological parameters, time parameters and power consumption parameters at the T-t moment are adopted for prediction, meanwhile, a prediction model is established through a random forest algorithm, and the prediction efficiency and the prediction accuracy are improved.

The invention discloses a regional power load real-time allocation system and method based on artificial intelligence, and belongs to the technical field of power distribution. According to the method, after an electric load data set and an alarm condition data set are integrated, a multi-dimensional feature interaction matrix is decomposed based on a factorization machine, a high-order interaction relation between electric load features and alarm condition features is extracted, hidden variable modeling is introduced, accurate division of risk levels of different areas is further achieved, and the accuracy of the risk levels is improved. Compared with a traditional single-domain feature analysis method, the method has the advantages that complex nonlinear relations can be captured, potential influence rules of regional power anomalies on alarm risks can be mined, and experimental verification shows that the method is high in accuracy and high in reliability. According to the modeling method, the recognition accuracy of the high-correlation region is improved by more than 15%, and a more scientific basis is provided for resource allocation decisions.

The invention discloses a trans-provincial and trans-regional electric power medium and long term transaction continuous operation model analysis system, and belongs to the technical field of electric power. A data acquisition and preprocessing module acquires electric power transaction data, new energy power generation data, meteorological data and market rule change information in real time or regularly; the market dynamic monitoring and analysis module dynamically monitors and analyzes the market supply and demand relationship, the price trend and the transaction rule change based on real-time and historical data; the new energy adaptability evaluation module evaluates the influence of the fluctuation and uncertainty of the new energy power generation on the medium and long term transaction of the power by comparing the actual data and the prediction data of the new energy power generation; and the transaction strategy optimization module formulates an optimal transaction strategy and a quotation scheme for two transaction parties by constructing a multi-objective optimization model and synthesizing cost, risk and income. According to the invention, the accuracy of analysis and prediction of medium and long term transactions of trans-provincial and trans-regional electric power in which new energy participates is improved.

The invention discloses a cross-regional collaborative power spot market supply and demand balance adjusting system, and relates to the technical field of supply and demand balance management, and the system comprises a multi-stage cross-regional adjustment framework construction module; the future power supply and demand prediction module is used for performing future power supply and demand prediction and generating a supply and demand trend chart; the cross-regional supply and demand balance model construction module is used for constructing a cross-regional supply and demand balance model by integrating a multivariate optimization target and combining a multi-stage cross-regional market participation framework; the multi-stage collaborative optimization module is used for performing multi-stage collaborative optimization by taking the supply and demand trend chart as model input; and a cross-regional power distribution module. The technical problems that in the prior art, traditional power dispatching is mostly concentrated in a single region, cross-region cooperation capability is lacked, and flexible and efficient power balance adjustment is difficult to achieve are solved, and the technical effect of high efficiency and flexibility of cross-region power resource allocation is achieved through multi-stage cooperation optimization and a new energy priority mechanism.

The invention provides a regional power grid equivalent damping coefficient evaluation method and system based on data collected by a synchronous phasor measurement device, and belongs to the technical field of power system parameter identification. The method comprises the following steps: firstly, selecting a frequency measurement result with relatively high data quality before and after a large disturbance event, and analyzing the influence of a determined component and a noise component of an active unbalance amount on a system frequency dynamic state in the large disturbance event; secondly, performing curve fitting on discrete frequency measurement results, and estimating a frequency response component caused by a noise component effect after a large disturbance event based on a superposition principle; on the basis, the equivalent damping coefficient of the regional power grid is estimated by combining the active power sudden change amplitude caused by the large disturbance event, the occurrence time of the large disturbance event and the inertia coefficient of the regional power grid.

