251 results about "Energy allocation" patented technology

The invention discloses a power distribution management system of an intelligent charging pile, and belongs to the technical field of electric vehicle charging facilities and intelligent power grids. The load prediction module carries out space-time alignment fusion on historical data and real-time monitoring data to generate a power distribution demand prediction value, and the edge calculation controller executes a model prediction control algorithm based on multi-source data to generate a real-time control strategy containing relay time sequence parameters and a capacitance compensation scheme. And the dynamic power distribution adjustment module executes strategy parameters through the solid-state relay array and the parallel compensation capacitor bank. Self-adaptive adjustment of the power distribution network is achieved through a real-time monitoring-prediction-optimization closed-loop control mechanism, the harmonic content of the power grid is effectively reduced, the energy distribution efficiency of the charging pile group is improved, and the method is suitable for intelligent electric energy management of public charging stations and other scenes.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention relates to the field of energy allocation, and discloses a regional building group multi-energy complementary cooperative regulation and control method, which comprises the following steps of: acquisition processing: acquiring multi-energy data in a regional building group by utilizing multiple sensors, and preprocessing to obtain processed data; load prediction: inputting the processed data into an LSTM-Attention mixed deep learning model, performing fusion calculation in combination with a grey prediction model, and outputting a cold and heat power ultra-short-term load prediction result; and cooperative regulation and control: based on the load prediction result and the real-time operation parameters of the building air conditioning system, the electric vehicle and the multi-element energy storage system of the building group. Multi-energy data in a regional building group are collected and preprocessed through multiple sensors, an LSTM-Attention mixed deep learning model and a grey prediction model are fused to calculate a load prediction result, an interactive regulation and control model is constructed based on load prediction and real-time equipment parameters, and a collaborative regulation and control strategy is formulated and executed.

The invention relates to the field of energy storage power supply management, in particular to a power supply management method, system and equipment based on a household energy storage power supply system. The method comprises the following steps: collecting a household power utilization monitoring data stream, carrying out equipment use behavior preference mining and multi-scale state transition deep learning, and generating a user equipment behavior prediction model; mining power utilization behaviors of power utilization terminals one by one based on the household power utilization monitoring data flow, performing multi-time-point power utilization demand prediction based on a user equipment behavior prediction model, and constructing a multi-time-point power utilization demand prediction map of the user; and obtaining meteorological environment parameters of a current user family area, performing climate disturbance demand adjustment on the power demand prediction maps at the multiple time points based on the meteorological environment parameters, and constructing a climate disturbance power demand prediction map. According to the invention, through dynamic user demand changes, the energy efficiency management level and the energy allocation precision of long-term operation of the energy storage system are improved.

The invention discloses an intelligent energy consumption distribution method for a multi-source energy system. The method comprises the steps of energy supply data fusion, dual-channel joint prediction, energy response lag correction, multi-objective optimization and intelligent energy consumption distribution. The invention relates to the technical field of data processing of power management and resource scheduling, and the method comprises the steps: carrying out the unified collection and archiving of operation control data and environment information of power grid energy, distributed photovoltaic energy and an energy storage system, and constructing an energy supply fusion data set; respectively extracting energy availability and user load characteristics by adopting a double-domain attention decoupling fusion joint supply and demand prediction method, and realizing double-channel joint modeling; modeling, estimating and correcting response lags of different energy sources in combination with a dynamic response curve reconstruction method; and further constructing a layered target penalty function containing real-time response error, economic cost, carbon emission and response penalty terms, and embedding the layered target penalty function into a particle swarm optimization algorithm to obtain an energy distribution strategy under multi-target optimization.

The invention discloses a reinforcement learning-based mobile vehicle charging and storage dynamic scheduling method and system, and solves the problems of insufficient scheduling flexibility and low peak-valley electricity price utilization rate of a fixed charging facility of an existing parking lot. A dynamic environment model is constructed, the real-time SOC of the mobile charging and storage vehicle, the position topological relation and the charging demand space-time distribution are integrated, and a deep reinforcement learning algorithm is adopted to train an intelligent body to generate a multi-dimensional collaborative optimization strategy. According to the method, a charging / discharging time sequence, a task path and energy distribution are autonomously planned, a reward function mechanism fusing dynamic path cost and energy constraint is innovatively designed, a multi-vehicle asynchronous collaborative decision framework is established, and dual targets of charging demand response efficiency and operation cost optimization are achieved. According to the method, an MCSV hardware embedded system which supports an ROS2 communication protocol and has a real-time sensor data processing capability is deployed, so that effective transition from a theoretical strategy to actual application is realized.

