392 results about "Energy dispatch" patented technology

The invention relates to the technical field of energy scheduling, in particular to a multi-modal data fusion method and system based on energy scheduling and a storage medium. The method comprises the following steps: obtaining multi-modal data, and carrying out abnormal fluctuation feature extraction to obtain a space-time fusion abnormal feature labeling set; deducing a multi-objective optimization path according to the space-time fusion abnormal feature labeling set to obtain a dynamic scheduling decision map; performing edge node game equilibrium calculation according to the dynamic scheduling decision map to obtain a trusted scheduling verification chain; performing digital twinborn constraint optimization on the trusted scheduling verification chain to obtain a closed-loop scheduling digital twinborn body; compiling a dynamic scheduling instruction set based on the closed-loop scheduling digital twin to obtain an anti-disturbance energy scheduling strategy library; and obtaining real-time energy supply and demand data, and performing scheduling deviation tracing on the real-time energy supply and demand data according to the anti-disturbance energy scheduling strategy library to obtain an energy distribution decision. According to the invention, the efficiency and reliability of energy scheduling can be improved.

The invention discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

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 invention provides a multi-park energy consumption prediction scheduling method based on digital twinning and a control system, and systematically solves the problem of the pain point of multi-park energy consumption management by constructing a technical chain from data perception to closed-loop optimization. The method comprises the following steps: firstly, by constructing a global unified digital twinborn model, standardized integration of dispersed and heterogeneous park assets and data is realized, and the problem of information islands is solved; secondly, prediction is carried out by adopting federated learning, cross-park knowledge sharing and joint modeling are realized on the premise of ensuring data privacy and security of each park, and the prediction precision of a single park under the condition of limited data is remarkably improved; and finally, through a'prediction-decision-execution-update 'closed-loop process, traditional passive and static energy consumption management is converted into active and dynamic prediction scheduling, so that the energy consumption peak can be stabilized prospectively, the energy distribution can be optimized, and the comprehensive energy consumption cost and carbon emission can be effectively reduced.

The invention provides an AI-based carbon asset and carbon inspection management method and platform, and belongs to the technical field of carbon emission management, and the method comprises the steps: inputting the operation activity data of a target object into a preset energy efficiency analysis model, and obtaining an energy efficiency analysis result; based on the energy efficiency analysis result, performing energy flow modeling and analysis on the operation activity data of the target object to obtain an energy flow analysis result; the energy flow analysis result is input into a preset energy efficiency balance optimization model, an optimized energy distribution scheme and carbon emission prediction values, predicted based on the energy distribution scheme, of all links are obtained, and the energy efficiency balance optimization model is configured as follows: under the condition that the production constraint condition of the target object is met, the energy distribution scheme is optimized; optimizing energy distribution by taking minimization of the total carbon emission as a target; comprehensively calculating the carbon emission predicted value of each link to obtain a target total carbon emission predicted value of the target object; and generating a corresponding target carbon asset management strategy according to the target total carbon emission prediction value, the carbon transaction market data and the carbon sink data.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

The invention relates to the technical field of building intelligent control and energy saving, and discloses a building equipment intelligent control optimization management system based on a green low-carbon building. The system comprises five subsystems, wherein an environmental parameter sensing subsystem is used for acquiring temperature and humidity gradients, illumination intensity distribution and personnel activity thermodynamic diagram data inside and outside a building through a distributed sensing network to form a multi-dimensional environmental state vector; the equipment operation state acquisition subsystem acquires an operation power curve, an energy efficiency conversion rate curve and an equipment health degree index of heating ventilation air conditioning, illumination and renewable energy equipment in real time; the dynamic load prediction subsystem establishes an energy consumption load space-time distribution prediction model according to historical data time sequence relevance; the control strategy generation subsystem is used for generating an optimized instruction set containing an equipment start-stop time sequence, a power regulation gradient and an energy distribution weight in combination with the current environment vector and a predicted load; the execution terminal adapts the subsystem to convert the instruction into a device compatible signal and calibrate a response delay.

