400 results about "Energy allocation" patented technology

The invention discloses a building carbon emission monitoring system and method based on big data acquisition, particularly relates to the field of building carbon emission monitoring, and is used for solving the problems of energy use optimization and carbon emission dynamic regulation and control. Through combination of a recursive feature elimination algorithm and Kalman filter dynamic correction, efficient identification of an electric appliance operation state and accurate correction of load data are realized. Time-delay-free synchronization of distributed energy and load requirements is realized through a time sequence cross-correlation algorithm, and the efficiency and precision of energy allocation and carbon emission monitoring are greatly improved. The dynamic carbon load pressure index and the global energy consumption difference index cooperate to assess changes of load and carbon emission, rapid response to abnormal energy consumption and carbon emission is realized, generation and prediction optimization of a regulation and control strategy are carried out in combination with a fuzzy control algorithm and an autoregression model, the carbon emission of buildings is effectively reduced, and the construction efficiency is improved. And intelligent and dynamic load and carbon emission management is realized.

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 provides a self-adaptive power supply energy-saving configuration method and system based on energy gradient utilization, and relates to the technical field of power supply management, and the method comprises the steps: collecting the operation parameters of electric equipment, building a power utilization model, and carrying out the self-adaptive correction; constructing a multi-stage energy distribution matrix according to the corrected power utilization model, and performing energy efficiency grading on the power utilization equipment; performing spatial-temporal feature extraction by adopting a bidirectional attention mechanism, generating an electric equipment priority strategy in combination with a self-calibration network, and further calculating an energy distribution proportion of each level of electric equipment; and finally, generating a control signal according to the energy distribution proportion, adjusting the power supply power of each stage of electric equipment, distributing the exceeded power to the electric equipment with the secondary high energy conversion efficiency when the power value exceeds a target power threshold value, and outputting a self-adaptive energy balance control instruction according to the prediction control model to adjust the operation state of the electric equipment.

The invention relates to the technical field of ship power systems, and discloses a ship comprehensive power system based on composite energy storage, comprising an energy generation module which comprises a diesel generator, a gas turbine and a renewable energy sub-module and is used for generating electric energy; the energy storage management module comprises a lithium battery, a super capacitor and a flywheel energy storage system and is used for storing and releasing electric energy; the energy distribution and scheduling module is used for controlling energy distribution between the energy generation module and the energy storage management module; the load management module is used for adjusting the load power requirement according to the load priority; and the health management and fault monitoring module is used for monitoring the operation states of the energy storage management module and the system in real time. Through cooperative work of multiple energy storage units and combination with optimization control of the energy distribution and scheduling module, characteristics of different energy storage devices are fully exerted. Therefore, loss in the energy conversion process is remarkably reduced, distribution and utilization of electric energy are optimized, and the overall energy utilization efficiency of the system is improved by about 15%-20%.

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 provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention provides an automatic waste heat recovery method which comprises the steps that waste heat pressure parameters, component media and flow characteristic data are obtained, waste heat data are classified, characteristic vectors of different types of waste heat are obtained, and the characteristic vectors of different types of preheating are stored in a waste heat characteristic database; analyzing and predicting randomness and intermittency rules of waste heat generation and a waste heat concentration area, and dynamically adjusting a cold absorption strategy or a heat absorption strategy; corresponding cold absorption or heat absorption operation parameters are called from the waste heat characteristic database, and cold absorption or heat absorption equipment is controlled to set a self-adaptive adjustment operation state according to the parameters; the cold and heat storage energy in the recovery process is monitored in real time, and the energy loss value of waste heat recovery is analyzed; and constructing a photovoltaic power storage energy optimization scheduling model, solving to obtain an energy distribution strategy, dynamically adjusting a use strategy of cold and heat storage energy, optimizing a distribution proportion and a time sequence of cold and heat energy, and realizing collaborative optimization control of photovoltaic power storage and waste heat recovery.

