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Intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, electronic device and storage medium
PCT designated stageWO2025251545A1Generation forecast in ac networkData processing applicationsEnergy regulationPower usage
The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.
Owner:BEIJING SMARTCHIP MICROELECTRONICS TECHNOLOGY CO LTD +1
Energy management method, device and equipment based on air conditioning system and medium
PendingCN120907215AMechanical apparatusLighting and heating apparatusProcess engineeringElectric consumption
The invention relates to an energy management method, device and equipment based on an air conditioning system and a medium. The method comprises the steps that in a preset period, a target cold storage capacity calculation model matched with the current working condition is selected from a plurality of candidate cold storage capacity calculation models according to the pre-obtained photovoltaic power generation power, the air conditioner system load and the change trend of the environment temperature; the environment temperature and the electric energy are input into the target cold storage capacity calculation model, and the cold storage capacity, output by the target cold storage capacity calculation model, of a phase change cold storage material in the air conditioner system is obtained; based on the cold storage capacity, the photovoltaic power generation power and pre-obtained planned electricity consumption probability distribution, analysis processing is conducted, and an energy management strategy of the air conditioning system is obtained; the energy management strategy is used for guiding the air conditioning system to adopt commercial power supply or photovoltaic power supply, whether to utilize the phase change cold storage material for cold storage or not and whether to start a compressor to assist in executing the cold storage process or not. By adopting the method, the power consumption cost can be reduced.
Owner:CHANGZHOU TIANHE SMART ENERGY ENG CO LTD
Lithium ion battery electric quantity accurate estimation method and system based on BMS
The invention relates to the technical field of battery electric quantity estimation, in particular to a lithium ion battery electric quantity accurate estimation method and system based on a BMS. The method comprises the following steps: collecting BMS original data, carrying out voltage current interval slicing, constructing a charging and discharging period node map, detecting node interruption joints, comparing slope differences to identify charging and discharging turning points, dividing independent charging and discharging periods according to the turning points, and determining current change time sequence data of each period. The actual charging and discharging electric quantity is calculated through the time sequence data, the charging and discharging difference is compared, the historical input and discharging electric quantity is further inquired, the fuzzy electric quantity range of the battery is deduced, and finally the real-time remaining available electric quantity is accurately calculated according to the fuzzy electric quantity range and the actual charging and discharging electric quantity difference and is uploaded to the BMS system. According to the invention, real-time monitoring and management of the electric quantity estimation result are realized, and the intelligent level of the battery management system is improved.
Owner:广东汇创新能源有限公司
Carbon emission prediction method and related apparatus
PCT designated stageWO2025222792A1Data processing applicationsMolecular entity identificationPredictive methodsBusiness enterprise
The present invention is applicable to the technical field of carbon emission monitoring. Provided are a carbon emission prediction method and a related apparatus. The method comprises: acquiring total carbon emission and total power consumption of each industry; calculating a correlation index of the total carbon emission and the total power consumption, so as to screen a target monitored industry; acquiring carbon emission and power consumption of the target monitored industry; on the basis of the carbon emission and the power consumption, calculating an electricity-to-carbon emission transfer coefficient based on the target monitored industry; on the basis of the target monitored industry, matching a target monitored enterprise, and on the basis of randomness, periodicity and climate factors, constructing a power consumption prediction model of the target monitored enterprise; acquiring actual power consumption of the target monitored enterprise and inputting same into the power consumption prediction model for training; matching an outputted predicted power consumption value with the electricity-to-carbon emission transfer coefficient; and generating a predicted carbon emission value. On the basis of the linear relationship between carbon emission and power consumption, the present invention uses power consumption prediction to estimate predicted carbon emission values, thus effectively improving the efficiency and accuracy of carbon emission prediction.