The invention belongs to the technical field of regional power consumption prediction, and aims to solve the problem that detailed power consumption data of each region can be directly exposed by an existing regional power consumption prediction method. The distributed calculation-based large-area power consumption prediction method comprises the following steps of: dividing a large area of which the power consumption is to be predicted into a plurality of small areas, and performing secondary division on the divided small areas into a plurality of areas; constructing a power consumption prediction model of each district, and updating the power consumption prediction model of the core district by using the encryption loss data and gradient data of the other districts as the power consumption prediction model of the small district to which the core district belongs; the core small area receives encryption model parameters of the other small areas and generates model gradients corresponding to the small areas; and constructing a power consumption prediction model of the whole large area through encrypted data transmission and model updating iteration between the core small area and the other small areas. According to the method, the leakage problem in data transmission can be avoided, and the prediction accuracy and safety are improved.

The invention relates to the technical field of smart grids and discloses a method for regional power load forecasting, comprising collecting historical power consumption data; constructing a KANs model for power load forecasting, using a part of time characteristics, meteorological characteristics and power consumption data in the historical data as a training set and another part as a test set; inputting the training set into the KANs model to obtain an initial power load forecasting model; inputting the test set into the initial power load forecasting model for verification to obtain a trained power load forecasting model, inputting the time characteristics and meteorological characteristics of a to-be-forecasted area in a specific time period into the power load forecasting model to obtain a power load forecasting result; the method can be applied to the forecasting of power loads at various time granularities and has the characteristic of high accuracy.

The invention discloses a cross-regional power transmission and transformation project environmental protection and water conservation quota adaptation method. The method comprises the steps of 1, constructing a multi-dimensional regional feature factor library, and integrating landform data, soil type distribution data, annual average rainfall data, ecological protection zoning data and local environmental protection policy texts of provinces along a target project; when the scheme is implemented, environmental protection policies and water and soil conservation requirements of different regions are automatically adapted by establishing a regional characteristic factor library and a quota association rule, manual coordination errors are eliminated, and cross-regional engineering compliance is improved; geographic parameters such as topographic gradient, soil erosion degree and vegetation coverage are fused, the water conservation facility quota is dynamically adjusted in combination with real-time meteorological data, excessive design or insufficient protection is avoided, and the total engineering cost is reduced. The quota model is continuously optimized based on historical engineering data and a machine learning algorithm, an intelligent recommendation scheme is provided for a new project, the quota compilation period is shortened, and the environmental protection management efficiency is remarkably improved.

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 regional power grid short-term load prediction method. The method comprises the steps of dividing time periods, obtaining historical load data and meteorological data information of a target regional power grid in different time periods and processing the historical load data and the meteorological data information, and constructing a training data set according to the time periods; constructing a short-term load prediction initial model for each time period, and training to obtain a short-term load prediction model of each time period; acquiring data information of a to-be-predicted day and performing load prediction of each time period of the to-be-predicted day; selecting a historical day most similar to the data information of the day to be predicted, and obtaining load data of each corresponding time period; and in combination with the load prediction result of each time period and the load data of each time period of the corresponding historical day, completing short-term load prediction of the target regional power grid on the to-be-predicted day. The invention also discloses a system for realizing the regional power grid short-term load prediction method. According to the method, time-phased prediction of the short-term load of the regional power grid can be realized, the reliability is higher, and the accuracy is better.

The present invention relates to a method for joint clearing of main and auxiliary resources of regional interconnected power grids considering the reserve capacity of power generation and consumption resources, which belongs to the technical field of regional power grid management, and includes the following steps: the power generation side and the power consumption side in the sub-region evaluate their own regulation capabilities; enter the day-ahead market declaration link, all power generators, user-side demand response and energy storage determine their own day-ahead bidding information, and determine the load information at the same time; the sub-regional power grid determines the preliminary day-ahead clearing plan and pushes the information to the regional center; the regional center conducts secondary coordination on each region, and returns the winning bid information to the sub-region after adjustment; each sub-region returns the winning bid information to the power generator, user-side demand response and energy storage operator, and each department preliminarily executes according to the day-ahead plan. The regional power grid market can be cleared more efficiently, and the clearing plans of various types of generator resources, user-side demand response resources and energy storage resources in the region in the day-ahead and real-time main and auxiliary markets can be optimized and calculated to promote the consumption of clean energy.