The invention provides a fused salt heat storage coupled compressed air energy storage cooperative power generation system and a control method thereof.The fused salt heat storage coupled compressed air energy storage cooperative power generation system comprises a data acquisition module, a multi-target optimization decision algorithm based on dynamic programming, an energy distribution module, a dynamic adjustment module and a safety protection module; a multi-objective optimization decision algorithm based on dynamic programming automatically switches system working modes, an energy distribution module optimizes fused salt and air flow of a high-temperature fused salt air heat exchanger, and a dynamic adjusting module adjusts fused salt pump flow, compressor rotating speed and valve opening in real time. And the safety protection module is used for monitoring and preventing over-temperature, fused salt solidification and pipeline corrosion. The problems that in the prior art, a fused salt heat storage type photo-thermal power generation system is insufficient in heat storage capacity under the extreme weather condition, a compressed air energy storage system depends on fossil fuel, the afterburning efficiency is low, and the energy loss is caused due to the low system coupling degree can be effectively solved.

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 relates to the technical field of intelligent charging, in particular to an edge intelligent calculation enabling type peak-valley energy storage charging system which comprises a power grid frequency judgment module, an energy storage dispatching output module, a load trend recognition module, a multi-station matching sorting module and a load dynamic distribution module. According to the invention, through the continuous data analysis of the power grid frequency, the energy storage unit power and residual energy, and the load current, the system can accurately identify the linkage relationship of various parameters during the frequency abnormal response period, refine the condition judgment of peak-valley energy storage triggering and scheduling, optimize the energy storage output decision path, and improve the reliability of the system. Through dynamic screening of load trends and changing stations, the real-time performance and flexibility of energy allocation among multiple stations are enhanced, the adaptive capacity of the system to complex supply and demand changes is further improved in station matching and priority ranking, energy storage resources and loads are dynamically allocated, the electric energy utilization rate and the cooperative regulation and control level of the energy storage system are effectively improved, and the energy utilization rate and the cooperative regulation and control level of the energy storage system are improved. And intelligent upgrading of energy management is promoted.

The invention relates to an energy management method and system for a hybrid electric vehicle, and the method comprises the steps: obtaining the actual driving condition data of a target vehicle under a current energy distribution strategy; based on the actual driving condition data, dynamically optimizing a first student model deployed in the target vehicle to obtain a second student model; the first student model is obtained by performing knowledge distillation based on a pre-trained teacher model; and processing the real-time sensor data of the target vehicle through the second student model to obtain a latest energy distribution strategy of the target vehicle, and controlling the target vehicle according to the latest energy distribution strategy. Through the method and the device, the problem of poor vehicle energy efficiency performance caused by lack of dynamic adaptation capability for complex working conditions in an actual driving scene is solved, online continuous learning and performance iterative optimization of the model are realized, and the working condition adaptation capability and generalization capability of the model are enhanced, so that the complex working conditions in the driving scene are dynamically adapted, and the vehicle energy efficiency is improved. And the vehicle energy efficiency performance is optimized.

The invention provides a new energy light truck energy distribution method and system, and the method comprises the steps: obtaining the position and track of a vehicle to obtain the road section data of a current driving road section, carrying out the data preprocessing of the road section data, and carrying out the fusion of the preprocessed data, so as to obtain the current load and driving state of the vehicle; inputting the preprocessed data into an initial energy consumption model, introducing a deep learning algorithm to dynamically adjust model parameters of the initial energy consumption model to obtain an iterated target energy consumption model, and obtaining energy consumption coefficient data according to the target energy consumption model; and a motor torque demand curve is generated in real time according to the current load, the road slope and the predicted endurance mileage, torque distribution data are adjusted in combination with an adaptive algorithm, the adjusted torque distribution data are transmitted into a vehicle power control system to obtain real-time operation data of the vehicle, and then energy distribution is conducted on the new energy light truck. According to the invention, the endurance prediction precision is improved, and the real-time adaptive optimization of torque distribution control is realized.

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 the field of water production systems, and discloses floor type air water production equipment which comprises a multi-stage energy sensing module, an energy budget core module, a dynamic energy distribution module, a water production switching module and an energy optimization closed-loop module. The multi-stage energy sensing module is used for collecting data information of solar input power, battery charge state, environment temperature and environment humidity in real time; the energy budgeting core module calculates current available energy and predicts available energy in a future short period in real time, and deduces an energy trend based on a calculation result; a hierarchical energy management module used for executing a hierarchical energy management strategy according to the budgeting result of the energy budgeting core module; the water production switching module comprises a plurality of water production working modes, and each water production working mode corresponds to different energy consumption and water production efficiency; the energy optimization closed-loop module adjusts the dynamic energy distribution module and the energy budgeting core module; the device can continuously and efficiently work even in a low-light or extreme environment.