Owner:GUANGXI POWER GRID LLC
Intelligent power grid load balance control method and system
The invention belongs to the technical field of intelligent power grids, and discloses an intelligent power grid load balance control method and system. The method comprises the steps of dividing a power grid into a plurality of regions based on a predetermined rule; based on the regional power supply quantity and the regional power consumption quantity, identifying an overload region with insufficient power supply quantity; formulating and executing a load balancing strategy for the overload area by adopting a natural heuristic optimization algorithm, wherein the load balancing strategy is a load scheduling strategy taking scheduling time minimization as a target; calculating the priority coefficient of the electric equipment in the overload area, and supplying power to each electric equipment according to the priority coefficient; effective balance and optimal allocation of intelligent power grid loads are achieved, emergency power supply is guaranteed to the maximum extent in natural disaster time, and therefore the power grid operation level and livelihood guarantee capacity are improved, and the power supply reliability and the power utilization efficiency of the power grid are improved.
Owner:HUNAN XILAIKE ENERGY STORAGE TECH CO LTD
Electricity consumption prediction method
The invention relates to the technical field of data reasoning, and discloses an electricity consumption prediction method, which comprises the following steps: generating a conventional data set and a holiday and festival data set; generating a weather-load coupling feature set according to the extracted load volatility of the historical power consumption data and according to the temperature abrupt change point and the load volatility; extracting a multi-scale time sequence characteristic sub-sequence, and adding elements one by one to obtain a conventional prediction model; performing cross-modal fusion on the date feature and the load feature of the electricity consumption based on the date key value pair to obtain a holiday and festival prediction model; selecting a prediction model according to the target date type, and outputting an electricity consumption prediction value in combination with the weather-load coupling feature set; when the deviation value between the predicted value and the actual electricity consumption of the target area exceeds the limit, the electricity consumption data with the deviation value exceeding the limit are fed back to the historical electricity consumption data in the S1, the prediction model is optimized, the final electricity consumption predicted value is obtained, and the accuracy of electricity consumption prediction can be improved.
Owner:XIAN GUANGLIN HUIZHI ENERGY TECH CO LTD
Optical storage layered collaborative optimization control method coupling photovoltaic priority absorption and time-of-use electricity price
ActiveCN120914916AGeneration forecast in ac networkBatteries circuit arrangementsNew energyElectric consumption
The invention discloses an optical storage layered collaborative optimization control method coupling photovoltaic priority absorption and time-of-use electricity price, and relates to the technical field of new energy power system optimization control. The method comprises the following steps: taking a photovoltaic generating capacity prediction value, a load electricity consumption prediction value and peak and valley electricity price information as input data; carrying out rolling optimization by utilizing a model prediction control framework, and generating a light storage plan table; and calculating a photovoltaic output regulation value and energy storage charging and discharging power based on the generated light storage plan table in combination with the photovoltaic power generation power and the load power consumption power which are acquired in real time, executing corresponding energy storage charging and discharging and photovoltaic output processing by applying a decision tree control mechanism, and optimizing light storage control according to a processing result. According to the method, the photovoltaic preferential consumption strategy is executed, the light abandoning amount is reduced, the photovoltaic consumption rate is improved, the time-of-use electricity price dynamic response mechanism and the energy storage efficiency compensation and light abandoning punishment mechanism are combined, peak-valley arbitrage is maximized, meanwhile, the comprehensive electricity utilization cost is reduced, and the power purchase demand of a power grid is reduced.
