2049 results about "Clean energy" patented technology

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

According to the optical storage direct flexible system scheduling method based on multi-objective optimization and an adaptive scheduling strategy, monitoring devices are installed on a photovoltaic array, an energy storage unit and a load side, a data sensing network covering the whole link of'source-storage-load-network 'is constructed, and key data are collected in real time. A multi-objective optimization model with maximization of economical efficiency, reliability and clean energy consumption rate as objectives is established, and a hybrid optimization mechanism of a genetic algorithm and particle swarm optimization is adopted to generate a day-ahead scheduling reference scheme. And designing an adaptive scheduling algorithm and a dynamic parameter adjustment mechanism based on fuzzy logic, and combining a rolling optimization window to realize real-time optical storage coordination control and power real-time balance. According to the invention, the operation efficiency, stability and flexibility of the optical storage direct-flexible system are effectively improved, the capability of coping with emergencies is enhanced, and the optimization of the overall performance of the system is realized.

The invention relates to the technical field of energy storage systems, in particular to a source network load storage coordination control method for multi-configuration network type energy storage coordination operation. According to the technical scheme, the source-network-load-storage coordinated control method for multi-construction-network-type energy storage coordinated operation comprises the following steps: generating a new energy output and load change trend through a dynamic prediction model based on historical power data, real-time load requirements and meteorological information; optimization is carried out to obtain a power generation plan containing a clean energy consumption target and economical efficiency constraints; decomposing the power generation plan into a photovoltaic power adjusting instruction, an energy storage charging and discharging instruction and a load control instruction according to the power grid safety priority, wherein the emergency control instruction is executed prior to the economic dispatching instruction; and controlling parallel operation of a plurality of pieces of networking type energy storage equipment, and realizing multi-machine power distribution and circulating current suppression through a virtual synchronous control technology. And through layered optimization scheduling and multi-machine cooperative control, the new energy consumption capability is remarkably improved, and the phenomenon of abandoning light and wind is reduced.

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 provides a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system, and the method comprises the steps: data collection and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, continuous learning and knowledge accumulation, etc. According to the method, through dynamic data driving, intelligent difference analysis and self-adaptive optimization, conversion of a water conservancy system model from static passive to dynamic active is realized. Compared with the prior art, the method has accuracy, real-time performance, economical efficiency and sustainability, provides a landing technical path for intelligent upgrading of a water conservancy system, and has wide application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production and the like.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

The invention relates to the related technical field of clean energy system optimization, and discloses a space-time Transform-based photovoltaic array shadow shielding optimization method and system, and a space-time Transform-based shielding modeling method, which can capture time change and space correlation at the same time, improve the prediction capability of short-time dynamic shielding (such as cloud layer change), and improve the prediction efficiency. The occlusion prediction is more real-time and accurate; by combining solar radiation prediction and power loss quantification methods, the influence of different shielding conditions on the power generation capacity of the photovoltaic module can be accurately calculated, and data support is provided for system optimization; meanwhile, through an intelligent optimization scheduling strategy, the working mode of the photovoltaic module is adaptively adjusted, including module angle optimization, MPPT dynamic adjustment and energy storage optimization scheduling, it is ensured that the photovoltaic system can still maintain efficient power generation in a complex illumination environment, and the long-term stability and benefits of the system are further improved.

The invention relates to the field of multi-energy complementary clean energy systems, in particular to a water-wind-light-storage multi-energy complementary clean energy base capacity configuration method and system, and the method comprises the following steps: collecting the related basic data of a hydropower station, wind power, photovoltaic, pumped storage and power transmission lines in a research area; analyzing and determining the wind and light electricity abandoning rate, the annual utilization hours of the power transmission channel comprehensive internet access channel, the comprehensive utilization demand of the reservoir and the pumping power limitation of the pumped storage power station; constructing a water-wind-light-storage multi-energy complementary clean energy base capacity configuration double-layer nested optimization model; and obtaining the optimal capacity configuration scheme of the clean energy base and the output process of each power supply under the capacity configuration scheme. Technical support can be provided for planning, development and construction of a watershed or regional water-wind-light-storage clean energy base, the wind-light-electricity absorption capacity of a hydropower station and a pumped storage power station is effectively improved, the wind-light-abandoning rate of a power grid is reduced, safe and stable operation of the power grid is guaranteed, and the energy conservation and emission reduction target of China is smoothly achieved.

