107 results about "Smart microgrid" patented technology

The invention relates to the technical field of power grid self-healing control, and discloses a power grid self-healing control intelligent optimization method and system, and the method comprises the steps: obtaining the topological structure information of a power grid, dividing n power grid toughness evaluation regions according to the topological structure information of the power grid, calculating a hierarchical toughness evaluation index, and forming a comprehensive toughness evaluation index according to the hierarchical toughness evaluation index; a self-healing control intelligent optimization model is constructed based on the comprehensive toughness evaluation indexes, a self-healing control optimal solution is obtained by solving the self-healing control intelligent optimization model, the self-healing control optimal solution is dynamically calibrated, and a self-healing control strategy is generated; according to the method, the topological structure, the historical fault data and the real-time operation state of the power grid are comprehensively considered, a layered evaluation and dynamic calibration combined mode is adopted, the intelligent level and adaptability of self-healing control of the intelligent micro-grid are improved, the toughness and recovery capacity of the micro-grid for coping with complex faults are enhanced, and safe, reliable and efficient operation of the power grid is guaranteed.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

The invention relates to a multi-energy collaborative intelligent micro-grid control method, device and equipment and a medium. According to the method, a micro-grid frequency signal is collected in real time, the change rate of the micro-grid frequency signal is calculated, the inertia requirement of a system is accurately evaluated, the maximum virtual inertia capable of being provided by each unit is dynamically calculated in combination with the physical characteristics and operation constraints of each energy unit, and the system requirement and the unit capacity are dynamically matched through grey correlation analysis to generate an optimal distribution scheme. And then the special control models of all the units are driven to generate differentiated power regulation instructions, and finally power support is cooperatively executed through the power electronic converter, so that accurate matching and efficient cooperation of multi-energy virtual inertia capability are realized, the frequency stability of a high-proportion new energy power grid is remarkably enhanced, and the system performance is improved. The problems of resource allocation mismatch, poor control adaptability and the like in a traditional method are effectively avoided, and the dynamic safety of the system is guaranteed under the complex operation working condition.

The invention provides an intelligent micro-grid panoramic sensing system based on digital twinning, which relates to the technical field of micro-grid panoramic sensing and comprises a data acquisition layer, a data processing layer, a digital twinning model layer, an application service layer and a user interaction layer. A sensor is divided into a core layer, key equipment / nodes, an edge layer and general equipment / non-key nodes, a self-adaptive sampling frequency adjustment mechanism is introduced, the sampling frequency is dynamically adjusted according to the running state of the micro-grid, the frequency is reduced when the micro-grid is stable, and the frequency is improved when the micro-grid is abnormal, so that the sampling efficiency is improved. The problems of data redundancy and insufficient real-time performance of a traditional sensor network are solved, high-precision collection of key data, efficiency optimization of non-key data, collaborative data processing of edge calculation and federated learning are considered, a lightweight deep learning model is deployed at edge calculation nodes, and real-time data preprocessing, filtering, denoising and feature extraction are achieved.

The invention provides a multi-microgrid load frequency control method based on dynamic event triggering, which can be applied to the technical field of intelligent microgrids. The method comprises the steps that a local sampling error of the micro-grid system is acquired, the local sampling error is obtained based on a global neighborhood error and a local neighborhood error of the micro-grid system, and the local neighborhood error is used for representing a state difference between the micro-grid system and a neighbor micro-grid system at a sampling moment; under the condition that the local sampling error meets a dynamic event triggering rule, updating an initial weight matrix of the action-evaluation neural network based on the local sampling error to obtain a target action-evaluation neural network; and determining a load frequency control strategy of the micro-grid system based on the system state of the micro-grid system at the sampling time and the target action-evaluation neural network. The invention further provides a multi-microgrid load frequency control device based on dynamic event triggering.

The invention discloses a fault identification and network efficiency improvement method for an intelligent micro-grid, particularly relates to the technical field of cross-domain cooperative avoidance, and is used for solving a resonance frequency band problem caused by coupling of an existing SDN flow table updating strategy and micro-grid power fluctuation. The method comprises the following steps: acquiring micro-grid power fluctuation data and SDN flow table updating parameters in real time, and performing timestamp calibration; extracting a power fluctuation frequency domain characteristic and a flow table updating time domain correlation coefficient; fusing frequency domain integration and time domain convolution through a cross-domain coupling model to generate a dynamic resonance risk index; predicting a future fluctuation main frequency band based on the phase change and generating a constraint condition in combination with a historical avoidance frequency band; dynamically generating a frequency avoidance weight according to the real-time network load and the frequency band effectiveness; triggering the period adjustment of the asymmetric flow table to realize the frequency domain avoidance of the update frequency and the fluctuation main frequency band; resonance risk identification and active avoidance of power and communication cross-domain data fusion are realized, and the transient stability and the network resource utilization rate of the micro-grid are remarkably improved.

