1163 results about "Energy scheduling" 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 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 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 mobile energy storage vehicle energy management, and discloses a mobile energy storage vehicle energy management method based on an intelligent algorithm, and the method comprises the steps: collecting the charging and discharging rate of a battery pack, the environment temperature, the power grid load fluctuation and other multi-dimensional energy state data; a heterogeneous layered architecture of edge computing nodes, a cloud collaboration platform and a vehicle-mounted control terminal is constructed, energy partitions are divided according to peak valleys of a power grid, and low-delay response, global optimization and local closed-loop control modes are configured; fusing the multi-source heterogeneous energy data streams and removing abnormal values; a dynamic optimization decision model is constructed through a battery degradation model and a power grid supply and demand balance equation, and a multi-parameter collaborative constraint relation is solved through simultaneous solving of a multi-target iteration solver and a parallel gradient descent algorithm; and generating a three-level adaptive response instruction sequence of local battery overload alarm, regional power grid frequency modulation early warning and global energy scheduling imbalance pre-judgment based on constraint boundary triggering conditions. According to the method, the accuracy, the collaboration and the robustness of energy management are improved.

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

The invention discloses a distributed energy collaborative scheduling optimization method based on edge computing. According to the method, a plurality of edge computing nodes are deployed in a distributed energy system, a multi-protocol compatible OPC UA communication channel is constructed through protocol conversion middleware to collect data, and after the edge computing nodes clean and normalize the data, a preliminary scheduling scheme is generated through an improved genetic algorithm; the improved genetic algorithm is optimized through cooperation of a deep reinforcement learning model and an adaptive attenuation mechanism. And uploading the preliminary scheduling scheme to a cloud end, and obtaining a global optimal scheduling strategy through a multi-target particle swarm optimization algorithm. And the cloud carries out credible evidence storage on the global optimal scheduling strategy abstract value through an alliance chain smart contract, and establishes a PoA consensus mechanism. And when the communication is interrupted, the edge computing node starts the local emergency scheduling module, and incremental data synchronization is performed after the communication is recovered. The distributed energy scheduling optimization problem is effectively solved, the energy utilization efficiency is improved, and the system stability and reliability are enhanced.

A park-factory-coordinative optimized scheduling method based on a Jacobi iteration method, which belongs to the technical field of energy scheduling optimization. A park-factory hierarchical game model is established, and by means of coordination and optimization between energy scheduling under a complex production constraint and production management and control that takes profit optimization as the goal, an optimal price regulating strategy and an optimal production scheduling strategy are obtained, such that a lower industrial park energy consumption and a higher factory production profit are achieved. A coordinative optimized scheduling model for a park control center and a factory management and control center is a multi-variable multi-constraint complex optimization issue; the solution of a high-order partial differential equation set involved in the model is calculated, and a cyclic iterative optimization algorithm is designed on the basis of a Jacobi iteration matrix, thereby greatly reducing the operation complexity; moreover, such a cyclic iterative algorithm requires a smaller storage capacity of a computer, and is thus suitable to be applied in practice by an engineer.

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system based on dynamic optimization control and a multi-mode energy scheduling method. Comprising a renewable energy power generation unit which comprises at least two distributed power supplies with different power generation characteristics; the multi-stage hydrogen production device is configured to receive the electric energy output of the renewable energy power generation unit and perform proton exchange membrane water electrolysis hydrogen production; the hybrid energy storage system comprises a super capacitor bank and a lithium ion battery pack which are connected in parallel to the direct current bus through a bidirectional DC / DC converter; the dynamic optimization controller is internally provided with a multi-time-scale coordination algorithm and is used for acquiring meteorological prediction data, load demand data and an energy storage SOC state in real time; the multi-mode scheduling module comprises three working modes including an island operation mode, a grid-connected operation mode and an emergency standby mode; according to the scheme, hydrogen production economy, equipment durability and power supply reliability can be considered at the same time.

