1506 results about "Energy management system" patented technology

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 photovoltaic power station photovoltaic power prediction method and system. A cloud layer motion time sequence library is constructed based on a historical cloud picture, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and the aerosol density in real time, calculates the light intensity attenuation gradient of the shadow area in combination with the model, and predicts the remaining time of the cloud cluster reaching the core area. Dividing a power prediction confidence interval according to remaining time, inputting satellite data into a multi-scale meteorological fusion architecture, and adjusting a meteorological feature extraction path of an attention mechanism based on cloud density. And fusing the surface reflectance correction value calculated by the scattering data of the light sensor to generate a short-term power prediction sequence of the sudden change cloud layer scene. And inputting the sequence into an energy management system, and outputting an active and reactive double-target scheduling instruction in combination with a grid-connected point voltage deviation and a reactive compensation parameter constrained by a power grid rule. According to the technical scheme provided by the invention, the cooperative scheduling precision of the photovoltaic power station in the cloud abrupt change scene is improved.

The invention relates to the technical field of data processing, in particular to a building light storage and charging integrated energy management system, which is used for solving the problems of data processing delay and resource coordination mismatch caused by insufficient multi-source heterogeneous data fusion in photovoltaic power generation, energy storage and grid connection links. The system comprises a data acquisition module, a protocol conversion module, a data fusion module, a scheduling optimization module, an instruction generation module and a feedback module. The protocol conversion module carries out Modbus, CAN bus and IEC 61850 protocol analysis on the photovoltaic output time sequence data, the energy storage charging and discharging state data and the power grid dispatching instruction to generate standardized data streams. And the safety verification module intercepts the overrun instruction and triggers an amplitude limiting strategy. Through protocol unification, dynamic fusion and closed-loop control, the system realizes improvement of photovoltaic consumption rate, reduction of energy storage circulation loss and optimization of power grid frequency modulation deviation, and significantly improves the efficiency and economy of light storage and charging cooperative operation.

The invention discloses an optical storage and charging integrated micro-grid energy management system, which relates to the related technical field of micro-grids and comprises an electric energy collection layer, a digital twinborn layer, a collaborative optimization layer, an application execution layer, a photovoltaic power generation unit, an energy storage unit, a charging load unit and a distributed measurement and control terminal. The invention further discloses an energy management method of the photovoltaic, storage and charging integrated micro-grid. The energy management method comprises the steps of data acquisition, digital twin modeling, multi-target strategy generation and optimization, strategy evaluation and screening, strategy issuing and execution and closed-loop feedback and dynamic correction. According to the invention, through short-term prediction of a digital twinborn layer and real-time generation of a charging and discharging strategy by a collaborative optimization layer, fluctuating renewable energy sources are preferentially consumed, the light abandoning rate is reduced, energy storage charging is automatically triggered in an illumination peak period, and power overflow is avoided; based on the simulation result of the digital twinborn model, the power distribution of energy storage and load is dynamically adjusted, so that the photovoltaic utilization rate is improved, and the dependence on a traditional power grid is reduced.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

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 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 discloses a micro-grid anti-countercurrent control method and system based on multi-objective optimization, and the method comprises the steps: generating a dynamic load prediction result according to the power grid side voltage, user side load power and energy storage system charge state data collected by an energy management system in real time; based on the dynamic load prediction result, obtaining a countercurrent risk quantitative index; generating a corresponding dynamic anti-reflux threshold curve according to the reflux risk quantitative index; the dynamic anti-reflux threshold curve is corrected through a real-time collaborative verification module, and an anti-disturbance optimization threshold is generated; and converting the anti-disturbance optimization threshold value into a PCS power regulation and control instruction, synchronously displaying a threshold value execution state and reverse feed early warning information on an energy management interface, and triggering adaptive power clamping protection when detecting that the voltage is out of limit or the power is reverse. According to the embodiment of the invention, the intelligent level of countercurrent prevention and control of the micro-grid can be improved, and safe and stable operation of the grid is guaranteed.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

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 provides an urban rail transit energy management system considering new energy and energy storage access. The urban rail transit energy management system comprises a new energy power generation unit, an energy storage unit, a regenerative braking energy recovery unit and an energy management control platform, the new energy power generation unit and the energy storage unit are connected to a rail transit power supply system; the energy management control platform is connected with the power supply network and controls the units in a communication mode, energy flow in the rail transit power supply system is monitored, analyzed, optimized and dispatched so as to coordinate utilization of new energy power generation power, energy storage charging and discharging and regenerative braking energy, the energy utilization efficiency is improved, and the requirement for power grid outsourcing electric energy is lowered. The peak load of the system and the power grid electricity purchasing cost are effectively reduced, meanwhile, the overall energy utilization efficiency is improved, and energy conservation and consumption reduction are achieved while safe and stable operation of the rail transit system is ensured.