Owner:NANJING XINGHE ENERGY TECH CO LTD
Electricity consumption anomaly detection method, system and device based on environmental perception graph convolutional network and medium
The invention discloses an electricity consumption anomaly detection method, system and device based on an environmental perception graph convolutional network and a medium, and belongs to the technical field of smart power grids. The method comprises the steps that historical electricity consumption of a power grid area unit and external environmental factors are acquired to construct a multi-dimensional space-time association graph; a periodic trend component and environment-related residual fluctuation are separated through the multi-dimensional space-time correlation diagram, and a two-channel diagram convolution feature is obtained; in combination with the convolution features of the two-channel graph, feature space decoupling of a periodic channel and a residual channel is realized, and decoupling features are obtained; and extracting a historical residual fluctuation sequence based on the decoupling features, and constructing an environmental state sensitive probability density function. According to the method, the historical electricity consumption and the external environment factors such as temperature, humidity or weather events are closely fused by constructing the multi-dimensional space-time association diagram, so that the space-time dependence of the electricity consumption behavior is effectively captured, and misjudgment caused by ignoring environment dynamics in a traditional method is avoided.
Owner:HAINAN POWER GRID CO LTD
Photovoltaic user electricity consumption abnormity monitoring method and system based on artificial intelligence
The invention relates to the technical field of power utilization monitoring, and discloses a photovoltaic user power utilization abnormity monitoring method and system based on artificial intelligence. The photovoltaic user electricity consumption abnormity monitoring system based on artificial intelligence comprises a data acquisition module which is used for acquiring photovoltaic power generation data, electricity consumption data and environment data of a user; the data preprocessing and feature engineering module is used for cleaning, aligning and normalizing the original data acquired by the data acquisition module and constructing a feature data set for model training and reasoning; and the artificial intelligence analysis engine module comprises an unsupervised learning unit, a supervised learning unit and a deep learning unit. According to the invention, the false alarm rate and the missing report rate can be effectively reduced, the accurate diagnosis of the abnormal type can be realized, and the intelligent and accurate operation and maintenance requirements of power grid enterprises on the power utilization monitoring of photovoltaic users are met.
Owner:STATE GRID SHANXI MARKETING SERVICE CENT
Power grid side flywheel energy storage stationing method considering source grid demand
ActiveCN120566524AAc network load balancingComplex mathematical operationsFlywheel energy storagePower grid
The invention relates to the technical field of power grid side energy storage, in particular to a power grid side flywheel energy storage stationing method considering source grid requirements, and aims to solve the problem of how to improve the adjusting effect of power grid side shared energy storage in the peak period of power utilization according to the source grid requirements. The invention provides a power grid side flywheel energy storage point distribution method considering a source network demand. The point distribution method comprises the following steps: determining pre-site selection data of power grid side shared energy storage according to an energy storage demand of a target area and a topological structure of cable equipment in the target area; acquiring cable equipment corresponding to the corrected site selection data, and calculating an energy storage demand corresponding to the corrected site selection data according to historical electricity consumption of the cable equipment; and obtaining a power supply unit in the target area, planning all the corrected site selection data according to the power supply capability and the energy storage demand of the power supply unit, and obtaining the actual distribution point of the power grid side shared energy storage.
Owner:XIANGSHAN ELECTRIC POWER IND CO LTD +1
Energy storage user identification method, system and equipment based on power grid energy consumption data
The invention discloses an energy storage user identification method, system and device based on power grid energy consumption data. The method comprises the steps of obtaining daily power consumption time sequence data of a target user; obtaining daily peak electricity consumption time sequence data and daily valley electricity consumption time sequence data according to the daily electricity consumption time sequence data; judging whether a date with negative daily peak electricity consumption exists in the daily peak electricity consumption time sequence data or not; if the date that the peak electricity consumption is negative exists, the target user is an energy storage user; if the date with the negative peak electricity consumption does not exist, daily valley electricity consumption data corresponding to a first date is obtained, and the first date is configured to be the date with the daily peak electricity consumption larger than a preset first threshold value; judging whether the dispersion degree of the daily valley electricity consumption data corresponding to the first date accords with a preset index or not; and judging whether the target user is an energy storage user. The problems that at present, an intelligent energy storage user identification method is lacked, and accurate identification is difficult to carry out are at least solved.