The invention discloses a power electronic distributed cooperative control method and system for an intelligent micro-grid. The method comprises the following steps: constructing a distributed communication architecture based on adaptive topology; based on a distributed communication architecture, executing a fusion cooperative control algorithm: dynamically coordinating a power distribution proportion based on a distributed average consistency algorithm, and introducing a dynamic weight mechanism; in combination with an LSTM prediction model, historical and real-time data are utilized to predict the output fluctuation and load change trend of the distributed power supply, and a control strategy is optimized in advance; setting an intelligent abnormal response mechanism; and based on the intelligent abnormal response mechanism, carrying out dynamic scheduling by adopting a flexible power regulation method. In the aspect of flexible power adjustment, fuzzy logic control is organically combined with maximum power point tracking and PWM pulse width modulation, and dynamic scheduling is performed according to the real-time operation state of the micro-grid, so that accurate and efficient control of the output power of the inverter is realized, and the overall performance and stability of the micro-grid are greatly improved.

The invention relates to an intelligent micro-grid frequency modulation and peak regulation and electric energy distribution method based on photovoltaic power generation, and relates to the field of intelligent micro-grids, and the method comprises the steps: carrying out the analysis to obtain a predicted fluctuation degree and an energy supply deviation amplitude, combining the predicted fluctuation degree with a historical fluctuation degree, and carrying out the calculation to obtain a fluctuation degree; a frequency modulation coefficient and a peak regulation distribution coefficient are obtained according to the fluctuation degree and energy supply deviation amplitude configuration; and randomly generating an electric energy distribution scheme, carrying out peak regulation and frequency modulation analysis according to the predicted photovoltaic power generation power sequence and the predicted demand load power sequence, carrying out calculation according to a frequency modulation coefficient and a peak regulation distribution coefficient to obtain distribution fitness, carrying out iterative optimization evaluation, and obtaining an optimal distribution scheme to carry out electric energy distribution. According to the invention, the technical problems that the traditional method cannot efficiently cope with the conditions of load fluctuation and insufficient photovoltaic power generation, it is difficult to ensure that the energy storage unit can respond in time and provide stable and accurate electric energy support, so that the operation fluctuation of the micro-grid in a high-load period is large, and the frequency modulation and peak regulation difficulty is high can be solved.

The invention relates to the technical field of intelligent power grids, and discloses an intelligent micro-grid energy storage optimization scheduling system, which comprises a linear programming module used for converting a regulation and control process of energy storage equipment into a linear programming problem, dividing the regulation and control process of the energy storage equipment into N time points, and regulating and controlling the time points in sequence; the target setting module is used for inputting a target function regulated and controlled by the energy storage equipment; the condition constraint module is used for setting constraint conditions for regulation and control of the energy storage equipment; the solution configuration module is used for configuring parameters of a solver and inputting the target function, the decision variable and the constraint condition into the solver for solution; the instruction issuing module is used for generating a regulation and control strategy according to the solving result and issuing the regulation and control strategy to the energy storage equipment for execution; the condition constraint module sets continuous charging and discharging times of the energy storage equipment, and linear processing is carried out on charging and discharging states by introducing a large M variable and a constraint condition; the problems that an existing energy storage scheduling system is complex in operation, slow and lack of conciseness and flexibility are solved.

The invention relates to the technical field of energy internet and intelligent micro-grids, and discloses a dynamic energy-carbon coupling modeling and collaborative optimization method for a wind and light storage and charging system, which comprises the following steps of: a, acquiring operation data of the wind and light storage and charging system; b, calculating to obtain the equipment-level dynamic real-time carbon potential of the output port of the energy conversion equipment; step c, updating the virtual total carbon load by using the state data of the energy storage unit and the real-time carbon potential; step d, constructing and solving a rolling horizon optimization problem to obtain an optimal control strategy covering the prediction horizon; and step e, issuing and executing a control instruction, and repeating the step a to the step e. According to the method, the physical working condition and the real efficiency of the equipment are dynamically mapped, the carbon emission intensity and the equipment operation state are associated in real time, and then static emission factors are replaced, so that the influence of the equipment on the carbon flow is accurately and dynamically reflected, and the precision and the real-time performance of microscopic carbon accounting are improved.

