4528 results about "Supply energy" 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 relates to the technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

A system and method for directing energy use within a facility comprising charging stations for charging a fleet of electric vehicles, using an energy optimisation engine. The energy optimisation engine receives energy supply characteristics and facility operational energy consumption characteristic of energy assets associated with the facility over a first time period, and electrical vehicle operational requirements for the electric vehicles during a second period; computes energy distribution instructions for the first time period and satisfying a designated energy condition corresponding at least in part with the energy supply characteristics; and directs operation of the charge stations or the energy assets in accordance with the energy distribution instructions.

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

The invention provides a rebalance dynamic adjustment method applied to a multi-source system and a service system, and relates to the technical field of smart energy, and the method comprises the steps: collecting a real-time operation data set of the multi-source energy system, covering energy supply, load demand and system operation state features, carrying out the dynamic supply and demand deviation feature extraction, and carrying out the dynamic adjustment of the dynamic supply and demand deviation features; a feature set containing supply, demand fluctuation and system stability deviation is generated, then a pre-trained multi-source collaborative prediction model is called, and a multi-source collaborative prediction result containing energy supply and load demand trend prediction features is generated based on the feature set; and dynamic adjustment strategy matching is carried out according to a multi-source collaborative prediction result and real-time operation data, and a strategy set of energy output adjustment, energy storage charging and discharging, load priority distribution and the like is obtained. And finally, executing an adjustment strategy and obtaining feedback data, optimizing parameters of the multi-source collaborative prediction model based on the adjustment strategy, triggering closed-loop adjustment operation, and realizing rebalance dynamic adjustment of the multi-source system.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

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

The invention discloses a pipe network health real-time monitoring system and an evaluation method. According to the invention, through real-time health monitoring and dynamic deduction capability, the flexibility and safety of energy supply are significantly improved. The healthy twin module constructs a digital mirror image of the full life cycle of energy equipment, solar hydrogen production efficiency fluctuation, hydrogen storage tank pressure change and fuel cell attenuation trend can be accurately tracked, and risk points such as electrolytic cell abnormal hydrogen evolution and proton exchange membrane aging can be recognized in advance. The all-weather transparent monitoring enables the system to dynamically adjust the ratio of hydrogen production to power generation, the fuel cell is automatically switched to supply power in cloudy and rainy days, continuous energy supply is guaranteed, and the system can generate a coping plan in advance by simulating extreme scenes such as sudden drop of photovoltaic output and hydrogen leakage, so that the system is convenient to use. The energy interruption risk caused by sudden equipment failure is greatly reduced, meanwhile, the service life of key components is prolonged, the maintenance cost of a distributed energy network is reduced, and meanwhile the overall energy efficiency utilization rate is also improved.

A vehicle provides an indication of status of an energy source that provides power for the vehicle. The vehicle includes an indicator, positioned on the vehicle at a location determined to be among a plurality of locations to most likely be intact during and after an accident. The indicator provides a predetermined indication that identifies status of an energy source that provides power for the vehicle. The indicator may provide a visual or acoustical indication or may provide a wireless message. The indication may provide an indication of one or more of make / model of the vehicle, battery charge level, type, danger condition, danger condition reoccurrence, danger condition suppression technique and a warning to not approach the vehicle.

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 present application relates to a multi-objective scheduling method and apparatus for an urban power grid, a computer device, a storage medium, and a computer program product. The method comprises: connecting hydrogen energy vehicles to an original urban power grid operation system to obtain an updated urban power grid operation system; determining an electrical-hydrogen coupling unit from the updated urban power grid operation system; establishing a corresponding mathematical model for each subunit in the electrical-hydrogen coupling unit; determining a first objective function on the basis of operation cost parameters of respective modules of the updated urban power grid operation system, determining a second objective function on the basis of energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and generating a multi-objective optimization and scheduling model on the basis of the first objective function and the second objective function; and solving the multi-objective optimization and scheduling model to obtain a target scheduling strategy.

The invention discloses an airport energy balance management method, system and device and a medium, and relates to the field of energy management. The method comprises the following steps: constructing an energy topological graph according to multi-source data; predicting energy load demands of the airport in different time periods according to the energy topological graph, determining the supply amount of renewable energy in different time periods according to the predicted illumination intensity and wind power, and determining a time-sharing energy supply and demand balance probability according to the energy load demands and the supply amount; determining a preliminary energy management plan according to the cost parameter, the time-sharing energy supply and demand balance probability and the equipment operation constraint, wherein the preliminary energy management plan comprises an energy storage system charging and discharging time sequence, a standby generator starting and stopping plan and a power grid electricity purchasing proportion; and executing the preliminary energy management plan through the digital twin of the airport to obtain first energy execution data, and adjusting the preliminary energy management plan through the difference between the first energy execution data and the second energy execution data at the current moment. By implementing the technical scheme provided by the invention, the energy cost is reduced.