The invention provides an elevator energy recovery application method and system based on a super capacitor, and relates to the technical field of energy recovery, and the method comprises the steps: collecting elevator operation data and super capacitor state data, constructing a state space and an action space, and constructing a reward function according to peak-valley electricity price economic benefits, super capacitor life loss and energy utilization efficiency; training a deep reinforcement learning model based on the double-Q network to obtain an energy recovery optimization strategy; when the elevator is braked, regenerative braking energy is stored in the super capacitor; when the elevator is driven, the stored energy is released for traction; the optimal storage allocation scheme of the multiple elevator rooms is calculated through the virtual energy storage unit, hierarchical control is implemented based on an optimization strategy and the allocation scheme, the upper layer adopts fuzzy self-adaptive weight to determine the energy storage priority, and the lower layer adopts a distributed algorithm to calculate the energy allocation coefficient and the target power; and the charge-discharge rate is updated according to the health state of the supercapacitor.

The invention discloses an energy management method of an electric bicycle system and the electric bicycle system.The method comprises the steps that battery parameters, motor parameters and whole vehicle kinetic parameters of an electric bicycle are obtained, and digital mapping processing is carried out to obtain a digital twin system; acquiring operation parameters of the battery, the motor and the whole vehicle, and performing distributed feature extraction processing to obtain feature data; training the bionic optimization model fusing the genetic algorithm and the ant colony algorithm to obtain a scenarized energy optimization strategy; performing hierarchical regulation and control processing on the control system to obtain a hierarchical control instruction sequence; and performing dynamic regulation and control processing on energy distribution to obtain a real-time energy optimization control instruction. According to the technical scheme of the invention, intelligent prediction and dynamic optimization distribution of energy demands are realized.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.

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 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.

The invention relates to the technical field of carbon emission data processing, in particular to a carbon emission data processing method, device and equipment and a readable storage medium. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, abnormal data identification and correction based on a hybrid machine learning model, energy optimization configuration assistance and real-time monitoring and feedback adjustment, and data acquisition and preprocessing: constructing a multivariate data acquisition network and preprocessing the acquired data; therefore, standard carbon emission data for subsequent analysis can be obtained. Through a multi-element acquisition network and fine preprocessing, the comprehensiveness, accuracy and timeliness of data are greatly improved, a foundation is built for accurate analysis, abnormity is accurately recognized and corrected through a hybrid machine learning model, the accuracy is improved, data reliability is guaranteed, association between carbon emission and energy is mined, enterprises are assisted in optimizing energy configuration, and the energy utilization efficiency is improved. The carbon emission is reduced, and the system stability is enhanced by real-time monitoring feedback.

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 flywheel energy storage, in particular to a flywheel energy storage management system for renewable energy integration, which comprises a state monitoring module, an energy access module, a material fatigue prediction and optimization module and an energy scheduling module, the state monitoring module collects the operation state of the flywheel energy storage unit in real time. The energy access module receives input power from renewable energy sources; the material fatigue prediction and optimization module generates an operation optimization strategy when a fatigue risk is predicted; and the energy scheduling module performs dynamic energy distribution among the flywheel energy storage unit, the power grid and the standby energy storage unit, and formulates an optimal energy flow strategy in combination with historical load data and real-time input power. According to the invention, the service life of the flywheel energy storage unit is greatly prolonged, and the overall safety and reliability of the system are improved.

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 provides a multi-energy coupling complementary light storage and charging management system. The multi-energy coupling complementation light storage and charging management system comprises a photovoltaic power generation module, an energy storage module, a lithium battery pack, a power grid input end, an energy conversion module, an energy storage management module, an energy scheduling module, an energy storage management module and a power supply management module, electric energy input by a photovoltaic power generation module, an energy storage module and a power grid is allocated through a multi-energy coupling algorithm, a charging management module is used for controlling the stability of the electric energy output outwards, and system control software is used. According to the multi-energy coupling complementary light storage and charge management system, through an innovative multi-energy complementary technology and an energy efficiency optimization algorithm, the problem of energy loss generated in the energy switching process in the prior art is effectively solved, a deep reinforcement learning algorithm and a dynamic energy distribution model are adopted, and the energy efficiency is improved. And electric energy from different sources is intelligently dispatched.