The invention relates to the technical field of electric vehicle charging control, discloses an electric vehicle intelligent charging and discharging method and system based on robust optimization, and aims to solve the problems that a traditional deterministic optimization method is difficult to deal with photovoltaic output, load requirements and electricity price fluctuation and does not consider the service life of a battery and economical efficiency. The method comprises the following steps of: establishing a deterministic charging optimization model which takes minimization of power grid electricity purchasing cost and battery depreciation cost as a target and comprises constraints such as battery charge state dynamics and an operation mode; on the basis of the information gap decision theory robust optimization model, load, photovoltaic and electricity price robust boundaries are defined, and a worst scene is constructed; solving to obtain a Pareto optimal solution set by using a multi-target particle swarm algorithm; and selecting the solution with the maximum minimum satisfaction degree as a final strategy through the fuzzy satisfaction degree. Economical, reliable and battery-friendly charging and discharging scheduling can be realized in an uncertain environment, the method is adaptive to an intelligent micro-grid and a V2G scene, and the comprehensive operation performance is improved.

The invention discloses an intelligent micro-grid multi-mode collaborative optimization method and system, and belongs to the field of intelligent power grids. The method comprises the following steps: collecting electrical data of a common connection point and a distributed unit, and quantifying a harmonic responsibility coefficient by using an improved complex independent component analysis and partial coherence method; constructing a dynamic electricity price game model, and solving an optimal excitation coefficient through a non-cooperative game; and building a multi-objective optimization model, correcting model prediction control parameters by means of a digital twinborn training reinforcement learning agent, and outputting a scheduling instruction in a rolling optimization manner to realize multi-mode switching. The system comprises a data acquisition layer, a regional collaboration layer, a cloud computing and digital twinning layer and an intelligent optimization core layer. According to the method, the electric energy quality and economic dispatching are fused, and the optimization intelligence and safety of the micro-grid are improved.

The invention discloses a multi-node data redundancy synchronization method for a user-level intelligent micro-grid monitoring system, and the method comprises the steps: deploying a file library server process on each file library server of an embedded file management system, and setting a one-main multi-standby architecture; a host concurrently monitors a data request message from a monitoring system client in real time, a data request service is executed in a local file database, and a file library server process adds the request message into a data synchronization buffer area; and the host file library server process sends the request message to all standby file library servers in real time. The lightweight embedded file management system and a universal data access interface are designed to realize data redundancy synchronization of a power grid model, a topological structure, alarm information, historical operation data and statistical data, so that the integrity and consistency of the data are guaranteed, and the operation and maintenance cost of the system is reduced; and the safe, stable and reliable operation level of the household-level comprehensive energy micro-grid system is improved.

The invention discloses a large-capacity easily-retractable optical energy storage container power station which comprises an optical energy storage container, and an energy storage system and a photovoltaic power generation system are arranged in the optical energy storage container. The photovoltaic power generation system comprises a folding and unfolding device, a track, a photovoltaic folding support, a photovoltaic assembly and a photovoltaic inverter, the track is arranged on the outer side of the light storage container, the photovoltaic folding support is arranged on the track in a sliding mode, the photovoltaic assembly is arranged on the photovoltaic folding support, and the photovoltaic inverter is arranged on the photovoltaic assembly. The photovoltaic assembly is connected with the energy storage system and the EMS energy management system through the photovoltaic inverter, and the photovoltaic folding support is connected with the folding and unfolding device. Based on the photovoltaic power generation technology, the energy storage technology, the container manufacturing technology and the intelligent micro-grid technology, the system is an energy-saving configuration mode for energy consumption of the field living quarters of the drilling crew, reduces the initial asset investment, is energy-saving and environment-friendly, and meets the requirements of oil field users for energy consumption and easy relocation of the living quarters.