The invention discloses a smart factory energy efficiency optimization method, system and device based on the industrial Internet of Things, and relates to the technical field of the industrial Internet of Things, and the method comprises the steps: collecting production energy consumption data based on equipment operation parameters and production plan information; constructing a factory digital twinborn model, and determining an equipment start-stop time sequence and an energy distribution strategy; deploying edge computing nodes, monitoring the vibration spectrum of the equipment, generating a state prediction map in a time sliding window, and performing adaptive compensation optimization; and meanwhile, an energy flow network is drawn up based on the equipment start-stop time sequence and the energy distribution strategy, and energy dynamic adjustment is carried out. The technical problems of low energy utilization efficiency and insufficient equipment operation stability of a smart factory in the prior art are solved, and the technical effects of realizing smart factory energy efficiency optimization based on the industrial Internet of Things and improving the energy utilization efficiency and the equipment operation stability are achieved.

The invention relates to an electric energy dispatching method for virtual power plant multi-subject system game, which comprises the following steps: acquiring and constructing a Wasserstein distance fuzzy set of electricity price and renewable energy output based on historical power plant data, and respectively establishing a superior power grid electricity price predicted value and a fuzzy set constraint of renewable energy output; establishing a VPP multi-subject collaborative transaction optimization model among an operator, an electric vehicle charging station and a producer, and reconstructing the VPP multi-subject collaborative transaction optimization model based on a Wasserstein distance fuzzy set and a maximum harsh lower bound method; and for the reconstructed VPP multi-agent collaborative transaction optimization model, establishing a game equilibrium model based on a non-cooperative game theory, and solving through a stationary point optimization method to obtain an electric energy scheduling scheme among multiple agents of the virtual power plant. Compared with the prior art, the method has the advantages that the robustness of game decision results is improved, and the flexibility and intelligence of electric energy utilization are improved.

The invention relates to the technical field of micro-grid control, in particular to a source-grid-load storage and charging integrated control method and system based on a micro-grid, and the method comprises the steps: considering the current SOC, health state and charging and discharging rate of a battery energy storage system, and evaluating the available capacity and charging and discharging capability of the battery energy storage system; according to the predicted charging load curve, the photovoltaic output curve and the energy storage state evaluation result, taking peak load shifting, photovoltaic consumption and charging station power supply guarantee as targets, generating a scheduling strategy by using an optimization algorithm, and determining a power distribution relationship among a photovoltaic system, a battery energy storage system and a charging station in each time period; and executing a scheduling strategy generated by the energy management module through three stages of regulation and control units, wherein the three stages of regulation and control units are respectively used for limiting grid-connected power fluctuation of the micro-grid, optimizing local energy distribution and island switching, regulating active power and reactive power and carrying out harmonic suppression. And stable operation of the micro-grid and stability of electric energy quality are ensured.

The invention discloses a multi-energy micro-grid power supply system based on a hydrogen fuel cell and a control method. The micro-grid power supply system comprises an intelligent grid-connected switch, an electrical load, an isolation transformer, an energy storage converter, a hydrogen fuel cell system, an energy storage lithium battery system and a local energy management system. The control method comprises the steps that when a power grid loses power or a PCC switch is switched off, an energy storage converter PCS is automatically switched to an off-grid operation mode or is switched to an off-grid operation mode according to an off-grid command, and stored electric energy is continuously used for on-load operation; when the power grid recovers or is converted into grid connection in a planned manner, the PCS is automatically synchronized to a state which is synchronous with the power grid or according to a grid connection command, and the intelligent static switch is closed to be converted into grid connection operation. According to a coordination control strategy among the PCS converter, the intelligent grid-connected switch and the energy storage battery and an SOC grading strategy between the fuel battery and the energy storage battery, functions of millisecond-level fast switching of grid-connected and off-grid operation, variable load fast energy scheduling control of a load and the like are realized.