The invention discloses a hybrid electric vehicle predictive energy management system based on multi-layer cooperative control, and aims to improve the vehicle performance and energy efficiency. According to the method, a hierarchical cooperative control architecture is adopted, and the reinforcement learning, vehicle speed multi-step prediction and model prediction control technologies are combined, so that joint optimization control over vehicle acceleration and power distribution is achieved. The system obtains the vehicle running state and the surrounding environment information in real time through the state sensing module, and determines the expected acceleration by using a strategy network trained by a PPO algorithm. In addition, the future vehicle speed is predicted in combination with an XGBoost multi-step regression model, the optimization module is designed through an MPC strategy, oil consumption, the battery state and driving smoothness are comprehensively considered, and therefore efficient management of the power system is achieved. According to the scheme, the obvious energy-saving advantage is achieved, the driving smoothness and comfort are improved, the good self-adaptive capacity and a real-time feedback mechanism are achieved, and the method is suitable for the complex urban road traffic environment.

The invention provides a power distribution network fault recovery method and device, a storage medium and computer equipment. The method comprises the steps of obtaining operation state data of a power distribution network; determining a fault detection result according to the operation state data in combination with a neural network algorithm and digital twinborn simulation; based on the fault detection result, after a fault area is determined, an isolation measure corresponding to the fault area is applied; and based on the topological information of the power distribution network, calculating a load transfer path, and according to the load transfer path, carrying out load transfer by adopting a flexible interconnection system and a household energy management system so as to recover power supply of a fault area. Therefore, cooperative scheduling of the power distribution network level flexible interconnection system and the user side household energy system is realized, the self-healing capability and the operation efficiency of the power distribution network are effectively improved, and the technical effects of rapid fault isolation and power supply recovery are achieved.

The invention discloses a smart city energy management system based on energy conservation and emission reduction, and relates to the technical field of smart city energy management. By using an intelligent sensor group, illumination related data of illumination equipment is obtained, an illumination related data set R is constructed, and a light attenuation factor Gs, a convective heat transfer index DL and a use intensity factor Qd are extracted; the method comprises the following steps: dynamically evaluating the thermal load intensity of an urban area, scientifically dividing thermal risk grades, combining performance brightness prediction and control strategy matching to realize subarea and lamp-divided refined dimming control, using a feedback self-evaluation and optimization regulation and control mechanism, carrying out power reestimation and strategy alternation on an area with a substandard control effect, and carrying out power control on the area with the substandard control effect. Redundant energy consumption is effectively reduced, heat accumulation is restrained, and the lighting efficiency and the environment comfort degree are improved.

The invention discloses a data acquisition method and device for an energy management system, equipment and a medium. The energy management system is used for managing multiple power plants. The method comprises the following steps: creating a to-be-constructed big data platform which comprises a data acquisition layer, a data processing layer and a data storage layer; deploying a multi-protocol adapter cluster on a data acquisition layer; constructing a data standardization pipeline in a data processing layer, and generating a multi-level security label in combination with a data source security level, a service association degree and an updating frequency; a hierarchical storage framework is designed in a data storage layer and used for constructing a redundant index table crossing storage nodes and a data blood relationship graph based on a graph database; and deploying a dynamic security gateway between the data acquisition layer and the data processing layer, dynamically selecting a corresponding encryption algorithm according to the data security label, and completing the construction of a big data platform, so as to complete the data acquisition of the plurality of power plants based on the big data platform. And high-reliability, traceable and safe data support is provided for cooperative management of multiple power plants.