Owner:STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
Data feature analysis method and system for intelligent electric meter
The invention discloses a data feature analysis method and system for an intelligent electric meter, and relates to the technical field of data processing analysis, and the method comprises the steps: collecting daily electricity consumption data of all electricity users in a target region in a preset time period, and carrying out the preprocessing, and obtaining a corresponding daily electricity consumption time sequence and a daily electricity consumption curve; calculating the similarity between the adjacent daily electricity consumption curves of each user, and obtaining the stability characteristic value of the electricity consumption change of the user; calculating an optimal Euclidean distance between the daily electricity consumption time sequences of different users in combination with the stability characteristic values of the electricity consumption changes of the users; clustering the users in the target area based on the optimal Euclidean distance; analyzing the power consumption behavior of the user based on the clustering result; by measuring the stability of the power consumption behavior of the user, the problem of clustering deviation caused by neglecting time sequence fluctuation in a traditional method is solved, and the clustering accuracy is improved, so that the precision of power consumption behavior analysis and the reliability of anomaly detection are effectively improved.
Owner:YOONO ENERGY TECH (JIANGSU) CO LTD
Building energy-saving potential assessment method and system based on energy consumption quota
The invention provides a building energy-saving potential assessment method and system based on an energy consumption quota, and the method comprises the steps: carrying out the energy consumption analysis of a to-be-assessed building through a clustering analysis algorithm and a regression analysis algorithm according to the energy consumption multi-factor data of the to-be-assessed building, and obtaining the energy consumption quota data and the carbon emission quota data; based on the energy consumption multi-factor data and the energy consumption quota data, performing power consumption prediction on the to-be-evaluated building by using a multiple linear regression algorithm to obtain power consumption prediction data; calculating target energy efficiency data according to the electricity consumption prediction data; according to the energy consumption quota data, the carbon emission quota data and the target energy efficiency data, performing energy-saving potential assessment on the to-be-assessed building to obtain a comprehensive potential score of the to-be-assessed building; according to the method, the energy-saving potential of the building is evaluated by combining the energy consumption quota data, the carbon emission quota data and the power consumption prediction data obtained by using the multiple linear regression algorithm, the actual energy consumption of the building can be comprehensively reflected, and thus the potential energy-saving opportunity of the building can be identified more accurately.
Owner:CHINA ELECTRIC POWER RESEARCH INSTITUTE CO LTD +3
Bidirectional metering correction method and system for electric energy meter
The invention belongs to the technical field of electric energy meter metering correction, and provides a two-way metering correction method and system for an electric energy meter, and the method comprises the following steps: obtaining the power generation amount of distributed energy in historical data and the power consumption of a user, recognizing a continuous time period in which the power generation amount and the power consumption have a significant difference as a mismatch high-incidence time period, and determining the mismatch high-incidence time period; performing selection analysis on the mismatch high-incidence time period, and determining a time period division rule for prediction analysis; and performing time interval division based on the determined time interval division rule to obtain an analysis time interval. By analyzing the metering abnormal proportions of different types of key mismatch time period groups, screening out high-proportion class groups, predicting the occurrence trend and time of the high-proportion class groups by using a neural network model, and performing electric energy metering correction compensation, the occurrence of the key mismatch time periods can be predicted in advance, and correction compensation is performed based on a historical metering deviation rate mean value. Settlement errors caused by inaccurate metering of the electric energy meter can be effectively reduced.
Owner:S P ELECTRIC
Line loss abnormity diagnosis method based on electric quantity fluctuation analysis
The invention relates to the technical field of electric power, in particular to a line loss abnormity diagnosis method based on electric quantity fluctuation analysis. The method comprises the following steps: acquiring an electricity user and electricity data; calculating a power supply quantity change rate and a power consumption change rate according to the daily power supply quantity of the transformer area and the fluctuation degree of the daily power consumption of the transformer area, and further judging a line loss abnormity type; if the line loss abnormity type is user side abnormity, screening the electricity users, and obtaining a user side abnormity type according to the screened electricity users; if the line loss abnormity type is power supply side abnormity, calculating a power supply quantity multiple, and performing fault attribution to obtain a power supply side abnormity type; and explaining the two exception types through the SHAP so as to generate a line loss exception diagnosis report. In this way, the adaptability and robustness of the diagnosis method in a novel scene containing a distributed power supply and the like can be enhanced, the interpretability of a diagnosis result is further improved, and a clear and credible feature basis is provided for field check.