The invention provides a hybrid vehicle energy management system and method based on a traffic state, a storage medium and a computer program product, and the system comprises the steps: constructing a congestion prediction model based on a deep learning model, the congestion prediction is used for predicting the traffic flow and the driving speed of a future time period according to the historical traffic flow data and the historical driving speed data; calculating a first congestion index according to the predicted traffic flow in the future period; calculating a second congestion index according to the predicted driving vehicle speed sequence of the future time period; performing weighted calculation on the first congestion index and the second congestion index to obtain a comprehensive traffic congestion index in a future time period; traffic jam types are divided according to the interval where the comprehensive traffic jam index is located; and determining an energy management mode and / or a target SOC of the hybrid vehicle based on the traffic jam type. According to the invention, multi-source data are collected and analyzed in real time, congestion is accurately quantified by using a deep learning algorithm, an energy distribution strategy of vehicles is dynamically adjusted, and the energy utilization efficiency is improved.

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.

A regional building cooling and heating load artificial neural network energy distribution optimization method disclosed by the present invention comprises the steps of obtaining inter-regional topological relation data and load-related data, constructing a graph network model to capture spatial dependency, fusing meteorological data and historical load records to generate a multivariable dynamic data set, and obtaining the optimal energy distribution of the cooling and heating load artificial neural network. Processing the multivariable dynamic data set by adopting a graph convolutional neural network to obtain an initial prediction result, adjusting the initial prediction result through an artificial neural network to generate adjusted load distribution data, and analyzing by combining a heat transfer rule and a time sequence to obtain spatial and temporal distribution characteristics; and integrating equipment efficiency constraint and cost fluctuation data by adopting a multi-objective optimization algorithm to obtain an optimized objective function value, adjusting equipment dynamic parameters through deep reinforcement learning to obtain a parameter adjustment scheme, and optimizing an energy distribution proportion by adopting a linear programming algorithm to obtain a final energy distribution scheme.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

The invention discloses a photovoltaic power supply power consumption management method, which comprises the following steps: establishing a multi-source fusion prediction model to calculate and predict power consumption and photovoltaic power generation by acquiring traffic flow, weather forecast, energy storage module state and charging demand data, and calculating a priority index according to the charge state, health state, temperature and residual life of an energy storage module. And generating an energy distribution strategy based on the net load power demand and the priority index, and controlling photovoltaic, energy storage and power grid cooperative power supply. The prediction precision is improved through multi-source data fusion, the priority of the energy storage module is dynamically calculated, optimal distribution of energy is achieved, the photovoltaic self-generation and self-use rate can be improved, dependence on a power grid is reduced, the power grid access frequency and power transmission loss are reduced, the service life of the energy storage module is prolonged, and the overall operation efficiency of the system is improved. And the power supply stability and reliability are guaranteed, and good economical efficiency and practicability are achieved.

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 invention discloses a micro-grid real-time optimization control method based on a micro-meteorological station and a deep learning model, and the method comprises the steps: collecting high-precision meteorological data, such as temperature, humidity, wind speed and solar radiation, in real time through the micro-meteorological station; inputting an LSTM deep learning model combined with a global attention mechanism to carry out load and renewable energy output prediction; and based on a prediction result, a micro-grid energy distribution scheme is optimized by adopting a genetic algorithm-adaptive weight particle swarm optimization algorithm, and dynamic adjustment is realized through model prediction control. The problems of insufficient utilization of meteorological data, low prediction precision, response lag and the like in traditional micro-grid management are solved, load prediction errors are reduced, the operation cost is reduced, the utilization rate of renewable energy sources is improved, and the stability, economy and sustainability of micro-grid operation are remarkably improved.

The invention discloses a micro-grid energy coordination control method considering communication topology dynamic change, which belongs to the technical field of electric energy control and management, and comprises the following steps: monitoring a communication connection state between control nodes, and generating a topology change event; evaluating energy supply capability and communication reliability and adjusting influence in energy coordination to generate adaptive weights; in combination with real-time sensor information and historical operation data, short-term energy prediction data is generated, in combination with adaptive weight, a global energy distribution consensus is achieved, and then an energy distribution decision is generated; in the execution process, the topology change event and the deviation between the short-term energy prediction data and the real-time energy data are monitored, and if a deviation threshold value is exceeded, an event signal is triggered to drive the control strategy to be updated. According to the method, the adaptive weight is generated by monitoring the communication topology and the energy state, and distributed negotiation and event triggering updating are performed in combination with short-term prediction, so that the energy coordination robustness and the operation stability of the micro-grid in a dynamic environment are improved.