The invention discloses an intelligent micro-grid network attack detection method and system based on a block chain, wavelet transform and a support vector machine, a medium and a processor, and relates to the field of power grid network attack detection. The method aims at solving the problems that normal and attack exception are difficult to distinguish, the missing and false detection rate is high, and data are tampered easily in a traditional method. The method comprises the following steps: collecting and preprocessing DC micro-grid data; decomposing into high and low frequency components through wavelet transform, and extracting amplitude, frequency and energy related characteristic parameters; constructing and training a support vector machine model for real-time attack detection; and after detecting full-process data hash processing, uploading the data to the block chain, and storing evidence based on a PoA consensus mechanism. According to the method, attack features are accurately captured through wavelet transformation, high-accuracy classification is realized in combination with the SVM, the block chain guarantees data credibility, a micro-grid dynamic scene can be quickly responded, and the network security protection capability is improved.

The invention provides an intelligent micro-grid control method based on multi-source collaborative optimization, and belongs to the technical field of micro-grid control, and the method comprises the steps: collecting the operation state data of a diesel generator set in real time, including the output power, the fuel consumption rate and the operation temperature of the diesel generator set, and the SOC and the charging and discharging power of an energy storage system; output voltage, current and illumination intensity of the photovoltaic array, and real-time power data of a load end; obtaining the total load demand and the schedulable energy storage capacity of the current micro-grid system; calculating an optimal generator set operation interval according to the load demand and the energy storage scheduling capability; and the output power of the generator set is dynamically adjusted, so that the generator set operates in the optimal fuel efficiency interval. Through real-time sensing and dynamic adjustment, the generator set always operates in the optimal fuel efficiency interval, fuel consumption of unit electric energy is remarkably reduced, the overall energy utilization efficiency is improved, and carbon emission is reduced.

This invention discloses a multi-source collaborative optimization control method for smart microgrids, relating to the field of energy technology. The method includes: connecting to the user side to predict the power supply demand of the target microgrid in a preset time zone; acquiring a hybrid energy supply module containing multiple distributed power sources; collecting historical power supply logs of the distributed power sources and generating relevant time sequences; performing energy curtailment rate analysis based on this to generate indicators and decisions; optimizing energy storage units after determining that the indicators meet the standards; and then performing multi-source collaborative power supply control according to preset demand. This method solves the technical problems of existing new energy power generation uncertainties that are difficult to accurately predict, and unreasonable energy storage module configurations that lead to significant energy losses during power generation, storage, and distribution, resulting in low energy utilization efficiency and unstable power supply quality. By optimizing and integrating new energy power generation, energy storage modules, and energy distribution in the microgrid, it achieves the technical effect of reducing energy losses and ensuring efficient and high-quality power supply to the microgrid.

The present application relates to the technical field of micro-grid control, and specifically provides a kind of intelligent micro-grid control method based on edge computing, comprising: according to control instruction, generate the calculation task for simulation;According to cost data, through the computing power scheduling method, obtain processing strategy;In the case where local edge convergent node executes calculation task, based on micro-grid holographic topology, through edge digital twin model, the control instruction is simulated, and performance index is obtained;In the case where target edge convergent node executes calculation task, send calculation task to target edge convergent node, and receive the performance index returned by target edge convergent node;In the case where performance index meets preset safety constraint set, the circuit device in micro-grid is controlled by control instruction, to solve the problem that centralized network communication architecture leads to slow control response and poor stability in the related art.

The invention relates to the technical field of intelligent micro-grids, and discloses an intelligent micro-grid selling integrated transaction system based on multi-energy complementation, which comprises an energy transaction master control module, a multi-source complementation optimization module, a dynamic transaction matching module and the like. The multi-source complementary optimization module generates an initial transaction strategy according to day-ahead, day-intra and real-time stages; the load prediction module collects electrical load, adjustable response capability and market price data. The risk assessment module generates a risk level quantitative index by combining a historical default record and a power grid operation state; the energy transaction main control module generates an optimized transaction instruction through a multi-target collaborative decision algorithm; the dynamic transaction matching module adjusts a supply and demand matching rule and feeds back an execution state; the system is further provided with a fault-tolerant correction module, a transaction strategy verification module and the like, the microgrid energy transaction efficiency and the risk management and control capability can be improved, and the system is suitable for intelligent microgrid selling integrated transaction.

The present invention belongs to the field of power system security technology and discloses a load frequency control system attack detection method based on reinforcement learning. The method aims to enhance the power system's ability to identify and defend against complex network attacks, and address the shortcomings of traditional detection methods in attack sample generation, unknown attack identification, and system adaptability. The present invention constructs an attack agent based on a Markov decision process and uses an improved reinforcement learning algorithm to generate highly concealed adversarial samples. It designs a dual-modal detection architecture that integrates LSTM supervised learning and autoencoder unsupervised learning, and introduces an adaptive weight fusion mechanism to achieve attack type identification and anomaly detection. Incremental learning is combined with a dynamic parameter adjustment mechanism to enable the detection model to have continuous learning and evolution capabilities. This method can be applied to power grid dispatching centers or smart microgrids to achieve real-time monitoring and attack defense of load frequency control systems, significantly improving the operational safety and robustness of power systems.