The invention provides an intelligent management method and system for photovoltaic energy storage equipment, and the method comprises the steps: analyzing the power fluctuation trend through monitoring the output power and environment parameters of the photovoltaic energy storage equipment in real time, and pre-judging the power fluctuation characteristics; according to the operation state parameters of the supercapacitor and the vanadium redox flow battery and the power fluctuation pre-judgment result, the power distribution proportion and the cooperative working mode of the energy storage element are determined; based on industrial load demand characteristics, an energy storage element is controlled to be flexibly switched between an active adjustment mode and a reactive compensation mode; and voltage regulation and harmonic compensation are realized by monitoring electric energy quality indexes of parallel connection points. According to the invention, the rapid response capability of the photovoltaic energy storage system to the industrial load can be effectively improved, the power supply stability and the electric energy quality are remarkably improved, the system power fluctuation is reduced, the utilization efficiency of the energy storage equipment is improved, and reliable clean energy support is provided for industrial users.

The invention discloses a green power computing power fusion scheduling model construction method and a scheduling platform, and relates to the technical field of green computing power. The green power computing power fusion scheduling model construction method comprises the steps of determining an adjustable reference load, determining a peak clipping and valley filling optimization load, determining a clean energy output load, determining a computing power center computing power task execution optimal load curve, monitoring a computing power center fusion scheduling task execution process and the like. A green electric power computing power fusion scheduling platform is constructed, and fusion scheduling task execution responsivity monitoring, computing power center electricity charge economy analysis, computing power center carbon emission reduction analysis and the like of a computing power center are carried out. The beneficial effects of the invention are that the system employs the technologies of big data, cloud computing and the like, builds a green power calculation power scheduling platform, monitors and analyzes the clean energy supply and demand conditions of the calculation power centers of the whole province, provides calculation power task scheduling services for the calculation power centers of the whole province, integrates clean energy consumption and green power calculation power, and improves the efficiency of the whole province. And the calculation power load active matching clean energy supply is realized.

The invention relates to the technical field of intelligent power supplies, in particular to an intelligent power supply for an unattended station. Comprising a multi-source power supply scheduling unit; an intelligent energy storage management unit; a double-link communication unit; the double-link communication unit is used for constructing a multi-redundancy communication link of wired, wireless and power line carriers, and the double-link communication unit is based on link quality data, adopts an automatic switching strategy to guarantee data transmission continuity, preprocesses monitoring data by using an edge side data clustering algorithm, and realizes offline data caching and transmission flow optimization. Through a multi-source energy dynamic scheduling strategy, according to photovoltaic real-time output, commercial power time-of-use electricity price and load electricity demand, power supply paths of photovoltaic, commercial power and a storage battery can be dynamically optimized, the consumption proportion of clean energy is improved, the dependency degree of high-cost commercial power is reduced, a complex energy structure of photovoltaic, commercial power and storage battery multi-energy complementation is effectively adapted, and the energy utilization rate is improved. And power supply flexibility is enhanced.

Described herein are devices, systems, methods, and processes for energy-efficient routing in a cloud environment. The system includes a load balancer that distributes incoming traffic across multiple application instances hosted within different availability zones (AZs) based on a preference for sustainable energy sources as communicated by a client device. The client device communicates its preference for sustainable energy sources through a sustainability flag included in a service request. The load balancer steers the service request to the suitable resource based on the sustainability flag. In subsequent interactions, the client device can send a service request with a configuration, such as a sticky cookie. The configuration may allow the load balancer to consistently direct requests from the same client device to the same resource. Accordingly, the embodiments enable energy-efficient routing, client preference for sustainable energy, and consistent service, and contribute to environmental protection.

The invention discloses an energy-carbon integrated management method based on digital twinning, and relates to the technical field of energy-carbon management, and the method comprises the steps: obtaining clean energy data, thermoelectric equipment data, outsourcing energy data, planned load data and production process data; predicting by using a preset first carbon emission propagation model to obtain a supply carbon emission propagation network; obtaining a required carbon emission propagation network by using a preset second carbon emission propagation prediction model; respectively calculating carbon emission factors according to the clean energy data, the thermoelectric equipment data and the outsourcing energy data; generating a preliminary regulation and control instruction according to the carbon emission factor, the supply carbon emission propagation network and the demand carbon emission propagation network; performing simulation in combination with the equipment execution instruction and the load regulation and control instruction to obtain a simulation result, and determining a carbon deviation result; and adjusting the equipment execution instruction and the load regulation instruction according to the carbon deviation result and a preset carbon integral mechanism to obtain an energy-carbon coordinated regulation instruction. According to the invention, the accuracy of carbon management can be effectively improved.