Owner:MARKETING SERVICE CENT OF STATE GRID LIAONING ELECTRIC POWER CO LTD
Power consumption management method for photovoltaic power supply
ActiveCN120675065AGeneration forecast in ac networkData processing applicationsElectric power transmissionMulti source data
The invention discloses a photovoltaic power supply power consumption management method, which comprises the following steps: establishing a multi-source fusion prediction model to calculate and predict power consumption and photovoltaic power generation by acquiring traffic flow, weather forecast, energy storage module state and charging demand data, and calculating a priority index according to the charge state, health state, temperature and residual life of an energy storage module. And generating an energy distribution strategy based on the net load power demand and the priority index, and controlling photovoltaic, energy storage and power grid cooperative power supply. The prediction precision is improved through multi-source data fusion, the priority of the energy storage module is dynamically calculated, optimal distribution of energy is achieved, the photovoltaic self-generation and self-use rate can be improved, dependence on a power grid is reduced, the power grid access frequency and power transmission loss are reduced, the service life of the energy storage module is prolonged, and the overall operation efficiency of the system is improved. And the power supply stability and reliability are guaranteed, and good economical efficiency and practicability are achieved.
Owner:明通装备科技集团股份有限公司
Enterprise-oriented multi-user electric quantity prediction method
The invention discloses an enterprise-oriented multi-user electric quantity prediction method, and belongs to the technical field of electric quantity prediction, and the method comprises the steps: obtaining enterprise electric quantity related data, and carrying out the preprocessing of the data, and obtaining a training set, a verification set, and a test set; inputting the training set and the test set into the long short-term memory network for training, and verifying the performance of the long short-term memory network based on the verification set; inputting the training set into the verified long-short-term memory network to obtain multi-dimensional features, and training an extreme gradient lifting algorithm based on the multi-dimensional features; constructing a hybrid model based on a long short-term memory network and an extreme gradient lifting algorithm; obtaining enterprise electric quantity related data in a specified time period, performing preprocessing to obtain a prediction data set, and inputting the prediction data set into the hybrid model to obtain an electricity consumption prediction value of the corresponding enterprise; according to the invention, the problem of low power consumption prediction accuracy of the corresponding enterprise caused by the influence of multi-dimensional unstable factors in the prior art is solved.
Owner:STATE GRID ZHEJIANG ELECTRIC POWER CO LTD YONGKANG POWER SUPPLY CO
Micro-grid energy optimization method and device based on distributed deep reinforcement learning
The invention provides a micro-grid energy optimization method and device based on distributed deep reinforcement learning, and relates to the technical field of micro-grid energy optimization and intelligent scheduling, and the method comprises the steps: building a feature sequence according to a time sequence based on historical electricity consumption, meteorological data and corresponding power supply amount; then training an LSTM model by using the two types of sequences to obtain a power consumption and power supply prediction model, obtaining a prediction error through a sliding window, then obtaining meteorological data of the next time period in the future, obtaining a power supply prediction value in combination with a historical sequence, and obtaining a prediction result based on historical meteorological combination error distribution; calculating the probability that the power supply quantity predicted value belongs to each interval as a first type of evidence; and then a second type of evidence is distributed by taking the future weather and working condition similarity as a weight, an abnormal fan is identified by adopting a dynamic time warping difference method, a power supply quantity predicted value is corrected, a third type of evidence is generated, finally D-S fusion is performed, a power supply quantity predicted value in the next time period in the future is output, and a power supply mode decision is made.