The invention provides an industrial carbon footprint dynamic prediction method fusing multi-source data, which comprises the following steps: step 1, collecting multi-source data, core data including electricity consumption, converted standard coal consumption, key industrial process product yield and carbon emission, and three types of extended data including new energy consumption data, process upgrading data and external data; 2, performing dynamic modeling according to the multi-source data obtained in the step 1, including dynamic carbon emission factor calculation and a process-environment coupling equation; 3, an industrial carbon-energy-economy multi-objective optimization model of dynamic coupling new energy consumption is constructed, minimization of carbon emission and operation cost is taken as an objective function, and constraint conditions comprise production requirements, energy supply and policy compliance; and 4, performing optimization solution on the target function of the multi-target optimization model by adopting an NSGA-II algorithm, and outputting a carbon emission-cost tradeoff curve.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention relates to the technical field of smart home, and discloses a smart home energy supply and demand collaborative optimization system based on reinforcement learning. The system comprises an energy data acquisition module, a reinforcement learning decision module, a supply and demand collaborative optimization module, a real-time regulation and control execution module and a system performance monitoring module. The energy data acquisition module is used for acquiring various energy and environment data and processing exceptions; the reinforcement learning decision-making module constructs a state and action space and generates a regulation and control strategy in combination with a reward function; the supply and demand collaborative optimization module performs multi-objective optimization on energy distribution; the real-time regulation and control execution module regulates and controls the equipment according to the instruction priority; and the system performance monitoring module tracks the state and performs feedback optimization. In addition, an abnormal mode recognition module and a multi-agent cooperation module are further arranged and used for recognizing energy consumption abnormity and achieving equipment cooperation respectively. The system can effectively optimize household energy supply and demand, reduce cost, and improve energy utilization rate and user satisfaction.

The invention provides a flexible preparation system and method for sustainable aviation fuel, and the system comprises a green power supply module which is used for outputting fluctuating green electric energy; the process execution device is connected with the green power supply module and comprises five process modules, namely an air carbon capture module, a co-electrolysis module, a Fischer-Tropsch synthesis module, a hydrogen production module and a hydrogenation refining module; the data acquisition module is used for acquiring the temperature, the pressure, the liquid level, the raw material input flow and / or the product output flow of each process module in the process execution device; and the decision control module is used for predicting the future power supply condition of the green electricity based on the historical weather data of the green electricity location, adjusting the distribution of the power in each process module according to the future power supply condition, and adjusting the temperature, pressure and / or raw material input flow in each process module. According to the invention, the cost, the reliability, the operation and maintenance performance, the product quality and the green power utilization rate can be substantially improved at the same time, and the flexible and stable operation of the system under the green power condition is realized.

The invention discloses a power supply method and a power supply system for on-line monitoring equipment of a lightning arrester, and the method specifically comprises the steps: activating a multi-source energy module according to a preset priority order based on the operation state and environment parameters of the lightning arrester, and supplying power to the on-line monitoring equipment; based on the output characteristics of the multi-source energy module and the residual electric quantity of the on-line monitoring equipment, a constant voltage-constant current hybrid power supply strategy in a conventional mode or an anti-interference power supply strategy in an abnormal mode is executed on the on-line monitoring equipment; and processing the historical leakage current, environmental temperature and humidity and vibration spectrum characteristics of the lightning arrester acquired by the online monitoring equipment through the LSTM prediction model, predicting the energy supply trend of the online monitoring equipment in the preset future time, and dynamically adjusting the working parameters of the online monitoring equipment according to the energy supply trend. According to the invention, stable power supply of the lightning arrester on-line monitoring equipment in a complex environment is realized, and the power supply reliability and the equipment availability are improved.

The invention discloses a combined cooling heating and power Carnot battery energy storage system which comprises a two-stage heat pump cycle unit, a heat storage cycle unit, a Rankine cycle unit, a cold storage cycle unit and an LNG vaporization utilization unit. The two-stage heat pump circulation unit, the heat storage circulation unit, the cold storage circulation unit and the LNG vaporization utilization unit are operated in the power utilization trough period of the power grid; and the Rankine cycle unit is operated in the power utilization peak period of the power grid. According to the invention, the vaporization process of Liquefied Natural Gas (LNG) is applied to the Carnot battery energy storage system, and a novel efficient energy storage system capable of realizing combined supply of cooling, heating and power is constructed, so that the purposes of relieving the peak load of a power grid, guaranteeing the stability of energy supply and the like are achieved.