The invention discloses a regional power load prediction method and device and a storage medium, and the method comprises the steps: querying historical meteorological parameters and historical power load data of a plurality of historical time periods for a specified region, and querying future meteorological parameters of a future time period; preprocessing the power load data; if the preprocessing is completed, searching the most similar historical time period as the optimal time period for the future time period according to the historical meteorological parameters and the future meteorological parameters, and searching the adjacent and similar historical time period as the adjacent time period; and predicting future power load data of a future time period according to the historical power load data of the optimal time period and the adjacent time period. According to the embodiment, the appropriate historical time period is searched according to the similarity of the meteorological conditions and serves as a high-value reference object, and the influence of information forgetting is reduced, so that the accuracy of power load prediction is improved, and the operation and planning quality of a power grid is improved.

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 discloses a China power industry carbon quota allocation method giving consideration to inter-generational and inter-provincial fairness. The method comprises the steps of obtaining national carbon emission data over the years and power industry carbon emission data over the years; based on the intergenerational emission reduction model, according to the national carbon emission data over the years and the carbon emission data of the power industry over the years, predicting the carbon emission of the power industry in the tth year; and multiplying the carbon emission of the power industry in the tth year by the adjusted accumulated carbon intensity of the power industry in the set region to obtain the power industry carbon quota of the corresponding set region in the tth year. According to the method, the upper limit of the trans-generational carbon quota is set, a carbon transfer responsibility adjustment mechanism based on regional power dispatching is introduced, and the problem of carbon responsibility imbalance caused by power trans-regional dispatching is effectively relieved; meanwhile, the emission reduction economic cost of each time period and each region is quantified, and a data basis is provided for carbon regulation and control.

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 invention relates to a regional power development planning evaluation method based on multi-level fuzzy comprehensive evaluation, and the method comprises the steps: collecting the power development planning data of a target region; then constructing a plurality of hierarchical indexes according to the power development planning data, allocating weights to the hierarchical indexes, calculating numerical values of the indexes, dividing evaluation grades according to the numerical values, and setting fuzzy values; and constructing a fuzzy evaluation matrix of each level index according to the fuzzy values, finally calculating a fuzzy comprehensive evaluation vector of the plurality of level indexes according to the fuzzy evaluation matrix, and calculating a comprehensive evaluation score according to the fuzzy comprehensive evaluation vector to evaluate regional power development planning. According to the method, global evaluation optimization is carried out from the perspective of comprehensively considering multiple factors, and all aspects of the power system are comprehensively evaluated by constructing a multi-level fuzzy comprehensive evaluation model.

The invention provides a method for establishing a function evaluation index system for evaluating a regional power market, and the method comprises the steps: determining a key income index of the regional power market based on a construction standard; based on the key income index of the regional power market, determining accounting data of each related province in the region and historical data of operation of each related province-level power market in the region; based on the accounting data of the related provinces in the region and the historical data of the operation of the related provincial power markets in the region, determining prediction data of the combined operation of the regional power markets; calculating the function evaluation index system based on the prediction data of the joint operation of the regional power market; wherein the function evaluation index is a three-dimensional comprehensive income index. The problem of insufficient scientificity of an advanced evaluation scheme for regional power market construction is solved.

The invention relates to an intelligent arrangement response method and system based on a power network environment, and relates to the technical field of power networks, and the method comprises the steps: collecting cross-regional power transmission data in real time through edge nodes, training a preset federated prediction model according to the cross-regional power transmission data, and outputting a parameter update quantity set, transmitting the parameter update quantity set to a global aggregator of the cloud to generate a global optimization target; based on the global optimization target and the historical data, outputting and executing a cross-regional power transmission strategy; establishing a weighted Chebyshev sub-problem based on a cross-regional power transmission strategy, cross-regional power transmission loss minimization, power supply reliability and carbon emission constraint, and outputting a Pareto final solution set; and acquiring actual operation data in real time, comparing the actual operation data with the Pareto final solution set, outputting a deviation value, if the deviation value is greater than a threshold value, updating the local model corresponding to the deviation value greater than the threshold value, uploading the local model to a global aggregator, and regenerating a global optimization target.