The invention relates to the field of clean energy, and provides a distributed secondary control method based on deep reinforcement learning, and the method comprises the steps: S1, deploying a DDPG intelligent agent in a distributed micro-grid, and enabling the DDPG intelligent agent to be used for secondary control; s2, establishing a communication graph model of the distributed micro-grid based on a graph theory, so that each distributed power supply in the distributed micro-grid only exchanges control signals and voltage information with the adjacent distributed power supplies; and S4, regulating and controlling the output power of the distributed power supply by using the intelligent agent, and dynamically regulating the frequency and voltage secondary control signal according to the decision of the intelligent agent. Therefore, the dynamic adaptability and the system-level reliability of the distributed power system are improved.

The invention provides a wind and light storage micro-grid dispatching method based on an ant colony algorithm, and relates to the technical field of micro-grid dispatching, and the method comprises the following steps: S1, building a wind and light storage micro-grid system, and building a model of each device in a micro-grid; s2, establishing a comprehensive cost model, and forming a target function considering both the economic cost and the environmental cost of the system; s3, various constraint conditions of the system are established; s4, establishing a mathematical model of microgrid optimization scheduling based on S1-S3, and obtaining various basic parameters; s5, initializing related parameters and a pheromone matrix of the ant colony algorithm; according to the method, the comprehensive energy system micro-grid model is constructed, the economic cost and the environmental cost are taken as indexes, the output condition of each device in the system is reasonably configured, so that the cost is reasonably dispatched and reduced, the micro-grid is taken as a small system capable of realizing power generation and distribution, the utilization rate of clean energy can be increased, and the energy utilization rate is increased. The method plays an important role in energy conservation and environmental protection.

The invention discloses a micro-grid system planning and design method and device based on double-layer optimization, and relates to the field of micro-grid system planning and design, and the method comprises the following steps: S1, collecting the operation data of wind power, photovoltaic, hydropower, an energy storage power station, an electrolytic cell and a hydrogen storage tank in a micro-grid system, and the load data of the micro-grid system; s2, constructing a micro-grid system double-layer optimization model based on a double-layer optimization method; the upper-layer model is a capacity optimization layer, and the maximum total income within the operating age limit is used as a target function; the lower-layer model is a scheduling optimization layer, the maximum operation net income in a scheduling period is taken as an objective function, and system power balance, equipment operation and capacity are taken as constraint conditions; and S3, solving the double-layer optimization model by using an improved grey wolf algorithm and a solver. The microgrid system is planned and designed through a double-layer optimization method, a clean energy power generation and energy storage device capacity configuration scheme is reasonably configured, and an energy scheduling strategy is optimized.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

Provided are a method and apparatus for optimizing an energy storage system, and a device and a storage medium. The method comprises: on the basis of an energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be acquired, net load fluctuation value to be acquired, cost discount rate and energy storage service life of an energy storage system to be optimized, constructing an energy storage configuration objective function; on the basis of a total demand response duration, demand response load, system output value, electricity price to be solved and unit clean energy curtailment cost of the energy storage system to be optimized, constructing a demand response objective function; on the basis of the energy storage configuration objective function, energy storage configuration constraints, the demand response objective function and demand response constraints, jointly solving the energy storage configuration to be solved and the electricity price to be solved to obtain a target energy storage configuration and a target electricity price, so as to realize the optimization of the energy storage system to be optimized. Thus, the operating costs are reduced and clean energy integration is also maximized.

The invention discloses a photovoltaic auxiliary greenhouse cultivation system which comprises a greenhouse body composed of a framework system and a photovoltaic ceiling, and the photovoltaic ceiling is laid on the top of the framework system, provided with a light splitting type solar thin film cell panel set with adjustable light transmittance and laid in a partitioned mode according to the illumination requirement. The dynamic light compensation system, the temperature balance system and the energy storage system are arranged at the top of the framework system, the energy storage system is used for storing energy and supplying power, the dynamic light compensation system is used for supplementing light for each indoor crop area according to photosynthetically active radiation and sun illumination conditions, and the temperature balance system is used for ventilating and humidifying the greenhouse; and the control machine is arranged on the greenhouse main body and is electrically connected with the dynamic light compensation system, the temperature balance system and the energy storage system so as to receive the monitoring data and control the operation of each electric appliance element. According to the system, lithium battery energy storage and a grid-connected inverter are integrated, the energy self-sufficiency rate is optimized, and the space utilization rate is increased through spectrum customization and three-dimensional planting; the method is suitable for tilled land shortage areas, and gives consideration to clean energy production and agricultural efficient output.