The invention discloses a virtual power plant energy scheduling method and system under a local data abnormal condition, and the method comprises the steps: recognizing a data type and an incidence relation related to abnormal data through an incidence matrix after the abnormal data of a virtual power plant are collected and recognized; and calculating an abnormal data correction value based on the association relationship function and the real-time association data, and performing weighted average on the abnormal data correction value and the original abnormal data to obtain correction data. And then calculating an abnormal data time difference and a correlation deviation degree, and distributing a credibility weight for the corrected data. And finally, calculating the correlation degree between the scheduling parameter of the preset scheduling scheme and the weighted correction value, and selecting the scheme with the highest correlation degree as a virtual power plant resource scheduling scheme. By implementing the technical scheme provided by the invention, the scheduling accuracy in a data exception state is improved.

The invention provides a dynamic fusion system for digital twin energy management BIM modeling and a method thereof, which are used for improving the intelligent level of energy management and optimizing energy scheduling. The system comprises a data sensing node used for collecting heterogeneous operation data of energy facilities; the local twin processor is used for carrying out real-time digital twin mapping on the data and generating a dynamic feature vector; region twinborn nodes perform local optimization based on edge calculation, and realize decentralized collaboration through a block chain or a knowledge graph; the cloud BIM fusion engine constructs a digital twin BIM model, generates a self-optimization energy scheduling scheme and predicts a long-term trend; and the operation control terminal is used for presenting a real-time view and providing a regulation and control instruction. According to the method, technologies such as multi-source data fusion, streaming aggregation optimization and adaptive twin mapping are combined, and the accuracy and dynamic response capability of energy management are improved.

The invention discloses an intelligent energy scheduling method and system for a flow battery, and relates to the technical field of flow batteries, and the method comprises the following steps: collecting energy operation parameters of the flow battery in real time, and dynamically estimating an electrolyte state to obtain multiple pieces of energy state information; a target to be dispatched is set, and a fuzzy control strategy is formulated through retrograde multi-target optimization; executing a fuzzy control strategy to generate a regulation and control signal set and scheduling; and finally, according to a scheduling result, performing reverse tracing to optimize a fuzzy control strategy, and updating a regulation signal set to obtain a multi-stage regulation instruction to perform intelligent scheduling on the energy of the flow battery. The method solves the technical problems of low matching degree between a scheduling strategy and an actual demand, large power loss and fast battery life attenuation caused by difficulty in accurately capturing an energy state in real time in a traditional flow battery scheduling method, and achieves the purposes of intelligent flow battery energy scheduling, power loss reduction, battery life prolonging and energy conservation. And the matching degree between the scheduling and the actual demand is improved.

The invention discloses a virtual power plant green energy consumption cooperation method and system fusing digital twinning and reinforcement learning, and the method comprises the steps: building a green energy power plant digital twinning model through a simulation tool, collecting data in real time, and carrying out the normalization and abnormal value cleaning; inputting the data into the digital twinborn model, mapping the operation state of a physical system, and rehearsing the influence of different energy scheduling strategies on the green energy consumption rate and the power grid frequency in a virtual environment; optimizing and updating the energy scheduling strategy based on a near-end strategy optimization PPO algorithm; the optimized and updated energy scheduling strategy is fed back to a physical system to be executed, the strategy execution effect is monitored in real time, the digital twin model parameters are updated, and closed-loop control is formed; the method can solve the problem that the intermittency of renewable energy sources is not matched with the dynamic demand of the load.

The invention provides an energy digitization platform resource scheduling method based on cloud edge cooperative computing, which comprises the following steps: acquiring real-time supply and demand data, an energy price signal and network topology information from a distributed energy management system, and preprocessing to obtain a structured dynamic supply and demand scene data set meeting a unified format requirement; aiming at a dynamic supply and demand scene data set, respectively detecting the fluctuation frequency and amplitude of an energy price on different time scales by adopting a time sequence analysis method, detecting the change condition of a network topology structure in real time by adopting a network analysis technology, and extracting key parameters reflecting scene dynamic characteristics from the change condition; and extracting a scheduling demand of cross-regional energy flow from the adjusted edge node permission configuration, and optimizing a cross-regional energy flow path in combination with real-time inter-regional supply and demand difference data and network state evaluation to obtain a globally optimized cross-regional energy scheduling scheme.