Owner:NANTONG INST OF TECH
New energy station load stability control method and system based on mutual conversion of electricity energy and hydrogen energy
The invention provides a new energy station load stability control method and system based on mutual conversion of electricity and hydrogen energy. Obtaining a generated power prediction curve of the new energy power generation facility, and determining a power difference value between the new energy power generation load and the power grid stability demand based on the prediction curve; a surplus hydrogen production electricity consumption instruction is issued, the operation number and the hydrogen production rate of an electric hydrogen production module are dispatched, and surplus electric energy is converted into hydrogen energy to be temporarily stored; receiving a real-time charging demand signal, dynamically issuing a charging permission load instruction based on the current new energy power generation load and the hydrogen gas turbine power generation load, and scheduling the power output of each charging pile; and according to the real-time states of the new energy power generation load, the electrical hydrogen production electrical load, the charging pile electrical load and the hydrogen gas turbine power generation load, starting, stopping and output power of the hydrogen gas turbine are adjusted, and reverse compensation of the new energy fluctuation power is achieved. New energy power generation fluctuation can be effectively stabilized, abandoned wind and abandoned light are reduced, and stable power supply of the distributed charging pile is achieved.
Owner:XIAN TPRI BOILER ENVIRONMENTAL PROTECTION ENG CO LTD
Social, industrial and enterprise electricity consumption analysis method based on DDPM and GRU
The invention discloses a method for analyzing electricity consumption of society, industry and enterprise based on DDPM and GRU. The method comprises the following steps: collecting electricity consumption data of society, industry and enterprise; constructing a multi-dimensional social development characteristic index system covering economic thermometer indexes, per capita power consumption, power consumption elastic coefficients and the like; performing standardization, forward diffusion and reverse de-noising processing on the industry power consumption data by using a DDPM algorithm, predicting the yield of a terminal product by combining the upstream and downstream industry chain power consumption data, eliminating noise interference and capturing dynamic relevance; and a GRU model is adopted to carry out time sequence modeling on the power consumption data and the non-power indexes of the enterprise. According to the method, the advantages of DDPM and GRU are fused, the accuracy and robustness of power consumption analysis are remarkably improved, scientific data support is provided for policy making, industrial planning and enterprise green transformation, and energy management optimization, carbon emission reduction and green energy utilization efficiency improvement are achieved.
Owner:NORTHEAST DIANLI UNIVERSITY
Device Energy Monitoring and Management
Method and system comprising determining electricity usage of a plurality of devices operating within one or more local networks. Maintaining in a data store external to each local network, data representing the determined electricity usage for each device of the plurality of devices and data identifying the device local network of the device.
Owner:VODAFONE GROUP SERVICES LTD
Resident daily electricity consumption prediction method and device based on dynamic clustering and time sequence fusion network, storage medium and system
The invention discloses a resident daily electricity consumption prediction method, device and system based on a dynamic clustering and time sequence fusion network, and a storage medium, and belongs to the technical field of resident daily electricity consumption prediction, and the method comprises the steps: obtaining the historical resident daily electricity consumption time sequence, key influence characteristics and holiday and festival data of a plurality of residents in the same time period; performing clustering analysis on the historical daily electricity consumption time series of the plurality of residents through a K value clustering analysis algorithm based on dynamic time warping to obtain a resident electricity consumption behavior mode of each resident; and inputting the key influence characteristics, the holiday and festival data, the historical resident daily electricity consumption time sequence of each resident and the resident electricity consumption behavior mode into a trained time sequence fusion converter model to obtain a resident daily electricity consumption predicted value of each resident in a future set time period. Through the dynamic clustering technology, the Shap algorithm and the time sequence fusion converter model, the prediction accuracy and reliability are improved.