The invention relates to the technical field of energy regulation and control, in particular to an inter-building energy regulation and control method based on the Internet of Things. The method comprises the following steps: predicting and judging whether the actual energy consumption of a building is abnormal or not; when the actual energy consumption is predicted and judged to be abnormal and the maximum energy consumption deviation value is positive, external climate data is associated to determine an execution mode of power dispatching; when the maximum energy consumption deviation value is negative, obtaining an identification type result of the current time period; and determining whether to execute power dispatching control based on the identification type result. By monitoring the energy consumption deviation value of each building in real time, the energy consumption deviation condition of the building in the to-be-monitored range is determined, energy consumption abnormity is found in time, power distribution is optimized, energy waste is reduced, external climate data and building types are associated to dynamically adjust a power dispatching strategy, the energy consumption requirement of the building is judged more accurately, and the energy consumption of the building is improved. Therefore, different environment and energy scheduling requirements can be met, power distribution can be rapidly adjusted, and the stability of energy supply can be ensured.

The invention discloses a balance scheduling system for energy management and a balance scheduling method thereof, and relates to the technical field of energy management scheduling, and the balance scheduling system comprises an energy data acquisition module, an energy demand trend analysis module, an energy supply and demand dynamic prediction module, an energy balance scheduling decision module, a scheduling execution module and a performance monitoring module. After the energy supply and demand dynamic prediction module integrates real-time external factor data of the energy data acquisition module and a long-term historical rule of the energy demand trend analysis module, a reference prediction baseline based on a long-term trend is generated, and then a dynamic correction value is superposed to respond to short-term disturbance. And meanwhile, through an error traceability mechanism for separating a trend baseline from external correction, the module performance can be optimized in a targeted manner, if an error is derived from long-term trend deviation, a trend analysis model is calibrated, and if external data is lagged, priority adjustment of a data acquisition module is triggered, so that system self-diagnosis and prediction precision closed-loop improvement are realized.

The invention relates to a virtual power plant load end resource optimization scheduling method and device and a medium, and is applied to the field of data processing, and the method comprises the steps: obtaining historical total electricity consumption corresponding to a plurality of historical periods of an energy supply range of a virtual power plant; according to the historical total electricity consumption corresponding to a plurality of historical periods of the energy supply range of the virtual power plant, estimating the first total electricity consumption required by the next period; for each region, according to the probability distribution model corresponding to the region and the first total electricity consumption, estimating the estimated electricity consumption of the maximum probability corresponding to the region; and performing power scheduling according to the estimated power consumption of the maximum probability corresponding to each region. According to the invention, after overall estimation, each region can be finely estimated based on the first total power consumption after overall estimation, overall data are considered, estimation data of each region are also considered, and the accuracy and reliability of power estimation are effectively improved.

The invention discloses a distributed energy system source load coordinated optimization method based on a quasi-potential game method, and the method comprises the steps: constructing a distributed energy system model, inputting system parameters, and predicting renewable energy power generation and initial load demands. In a source side optimization stage, a leader layer potential function of a quasi-potential game is established by taking minimization of source side cost # imgabs0 # as a target, and an initial power generation plan is generated by comprehensively considering economical efficiency and carbon emission constraints; and then, based on a scheduling result, calculating a carbon potential epsilon t of each node and a dynamic carbon emission factor # imgabs1 # of each stage, optimizing a load side response based on an LCDR scheme, constructing a local potential function of a follower layer, reflecting a relationship between a user profit maximization target and carbon emission, and adjusting user behaviors through a distributed decision. And the updated load demand is fed back to the source side, and the source side optimizes the output plan of each unit based on the updated load, so that an iterative process of source side potential function optimization-load side equilibrium response is formed, and an optimal scheduling strategy and scheduling result of the energy supply side are obtained. According to the framework, the global consistency requirement of a traditional potential game is relaxed, independent optimization of source-load two sides under the guidance of respective potential functions is allowed, and a Nash equilibrium state is finally achieved only by ensuring monotonous convergence of total potential energy of a system in an iteration process. According to the method, the convergence advantage of the potential game is reserved, the method is also adapted to the characteristics of a source-load heterogeneous decision subject, efficient consumption of renewable energy and collaborative optimization of carbon emission are realized through bidirectional transmission of the carbon potential signal, and the overall efficiency of the system is remarkably improved.

The invention relates to a virtual power plant scheduling method considering source-load coupling and multi-agent game. The method comprises the following steps: establishing a multi-temporal-spatial-scale model of a multi-energy system in a virtual power plant; the uncertainty of the source load is quantified; on the basis of quantification of source load uncertainty and a multi-temporal-spatial-scale model, a multi-subject scheduling model under a master-slave game framework is constructed, and in the multi-subject scheduling model, a VPP operator serves as a leader, and the market is regulated and controlled through dynamic electricity price and heat price; an energy supply aggregator, a user side aggregator and a carbon processing aggregator are used as followers, unit output, load adjustment and carbon processing strategies are optimized respectively, the two parties perform multi-stage dynamic gaming to form balance, and the user side aggregator comprises CSRLA and EVA; and solving the multi-subject scheduling model to obtain a virtual power plant scheduling optimization strategy. Compared with the prior art, the method has the advantages of realizing dynamic balance of interests of multiple parties, privacy protection and the like.