The invention discloses a multi-type energy scheduling method based on a trusted data space, and the method comprises the steps: accessing a multi-type energy provider through a block chain node, and constructing a cross-domain trusted data space; obtaining energy data of each party; processing to obtain an energy data set; calling a federated learning model, training a load prediction neural network, and predicting to obtain a future load demand; constructing a multi-target optimization model by taking the maximum consumption efficiency and the minimum energy abandoning rate of each type of energy as targets, and solving the multi-target optimization model based on the predicted future load demand and the output curve of each type of energy to obtain an optimal scheduling strategy; and storing the optimal scheduling strategy on the chain, and obtaining the optimal scheduling strategy on the chain by each type of energy providers to perform energy scheduling. A decentralized trusted data space is constructed through a block chain technology, data source traceability and non-tampering operation are realized, a centralized platform trust risk is avoided, and data privacy zero leakage during cross-domain sharing is ensured by adopting privacy calculation.

The invention discloses a dynamic signal synchronization control method and system for an energy scheduling network, and relates to the technical field of dynamic signal control, and the method comprises the steps: carrying out the analysis and disassembly of the energy scheduling network, and determining signal transmission delay data and signal transmission error data; configuring a first energy distribution vector; configuring a second energy distribution vector; fusing the first energy distribution vector and the second energy distribution vector, and configuring a regulation and control strategy matrix of an energy stable distribution level and a signal synchronization correction parameter; and performing multi-node cooperative control, and feeding back a regulation and control effect to the coordinated optimization network to iteratively optimize the attention weight. According to the invention, the technical problems of poor network coordination and weak anti-interference capability caused by low signal synchronization precision and unstable distribution of the energy scheduling network in the prior art are solved, and the technical effects of realizing dynamic synchronization and stable distribution of the signals in the energy scheduling network and improving the coordination and anti-interference capability of the network are achieved.

The invention discloses a light storage charging station electric vehicle ordered charging control method based on bilevel planning, and the method comprises the steps: firstly constructing a bilevel planning model of a light storage charging station, and enabling the upper layer planning model to be used for optimizing the overall energy scheduling of the charging station; the lower-layer planning model performs optimization scheduling on the charging behavior of the electric vehicle in the charging process according to the energy distribution result of the upper-layer planning model; and then, iteratively solving the bilayer planning model by adopting a dynamic alternating direction multiplier method, taking the output of the upper layer planning model as the input constraint of the lower layer planning model, and feeding back the result to the upper layer planning model by the lower layer planning model until the target function value of the upper layer planning and the lower layer planning converges or reaches a preset number of iterations, and the orderly charging power of the electric vehicle is obtained, and a control strategy is dynamically adjusted in case of operation deviation. According to the method, through collaborative optimization of upper-layer planning and lower-layer planning, the overall operation benefit of the power system is optimized, and a flexible charging strategy is provided for an electric vehicle user.

The invention relates to the technical field of energy data management and control, and discloses a smart park energy data dynamic management and control system. The energy data acquisition module of the system acquires and preprocesses the operation data flow in the park in real time. The energy feature analysis module performs multi-dimensional feature extraction on the data stream, and identifies feature vectors such as peak load and steady-state operation. The event trigger engine generates an energy management event signal based on conditions such as feature vector and threshold comparison, timing pattern matching and the like. The parameter node identification module analyzes an event signal time sequence and positions key nodes such as equipment start and stop and load sudden change. The offset calculation module calculates the offset of the energy parameter relative to the historical reference for the key node. The similar parameter derivation module derives a migratable parameter set containing general control parameters and scene adaptive parameters according to the offset and a historical database. And the dynamic regulation and control module performs real-time adjustment such as power distribution and equipment scheduling on park energy distribution by using the set.