The invention discloses an electric power control method based on a diesel generator, commercial power and photovoltaic grid connection. The method comprises the following steps: acquiring operation data of commercial power, a diesel generator and photovoltaic-energy storage through an energy management system, constructing a multi-target dynamic optimization model, and distributing power by taking the lowest comprehensive operation cost, the highest power supply reliability and the minimum carbon emission as targets; the power analysis and regulation unit adopts a double-loop mechanism of outer-loop Kalman filtering prediction compensation and inner-loop model prediction control, synchronizes the voltage, frequency and phase of the side to be connected with the grid and the power grid side, and smoothly switches the power change rate of each energy source in the process through a self-adaptive sliding mode control strategy; and the grid-connected switching unit intelligently pre-judges the grid-connected switching opportunity by using a long short-term memory (LSTM) network and fuzzy logic fusion model. According to the invention, the energy utilization efficiency and the power supply stability are improved, multi-energy intelligent dynamic regulation and control are realized, the comprehensive operation cost of the system is effectively reduced, and reliable power supply under complex working conditions is ensured.

The embodiment of the invention provides a digital simulation modeling method and device of a tandem type hybrid electric propulsion system, computer equipment and a medium, and relates to the technical field of simulation modeling, and the method comprises the following steps: constructing a turboshaft prime mover model, constructing a generator model, connecting the turboshaft prime mover model and the generator model in series to generate a generator set, the power generation groups are connected in parallel to construct a turbine power generation system; constructing a propulsion system; constructing a battery energy storage system; constructing a power transmission and distribution system, and respectively connecting the power transmission and distribution system with a turbine power generation system, a propulsion system and a battery energy storage system; and constructing a comprehensive control and energy management system, and outputting a digital simulation modeling result through a turbine power generation system, a power transmission and distribution system and a battery energy storage system. According to the scheme, a simulation modeling method based on multi-physics field coupling double-power-source dynamic response and energy flow regulation and control provides reliable modeling technical support for engineering application of a tandem type hybrid electric propulsion system.

The invention presents an energy management system for power distribution networks. It features a hierarchical network with multi-level metering nodes, each measuring energy usage. Data from these nodes are transmitted via a secure communication gateway to a cloud-based platform, which includes servers for processing and storage. A key component is a machine learning algorithm within the server, designed to analyze energy data, normalize and transform inputs, and identify anomalies. The algorithm generates anomaly scores for each power meter, reflecting potential operational issues. A user interface, accessible through client devices, displays the processed data and anomaly scores. This allows users to effectively monitor and manage the energy distribution, making informed decisions for optimization. The system enhances the efficiency and reliability of power distribution, offering advanced analysis and user-friendly monitoring capabilities.

The invention discloses a power distribution network harmonic prediction method and device based on a cascade space-time diagram network, and belongs to the field of power distribution network electric energy quality monitoring. According to the method, historical harmonic voltage content ratio and injection power data of a measurement node are taken as input, and an HRU predicted value of the measurement node is output through a single-point time sequence prediction module based on a Transform architecture; the topology and power prediction data of the power distribution network and the HRU predicted value of the measurement node are fused, an inter-node harmonic propagation path is modeled through a space estimation module based on the graph neural network, the HRU value of the non-measurement node is estimated, and the predicted value of the THD is obtained based on index fusion calculation. According to the method, time sequence-space joint modeling is applied to harmonic analysis, through network cascading of multifunctional modules, the node harmonic THD prediction precision in dynamic topology and sparse measurement scenes is effectively improved, the method can be integrated in a power grid energy management system, and real-time decision support is provided for harmonic treatment.

An intelligent energy management system, includes: a drive device including an engine configured to output power to a wheel of the vehicle, a drive motor configured to output power to the wheel, and an electric generator connected to the engine and driven by the engine to generate electricity; a power battery configured to supply electricity to the drive motor and charged with an alternating current outputted from the electric generator or the drive motor; and a control device configured to acquire multi-domain data fusion information, predict, according to the multi-domain data fusion information, a route-specific vehicle energy consumption corresponding to a preset travel route, plan, according to a road section-specific vehicle energy consumption corresponding to each road section, a target SOC corresponding to each road section, and control, according to the target SOC and an actual vehicle demand corresponding to each road section, the drive device.

