1856 results about "Energy management system" patented technology

The invention discloses an energy storage energy management system for improving a demand protection algorithm, and the system comprises a data collection module which is used for obtaining energy storage system load data, distributed energy operation data, and energy storage system operation data; the correlation feature analysis module is used for acquiring a correlation feature set between the load and the energy supply; the control strategy generation module is used for adjusting a charging and discharging threshold value and formulating a charging and discharging control strategy; the control strategy execution module is used for formulating a charging and discharging control strategy and adjusting the working mode of the energy storage converter; the cooperative execution module is used for dynamically optimizing a power distribution strategy by dispatching distributed energy and power grid cooperative work; and the dynamic power adjustment strategy adjustment module is used for adjusting the charging and discharging protection threshold value and the power adjustment strategy through a feedback mechanism. Through an intelligent control and scheduling mechanism, the charging and discharging efficiency of the energy storage system is improved, the stability and flexibility of the power grid are enhanced, and effective guarantee is provided for safe operation of the power grid.

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.

The invention relates to a distributed energy management system for prolonging the cycle life of a lithium battery. The system comprises an acquisition unit used for acquiring battery state characteristic data of single batteries in a lithium battery pack; the identification unit is used for carrying out charge and discharge abnormal mode identification on the battery state characteristic data to obtain charge and discharge abnormal event data; the decomposition unit is used for performing hierarchical characteristic decomposition and multi-dimensional fusion based on the charging and discharging abnormal event data to obtain a cycle life attenuation characteristic matrix; and the equalization control unit is used for carrying out life equalization control based on the cycle life attenuation characteristic matrix to obtain a target charge and discharge coefficient. According to the invention, the local over-charge and over-discharge phenomena are effectively inhibited, the service life loss of the battery pack is reduced, and the energy balance adjustment of the multi-node control unit is realized.

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 relates to the technical field of electric power peak regulation of energy storage equipment, in particular to a dispatching energy management system for assisting electric power peak regulation, which comprises a load prediction unit, a dynamic adjustment unit and a model optimization unit. A dynamic adjustment unit monitors multiple parameters of an energy storage equipment power system through principal component analysis dimensionality reduction, a peak regulation threshold value of the energy storage equipment is dynamically adjusted according to working conditions and load prediction in combination with a fuzzy logic reasoning model, and a model optimization unit analyzes operation data, compares deviation, adjusts fuzzy rules and evaluates the peak regulation effect of the energy storage equipment. The method can flexibly adjust the threshold according to the system operation parameters and the load prediction, improves the peak regulation accuracy of the energy storage equipment, guarantees the stable operation of a power grid and the efficient utilization of energy, further optimizes the peak regulation effect of the energy storage equipment through the feedback improvement rule, and solves the problem of insufficient adaptability of the peak regulation strategy of the existing energy storage equipment.

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.

An energy management system can comprise a housing configured to receive energy storage assemblies within an interior region of the housing via one or more openings on a surface of the housing. One or more guide tracks positioned within the interior region can mechanically and electrically couple to the energy storage assemblies. One or more switches can electrically couple the energy storage assemblies in series to increase a voltage provided from energy storage assemblies to an electrical load and can electrically couple the energy storage assemblies in parallel to facilitate charging the energy storage assemblies from an energy source.

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 discloses a gas generator and energy storage battery hybrid energy supply scheduling strategy, belongs to the technical field of storage and charging, and mainly solves the problems that in an energy supply mode of a gas generator in existing oil and gas drilling operation, the power generation efficiency of the gas generator is low, and the gas generator is prone to flameout when coping with an impact load. Measuring and acquiring data of voltage and current of oil and gas drilling operation energy consumption equipment, the starting number of gas generators and the residual electric quantity of an energy storage battery; 2, calculating the real-time total power, the power abrupt change rate and the load rate of the gas generator according to the measured data; 3, the number of gas generators needing to be started and the charging and discharging state of energy storage electricity are determined according to the calculation and analysis result; and 4, repeating the steps 1-3 until the drilling operation is finished. The scheduling strategy depends on an existing energy management system, the starting number of the gas generators and the charging and discharging states of the energy storage batteries are reasonably controlled by combining the total power fluctuation characteristics of oil and gas drilling operation, comprehensive management of hybrid energy supply of the gas generators and the energy storage batteries in the oil and gas drilling operation is achieved, the power generation efficiency of the gas generators is improved, and the energy consumption is reduced. Meanwhile, flameout of the gas generator is avoided, and operation safety is guaranteed.