The invention provides an energy optimization type motion control system for an unmanned factory, belongs to the field of unmanned factory control, and solves the problems of low motion control integration efficiency, poor transfer precision, poor energy distribution effect and the like of an existing unmanned factory. Comprising a load detection module, a self-adaptive control module, a regenerative braking module, a cooperative scheduling module, an Internet of Things communication module, a positioning detection module, a path planning module, an intelligent decision and optimization module, a safety and fault tolerance module, a man-machine interaction and monitoring module and a stacking machine, a movable frame mechanism is arranged between the ground rail and the sky rail, a cargo carrying table mechanism is arranged on the movable frame mechanism in a transmission mode, a lifting transverse movement fine adjustment mechanism is arranged at the upper end of the cargo carrying table mechanism, a rotating position fine adjustment mechanism is arranged at the upper end of the lifting transverse movement fine adjustment mechanism, and a telescopic fork plate mechanism is arranged at the upper end of the rotating position fine adjustment mechanism. According to the system, multiple modules are intelligently integrated for cooperative work, energy distribution is optimized, and the three-dimensional space pallet forks are accurately aligned with the goods locations.

The invention relates to a power distribution network dispatching optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring power supply output information and load demand information of a plurality of polymers in a power distribution network; according to the power supply output information and the load demand information, determining reference interaction power among the plurality of polymers; the reference interaction power is optimized according to the parameter information of the power supply devices in the polymers through the polymers until a preset convergence condition is met, and target interaction power among the multiple polymers is obtained; according to the target interaction power and the parameter information of the power supply equipment in each aggregate, target output information of the power supply equipment in each aggregate is determined; and performing energy scheduling on the power supply equipment in each polymer according to the target output information. By adopting the method, the dispatching optimization efficiency and effect of the power distribution network can be improved.

The invention belongs to the technical field of power equipment, and discloses a smart energy system based on a power electronic transformer and a monitoring method, and the method comprises the steps: installing a sensor; equipment operation data is collected in real time, and environment monitoring equipment collects environment data; the data processing module preprocesses the collected data; the abnormity monitoring module judges whether an abnormal condition exists or not; the fault detection module judges whether the system has a fault hidden danger or has a fault; the energy scheduling control module formulates an energy scheduling strategy; the electric energy quality collaborative optimization module ensures that the electric energy quality of the whole system meets the standard; and the load management unit manages different types of loads according to the hierarchical load control strategy. According to the invention, abnormal conditions and potential fault hidden dangers can be found; intelligent energy scheduling can be realized, energy utilization efficiency can be improved, electric energy quality can be cooperatively optimized, hierarchical load management can be realized, and key load power supply can be guaranteed; and a hierarchical load control strategy is adopted to carry out differential management on loads.

The invention relates to the technical field of ship energy consumption control, and particularly discloses an FSRU ship energy consumption and carbon emission intelligent management and control system based on route big data, and the system comprises a route big data collection unit which is used for obtaining the multi-source data of an FSRU ship in the full-route-segment navigation process, and transmitting the multi-source data to an FSRU ship. The multi-source data at least comprises ship operation data, route environment data, carbon capture data and waste heat data; wherein the FSRU ship is a floating type storage regasification device ship; and the data preprocessing unit is connected with the airline big data acquisition unit and is used for cleaning the multi-source data and outputting preprocessed data. According to the method, the staged cooperative control model is established, the unique working condition requirements of the FSRU ship in different operation stages such as berthing, loading and unloading, offshore cruising and open sea navigation can be accurately identified and adapted, the limitation of a traditional single control strategy in variable scenes is overcome, and refinement and dynamics of energy distribution and emission control are achieved.