The invention discloses a distributed photovoltaic grid management method and system, and relates to the technical field of distributed photovoltaics, a distributed photovoltaic efficiency analysis module of the system monitors and evaluates the power utilization efficiency Psys of the whole system in real time by constructing a power efficiency formula, and compares the power utilization efficiency Psys with a preset efficiency threshold value X. When the operation efficiency of the system is lower than a set threshold value, the intelligent scheduling and energy distribution module starts an optimization strategy according to an electric power balance scheduling analysis result, redistributes electric power supply, preferentially meets a high-demand area or an area with insufficient energy storage, and ensures electric power supply and demand balance. According to the system, through an intelligent scheduling algorithm, power distribution is flexibly adjusted based on the priority factor of each micro-grid unit, and the operation efficiency of the system is improved. An intelligent feedback and iterative scheduling mechanism enables the system to still maintain efficient operation under continuously changing load and power generation conditions, power loss is reduced, resource waste is reduced, and the overall power utilization rate is improved.

The invention relates to a hybrid power mining truck energy management strategy implementation method and system. The hybrid power mining truck energy management strategy implementation method comprises the steps that a power system model of a hybrid power mining truck is constructed according to kinetic parameters of the hybrid power mining truck; based on the power system model, carrying out modeling on the driving working condition of each stage of the hybrid power mining truck to form a target function; solving the target function by using a dynamic programming algorithm to obtain a global optimal energy management strategy of the hybrid power mining truck under each working condition; inputting the global optimal energy management strategy under each working condition as training data into a BP neural network model for training to obtain an energy distribution model; and optimizing an energy management strategy of the target hybrid power mining truck by using the energy distribution model. By using the dynamic programming algorithm and the BP neural network, the energy management strategy of the hybrid power mining truck can be dynamically adjusted according to different working conditions, and the adaptive capacity of the truck in various environments is improved.

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 discloses an OPSA-ECMS hydrogen fuel hybrid power unmanned aerial vehicle energy management method, and the method comprises the steps: building an unmanned aerial vehicle hybrid power system model employing a hydrogen fuel cell as a main power supply and a lithium ion battery as an auxiliary power supply, and building an energy management system model. According to the method, the OPSA and the ECMS are combined, the minimum hydrogen consumption and the highest efficiency are taken as optimization objectives, the OPSA is adopted to calculate an optimal reference energy distribution scheme in an off-line manner under a known preset route, and the ECMS is adopted to optimize the reference energy distribution scheme in an on-line manner in the task execution process of the unmanned aerial vehicle, so that the optimal energy distribution of the system is realized. According to the method, OPSA off-line calculation and ECMS on-line optimization are combined, the high energy density of hydrogen fuel and the quick response characteristic of the battery are exerted to the maximum extent, and the overall energy utilization efficiency is improved.

The invention provides an energy distribution optimization adjustment method for an energy storage system, and belongs to the technical field of energy storage management, and the method comprises the steps: obtaining a basic structure of the energy storage system, carrying out the structural decomposition of the basic structure, and deploying a sensor according to a structural decomposition result. Obtaining internal system data and external environment data of the energy storage system based on a deployment result, determining an energy optimization target, carrying out data fusion on the internal system data and the external environment data according to the energy optimization target, extracting influence distribution characteristics from the fused data, and establishing a prediction model; key factors and distribution trends of each energy optimization target are identified according to the prediction model, an energy distribution strategy is determined, energy storage system configuration is optimized according to the energy distribution strategy, a configuration result is fed back to the prediction model, dynamic optimization and intelligent management are achieved, power demand changes are responded in real time, the energy distribution strategy is optimized, and energy storage system configuration is optimized. And the overall efficiency of the system is improved.

The present application relates to a microgrid energy management optimization method and system in a micro-grid and distribution network collaborative interaction scenario, and relates to the technical field of power grid regulation and distribution. It includes: establishing a power consumption data acquisition base to monitor the power consumption data of the main power grid and the microgrid, and analyzing the power consumption merchant information of conventional power consumption loads and new power consumption loads; analyzing based on the power consumption merchant information to determine the power consumption order and power consumption load priority in the power consumption merchant information; sending an automatic allocation instruction to a preset distribution collaboration system according to the power consumption load priority and power consumption order, and calculating a power energy allocation balance coefficient; performing power energy allocation based on the automatic allocation instruction, and when the power energy allocation balance coefficient is less than a preset good balance allocation coefficient, sending a power energy interaction management instruction. The present application has the advantages of improving the interaction and collaboration ability between the microgrid and different distribution network ends and improving the power energy allocation balance effect.