The invention belongs to the technical field of anti-explosion electric cabinets, and particularly discloses an anti-explosion electric cabinet for new energy supply, which comprises an anti-explosion electric cabinet and an active cooling and fire extinguishing system, and is characterized in that the active cooling and fire extinguishing system comprises a centralized cooling and fire extinguishing unit, a confluence controller, a control module, a plurality of pressure pipelines and a plurality of temperature feedback devices; a plurality of working parts in the explosion-proof electric cabinet are respectively provided with a monitoring unit; the explosion-proof electric cabinet has the advantages that the monitoring unit is arranged in the explosion-proof electric cabinet, real-time monitoring of the environment of the electric cabinet is achieved, and especially under the abnormal conditions of high temperature and the like, the active cooling and fire extinguishing system can rapidly respond, automatically increase the spraying amount of the fire extinguishing material and improve the fire extinguishing effect. The system integrates a passive pressure relief structure, an active explosion venting system, an inerting explosion suppression system and automatic water mist spraying, multi-level safety protection is provided, and safety and stability of electric power distribution facilities, especially an electric cabinet and a distribution box in a new energy grid-connected system in a complex environment are ensured.

The invention discloses an energy-saving air pollution monitoring device based on environmental protection, which comprises a load-bearing energy supply mechanism, the load-bearing energy supply mechanism comprises a load-bearing platform, the top of the load-bearing platform is provided with a monitoring protection mechanism, the top of the load-bearing platform is fixedly connected with a linkage top frame, the top of the linkage top frame is provided with a circular ring opening, and the top of the linkage top frame is fixedly connected with the monitoring protection mechanism. The invention relates to the technical field of air pollution monitoring. According to the energy-saving air pollution monitoring device based on environmental protection, the load-bearing energy supply mechanism and the monitoring protection mechanism are combined for use, and the solar panel can be used for auxiliary power supply through the arrangement of the two mechanisms, so that the energy-saving performance is achieved; and meanwhile, the driven motor can drive the air monitoring sensor to ascend and descend to monitor air at different heights, meanwhile, the isolation net barrel and the scraper arc plate can rotate mutually through linkage between the structures, the air monitoring sensor is protected, and the influence of external factors on the air monitoring sensor is reduced.

The invention discloses a dynamic resource allocation system and method for smart travel. The system comprises a multi-source data acquisition module, a data fusion module, a prediction module, a framework construction module, an algorithm optimization module, a feedback module and a resource allocation module. According to the method, environment data, energy consumption data, renewable energy supply data and tourist flow data in a plurality of sub-regions of the tourist attraction are collected, and fusion and deep learning prediction are carried out on the data, so that future energy consumption, energy supply and flow data are obtained. And a reinforcement learning scheduling framework is constructed, and intelligent scheduling of the charging pile, the electric sightseeing vehicle, the lighting equipment and the monitoring equipment is realized. And carrying out global optimization on the resource scheduling scheme by adopting a speed pause particle swarm optimization algorithm so as to output a resource allocation scheme with optimal energy consumption. The scheduling scheme is further optimized based on user feedback data, and the dynamic adaptability of resource configuration is ensured. According to the invention, the energy utilization efficiency in the smart travel scene is improved, and the operation cost is reduced.

The invention discloses a fuel cell coupled flow battery energy supply system and an operation control method thereof. The energy supply system comprises a basic energy supply unit, an energy storage peak regulation unit, a renewable energy power generation unit, an electrolytic hydrogen production unit, a hydrogen storage unit, a first-stage waste heat recycling unit, a second-stage waste heat recycling unit and a heat storage unit. The basic energy supply unit is used for providing a basic power load of the target building group; the energy storage peak regulation unit is used for providing fluctuation power load of the target building group; the first-stage waste heat recycling unit receives first-stage waste heat generated by power generation of the fuel cell and is used for indoor heating or indoor refrigeration. And the second-stage waste heat recycling unit directly receives the first-stage waste heat or second-stage waste heat from the first-stage waste heat recycling unit and is used for preheating the electrolyzed water and / or the hydrogen storage unit. By arranging the first-stage waste heat recycling unit and the second-stage waste heat recycling unit, waste heat is utilized in a multi-stage mode according to requirements, and the waste heat utilization rate is high.