The invention relates to the technical field of machine room energy consumption management, and discloses a machine room energy-saving control method and system based on cloud computing, and the method comprises the steps: collecting equipment operation data in real time through a multi-dimensional sensor network, building a power consumption prediction model and an energy distribution optimization model, and extracting key features through principal component analysis; and constructing a multiple regression model to determine an energy efficiency coefficient, and predicting a future power demand by adopting a long-short-term memory neural network. And an energy distribution scheme is optimized based on a genetic algorithm, and accurate power control and dynamic balance are realized. According to the invention, a self-adaptive energy consumption management decision mechanism is also established, the energy consumption trend is analyzed through a sliding window, an emergency adjustment program is started, state estimation is corrected by using a Kalman filtering algorithm, and parameters of a prediction model are dynamically adjusted. According to the method, intelligent management of energy utilization of the data center can be realized, the total energy consumption cost is effectively reduced, the energy utilization efficiency is improved, and technical support is provided for energy conservation and emission reduction of a large machine room.

The systems and methods provide techniques for a distributed energy resource management system (DERMS). The system can optimize energy distribution by leveraging Distributed Energy Resources (DERs) (such as thermostats for HVAC systems, electric vehicle (EV) chargers, and home battery storage) by shifting energy among devices and home. During peak demand, the VPP can minimize the cost to the company, reduce demand on the grid, and ultimately reduce long-term consumer costs. The system can utilize home sensors to determine a state of occupancy (such as a home is occupied, unoccupied, or the residents are on vacation). The system can consider a desired comfort level. The various sources of data (e.g., energy data, cost data, and home sensor data) can be used to manage energy distribution in a VPP and generate demand response events or other instructions for energy usage based on real-time occupancy data.

The invention relates to the technical field of electric power, in particular to an accurate load prediction model and method for a regional building group source network load storage and utilization system, and the method comprises the steps: collecting multi-source heterogeneous data, including meteorological data, historical load data, building operation data, energy storage system state data and building behavior data, of a regional building group; performing spatial-temporal feature fusion processing on the multi-source heterogeneous data, and constructing a dynamic feature matrix; inputting the dynamic characteristic matrix into a pre-trained load prediction model, and outputting a refined load prediction result of the building group in a future time period; and optimizing an energy scheduling strategy of the source network load storage and utilization system based on the prediction result. The method has the advantages that prediction accuracy can be improved by considering multiple elements, and regional power supply stability is improved.

The invention relates to the technical field of harmonic suppression, and particularly discloses a ship power grid harmonic active suppression and energy management method and system, and the method comprises the steps: collecting the basic electric parameters of a ship power grid, the operation parameters of energy equipment, and the transient characteristics of a nonlinear load; and based on the collected multi-dimensional data, utilizing a deep learning hybrid model containing an attention mechanism to predict the harmonic trend and the energy demand of the ship power grid, and performing online iteration updating on model parameters according to a prediction error. According to the invention, active harmonic suppression and energy management are deeply coupled, so that conversion from passive compensation to prospective intervention is realized; the system can dynamically coordinate the strategy of harmonic treatment and energy distribution based on accurate prediction of the harmonic trend and energy demand of the power grid, and significantly improves the overall energy efficiency and operation economy of the ship power grid on the premise of ensuring that the electric energy quality strictly reaches the standard.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.

The invention discloses a multi-energy complementary low-altitude aircraft energy supply method, and relates to the field of low-altitude aircraft energy supply, and the method comprises the steps: collecting the flight state and the energy state of a low-altitude aircraft, carrying out the filtering processing of the flight state and the energy state, constructing a demand prediction model, and predicting the energy demand of the low-altitude aircraft in the flight stage; combining the energy demand with the flight stage rule to initialize an energy distribution proportion, optimizing the energy distribution proportion by using a target optimization algorithm and an optimization target, and outputting an optimal energy distribution scheme; and based on the environment information in the flight range of the low-altitude aircraft, establishing an energy use weight coefficient table, combining the energy use weight coefficient table with the optimal energy distribution scheme to dynamically adjust the energy priority, and generating an execution instruction according to the energy priority to drive an energy storage device so as to supply energy to the low-altitude aircraft. Through cooperative work of various energy sources, more stable and lasting power is provided for the aircraft, and the overall performance and the use efficiency of the aircraft are improved.