The invention relates to the technical field of battery charging and discharging optimization, and discloses an energy management system data analysis method, device and equipment and a storage medium, and the method comprises the steps: collecting a working condition feature data set of a robot energy storage battery through an energy management system; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, accurate prediction of battery performance is further achieved, and stable operation of an energy management system is ensured.

The invention discloses a power distribution method and system based on fuel cell multi-stack health state difference, and belongs to the technical field of fuel cell application. The specific method comprises the following steps: monitoring operation parameters of a plurality of fuel cell stacks through a sensor; obtaining the health state of each fuel cell stack and the health state of the lithium battery; establishing a hydrogen consumption model, a fuel cell SOH loss model, a multi-stack SOH difference equivalent loss model, a lithium battery SOH loss model and a lithium battery start and end SOC loss model; the calculation loss is converted into equivalent hydrogen loss, and a target function model is constructed; and optimizing the target function by using a reinforcement learning algorithm, distributing power output of the fuel cell and the lithium battery according to the current required power and the health state through an energy management system, and regularly updating parameters of a parameter identification module and a model to realize dynamic optimization. The method can identify the life difference between multiple piles, balance the life difference to realize multi-pile performance balance and improve the overall life of a multi-pile system.

A method of operating a virtual power plant (VPP) controller, that manages an electric vehicle (EV) charging station connected to a power grid, a battery storage system, and an independent power plant, includes: obtaining a first data set including time-series information of power output from the independent power plant; training a machine learning (ML) model that predicts power output from the independent power plant over a predetermined prediction horizon; obtaining a second data set including time-series information of: power demand information from the EV charging station; and power availability information from each of the battery storage system, the power grid, and the independent power plant; generating a predicted schedule of power output from the independent power plant over the predetermined prediction horizon; generating a command by inputting the second data set and the predicted schedule into an energy management system (EMS) policy.

The invention discloses an intelligent photovoltaic power generation system control method, and relates to the technical field of photovoltaic power generation management, and the method comprises the steps: collecting data in real time based on a sensor in a photovoltaic system, employing an edge computing technology, and transmitting important information to a cloud end; the future illumination change is predicted, so that the output of the inverter is adjusted in advance; based on an energy management system, the power demand and the generating capacity are monitored in real time, and interaction of photovoltaic power generation, energy storage and a power grid is coordinated; acquiring historical data, identifying a fault mode, and performing fault detection and prediction maintenance; based on the AR technology, the system state is displayed in real time. According to the method, environment data such as illumination, temperature and wind speed are collected in real time through a sensor, the edge computing technology is combined, the system state and the abnormal condition can be rapidly detected, the power generation efficiency is improved, illumination prediction based on the deep learning algorithm can dynamically adjust the working point of the inverter, the power generation capacity of the photovoltaic system is improved to the maximum extent, and the power generation efficiency is improved. And the method adapts to rapidly changing environmental conditions.

The invention discloses a plateau multi-energy complementary agricultural energy management system and a plateau multi-energy complementary agricultural energy management method, and belongs to the technical field of new energy. Wind, light and terrestrial heat multi-energy complementation is cooperated with a battery energy storage system and a phase change heat storage system, and a central intelligent management system is used for carrying out priority scheduling and real-time monitoring and prediction on a load; efficient energy supply of irrigation, processing and greenhouse heating is achieved, the renewable energy utilization rate and the load satisfaction degree are remarkably improved, the operation cost and environmental pollution are reduced, safe and efficient operation of the system is ensured, and good economic and environment-friendly effects are achieved.