This invention relates to the fields of new energy and smart grid technology, and discloses a single-point grid-connected distributed photovoltaic system based on energy storage and a smart microgrid. The system includes: a distributed photovoltaic cluster, an electrochemical energy storage system, a smart microgrid management system, and a single public grid connection point. By constructing a "single-point grid connection, unified control" architecture, this invention integrates the originally dispersed, multi-point grid-connected distributed photovoltaic clusters into a single entity. Through a unified smart microgrid management system for coordinated control, it achieves centralized management and unified scheduling of large-scale distributed power sources, solving the problems of "difficult grid scheduling and 'invisibility and uncontrollability' caused by dispersed multi-point grid connection." It transforms disordered access into orderly access, greatly simplifying the complexity of grid scheduling and management, and improving the grid's capacity to accept high-penetration distributed photovoltaic power and its operational safety.

The invention discloses an energy management method and system for an intelligent micro-grid, and belongs to the technical field of power electronics, and the method comprises the steps: carrying out the multi-source data collection of a running micro-grid, and obtaining the operation state data of the micro-grid; on the basis of the operation state data, predicting the source load power of the micro-grid in a future time period to obtain a probability distribution characteristic value of the source load power; according to the probability distribution characteristic value and the operation state data, through a preset reward function, energy management optimization is carried out on interaction of a generator set, an energy storage system and a power grid of the micro-grid, and an energy scheduling decision is constructed; and sending an energy regulation and control instruction to the micro-grid according to the energy scheduling decision. Therefore, by implementing the method and the device, the problem that the reliability of energy scheduling and management is insufficient due to the fact that the influence of uncertain factors such as energy storage equipment and environment change on energy management in the operation process of the micro-grid is not considered in the prior art can be solved.

The invention discloses a V2G-microgrid cooperative scheduling system and method based on an improved particle swarm algorithm, and belongs to the technical field of intelligent microgrid and electric vehicle energy management. In order to solve the problems of slow response of fixed energy storage, low utilization rate of V2G mobile energy storage, uncontrollable battery loss and the like, a V2G electric vehicle and a fixed storage battery are constructed into a hybrid energy storage system, and an improved particle swarm optimization algorithm is adopted for optimization. According to the method, the frequency modulation precision, the operation economy and the battery life are synchronously optimized in a fitness function through a double-layer architecture of optimizing the total power through an upper layer and dynamically distributing instructions through a lower layer. Finally, the V2G cluster responds to high-frequency power fluctuation in a millisecond level, and a basic load is supported in a fixed energy storage minute level, so that advantage complementation is formed. According to the invention, the frequency modulation response speed and the renewable energy consumption capability of the system are effectively improved, the operation cost is reduced, and the service life of the battery is prolonged.

The present invention relates to a power management method for a frequency-modulated and peak-shaved energy storage type intelligent microgrid, and relates to the technical field of power grid connection, including: obtaining the electricity consumption demands of users at the first node, the second node, up to the Nth node; obtaining the time series information of the pre-allocated output power of the large power grid in the first substation area and inputting it into the power grid frequency trend prediction model to output the first power grid frequency trend prediction result; when deviating from the reference power grid frequency, configuring the grid-connected output power of the energy storage type intelligent microgrid at the first node, the grid-connected output power of the energy storage type intelligent microgrid at the second node, up to the grid-connected output power of the energy storage type intelligent microgrid at the Mth node; obtaining the second power grid frequency trend prediction result; when not deviating from the reference power grid frequency, performing power grid connection management. It solves the technical problem in the prior art that due to using real-time monitoring of the power grid frequency for frequency modulation and peak shaving control, there is a large time delay and it is impossible to respond to the change of the power grid frequency in a timely manner.