The invention relates to the field of photovoltaic energy storage, and discloses an off-grid photovoltaic energy storage system control method comprising the following steps: collecting photovoltaic power generation data, load demand data, meteorological prediction data and battery state data and carrying out data preprocessing; constructing a deep reinforcement learning model based on the collected data, wherein the deep reinforcement learning model is used for optimizing a charging and discharging strategy of the energy storage system; constructing a multi-objective optimization algorithm for optimizing battery management, energy efficiency and load stability and determining an optimal charging and discharging strategy; dynamically adjusting a charging and discharging strategy based on the photovoltaic generating capacity, the load demand and the battery state monitored in real time, and performing cross-seasonal energy scheduling according to meteorological prediction data; and based on real-time feedback and system evaluation, the deep reinforcement learning model is updated, and a multi-objective optimization algorithm and a discharge strategy are adjusted. By constructing a closed-loop feedback mechanism based on the state of charge and the power supply error, the dynamic correction of the energy storage scheduling strategy is realized.

The invention relates to the technical field of comprehensive application of renewable energy sources and hydrogen energy, in particular to a wind-solar-hydrogen multi-energy complementary intelligent scheduling platform. According to the technical scheme, the wind-light-hydrogen multi-energy complementary intelligent scheduling platform comprises a wind-light power generation unit which monitors wind power and photovoltaic power fluctuation in real time; the hybrid energy storage unit comprises a super capacitor and a flywheel for energy storage and respectively responds to millisecond-level and second-level power fluctuations; the hydrogen energy subsystem comprises an electrolytic hydrogen production device, a hydrogen storage tank and a fuel cell and responds to minute-level power scheduling; and the multi-time scale controller is configured to execute millisecond-level, second-level and minute-level cooperative control and dynamically match the wind-light fluctuation and the hydrogen energy response speed. According to the method, through multi-time scale dynamic cooperative control, the matching problem between the wind-solar power generation volatility and the hydrogen energy response lag is effectively solved, and seamless connection between second-level high-frequency fluctuation suppression and minute-level energy scheduling is achieved.

The invention discloses a source-load interaction method based on an electricity utilization information acquisition system, and relates to the field of power distribution network operation and optimization. The method comprises the following four steps: S1, power consumption data acquisition: dynamically acquiring user power consumption, distributed energy output and power grid state data through intelligent equipment, and ensuring real-time accuracy; s2, load characteristic analysis: constructing a quantitative model to analyze characteristics such as a user load elastic coefficient and adjustable potential, establishing a supply and demand balance evaluation system in combination with distributed energy fluctuation, and determining peak and valley time periods and gaps; s3, generating an interactive strategy, and generating and issuing dynamic strategies such as electricity price excitation, load regulation and energy scheduling based on an analysis result; and S4, executing and feeding back the strategy, monitoring the execution effect in real time, and optimizing the strategy through a closed loop mechanism. According to the method, the problems of staticization, single mode and low efficiency of a traditional method are solved, the power grid supply and demand balance capability, the renewable energy consumption rate and the strategy accuracy are improved, and low-carbon and intelligent operation of the power distribution network is supported.

The invention provides an intelligent power supply management system based on multi-energy complementation, which relates to the technical field of intelligent power supply management and comprises a multi-energy supply module, a data acquisition module, a multi-source data fusion module, a scheduling optimization module, a demand prediction and response module, a supply safety evaluation module, an energy storage and load balancing module and an energy scheduling module. The multi-energy supply module comprises a solar energy supply sub-module, a wind energy supply sub-module, an energy storage system and a traditional power grid access sub-module, and by combining multi-source data and an advanced algorithm, the precision of power load prediction is remarkably improved. Compared with a traditional prediction method based on historical data, the method comprehensively considers the influence of various external factors such as climate change, holidays and festivals, and can more accurately predict the power demand fluctuation. The system can dynamically adjust the load prediction model in real time, improves the reliability of power supply, and can effectively cope with challenges caused by demand changes, environment changes and emergencies.