The invention relates to the technical field of data processing, and discloses a green cloud data center energy saving method and system based on a sensor network. The method comprises the following steps: acquiring environmental parameters and energy consumption data of a data center by utilizing a multi-type sensor network; encrypting the data through a quantum key distribution technology; inputting a multivariate regression analysis model to construct an energy consumption evaluation model; adjusting ventilation and refrigeration parameters according to an analysis result; monitoring a renewable energy source state and formulating a priority strategy; load distribution and energy allocation are optimized, and intelligent control is achieved. Through safe and efficient data acquisition and processing, multi-dimensional energy consumption analysis and intelligent cooperative control, the problems of low energy efficiency and high carbon emission of a traditional data center are solved.

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.

Examples described herein provide a system for controlling power flow in an energy distribution network. The system includes a plurality of inverters, each having an inverter controller associated therewith. The system further includes a utility controller associated with a utility and configured to communicate with at least a subset of the plurality of inverters. The system further includes a customer controller associated with a customer of the utility and configured to communicate with at least the utility controller. The system further includes a direct current (DC) breaker associated with a battery of the customer. The system further includes a DC meter of the customer, the DC meter and the DC breaker each configured to communicate with the utility controller and the customer controller. The utility controller is configured to determine a current operational scenario of the energy distribution network and to control power routing within the energy distribution network.

The embodiment of the invention discloses a power grid optimization method and device based on an improved fast marching algorithm. The method comprises the steps of obtaining topological structure data and operation data of a power grid; predicting the dynamic load change and operation state of the power grid by using a pre-trained artificial intelligence model, and generating an optimization initial condition set and a dynamic adaptive speed function; initializing an improved fast marching algorithm based on the optimization initial condition set, constructing a non-uniform propagation model by loading the dynamic adaptive speed function, and calculating an energy transmission optimal path meeting multi-target constraints, wherein the multi-target constraint comprises the steps of minimizing the active loss of the system, maximizing the static stability margin and optimizing the transient response time; and generating an energy distribution scheme including a generator output instruction and a topology switching strategy according to the optimal path, and optimizing the power grid according to the energy distribution scheme. According to the invention, efficient real-time optimization control of the power grid is realized.

The invention provides a one-dragging-N energy storage type elevator kinetic energy recovery management method and system, relates to the technical field of energy management, and is applied to an energy management framework comprising an energy storage device, a power grid, an energy dispatching module and N elevators, and the energy storage device, the power grid and the N elevators are all connected with the energy dispatching module. The energy scheduling module comprises a state monitoring unit and an energy distribution unit; comprising the following steps: acquiring operation data of each elevator through a state monitoring unit; the running state of each elevator is determined; determining an elevator kinetic energy quality coefficient in a power generation state; kinetic energy recovery is carried out according to the kinetic energy recovery priority in direct proportion to the kinetic energy quality coefficient; based on the dynamic impedance matching principle, an energy distribution unit distributes the SOC of the energy storage device after kinetic energy recovery to a load elevator in a power consumption state; and kinetic energy recovery management of each elevator is realized. The system life is prolonged, and the operation cost and the power grid instantaneous impact are reduced.

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.

The invention relates to the technical field of energy and power engineering, and discloses an efficient thermoelectric decoupling system based on phase change energy storage, which comprises an energy storage unit module, a regulation and control algorithm module and a scheduling optimization module. The energy storage unit module adopts a nanotechnology novel phase change material, so that the energy storage density and stability are improved; the regulation and control algorithm module realizes accurate control of charging and discharging through a multi-stage regulation and control strategy; and the scheduling optimization module predicts energy demand and production by using a machine learning algorithm, and dynamically adjusts an energy distribution strategy. The problems that a traditional heat storage material is low in energy storage density and poor in stability and lacks an intelligent scheduling mechanism can be solved, the energy utilization efficiency and operation reliability of a thermoelectric system are improved, and the energy cost is reduced.

The present invention discloses a method and system for dynamic scheduling of multiple energy storage cabinets, comprising: obtaining a registration request from each energy storage cabinet, assigning a unique identifier to each energy storage cabinet, and then establishing a UDP communication connection with each energy storage cabinet; obtaining operating status data periodically sent by each energy storage cabinet; calculating real-time load demand based on a power grid electricity price signal, generating a scheduling instruction based on the operating status data and the real-time load demand, and sending the scheduling instruction to the corresponding energy storage cabinet via a communication connection. The present invention uses UDP to establish a connection with each energy storage cabinet, realizing the application of the UDP protocol in the master-slave architecture of the energy storage system, significantly reducing communication delays, meeting the requirements of dynamic scheduling in seconds, enabling the energy storage system master control terminal to obtain the operating status data of each energy storage cabinet in real time, and quickly generating corresponding scheduling instructions based on the real-time load demand and electricity price signal, thereby realizing efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets and improving the response speed and scheduling flexibility of the energy storage system.