The invention relates to the technical field of intelligent power grids, in particular to a virtual power plant multi-level collaborative optimization control method based on digital twinning. According to the invention, data of the power generation end, the power storage end, the power transmission end and the load end are collected and processed in real time, a digital twin model is established, and accurate modeling and real-time monitoring can be carried out on each device of the virtual power plant. Through real-time optimization scheduling and cooperative control, energy loss can be minimized, supply and demand can be balanced, the energy use efficiency is improved to the maximum extent, and the cost is reduced; according to the method, the genetic algorithm and the particle swarm optimization algorithm are combined, scheduling optimization is carried out based on a multi-level collaborative optimization model, a virtual power plant can rapidly respond according to changes of real-time environment and load requirements, fossil energy reduction and clean energy increase are preferentially considered in the power generation equipment scheduling process, and greenhouse gas emission reduction is facilitated.

The invention relates to a clean energy combustion technology, and aims to provide a rotational flow on-duty multi-element micro-mixing coupling type low-nitrogen combustion device for hydrogen-doped natural gas. Comprising an air cooling cavity, an air bin fixing cavity and a gas bin fixing cavity which are sequentially stacked from top to bottom. An on-duty combustion unit of a sleeve structure is arranged in the center of the burner in a penetrating mode, and micro-mixing combustion units, independent air bins and independent fuel gas bins are evenly arranged in the circumferential direction in a surrounding mode. A plurality of micro-mixing spray pipes are arranged in an air cooling sleeve of the micro-mixing combustion unit in the axial direction in an array mode, and fuel spray pipes are arranged on a second fixed disc in the axial direction in an array mode. The closed end of the fuel spray pipe extends into the micro-mixing spray pipe corresponding to the fuel spray pipe, and a fuel gas inlet is formed in the bottom of the fuel gas bin fixing cavity. After external air is introduced through an air circulation path from top to bottom to cool the head of the combustor, the heated air is mixed with fuel for combustion, and safe, efficient and low-emission application of natural gas large-proportion hydrogen-doped fuel is achieved.

The invention relates to the field of clean energy, and provides a distributed secondary control method for an island micro-grid system based on event triggering, which comprises the following steps: S1, connecting adjacent distributed units in an island micro-grid through a local communication link, and constructing a distributed control architecture to eliminate dependence on a central controller; s2, triggering communication of the local communication link based on a preset threshold value, and transmitting a secondary control signal when the system state deviation exceeds the threshold value; s3, performing cooperative calculation on the secondary control signal by using the distributed control architecture, adjusting frequency and voltage deviation, and shielding original power data; and S4, connecting a newly added distributed unit to the distributed control architecture through the local communication link, thereby reducing high communication burden and single-point fault of centralized control of the island microgrid.

The invention relates to the technical field of power generation prediction data processing, and discloses a photovoltaic power station power generation prediction method based on multi-model fusion, which combines a photovoltaic physical model, a power generation statistical model and a power generation prediction model, not only considers a physical rule, but also combines statistical learning and machine learning to improve the stability and accuracy of prediction, and improves the prediction efficiency. And predicting the generating capacity of the photovoltaic power station by using environmental meteorological data, electrical parameters of the photovoltaic power station and historical power generation data. Accurate photovoltaic power generation power prediction is beneficial to better arrangement of power generation plans and resource scheduling of the power grid. The prediction precision and stability are improved through multi-model fusion, so that power grid dispatching is more accurate and efficient, and power grid instability factors caused by photovoltaic power generation fluctuation are reduced. Therefore, photovoltaic power generation can be connected to a power grid more stably, the power demand is met, and wide application and development of clean energy are promoted.

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The invention discloses a virtual power plant scheduling method and device considering a user side response priority. The method comprises the following steps: constructing a schedulable response model of a load of a user side; obtaining a performance index influencing the schedulable response performance, calculating a comprehensive performance index of each user according to the performance index, and determining the priority of the load of the user side according to the comprehensive performance index; and constructing a priority objective function according to the schedulable response model and the priority of the load, constructing a constraint condition, solving the priority objective function and the constraint condition to obtain a load scheduling optimal solution, and performing power plant scheduling according to the load scheduling optimal solution. According to the invention, the technical problems that the participation of distributed clean energy in coincidence scheduling is mainly considered, the participation of scheduling of a user side is not fully considered, the power consumption type of the user side is singly considered, and the response priority of load scheduling of the user side is ignored in the related technology are solved.