The invention discloses a multi-objective collaborative optimization method for a photovoltaic-energy storage-charging micro-grid. The method comprises the following steps: configuring a photovoltaic-energy storage-charging facility micro-grid system; evaluating the autonomous operation capability of the micro-grid through the dynamic internal energy autonomy index; a multi-objective optimization model is established, and the new energy consumption rate, the operation cost and the system autonomy are collaboratively optimized; an improved decomposition type multi-objective evolutionary algorithm is adopted to solve a Pareto optimal solution set; dynamically adjusting a system operation strategy through the energy management system; according to the method, a probabilistic IEA index is provided, the autonomy risk of the micro-grid is quantified, and energy storage optimization configuration is guided; a self-adaptive weight MOEA / D algorithm is designed, and multi-target efficient collaborative optimization is achieved; and a light-storage-charging multi-target cooperative control strategy is developed, and the economical efficiency and reliability of the system are improved. The invention provides a systematic solution for autonomous operation of the renewable energy microgrid, and is suitable for scenes such as intelligent charging stations, industrial parks and the like.

The invention discloses a wind power plant energy management system and a dispatching optimization system, and belongs to the field of wind power generation. The system comprises a data acquisition module, a wind speed prediction module, a wake effect analysis module, a power prediction and distribution module, an optimization scheduling module, a dynamic adjustment module, an energy efficiency evaluation module and a communication control module which work cooperatively. Through multi-source data fusion and dynamic collaborative optimization, the comprehensive performance of the wind power plant is remarkably improved, on the operating efficiency level, the system combines the space-time convolutional neural network and the multi-target optimization algorithm, high-precision prediction of minute-level wind speed and optimal distribution of whole-field power are achieved, energy loss caused by the wake effect is effectively reduced, and the wind power generation efficiency is improved. The output strategy is dynamically adjusted according to the health state of the fan, and the fatigue loss of the equipment is delayed while the generating capacity is maximized; on the power grid adaptability level, a self-learning mechanism based on the frequency disturbance qualified rate is introduced, and frequency modulation response parameters are optimized in real time.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention is applicable to the technical field of fuel cells, and provides a power-heat cooperative control method for a network-connected fuel cell vehicle, and the method comprises the steps: constructing a speed prediction model and a vehicle dynamics system model; establishing a power battery system model; establishing a fuel cell thermal management system model; establishing a passenger compartment thermal model; analyzing the coupling relationship between the energy management system and the fuel cell-passenger compartment coupling thermal management system; and designing a power-heat hierarchical optimization framework, and realizing efficient energy utilization and real-time control through a hierarchical collaboration mechanism. According to the method, the problem of comprehensive operation cost optimization of the integrated heat management system considering energy and heat coupling can be effectively solved, and efficient energy utilization and real-time system response are realized. The real-time responsiveness and stability of the system are improved, and it is ensured that efficient operation can be kept under various conditions.

The invention discloses a state monitoring and real-time evaluation system and method for an energy storage power station, and relates to the field of energy storage power station monitoring, and the method comprises the steps: collecting multi-source heterogeneous data of the energy storage power station through constructing a hierarchical multi-scale sensing system; the collected multi-source heterogeneous data is preprocessed; inputting the processed data into the first model and the second model, carrying out classification judgment on the state of the energy storage battery, and outputting an evaluation result; the assessment result is transmitted to a safety early warning unit, the early warning unit maps and judges the state assessment result according to a preset risk grading rule, and early warning grade information and a risk label are output; and transmitting an evaluation result and an early warning result to an energy management system, and performing visual display and early warning prompt on the real-time state. An evaluation method combining a battery behavior mechanism and an artificial intelligence algorithm is introduced, dynamic prediction and trend analysis of key indexes are realized, and the accuracy and adaptability of the second model are improved.

The invention relates to a light storage integrated system and a control method, the system comprises a direct current bus, a photovoltaic access unit, an energy storage unit, a power conversion system and an energy management system, the energy storage unit and the photovoltaic access unit are accessed to the direct current bus, and the energy storage unit is provided with a plurality of SOC parameters including a lower discharge limit SOCmin, an upper charge limit SOCmax and an off-grid recovery threshold SOCon; the energy management system configures an energy management strategy in each mode through a plurality of SOC parameters set by an energy storage unit, realizes intelligent control and mode switching in a grid-connected mode and an off-grid mode, and in the grid-connected mode, preferentially utilizes photovoltaic power to supply power to a load, switches to an anti-countercurrent mode after the stored energy is fully charged, and dynamically limits the photovoltaic power to avoid countercurrent; in the off-grid mode, the power conversion system is controlled to operate in a VF mode, and photovoltaic power reduction is triggered when set conditions are met. Compared with the prior art, the method has the advantages that the countercurrent suppression efficiency is improved, and the system safety, reliability and stability are enhanced.

