9648 results about "Power usage" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

The invention provides a BIM-based building energy consumption simulation analysis optimization method and system, and relates to the technical field of building energy consumption, and the method comprises the steps: building a building energy consumption digital twinborn model through integrating a building information model (BIM) and real-time operation data, such as indoor and outdoor temperature, equipment operation, electrical load and personnel density, collected by an Internet of Things sensor. An LSTM and GNN mixed deep learning algorithm is used to predict building energy consumption, and clustering analysis is used to identify an energy consumption abnormal mode and a peak period. And finally, based on a reinforcement learning algorithm, constructing an energy consumption optimization model taking total energy consumption minimization as a target and taking indoor thermal comfort and air quality as constraints, outputting equipment optimization operation parameters such as air conditioner temperature, fresh air volume, illumination brightness and the like, and realizing intelligent optimization control of building energy consumption, so that the building energy consumption is reduced, and the building operation efficiency is improved.

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

The invention relates to a control method and system for a photovoltaic energy storage system, and the method comprises the steps: obtaining the real-time irradiance data of the photovoltaic energy storage system, carrying out the power generation prediction of an irradiance region, and obtaining a dynamic power generation prediction value; acquiring charge state parameters of a photovoltaic energy storage system and aggregated power load demand data of a target power utilization area, and performing energy storage scheduling analysis in combination with the dynamic power generation power prediction value to obtain a reference energy storage scheduling strategy; acquiring energy consumption equipment parameters of the target power utilization area, and performing equipment scheduling control with the reference energy storage scheduling strategy to obtain an equipment-level control instruction set; and performing grid-connected mode and off-grid mode collaborative analysis on the dynamic generation power prediction value, the reference energy storage scheduling strategy and the equipment-level control instruction set to obtain an energy storage global control strategy. According to the invention, multi-level collaborative optimization can be realized, and the economical efficiency and sustainability of the system are improved while stable power supply is guaranteed.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

The invention relates to the technical field of data processing, in particular to an intelligent analysis system for electric energy quality and read data, which comprises a data fusion module, a modeling module, an analysis module, a detection module, an optimization module and a control module. The data fusion module aligns data of an electric meter terminal, power grid monitoring equipment and an environment sensor through a sliding time window, and the analysis module calculates a power grid node loss transfer coefficient based on a graph neural network and fuses transformer no-load loss and line contact resistance parameters to generate a dynamic line loss evaluation matrix. And the detection module identifies the power consumption characteristic deviation degree through a random forest classifier, and generates a priority management strategy in combination with a multi-dimensional abnormal scoring model. The control module adaptively selects a communication protocol to execute a regulation and control instruction according to a network state, a heartbeat detection mechanism feeds back operation data of a governance device in real time, model parameters are driven to be iteratively updated, and dynamic cooperation of power supply quality optimization and line loss governance is achieved.

The invention provides a power distribution network carbon emission analysis method, which comprises the steps of constructing a user power consumption behavior portrait based on user electric quantity data, learning an influence weight of a user power consumption habit on carbon emission by utilizing an attention mechanism, mining carbon emission modes under different power consumption behaviors through a clustering algorithm, and forming a user-oriented carbon emission interpretation model; an Apriori association rule mining algorithm is adopted, a frequent pattern in carbon emission data is extracted from three dimensions of a time period, equipment and a user, a key factor combination which obviously influences the carbon emission is screened out, and a rule is embedded into a block chain through an intelligent contract to execute carbon emission traceability; the diversity of carbon emission data is expanded by utilizing the generative adversarial network, carbon emission conditions in different extreme scenes are simulated, visual tracing of carbon emission whole-process data is realized through a block chain browser, and a decision basis is provided for accurately checking carbon emission and formulating an emission reduction policy.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention discloses a city expansion area transformer substation layout method and system based on an artificial intelligence algorithm. The method comprises the following steps: predicting future electrical load demand data of an area by mining an association rule between geographic space data and historical load data of the city expansion area; analyzing the prediction data by using a large language model, partitioning the city expansion area according to an analysis result, obtaining each sub-area and load difference data thereof, combining the data with historical power distribution network grid structure data of the city expansion area, and processing the data through the large language model to obtain an initial layout scheme of the transformer substation; by simulating the power supply capacity of each sub-region at different load growth rates, the initial layout scheme of the transformer substation is optimized, an optimization scheme is obtained and executed, and the optimal layout of the transformer substation is obtained by comprehensively applying artificial intelligence algorithms such as a large language model and a machine learning algorithm dynamic planning algorithm. Accurate prediction of the electrical load demand of the urban expansion area and effective optimization of the transformer substation layout scheme are realized.