The invention discloses a photovoltaic power generation management method and system, and relates to the technical field of photovoltaic power generation management, and the method comprises the steps: S1, carrying out the real-time monitoring and classification of loads: carrying out the real-time monitoring of various types of electricity loads in an off-grid system through an intelligent electric meter and a sensor; s2, implementing an energy distribution algorithm: performing energy distribution by using a dynamic energy distribution algorithm according to the battery electric quantity, the photovoltaic power generation power and the load demand real-time monitoring data; s3, power generation equipment cooperative management: establishing a cooperative control mechanism of a photovoltaic panel and a diesel generator by means of the monitored environmental parameters; s4, intelligent management of energy storage equipment: monitoring parameters such as voltage, current, temperature and residual electric quantity of the battery in real time; s5, equipment state monitoring and data analysis; and S6, executing the active maintenance mechanism. According to the photovoltaic power generation management method and system, energy can be accurately distributed according to the load requirement, the generated power and the electric quantity of the battery which are monitored in real time.

The invention relates to the technical field of new energy management and edge computing, and particularly discloses a heterogeneous computing power aware new energy cluster collaborative scheduling method and system. According to the method, information such as static attributes and dynamic loads of various energy devices and control units in a cluster is collected through a lightweight probe, and a dynamically updated heterogeneous computing power resource portrait is constructed after processing; analyzing the structured scheduling request, and combining a cluster physical layout to construct a space-time-capability-dependency-computing power-energy consumption five-dimensional coupling adaptation degree model; searching an optimal scheduling plan through a hybrid optimization algorithm, wherein the optimal scheduling plan covers energy distribution, energy storage scheduling and transmission path planning; after the stability is verified by the digital twin sandbox, an instruction is issued; real-time monitoring is carried out during operation, and computing power-physical joint rescheduling is triggered. Intelligent perception, accurate prediction, simulation verification and dynamic collaborative optimization of the heterogeneous computing power resources of the new energy cluster are realized, and the overall efficiency, stability and adaptive capacity of an energy system are remarkably improved.

The present invention discloses a system and method for optimizing energy distribution in renewable energy plants. The system (100) comprises a solar power plant or a wind power plant to generate electrical energy. A renewable energy controller (108) monitors real-time energy output and manages energy flow to a grid (112). The renewable energy controller (108) optimizes charging cycles based on forecasts. The renewable energy controller (108) generates dispatch schedules through an intelligent bidding unit (114), considering market prices and grid conditions, while ensuring grid code compliance. A battery digital twin unit (122) is operatively connected to the renewable energy controller (108), to simulate future states of the plant based on historical data, real-time operational data, and weather forecasts, providing inputs for the intelligent bidding unit (114) to optimize energy dispatch and storage.

The invention provides an SAGD underground heater intelligent energy supply system based on multi-energy complementation, and belongs to the technical field of thickened oil recovery, and the SAGD underground heater intelligent energy supply system comprises a new energy power generation module which is used for generating power by using solar energy and / or wind energy; the energy storage module is used for storing electric energy when the new energy power generation module generates excessive power and releasing the electric energy to the underground heater module when the new energy power generation module generates insufficient power; and the intelligent control module is used for regulating and controlling the new energy power generation module, the energy storage module and the underground heater module according to the power generation data of the new energy power generation module, the energy storage state data of the energy storage module and the operating parameters of the underground heater module which are acquired in real time. The system has the advantages that new energy power generation and energy storage are organically combined, and smooth output and efficient utilization of energy are achieved; system operation data are monitored and analyzed in real time, an energy distribution strategy and operation parameters of an underground electric heater are dynamically adjusted, accurate heating is achieved, the energy utilization efficiency is improved, and energy consumption and carbon emission are reduced.