The invention provides an energy optimization scheduling method and device, a storage medium and computer equipment, and relates to the technical field of energy scheduling, during energy scheduling, original data of each edge node is collected, and after a data hash value and key metadata of the original data are determined, blockchain fragmentation storage is performed, so that the energy scheduling efficiency is improved. Meanwhile, original data are encrypted into edge data by adopting a quantum key distribution method, so that the data cannot be tampered; next, edge data training is adopted to obtain a digital twin sub-model corresponding to each edge node, then federal aggregation is performed to form a global model, decentralized cooperation is realized, and carbon energy flow is simulated in real time; and then, dynamically optimizing the global model to generate a scheduling instruction, if the scheduling instruction is not in an expected range, performing incremental optimization correction on the global model until the scheduling instruction generated by the global model is in the expected range, and issuing the scheduling instruction to each edge node to perform energy scheduling. Therefore, millisecond-level real-time scheduling response can be realized while data privacy security is ensured.

The invention discloses a charging user behavior pattern analysis system and method, and relates to the technical field of electric vehicle charging management, and the method comprises the steps: obtaining and building a multi-source data pool, fusing the multi-source data pool, analyzing a charging behavior pattern of a user, calculating the abnormal behavior probability, building a dynamic scheduling rule according to the charging behavior pattern of the user, and carrying out the dynamic scheduling according to the abnormal behavior probability. According to the method, through multi-source data collection and analysis, the user charging behavior is accurately predicted, resource distribution is dynamically optimized, the charging station operation efficiency and the user satisfaction degree are improved, virtual power plant integration is promoted, and the power generation efficiency is improved. According to the method, a vehicle-to-power grid strategy optimization module is constructed, the user participation power grid peak regulation potential is quantified, regional energy scheduling is optimized, in addition, an anomaly detection and response mechanism is provided, safety and stability are ensured, and the charging service is promoted to develop towards the efficient, intelligent and sustainable direction.

The invention relates to the technical field of electric power energy monitoring, and discloses a remote monitoring analysis method and system based on electric power equipment operation data, and the method comprises the steps: collecting the multi-element energy operation parameters of a multi-source energy system and the multi-element energy use parameters of a user side in real time; after the collected data are preprocessed, the preprocessed equipment operation state data are evaluated, and an electric power equipment operation state index is obtained; whether equipment faults exist or not is judged according to power equipment operation state indexes, virtual mapping of a multi-source energy system is constructed, and system operation conditions under different energy scheduling strategies are simulated in real time by combining user energy demand prediction and external environment changes; when potential energy supply abnormity or equipment failure is detected, a multi-stage early warning mechanism is triggered, and a decision report including abnormity positioning, reason analysis, energy scheduling optimization and equipment maintenance disposal suggestions is generated, so that potential problems can be found in time, and the stability and reliability of energy system operation are improved.

The invention provides a smart park operation and maintenance management method and system based on CIM and a medium, and belongs to the technical field of smart park management. The method comprises the following steps: constructing a CIM digital twinborn model based on a park building group, a geographic position, Internet of Things equipment and business data, and synchronously extracting equipment state and energy consumption information; analyzing the state data by using the equipment fault prediction model and generating a repair work order to realize predictive maintenance; performing gridding processing on the energy consumption data, generating a thermodynamic diagram and identifying an abnormal energy consumption area; and positioning a high-energy-consumption grid by combining an energy consumption analysis model, and generating a comprehensive operation and maintenance scheme of equipment regulation and control, energy scheduling and system optimization through matching of an energy management strategy library. According to the invention, the digital twinning technology and the CIM platform are fused, so that full-life-cycle management and dynamic energy consumption optimization of the equipment are realized, the operation and maintenance efficiency is remarkably improved, the failure rate and energy waste are reduced, and both economy and environmental protection are achieved.