The invention discloses an intelligent electric meter abnormal electricity utilization detection method and system based on data analysis, and belongs to the technical field of electricity utilization abnormal detection, and the method specifically comprises the steps: obtaining the historical electricity utilization data of a user according to an intelligent electric meter, and carrying out the preprocessing of the obtained historical electricity utilization data of the user, extracting features of the preprocessed historical power consumption data of the user, carrying out three-layer clustering analysis on the extracted features, analyzing historical power consumption behaviors of the user, identifying abnormal behaviors obviously deviating from a normal power consumption mode according to the historical power consumption behaviors of the user, and adaptively adjusting model parameters through a feedback mechanism. The abnormal electricity utilization of the intelligent electric meter is monitored in real time, the method can adapt to the electricity utilization mode change of the user, the dynamic threshold value adjustment can be adaptively adjusted according to the electricity utilization behavior change of the user, and therefore more accurate anomaly detection is achieved.

The invention discloses a multi-user energy storage power station charging and discharging control method and system based on electricity price, and the method comprises the steps: arranging a monitoring terminal at energy storage equipment, and collecting the load data of each energy storage equipment; carrying out data preprocessing on the collected load data; constructing a cascade neural network prediction model, training the model by using the preprocessed load data, and generating a regional total load demand prediction value in a future time period; designing a deep reinforcement learning model DRL by using the regional total load demand predicted value to obtain a preliminary power adjustment amount; designing a target function considering the battery aging cost by using the initial power adjustment amount, and determining an initial value range of a coefficient in the target function of the optimization model according to a constraint condition; and a multi-objective optimization problem is considered, a Pareto optimal solution set is obtained based on the initial value range, and a final power distribution scheme is determined. The method can accurately predict the load, optimize the charging and discharging strategy, reduce the power consumption and operation cost, balance the service life of the battery and the economic benefit, and improve the intelligent and efficient level of the energy storage power station.

The invention relates to the technical field of electric power government affair management, and provides a cross-department government affair big data business co-processing method based on a computing power platform and data fusion, comprising the following steps: S1, deploying a heterogeneous computing power cluster, and establishing a computing power resource dynamic scheduling data processing platform comprising a CPU, a GPU, an FPGA and an edge computing node; s2, acquiring multi-dimensional data of power grid equipment data, power grid load data, industry power consumption data and government policy data; and S3, designing a cross-department data access system based on the zero-trust architecture, and developing a multi-dimensional model of a power grid risk prediction model, an industry energy consumption analysis model and a carbon emission accounting model in power management. The space-time joint probability prediction and conflict priority ranking algorithm is applied to power-government affair emergency disposal, intellectualization and precision of cross-department business collaboration are achieved, power faults with serious consequences are rapidly handled according to the priority level, and the harmfulness and unpredictability of the power faults can be reduced.

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.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention discloses a multi-source data fusion-based optical storage load collaborative optimization scheduling system and method, and relates to the technical field of photovoltaic energy storage optimization, and the method comprises the steps: building a photovoltaic power generation scene tree model and a load prediction model through obtaining historical photovoltaic power generation data, load power consumption data and meteorological data; constructing a first optimization target and a second optimization target by considering the operation income and the energy storage aging cost, considering the photovoltaic uncertainty based on the photovoltaic power generation scene tree model, and establishing an adaptive optimization scheduling model in combination with the load demand predicted by the load prediction model; the photovoltaic power generation scene tree model and the load prediction model are embedded into the adaptive optimization scheduling model, and the optimal energy storage charging and discharging strategy of the current time period is solved, so that the robustness and reliability of energy storage scheduling are improved while the economical efficiency of photovoltaic power generation is ensured.

