1384 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 provides a dynamic balance system for wind, light, fire and nuclear storage integrated regulation and control of a power grid in a cold region, which relates to the technical field of power grid regulation and control and comprises an energy data acquisition and preprocessing module, a multi-energy coordinated regulation and control module, an energy storage system optimization management module, an energy scheduling optimization module, an intelligent decision and monitoring module and a system communication and safety module. A multi-level, multi-module and intelligent comprehensive framework is constructed, collaborative management and optimal scheduling of various energy sources are achieved, stable operation, efficient energy utilization and sustainable development of a cold region power grid are ensured, global optimization is achieved through deep reinforcement learning, local precise control is conducted through fuzzy logic, and the energy utilization efficiency is improved. The system can intelligently distribute the generated power of each energy source of wind, light, fire and nuclear storage according to the real-time state and environmental conditions of the cold region power grid, gives full play to the advantages of different energy sources, realizes the complementary utilization of the energy sources, realizes the collaborative optimization of multiple energy sources, and improves the utilization efficiency and stability of the energy sources.

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a park-level adjustable load-oriented resource regulation and control method and system, and the method comprises the steps: formulating an initial energy scheduling scheme according to the predicted energy demand in combination with the output characteristics of distributed energy in a park and the load condition of a power grid; and training a load regulation and control strategy model by adopting a reinforcement learning algorithm based on the initial energy scheduling scheme, and generating a personalized load regulation and control instruction for each energy consumption unit. According to the scheme, the real-time energy consumption data of the plurality of energy consumption units in the park, the output characteristics of the distributed energy and the power grid load condition can be comprehensively considered, a scientific and reasonable energy scheduling and load regulation and control strategy is formulated, and efficient utilization and green low-carbon development of park energy are realized. Meanwhile, the method also needs to have the characteristics of data sharing, accurate prediction, intelligent scheduling, rapid response and the like so as to adapt to complex and changeable energy requirements and environmental protection requirements of the park.

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, in particular to an energy system capacity optimization and mobile resource space-time decoupling cooperative scheduling method, system and device under extreme disaster disturbance and a medium. Load change parameters are coupled to form a disaster-causing response database; renewable energy output fluctuation and user load demand deviation are converted into fuzzy variables, and a capacity configuration optimization model is established in combination with a disaster-causing response database to generate multi-energy complementary capacity configuration; mobile resources are introduced to establish an elastic lifting scheduling model, and an elastic lifting scheme is generated by implementing damaged road network topology reconstruction of the mobile resources, space-time transfer chain modeling and action state coordinated regulation and control. According to the method, collaborative decision-making of capacity optimization of equipment and dynamic scheduling of mobile resources is driven through the disaster-causing response database, and dual elastic gains of fixed resource optimization and after-disaster quick response of the mobile resources under extreme disaster events are achieved.

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 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 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 relates to the technical field of digital twinning, in particular to a digital twinning service platform construction method based on cloud edge end collaboration. The method comprises the following steps: acquiring energy system basic data through an edge end device; carrying out preliminary state modeling on the edge end device by utilizing the energy system basic data, constructing a virtual energy system model, and transmitting the virtual energy system model to the cloud server; controlling the cloud server to receive the virtual energy system model uploaded by the edge end device, and performing digital twinborn model construction on the virtual energy system model to obtain an energy system digital twinborn model; and performing energy scheduling optimization according to the digital twin model of the energy system to obtain energy scheduling data of the energy system, and sending the energy scheduling data to an edge end device to perform energy system scheduling optimization operation. According to the invention, through multi-scene simulation and strategy correction, a power supply gap can be predicted and adjusted in advance, and stable operation of energy supply under various meteorological conditions and load fluctuation conditions is realized.

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 discloses an energy consumption optimization management system and method, and particularly relates to the field of energy management, and the system comprises an intelligent information collection terminal collection module, an energy data preprocessing module, an intelligent prediction module, a multi-target optimization scheduling module, a real-time feedback module, an abnormity alarm module, and a scheduling scheme output module. According to the energy consumption optimization management system and the method thereof, data are acquired through the intelligent information acquisition terminal acquisition module by means of various intelligent information acquisition terminals, so that efficient management of energy consumption is realized; dynamic prediction, optimal scheduling and efficient management of energy consumption are realized through the intelligent prediction module, and the accuracy of energy consumption trend prediction is improved. And a multi-target optimization scheduling scheme is obtained by applying a multi-target optimization algorithm through the multi-target optimization scheduling module, so that the scientificity and rationality of energy scheduling are improved, and energy waste caused by unreasonable scheduling is reduced.

The invention relates to the technical field of energy, and discloses a distributed electric power energy storage system and method, and the system comprises a decommissioned battery sorting and modularization module which is used for carrying out the performance evaluation, grading and standardized modularization processing of decommissioned power batteries; the energy storage unit module is used for assembling the retired batteries into an energy storage unit and storing and releasing electric energy; the energy management module is used for monitoring the running state of the energy storage unit and optimizing energy distribution; the communication network module is used for carrying out data transmission and cooperative control between the modules; and the power grid and user interface module is used for interacting the energy storage system with a power grid and a user side. According to the method, the energy scheduling unit can optimize the charging and discharging strategy of the energy storage unit in real time by combining reinforcement learning and federated learning algorithms, and meanwhile, the adaptability of a scheduling model in multiple scenes is improved by utilizing cooperative training of distributed nodes.