The invention relates to the technical field of comprehensive energy systems, in particular to an optimal scheduling method, device and equipment for a comprehensive energy system and a medium, and the method comprises the steps: carrying out mechanism modeling on a hydrogen fuel cell, and carrying out the quantitative analysis of the variable working condition cogeneration characteristics of the hydrogen fuel cell in a feasible region through a piecewise linearization method; taking the minimum sum of the stepped carbon transaction cost, the energy purchase cost and the wind curtailment cost as a target, and establishing a target function of the integrated energy system optimization scheduling considering the thermoelectric variable working condition characteristics of the hydrogen fuel cell; establishing an optimization scheduling constraint condition considering the thermoelectric coupling hydrogen fuel cell; on the basis of a thermoelectric coupling curve obtained by the mechanism model, constructing a comprehensive energy system scheduling model considering the thermoelectric variable working condition characteristics of the hydrogen fuel cell by using a target function and constraint conditions; and solving the integrated energy system scheduling model to obtain the integrated energy system optimal scheduling method. Efficient integration and utilization of renewable energy sources, hydrogen energy and other clean energy sources are promoted.

The invention relates to a port energy station operation optimization method and system considering logistics whole link refinement, and the method comprises the following steps: determining typical energy equipment based on a typical structure of a port energy station under energy flow-logistics coupling, and constructing an energy equipment operation constraint; on the basis of the operation characteristics of port logistics activities, port logistics full-link operation characteristics and energy consumption characteristics are obtained, and logistics full-link operation constraints are constructed; based on constraint conditions including the energy equipment operation constraint and the logistics full-link operation constraint, establishing a port energy station operation simulation model considering the logistics full-link energy consumption time sequence adjustable characteristic; and effectively solving the port energy station operation simulation model to obtain a port energy station operation optimization scheme. Compared with the prior art, the method has the advantages that the operation cost of the port energy station can be effectively reduced, the clean energy consumption capability is improved, and the logistics load peak-valley difference is reduced.

The invention belongs to the technical field of watershed water-wind-light integrated operation, and relates to a watershed water-wind-light integrated key operation risk identification method. The method comprises the following steps: firstly, starting from complex coordinated operation requirements such as hydroelectric comprehensive utilization requirements, multi-level scheduling, multiple time scales and water-wind-light complementation, constructing a cascade hydroelectric operation risk system and constructing seven quantitative indexes of the system; secondly, constructing a watershed water-wind-light clean energy base annual 8760h panoramic time sequence operation simulation model, and comparing and calculating cascade hydropower operation risk change levels before and after new energy access; and finally, proposing a risk sorting method coupling TOPSIS and AHP, eliminating different risk index units and magnitude differences, and identifying key risks. By taking a clean energy base at the downstream of the Jinsha River as an application example, various risk distribution characteristics are disclosed, water and electricity waste water, output fluctuation and peak regulation pressure risks are found to be more sensitive to new energy access, and the sensitivity of the risks to seasons and new energy installation is explored and analyzed.

The invention discloses a process for synthesizing ammonia by coupling low-temperature plasma with electro-catalysis in the field of clean energy and green chemical industry, which comprises the following specific steps: mixing air and water vapor, feeding the mixture into a dielectric barrier discharge reactor, filling the reactor with a multi-stage heterojunction gradient doped carbon coated catalyst, generating active gas containing NOx under pulse high voltage, and condensing and then entering an electrocatalytic reduction unit. And an electro-catalysis product enters a photo-thermal separation module integrated with a Janus type photo-thermal electrode, the temperature of the electrode is locally increased under visible light irradiation, NH3 desorption is promoted, and finally high-purity ammonia is obtained through polyimide membrane separation. The multistage heterojunction gradient-doped carbon-coated catalyst adopted in the process is prepared through multiple steps and contains gradient-doped boron, sulfur and Au nanoparticles on the surface, NOx adsorption, electron transport and sintering resistance are synergistically improved, and efficient ammonia synthesis under mild conditions is achieved.

The invention relates to the technical field of thermal management optimization, and discloses an active cooling and power optimization system of a plateau turbine generator, which comprises a multi-physics field modeling module, a reinforcement learning decision module, a strategy optimization training module and an edge execution control module, and is used for resolving a temperature field / flow field / stress field coupling state in real time, providing environment dynamic characterization, and optimizing the power of the plateau turbine generator. According to the method, multi-source sensing data is processed, a cooling liquid flow rate and cooling fin angle control instruction is generated, strategy iteration optimization is carried out in a digital twin environment, the physical rationality and energy efficiency optimality of a control algorithm are ensured, a lightweight strategy network is deployed, and sub-second-level real-time response and fault safety protection are achieved. Through intelligent control and multi-physical field cooperation, the temperature adjusting precision and the system response speed are improved, efficient power output of the turbine is kept under different altitudes and climate conditions through a self-adaptive strategy, and a reliable clean energy solution is provided for high-altitude areas in combination with multiple safety protection mechanisms.