The invention discloses an electric equipment energy-saving control method and system based on the Internet of Things, and relates to the technical field of smart power grid and Internet of Things application, and the method comprises the steps: S1, obtaining the operation data and historical use records of user electric equipment, extracting the habit diversity and personalized preference analysis characteristics of a user, obtaining an equipment use preference model, and carrying out the operation of the user electric equipment; s2, extracting an operation rule of each device from the preference model, judging a priority adjustment parameter of the device, and obtaining a dynamic priority sequence, and S3, calculating an available power threshold value under current power resource limitation through real-time power grid load monitoring data, and determining a distribution power value required by important device guarantee; according to the electric equipment energy-saving control method and system based on the Internet of Things, the preference model and the priority sequence can be updated in time, and optimal distribution of electric power resources is achieved. The method can effectively balance the power demand of the user and the load of the power grid, improves the utilization efficiency of power resources, and provides a new solution for the management of the intelligent power grid.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention relates to the technical field of electrical safety monitoring, in particular to an intelligent detection method and system for indoor electrical potential safety hazards, and the method comprises the following steps: constructing a synchronous data set based on electrical equipment and environment data, extracting a power current sequence, analyzing the change rate, screening abrupt change signals, and combining with environment recognition risk fragments; and extracting a fluctuation interval to judge critical offset, positioning an electrical sequence offset distance, and counting risk frequency to mark a high-incidence area to generate an alarm node list. According to the method, high-precision synchronization of power consumption behaviors and environmental conditions is realized by collecting the electric participation environment data of the electric connection points and unifying time labels, the anomaly identification accuracy is improved, the power and the current sequence change rate are extracted, the line offset is dynamically pre-warned, and the hidden anomaly in the stable stage is revealed through track mapping; a high-incidence area is positioned through frequency statistics and space mapping, closed-loop monitoring from multi-dimensional data fusion to risk area marking is achieved, and the intelligence and foresight of electrical hidden danger recognition are remarkably improved.

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention discloses a green data center scheduling optimization method under computing power and electric power coupling, data load types of a data center comprise a transferable load, a reducible load and a rigid load, and the method comprises the following steps: constructing a double-target scheduling optimization model by taking the minimum power consumption cost of the data center and the maximum green power fitting degree as targets; the power consumption cost of the data center is used for representing the sum of expenses generated by the data center for purchasing power from a power grid in order to meet the power required for processing the data load in each time period, and the green power fitting degree is used for evaluating the matching degree between the power consumption demand of the data center and the renewable energy source supply; solving the dual-target scheduling optimization model by using a multi-target dogvessel swarm algorithm to obtain a Pareto frontier; and determining an optimal scheduling scheme based on the Pareto frontier, wherein the optimal scheduling scheme comprises a data center power consumption curve, an energy storage power station charging and discharging curve and a transferable load scheduling strategy. According to the invention, the power consumption cost of the data center can be reduced, and the renewable energy consumption capability can be improved.

The present application discloses a calculation method for multi-scale electric-carbon energy efficiency optimization for a power system, which includes designing a carbon flow calculation improvement model of the power system that takes into account a photovoltaic cluster auxiliary service, converting a power lossy transmission network of the power system equivalent to a lossless power consumption network, and calculating a carbon flow of the power system that takes into account a photovoltaic reactive power cluster auxiliary service; designing a decision-making system for low-carbon operation of the power system based on a coupling of electric-carbon energy efficiency, and calculating a correlation between electric quantity and the carbon flow of the power system that takes into account the photovoltaic cluster auxiliary service; the output varies in the range of 0-1, larger values represent a stronger correlation between electric-carbon energy efficiency.

The invention relates to the technical field of energy dispatching, in particular to a power dispatching optimization method based on optical energy storage, which comprises the following steps: extracting photovoltaic output and charge state fluctuation based on partitions, synchronously identifying abnormal intervals, analyzing direction matching and energy connectivity, screening abnormal units, and extracting linkage units in combination with load and power utilization characteristics. Evaluating power flow deviation, determining an unbalance level, matching a response list, and outputting an automatic linkage scheduling adjustment instruction set. According to the method, photovoltaic output and energy storage state fluctuation are identified in partitions, the accuracy of capturing an abnormal space-time coupling phenomenon is enhanced, the identification depth of scheduling imbalance hidden danger is improved, regional load distribution and power utilization behavior consistency judgment are combined, dynamic load feature evaluation of abnormal units is completed, the abnormal units are scheduled in a layered mode according to risk levels, and the scheduling efficiency is improved. The scheduling range is accurately adjusted, and the response precision and the handling capacity of the scheduling mechanism to the dynamic difference of the load side under new energy fluctuation are improved.