The invention provides an energy-saving system applying green energy and an intelligent micro-grid, and relates to a data processing technology and various energy equipment, and the energy equipment at least comprises green energy equipment, commercial power energy equipment and energy recovery equipment of special equipment. The various energy devices are connected with the special device, the green energy device, the mains supply energy device and the energy recycling device of the special device are used for supplying power to the special device, and the energy recycling device of the special device is used for recycling energy of the special device. The control equipment is used for acquiring the working state of the energy equipment, controlling the energy recovery equipment to perform energy recovery based on the working state, and controlling power supply to the special equipment based on the states of the green energy equipment, the mains supply energy equipment and the energy recovery equipment; or the electric energy of the green energy equipment and the recycled energy equipment is connected to the mains supply energy equipment in a grid mode, green energy is efficiently utilized, energy management is optimized, and energy conservation and emission reduction of special equipment are achieved.

This invention relates to the field of energy control, specifically to a multi-energy collaborative intelligent microgrid control system. It includes a management center communicatively connected to a grid acquisition module, a grid analysis module, and a grid control module. The management center acquires historical energy data and historical electricity consumption data, and, combining these data, establishes an energy grid model. It then analyzes the energy grid to obtain energy region data. Based on the energy observation cycle and energy region data, it establishes a two-dimensional grid coordinate system and constructs a time-series prediction model to obtain predicted grid data. Using the predicted grid data, it constructs a predicted grid matrix and uses this matrix to regulate energy in the power region, obtaining pre-regulation strategies and optimal energy distribution points. Finally, it analyzes the pre-regulation strategies based on real-time electricity consumption and real-time power generation to obtain actual regulation strategies. This invention enhances the stability and flexibility of the microgrid.

The invention provides a new energy, fresh air shutter door and window and intelligent micro-grid cloud control system, which comprises a frame body of the door and window, a cabin body, a shutter and a fresh air mechanism, the cabin body is fixed at the top position in the frame body, the fresh air mechanism is arranged in the cabin body, a cabin body air outlet is formed in the indoor side of the cabin body, and a return air inlet is formed in the side surface of the cabin body air outlet; a cabin body air inlet is formed in the bottom of the outdoor side, and the shutters are rotatably arranged on two side frames of the frame body; the fresh air mechanism comprises a heater, a filtering device and a fan, the heater is arranged at the position of the air inlet of the cabin body, the filtering device is fixed above the heater, the fan is arranged above the filtering device, and the fan faces the position of the air outlet of the cabin body. Meanwhile, multiple functions of sun shading, power generation, dimming and the like are achieved, air entering a house is sterilized and filtered, the air quality of the indoor environment is improved, the comfort level of a user is increased, and the body health of the user is protected.

The present invention discloses an energy dispatching method and system for a smart microgrid, and relates to the field of electric energy dispatching. The energy dispatching method includes the following steps: judging whether an energy storage system is grid-connected and then controlling the working state of a PCS; if the energy storage system is grid-connected, the PCS operates in a P‑Q mode; if the energy storage system is off-grid, the PCS operates in a V / F mode, and after the diesel generator is started, the PCS switches from the V / F mode to the P‑Q mode; obtaining the working state of the energy storage system and the online state of the smart microgrid and the grid, the working state of the energy storage system includes shutdown, standby, discharge and charging; the online state of the smart microgrid and the grid is a grid-connected state or an off-grid state; adjusting the sending parameters of the EMS based on the working state of the energy storage system and the online state of the smart microgrid and the grid, and realizing energy dispatching based on the adjustment of the sending parameters of the EMS. The present invention can improve the utilization efficiency of photovoltaic power and ensure normal power supply in an off-grid situation by designing energy dispatching for the grid, energy storage, photovoltaic power generation, and diesel generation.

The invention provides an intelligent micro-grid control method, device and equipment for an oil field mechanical extraction device, and belongs to the technical field of micro-grids of oil mechanical extraction systems. The method comprises the following steps: acquiring weather prediction information, generating a wind-light output prediction curve according to the weather prediction information, predicting user power consumption behaviors according to power consumption data over the years, and generating a load prediction curve; generating a day-ahead scheduling plan according to the wind and light output prediction curve and the load prediction curve; and determining a power difference according to the wind-light output in the wind-light output prediction curve and the load output in the load prediction curve, and making an energy storage system scheduling plan based on the power difference, so that the natural complementarity of carbon emission through the wind-light output and the energy transfer characteristic of the energy storage device are reduced, and the power supply reliability is improved. The energy storage system adopts a hybrid energy storage mode of a storage battery and a super capacitor, the service life of the energy storage system is prolonged, further through flexible load scheduling, source-load interaction is realized, and the capacity demand of the storage battery is reduced.