Owner:JIANGSU FRONTIER ELECTRIC TECH
User electricity consumption data analysis modeling method and system based on semi-supervised learning
ActiveCN120597051AData processing applicationsNeural learning methodsGrey correlation analysisElectric power system
The invention provides a user electricity consumption data analysis modeling method and system based on semi-supervised learning, and belongs to the technical field of power system data analysis and prediction. The problems of low data inspection accuracy, large characteristic correlation degree deviation and the like in the prior art are solved. The method comprises the following steps: on the basis of user electricity consumption characteristic data and climate characteristic data, constructing a semi-supervised learning driven electricity consumption data inspection and tracking feedback model by adopting a graph convolutional network; calculating the Pearson's correlation coefficient among the electricity consumption characteristics of different types of users, constructing a characteristic correlation model based on a deep belief network, and analyzing the nonlinear relationship among the user types, the user electricity consumption time sequence characteristics and the climate characteristics; based on a grey correlation analysis method, capturing distribution characteristics of electricity consumption changes; the method is suitable for power system resource optimization, and especially improves the operation efficiency of a power grid under climate variability.
Owner:STATE GRID SHANXI MARKETING SERVICE CENT
Industrial electricity consumption influence factor contribution degree quantification method and device based on multi-source heterogeneous data
The invention relates to the technical field of energy management, and particularly provides an industry electricity consumption influence factor contribution degree quantification method and device based on multi-source heterogeneous data, and the method comprises the steps: constructing a machine learning model for predicting industry electricity consumption based on industry electricity consumption influence factors; and quantifying the contribution degree of the industrial electricity consumption influence factors by using the machine learning model and a Shapley value method. The technical scheme provided by the invention provides support for the power grid to accurately grasp the power consumption change characteristic, supports the power supply guarantee work of a power grid company, and guarantees the safe and stable operation of the power grid under the novel power system.
Owner:CHINA ELECTRIC POWER RESEARCH INSTITUTE CO LTD +4
Power carbon emission dynamic distribution method based on multi-level responsibility main body
PendingCN120914789AData processing applicationsAc networks with different sources same frequencyPower flowNew energy
The invention relates to the technical field of electric power systems, and discloses an electric power carbon emission dynamic distribution method based on a multistage responsibility subject, and the method comprises the steps: firstly determining a dynamic carbon emission factor based on the real-time load data of a generator set, and calculating the direct carbon emission; then, node carbon emission factors of all nodes in the power system are calculated on the basis of the carbon emission flow theory and in combination with power flow distribution; and finally, dividing the power network into multi-level responsibility subjects, calculating regional carbon emission factors of the subjects according to node carbon emission factors, and calculating indirect carbon emission by combining the net power consumption of the subjects, thereby realizing dynamic and accurate distribution of carbon emission responsibilities. According to the method, the problems that a traditional accounting method is not accurate and responsibility distribution is not public are solved, the distribution accuracy and fairness are improved through dynamic factors and multi-level tracing, and a scientific basis is provided for guiding emission reduction of a user side and promoting new energy consumption.
Owner:NORTH CHINA ELECTRIC POWER UNIV
Power demand response system
The invention discloses a power demand response system which comprises the following steps: S1, data acquisition and monitoring: the system acquires detailed power consumption data such as power consumption, power consumption time, power consumption power and the like of a user in real time through widely distributed intelligent electric meters, and uses sensors installed at key nodes of a power grid to determine the power consumption of the user; operation parameters such as voltage, frequency and load of a power grid are monitored in an omnibearing manner; and S2, data analysis and load prediction: fusing and sorting various types of data from a user side and a power grid side, and eliminating data format differences and repeated data. According to the power demand response system, a power consumption mode is remodeled through two-way interaction, the operation efficiency and economical efficiency of a power grid are improved, key support is provided for energy transformation and carbon neutralization targets, and the power demand response system is an important tool for achieving an intelligent power grid and a sustainable energy system and has wide application prospects. The successful implementation of the method depends on policy support, technical innovation and collaborative optimization of a user participation mechanism.