The present application relates to a multi-terminal linkage energy scheduling method and apparatus. The method comprises: acquiring multi-terminal data, wherein the multi-terminal data comprises power supply side data, power grid side data, load side data, and energy storage side data; on the basis of the acquired multi-terminal data, predicting multi-terminal electricity amount related information within a future preset time period by means of a prediction algorithm, wherein the multi-terminal electricity amount related information comprises photovoltaic power generation related information, power grid power generation related information, power consumption information of a load side device, and electricity charging and discharging related information of an energy storage device; on the basis of an energy efficiency target, acquiring energy regulation information by means of the multi-terminal electricity amount related information; and controlling the energy storage device and / or the load side device on the basis of the energy regulation information. According to the solution of the present application, by means of a multi-party linkage mechanism, a scheduling plan formed on the basis of a preset scheduling target is more suitable for actual requirements and more accurate, and the energy storage device and the load side device can be automatically controlled on the basis of the scheduling plan.

The invention relates to a source load storage control method for sustainable and stable output of new energy output power of an active power grid, and belongs to the technical field of new energy power systems. The method mainly comprises the following steps: based on ARIMA model prediction and load elastic response, guiding a user to optimize power consumption through real-time state perception, power prediction and time-of-use electricity price; a reference value is set and dynamically adjusted through multi-source data fusion, historical data and a scheduling instruction are integrated, and an output value of the source-storage combined system is corrected and output through an ARIMA model; a collaborative optimization mechanism is constructed, and load prediction, energy storage life and power grid interaction economy are optimized through multiple objective functions. Dynamic monitoring is realized through generalized power modeling and bidirectional flow discrimination, precision is improved by combining ARIMA prediction and rolling correction, and abandoned power is reduced. The load side actively participates in the electricity market, and the smooth curve relieves peak pressure; the intelligent charging and discharging strategy prolongs the energy storage life, optimizes economical efficiency and environmental protection, and provides a feasible technical path for a high-proportion new energy power grid.

The embodiment of the invention provides an intelligent dynamic load regulation and control method and system for an intelligent power grid. Wherein power parameters of key nodes are collected in real time through time sequence analysis and a deep learning algorithm, future power demand fluctuation is predicted, and a power grid topological structure model is constructed to identify the key nodes and a potential influence range; a genetic algorithm, particle swarm optimization and other multi-objective optimization algorithms are adopted to generate specific operation instructions for different power utilization areas or devices, and optimization objectives comprise minimization of energy cost, maximization of renewable energy utilization rate and guarantee of power supply stability; converting the strategies into guidance suggestions, pushing the guidance suggestions to a terminal user, collecting feedback information, generating a preference model by applying a reinforcement learning algorithm, and selecting an optimal strategy for implementation based on user acceptance and feasibility evaluation; risks and abnormal conditions are detected through the Bayesian network, and the strategy is adjusted in time. According to the technical scheme provided by the embodiment of the invention, the efficient distribution of power resources and the energy utilization efficiency in the smart power grid are improved.

The invention discloses a hybrid energy storage system charging prediction and energy regulation and control method, and relates to the technical field of power systems and energy management, and the method comprises the following steps: obtaining the real-time operation data of an optical storage charging system; by adopting the dynamic regulation and control algorithm and the self-adaptive learning mechanism, the light storage charging system can predict and correct the power change of photovoltaic power generation in real time, the photovoltaic power is utilized to the maximum extent, the power consumption of a power grid is reduced, and the energy utilization efficiency and the economic benefit are improved. Meanwhile, the dynamic charging and discharging strategy based on error correction effectively avoids the problems of overcharging and deep discharging of the battery, prolongs the service life of the battery, and improves the long-term stability and safety of the system. The system optimizes the charging and discharging plan according to the power grid electricity price fluctuation, and combines an early warning mechanism to monitor the battery state in real time, thereby reducing the operation cost, reducing the potential safety hazard, and guaranteeing the efficient and reliable operation in the whole life cycle.