The invention discloses a multi-stage dynamic optimization method and system for a new energy flexible hydrogen production system, and the method comprises the steps: a day-ahead scheduling plan stage: formulating a next-day scheduling plan based on day-ahead prediction data; in the real-time monitoring stage, the output condition of the new energy power generation unit is monitored in real time through a sensor network; in the dynamic adjustment stage, the intelligent energy management system is used for dynamically adjusting the water electrolysis hydrogen production unit; in the hydrogen management and optimization stage, a hydrogen storage and transportation unit is intelligently managed; and in the multi-stage circulation optimization stage, the operation efficiency and economy of the system are continuously optimized through a closed loop feedback mechanism. The system comprises a new energy power generation unit, a water electrolysis hydrogen production unit, an intelligent energy management system and a hydrogen storage and transportation unit. Through the steps of real-time monitoring, dynamic adjustment, hydrogen management and optimization and multi-stage circulation optimization, the energy utilization efficiency is remarkably improved, the hydrogen production cost is reduced, the flexibility and stability of the system are enhanced, and powerful support is provided for development of the hydrogen energy industry.

The invention provides a safe communication method and system for a V2G charging pile and an energy storage system and a storage medium. The system comprises an energy management system EMS, a charging pile EVSE, a vehicle battery management system BMS and an energy storage battery management system BMS. A session key is established through certificate bidirectional authentication and ECDH negotiation, encryption and anti-replay are realized based on AES / SM4-GCM and additional authentication data, and digital signature verification and REST / TLS transmission are performed on a cross-domain instruction; and when the cloud is unreachable, the EVSE locally executes cooperative control and virtual droop voltage stabilization, or the EMS adopts model prediction control to issue an optimal charging and discharging track. According to the protocol, the confidentiality, integrity and availability of V2G communication and control and the stability of the micro-grid are improved.

The invention provides a zinc-nickel air flow battery energy storage cabinet. The zinc-nickel-air flow battery energy storage cabinet comprises a control module and a plurality of flow battery energy storage modules, each flow battery energy storage module comprises a plurality of supporting layers, an electrolyte storage tank is fixed to a first supporting layer in the supporting layers in a limited mode, a galvanic pile is fixed to a second supporting layer above the first supporting layer in a limited mode, and the control module is connected with the control module. The third supporting layer above the second supporting layer is used for installing an electric pile sensor module, a liquid delivery pump is arranged below the first supporting layer, and the electric pile, the liquid delivery pump and the electrolyte storage tank are connected through a liquid circulation pipeline; the control module comprises an air compressor; the air compressor is connected with the electric pile through an air pipeline; the control module further comprises an electric energy management system, and the electric energy management system comprises an energy management system, a power conversion system and a battery management module. According to the embodiment provided by the invention, the integration of the zinc-nickel air flow battery is realized.

The invention discloses an energy management system and method for a charging station, and relates to the technical field of management analysis, and the method comprises the steps: carrying out the charging demand space-time distribution prediction based on the operation data of the charging station, and generating a charging load distribution prediction map; topological structure analysis is carried out on charging station power grid connection nodes to obtain power grid topological parameters, and charging and discharging strategy optimization is carried out in combination with real-time electricity price fluctuation data and energy storage system state data to generate an optimal charging and discharging strategy; planning a charging and discharging path of the energy storage system based on the optimal charging and discharging strategy to obtain a charging and discharging path, and adjusting the charging and discharging path according to a power grid load balancing index to generate a power grid safety interaction strategy; and inputting the power grid safety interaction strategy into the charging station energy management digital twin model for strategy verification to obtain an optimized energy management strategy, and issuing the optimized energy management strategy to a charging station control terminal for execution. The method has the effect of improving the energy management efficiency of the charging station.