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 an energy management system based on a cloud and edge integrated optical storage system. The energy management system comprises a cloud module and an edge module, the EMS controller is used for collecting information of the optical storage equipment module and the load module and uniformly sending the collected data to the cloud module; the cloud module predicts future photovoltaic power generation power / electric quantity through a photovoltaic power generation prediction model by receiving data acquired by the EMS controller; predicting a future load through the load prediction model; analyzing peak and valley time periods of the electricity price through the electricity price management model, and sending prediction data to the edge module; and the edge module carries out energy management through the control logic of energy management according to the battery SOC, the chargeable amount, the photovoltaic predicted power and the generating capacity, and superposition peak-valley arbitrage and safe anti-countercurrent logic. Through the arrangement, the whole energy management system has high intelligence and automation levels, energy distribution and use are optimized, and the energy utilization efficiency is improved.

The invention provides a harbor district energy double-layer coordination optimization method oriented to multiple interest subjects. The method comprises the steps of performing fusion processing and data cleaning on real-time operation data, historical operation data and environmental parameter data of a harbor district energy management system; performing multi-scale decomposition and feature importance evaluation by adopting a time sequence decomposition algorithm; probability modeling and multi-scale prediction are carried out on the basis of feature recognition uncertainty factors; constructing an upper-layer government regulation and control model and a lower-layer harbor district-ship east game model, and calculating a balance coefficient; and performing monthly strategy optimization, weekly scheduling optimization and hour-level real-time control based on the balance coefficient. By constructing a complete technical chain of data processing, feature extraction, prediction modeling, game optimization and real-time control, dynamic balance of benefits of the government, the harbor district and the ship eastern is realized, and the operation efficiency and the control precision of the harbor district energy system are remarkably improved.

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 unmanned aerial vehicle hydrogen-electricity hybrid power system comprises a vehicle body, a hydrogen fuel cell system, a power cell system and an energy management system are arranged in the vehicle body, and electric energy output by the hydrogen fuel cell system and the power cell system supplies power to a motor through the energy management system; the motor controls the spiral blades, the rotating speed of the motor is independently adjusted, and the posture and movement of the unmanned aerial vehicle are controlled by changing the direction and size of the lifting force; the energy management system is used for performing energy conversion during air speed change and braking of the unmanned aerial vehicle, converting mechanical energy into electric energy, inputting the electric energy into the power battery system, and meanwhile, converting the electric energy to judge whether the unmanned aerial vehicle has a high-power energy supply demand currently so as to adjust energy supply; according to the invention, the service life of the galvanic pile monomers can be prolonged, so that the service life of the hydrogen-electricity hybrid power system module can be prolonged; according to the energy management method, unnecessary energy loss is reduced, and the endurance time of the unmanned aerial vehicle is further prolonged.

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 optical storage grid-connected and off-grid operation method, which relates to the field of optical storage grid-connected and off-grid operation, and comprises a photovoltaic power generation unit, an energy storage unit, an intelligent switching unit, an energy management system (EMS), and grid-connected operation and off-grid operation controlled by the system, the grid-connected operation comprises the following steps: system connection: uniquely connecting a photovoltaic system with a power grid through an intelligent switch to realize second-level automatic grid connection; in the operation mode, the daily operation mode is grid-connected operation, and the energy storage system and the power grid jointly supply power to the load; and the system automatically or manually switches back to the grid-connected mode after detecting that the power grid recovers normal power supply. Through a current type control method, the voltage of the power grid is tracked, the magnitude of the output current is set, stable grid-connected operation is achieved, the islanding effect is avoided, it is ensured that the photovoltaic power generation system is automatically powered off when the power grid is powered off, the power grid safety is protected, and the charging and discharging process of the energy storage system is reasonably scheduled according to the load requirement and the photovoltaic power generation condition; the energy utilization efficiency is improved.

The invention relates to new energy, and provides a battery management method, an energy management system and an energy storage system. The method comprises the steps that the energy management system and the battery management system form communication line connection; and the energy management system simulates the original vehicle battery pack to communicate and interact with the battery management system in a running environment of an actual vehicle, so as to obtain battery running parameters of the original vehicle battery pack from the battery management system in real time, and charge and discharge the energy storage unit through the energy storage converter unit based on an energy scheduling strategy. Therefore, the operation environment is simulated through the energy management system, the redundant original vehicle battery pack can be utilized to construct the energy storage system, the maintenance cost problem of the redundant original vehicle battery pack is solved, and new energy consumption and power supply guarantee are effectively supported.