Owner:KAIFENG POWER SUPPLY COMPANY STATE GRID HENAN ELECTRIC POWER
Electricity stealing detection method based on line loss, non-intrusive monitoring and dynamic time warping
The invention provides an electricity stealing detection method based on line loss, non-intrusive monitoring and dynamic time warping, and the method comprises the following steps: analyzing the electricity consumption of a user in a power distribution area with an abnormal daily line loss rate, and generating the line loss evidence of a suspected user; comparing the load data of the suspected user obtained based on the non-intrusive load monitoring with the power consumption behavior of the suspected user, and generating a contradictory evidence that the power consumption behavior has contradictions; based on the current waveform library of the electricity stealing behaviors, analyzing the similarity between the user current waveform of the suspected user and an electricity stealing waveform template recorded in the current waveform library, and generating waveform matching evidence; and based on the line loss evidence, the contradiction evidence and the waveform matching evidence, weighting and fusing to obtain the comprehensive score of the suspected user, and screening the target electricity stealing user. The electricity larceny detection method based on line loss, non-intrusive monitoring and dynamic time warping has the advantages of high efficiency, high precision, low cost and strong interference capability, and can effectively identify electricity larceny users.
Owner:STATE GRID JIANGSU ELECTRIC POWER CO ZHENJIANG POWER SUPPLY CO
Robot charging control method and system, electronic equipment and storage medium
PendingCN121791372ABatteries circuit arrangementsSecondary cells charging/dischargingSimulationElectric consumption
The invention discloses a robot charging control method and system, an electronic device and a storage medium, and the method comprises the steps: monitoring the first average electric quantity of all available robots, and generating a charging task according to the remaining electric quantity of the available robots and an electric quantity rapid pull-up strategy under the condition that the first average electric quantity is smaller than a low electric quantity threshold value, and the charging task is sent to the target available robot, the second average electric quantity of all the available robots after one-time charging is completed is calculated, and when it is determined that the second average electric quantity is larger than or equal to the safe electric quantity threshold value, the electric quantity rapid pulling-up strategy is controlled to be ended. When it is determined that the first average electric quantity of the all-field available robots is smaller than the low electric quantity threshold value, the electric quantity rapid increasing strategy is started, the target available robot is charged based on the number of the idle charging piles, it is ensured that the phenomenon that the electric quantity of the all-field available robots is too low, and consequently a large number of automatic shutdown occurs is avoided; and manpower waste caused by manual charging intervention on the available robot is reduced.
Owner:HEFEI JIZHIJIA ROBOT CO LTD
Method and device for determining allowable power of battery, equipment and storage medium
Owner:VOYAH AUTOMOBILE TECH CO LTD
Energy storage system capacity configuration optimization method and device and storage medium
The invention discloses an energy storage system capacity configuration optimization method and device and a storage medium, and relates to the technical field of energy storage system capacity configuration.The energy storage system capacity configuration optimization method comprises the following steps that daily total electricity utilization power information of a factory is continuously collected, corresponding weather information is obtained, and factory power weather data is obtained; denoising processing is carried out, an electricity consumption power prediction model is constructed, and the daily total electricity consumption power of the factory is predicted; calculating a prediction error, carrying out error rule analysis, and correcting the predicted total power consumption power to obtain corrected power prediction data; calculating predicted electricity consumption, and performing configuration optimization on the capacity of the energy storage system of the factory according to the predicted electricity consumption; the method is used for solving the problems that when an existing energy storage system capacity configuration technology configures the capacity of an energy storage system of a factory through a power consumption prediction model, the rule of prediction deviation of the model cannot be analyzed, meanwhile, a prediction result cannot be corrected, and the accuracy of capacity configuration of the energy storage system cannot be improved.
Owner:JIANGSU LONGTU ELECTRIC POWER TECHNOLOGY CO LTD