The invention discloses a Nash negotiation-based energy management scheduling method for an optical storage and charging integrated energy station, and the method comprises the steps: constructing a multi-target optimization model which comprehensively considers the interest demands of an electric vehicle user, a charging station operator and a power grid operator; determining participants of Nash negotiation as a charging station operator, a power grid operator and an electric vehicle user, and determining a strategy space of each participant; on the basis of the Nash negotiation theory, all the benefit subjects are regarded as negotiation participants, utility functions of all the benefit subjects are constructed, and negotiation reference points of all the benefit subjects are determined; solving a Nash equilibrium solution of the multi-objective optimization model, and determining an optimal energy management scheduling strategy through iterative negotiation; and according to the optimal energy management scheduling strategy, real-time control and scheduling are carried out on the optical storage and charging integrated energy station. According to the invention, the flexibility, adaptability and economy of an energy scheduling scheme can be improved, and multi-party win-win and stable and efficient operation of the system are realized.

The invention relates to the technical field of micro-grid energy optimization, in particular to a distributed federal reinforcement learning-based hydrogen-containing micro-grid energy scheduling method, which comprises the steps of constructing a multi-region hydrogen-containing micro-grid system model, designing a state space, an action space and a reward function of an intelligent agent, constructing an Actor-Critic network and an experience pool, and completing environment initialization. The intelligent agent inputs the operation state of the equipment into the Actor network, updates the state of the equipment according to the output action, verifies the constraint and outputs a reward value; tuples are extracted from the experience pool to update local network parameters, and the exploration rate is updated regularly; when a federation interaction period is reached, exchanging Critic network parameters and updating federation parameters; and when the training round arrives, outputting an equipment operation plan, deploying the model to the local hydrogen-containing micro-grid in the island mode, and outputting an equipment output value. According to the scheme, strategy sharing and learning collaboration are realized through neighborhood collaboration and local communication among the regional intelligent agents, so that dependence on a central node is avoided.

The invention discloses an intelligent charging and discharging strategy optimization method for a ship battery, and particularly relates to the technical field of battery management. Propulsion power data, auxiliary equipment power data and environment disturbance parameters generated in the ship operation process are collected, a high-frequency time sequence working condition input sequence is built, a load nonlinear evolution model is built through a multi-scale dynamic modeling algorithm, and a disturbance perception prediction sequence is generated; a heterogeneous state characteristic matrix is constructed by combining historical charging and discharging state records of a target battery and current sensing data, voltage sensing data and temperature sensing data, and then the state of charge and the state of health of the battery are evaluated; generating a multi-dimensional strategy candidate set by constructing an energy scheduling constraint map according to the evaluation results of the state of charge and the state of health of the battery; and selecting an optimal strategy path from the multi-dimensional strategy candidate set, outputting a target charging and discharging control instruction, and realizing comprehensive optimization control of dynamic sensing, state driving and strategy self-adaption of the ship battery.

The invention discloses an AI-based intelligent park source, network, load, storage and charging integrated scheduling method, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting park equipment data in real time, carrying out the preprocessing, and generating a standardized time series data matrix; time sequence information is used for screening and extracting data features of the standardized time sequence data matrix, a hybrid neural network frame is used for constructing a source network load storage and charging optimization model, and an embedded vector is obtained; optimizing the preliminary scheduling strategy by using a game theory, and generating a collaborative scheduling instruction set; converting the collaborative scheduling instruction set into an equipment control instruction by adopting protocol conversion, and executing the equipment control instruction; dynamic threshold value filtering is used for monitoring the equipment operation state, the safety boundary and the economical efficiency threshold value in real time, and online learning is used for optimizing an equipment control instruction. According to the invention, by using the near-end strategy to optimize the cutting algorithm and the Nash equilibrium game, the economical efficiency of the scheduling strategy is improved.