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 relates to the technical field of micro-grid energy dispatching, in particular to a micro-grid energy dispatching carbon emission reduction system based on reinforcement learning dual-objective optimization. The data acquisition module is used for acquiring information such as equipment energy consumption data, environmental parameters and carbon emission coefficients in the micro-grid in real time; the data processing module is used for cleaning acquired data, extracting features and performing standardization processing; the intelligent scheduling module is used for extracting information features and a deep reinforcement learning algorithm, optimizing by using a model to obtain an optimization strategy, and forming a scheduling instruction set; the execution feedback module is used for dynamically scheduling the operation state of equipment in the micro-grid according to the scheduling instruction set; the man-machine interaction module provides a user interface and is used for displaying the carbon quota use condition and the system operation state and allowing a user to input an adjustment demand; compared with the prior art, the energy system of the park is used as a unified network, mutual influence of multiple systems is considered, accurate scheduling is achieved, environmental protection and high efficiency are achieved, and the operation cost is reduced.

The invention discloses a park integrated energy scheduling method and system based on a master-slave game model, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting basic operation parameter data in a park integrated energy system; constructing an upper-layer game model taking an operator as a dominant, setting constraints, and making an electric heating and cooling price strategy and a unit output plan by taking operator profit maximization as a target; constructing a lower-layer game model, and making a load energy consumption strategy by taking user energy consumption cost minimization as a target; and converting the upper-layer game model and the lower-layer game model into a mixed integer linear programming problem, and obtaining an electric heating cold price scheduling scheme and an output scheduling scheme of each unit by adopting an optimization solution algorithm. The building thermal inertia and demand response mechanism is introduced, the KKT condition, the linear relaxation technology and the strong dual theory are used for converting the complex problem into the MILP model easy to solve, optimization of operator and user benefits is achieved, and the calculation speed and the solving efficiency are remarkably improved.

The invention discloses an energy storage method and system for base station management, and the method comprises the steps: carrying out the multi-modal data fusion processing according to the historical business volume data, real-time user movement mode data and meteorological data of each virtual base station in a target region, and obtaining a dynamic load prediction result of each virtual base station in a target time period; based on a dynamic load prediction result, adopting a distributed constraint optimization algorithm to dynamically allocate energy storage resources, and generating an optimal allocation strategy containing a cross-base-station energy scheduling path; and according to the wireless network topology structure characteristics of the target area, constructing an energy scheduling decision model based on a graph theory, optimizing an energy storage scheduling time sequence, and generating a dynamic energy storage scheduling scheme with a fault-tolerant mechanism so as to realize energy storage for base station management. By utilizing the embodiment of the invention, the energy efficiency of the base station can be improved, energy waste is reduced, and efficient configuration and real-time scheduling of energy storage resources are realized.

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 an environment energy management optimization method and system for a tag chip, and the method comprises the steps: obtaining the temperature data and environment electromagnetic data of a chip environment, and carrying out the spatial distribution analysis, and obtaining the corresponding environment parameter characteristics; acquiring real-time power consumption data of the tag chip, and performing correlation analysis on the real-time power consumption data and the environmental parameter characteristics to obtain corresponding energy consumption information; acquiring chip circuit acquisition data of the tag chip, and performing resonance frequency analysis on the chip circuit acquisition data according to a preset resonance optimization algorithm; performing fault diagnosis on the chip circuit acquisition data to obtain corresponding system state information and a corresponding system operation prediction scheme; and performing strategy optimization on the initial energy scheduling scheme according to the system operation prediction scheme to obtain a corresponding global optimization strategy. According to the invention, the system can flexibly cope with energy demand changes of different application scenes.

The invention discloses an active power distribution network optimal scheduling method and system considering a multi-agent cooperative game, and relates to the technical field of power distribution network optimal scheduling, and the method comprises the steps: considering the interaction among multiple market agents in an active power distribution network, and constructing a multi-agent cooperative transaction architecture; based on a multi-subject collaborative transaction architecture, taking cost minimization as a target, and combining constraint conditions to build an operation and income model of each subject; wherein a distributed energy storage operator is used as one of main bodies, and the operation of distributed energy storage is restrained by using a distributed energy storage distribution strategy based on distributed energy storage SOC balance; according to the Nash bargaining theory, a multi-subject operation and income model is integrated, and a multi-subject cooperative game model is constructed; and solving the multi-agent cooperative game model based on the actual energy demand quantity of the user side to obtain an optimal scheduling strategy of the energy in the active power distribution network, and performing energy scheduling according to the scheduling strategy. The charging and discharging power balance between the DES units can be realized, and the economic benefit is improved.

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 discloses a UWB-based multifunctional integrated vehicle-mounted safety control method and system, and the method comprises the following steps: collecting the dynamic environment data around and in a vehicle in real time based on a plurality of sensors, carrying out the path adjustment and driving mode optimization through generating a multi-level three-dimensional environment model, and carrying out the control of the driving mode. The system performs risk assessment and dynamic adjustment by using external data such as obstacles, road conditions, weather and the like collected by a sensor and combining a behavior mode of a driver and a health state of a passenger, and in a collision prediction and protection mechanism, the system gradually activates a protection system by analyzing factors such as vehicle speed and acceleration, so that the safety is guaranteed, and the safety is ensured. The system optimizes the energy distribution of a key system through an energy scheduling model, improves the energy efficiency, and preferentially guarantees the operation of a safety system in an emergency. According to the invention, a self-adaptive learning algorithm and a multi-scene optimized driving assistance function are integrated, and self-adaptive adjustment and safety control of the vehicle in different driving environments are realized.