The present invention relates to a decision-making method for reducing the carbon cost of regional power generation. Based on real-time monitoring and cloud-edge collaboration, carbon emission data is captured through a continuous flue gas emission monitoring system to ensure the timeliness and reliability of the detection data, providing a reliable data basis for cloud platform prediction and decision-making. An enterprise-level edge computing gateway is used to achieve local information collection and data transmission continuation in case of network disconnection, ensuring information integrity and data reliability. By combining the distribution of edge computing and the centralization of the cloud platform, accurate prediction of the enterprise's carbon emission cost for the current year is achieved. Through cloud-edge collaboration, numerous carbon emission enterprises are connected into a whole, realizing interconnection and interoperability among enterprises, achieving comprehensive carbon emission benefits on a large scale, and having a very significant emission reduction effect.

The application discloses an intelligent optimization method and system for cross-regional power dispatching based on federal learning, and the application is implemented. Through heterogeneous data fusion, implicit association in unstructured data is converted into a calculable quantitative feature, which provides more comprehensive state representation information for subsequent model training, avoids the loss of key information caused by a single data type, and is beneficial to improving the accuracy of training data selected by the model. A model structure decoupled from public / private parameters is adopted to realize the compatibility of common sharing and individual isolation, and the practicality of the obtained model is improved. Through the execution of a differential privacy federal aggregation operation driven by differentiated weights, the dispatching model can iterate towards dispatching urgency, improve the adaptability of the dispatching model to the actual application scenario, and improve the security of the dispatching model. Finally, by setting an output mechanism of linkage between optimization and fault checking, the accuracy, reliability and security of the final output of the cross-regional dispatching scheme are 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 present invention discloses a cross-region power peak-valley node complementary visualization method and device. The method includes: obtaining cross-region power characteristic data, generating a regional power characteristic plane visualization graph, including a load change region and a power supply adjustable region, taking the union thereof to determine a first filling region including: an adjustable region and a non-adjustable region, where the adjustable region and the non-adjustable region are respectively the power supply adjustable region and the load change region except for the intersection of the load change region and the power supply adjustable region; calculating the adjustable region and the non-adjustable region of any two different regions by using the peak-valley node complementary method, establishing a peak-valley node characteristic visualization graph, and performing complementary calculation for different days within a planned time to generate a spatio-temporal arrangement visualization graph of the power peak-valley complementary characteristics of different regions. By dividing the adjustable region and the non-adjustable region and using the peak-valley complementary method to generate a visualization graph, the present invention improves the phased allocation efficiency of cross-region power resources.

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.

The invention provides a regional electricity market economic benefit dynamic analysis method, and belongs to the technical field of electricity market economic benefit evaluation. And calculating the comprehensive market economic benefit of each region by calculating the dynamic and static weights and the dynamic and static positive and negative difference degrees of the regional power market. The method has the capability of realizing regional power market economic benefit dynamic analysis at different times, and solves the problem that the regional power market economic benefit index value is difficult to calculate when being equal to the positive and negative difference degree values by considering the trend change problem of the data sequence.

The multi-energy complementary scheduling control method comprises the following steps: dividing the power grid into N areas according to a power grid structure and a tie line, and determining an area power supply in each area; carrying out load prediction on each region Ri by utilizing historical load data and load influence factors to obtain a region load demand Li (t) based on a time period; predicting the regional comprehensive power generation capacity based on the time period according to the regional power supply; generating a regional power generation and transmission plan; the regional power generation and transmission plans of all the regions are integrated, and a power grid integrated power generation and transmission plan is generated in combination with whole-grid power balance constraint conditions; dividing the scheduling time period of the power grid into a plurality of segments, and distributing an energy scheduling target for each segment; and optimizing the comprehensive power generation and transmission plan of the power grid according to the energy scheduling target to generate a multi-energy complementary ultra-short-term power generation and transmission plan guidance real-time scheduling scheme. The power generation capacity and the expected load of various power supplies in the power grid can be integrated to efficiently and accurately dispatch various power supplies, and the absorption capacity of new energy can be fully guaranteed.

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.