The invention discloses a super capacitor energy scheduling method and system based on multi-time scale collaboration, and relates to the technical field of capacitor energy scheduling, and the method comprises the steps: calling a super capacitor energy scheduling log, and carrying out the division according to a time resolution hierarchy, and obtaining a plurality of time hierarchies; receiving a power grid dispatching instruction, performing power analysis on the load prediction data, and drawing a power reference curve of the super capacitor; dynamically correcting the power reference curve to generate a fluctuation stabilizing instruction; obtaining instantaneous charging and discharging control parameters of the super capacitor; and carrying out multi-time scale collaborative fusion, generating an energy scheduling instruction, carrying out scheduling verification, and determining an energy scheduling optimization instruction. According to the method, the technical problems of low scheduling efficiency and poor power grid stability caused by lack of multi-time scale collaborative analysis and difficulty in dynamically adapting to power grid fluctuation of super-capacitor energy scheduling in the prior art are solved, and the technical effects of improving the super-capacitor energy scheduling efficiency and the power grid operation stability are achieved.

The invention relates to the field of new energy technology and power electronics, and discloses a photovoltaic diesel generator energy storage hybrid off-grid and grid-connected household energy storage system, which comprises a data acquisition and state monitoring module for acquiring photovoltaic, diesel generator, energy storage, load and commercial power states in real time and generating system state parameters; the central control and decision module generates a control strategy containing an operation mode and an energy scheduling instruction; the energy flow and conversion execution module executes energy conversion and flow; the seamless switching control module realizes non-impact switching between modes; the self-adaptive safety protection module dynamically adjusts protection parameters; and the emergency response and recovery module executes black start and active recovery in an emergency state. According to the invention, through the unified central control and decision-making module, photovoltaic, diesel generator, energy storage and commercial power scheduling logics are integrated to form a collaborative energy management core, and the system can carry out global optimization on energy flow in grid-connected and off-grid modes according to a preset priority principle.

The invention relates to the technical field of intelligent heat supply, in particular to a multi-source energy fusion agricultural greenhouse intelligent heat supply and regulation and control system, which comprises a multi-source energy acquisition module for acquiring energy output parameters of a solar heat collector, a geothermal well and a biomass boiler in real time and monitoring external meteorological data of a greenhouse; the energy dynamic matching module is used for generating an energy scheduling strategy through a dynamic priority algorithm and outputting a target energy type and supplied energy; the energy storage buffer module is used for storing or releasing the supplied energy and outputting the temperature and the residual capacity of an energy storage medium; the multi-parameter coupling regulation and control module is used for generating a regulation and control instruction through an environment parameter coupling model; the actuating mechanism driving module is used for converting the regulation and control instruction into control signals of a heater, a fan and a humidifier; the self-learning optimization module is used for dynamically correcting the dynamic priority algorithm and the environment parameter coupling model; according to the invention, through multi-parameter cooperative regulation and dynamic optimization, crops are ensured to be in the optimal environmental conditions in different growth stages.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a control method and system for an intelligent calculation center comprehensive energy supply device, and the method comprises the steps: collecting the operation data of energy supply equipment and an environment sensor in real time through an SCADA system, and carrying out the preprocessing; based on the collected data and simulation data generated by a simulation environment, performing offline mixed training on the reinforcement learning model to generate an energy scheduling strategy; inputting a real-time state into the trained reinforcement learning model to generate a preliminary scheduling instruction, performing security verification and interpretability analysis by using a large language model, and optimizing a strategy; and fusing the preliminary scheduling instruction with the suggestion of the large language model, generating a final scheduling command through the energy router control unit, and issuing the final scheduling command to the energy supply equipment for execution. The problems that in the prior art, black box decision making and simulation are not accurate, and experience is difficult to solidify are solved, and the purposes of decision making transparency, simulation high fidelity and experience structuring are achieved.