378 results about "Energy demand" patented technology

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

The invention discloses an energy management method of an electric bicycle system and the electric bicycle system.The method comprises the steps that battery parameters, motor parameters and whole vehicle kinetic parameters of an electric bicycle are obtained, and digital mapping processing is carried out to obtain a digital twin system; acquiring operation parameters of the battery, the motor and the whole vehicle, and performing distributed feature extraction processing to obtain feature data; training the bionic optimization model fusing the genetic algorithm and the ant colony algorithm to obtain a scenarized energy optimization strategy; performing hierarchical regulation and control processing on the control system to obtain a hierarchical control instruction sequence; and performing dynamic regulation and control processing on energy distribution to obtain a real-time energy optimization control instruction. According to the technical scheme of the invention, intelligent prediction and dynamic optimization distribution of energy demands are realized.

The invention discloses a carbon emission real-time regulation and control method and system based on multi-source data fusion and AI decision, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes, in the method, real-time production data is collected through Internet of Things equipment, and the real-time carbon emission intensity is calculated in combination with a carbon accounting engine. And constructing an AI optimization layer comprising a process knowledge graph, a long-short-term memory network and a reinforcement learning agent, converting process constraints into mathematical boundary conditions, predicting energy demands, carrying out iterative optimization by taking carbon emission intensity minimization as a target, generating a dynamic scheduling strategy, and realizing real-time adjustment of operation parameters of production equipment. The method and the device are used for improving the accuracy of carbon emission monitoring data on production scheduling and reducing the risk of high-carbon-intensity production caused by improper time period selection of enterprises.

The invention relates to the technical field of industrial waste heat recovery optimization, in particular to an industrial circulating cooling water waste heat multi-source coupling system based on data driving, and the system comprises a waste heat collection unit, a data processing unit and an intelligent coupling regulation and control unit. After the data processing unit preprocesses data, high, medium and low temperature layers are divided according to temperature, a clustering algorithm is combined to subdivide a calorific value sub-layer, a dynamic characteristic file containing a parameter change curve, fluctuation amplitude and duration is established, the intelligent coupling regulation and control unit compares the file with pre-stored user heat demand characteristics, the matching degree is calculated preferentially according to the temperature contact ratio, and the matching degree is calculated preferentially. A single or multi-source cooperative coupling path is generated, target thermal parameters are synthesized through thermal capacity weighting in a multi-source mode, the supply and demand matching precision and the utilization efficiency of waste heat are improved, diversified energy consumption requirements of industrial scenes are met, and energy conservation and emission reduction are facilitated.

The invention relates to the technical field of electric power energy monitoring, and discloses a remote monitoring analysis method and system based on electric power equipment operation data, and the method comprises the steps: collecting the multi-element energy operation parameters of a multi-source energy system and the multi-element energy use parameters of a user side in real time; after the collected data are preprocessed, the preprocessed equipment operation state data are evaluated, and an electric power equipment operation state index is obtained; whether equipment faults exist or not is judged according to power equipment operation state indexes, virtual mapping of a multi-source energy system is constructed, and system operation conditions under different energy scheduling strategies are simulated in real time by combining user energy demand prediction and external environment changes; when potential energy supply abnormity or equipment failure is detected, a multi-stage early warning mechanism is triggered, and a decision report including abnormity positioning, reason analysis, energy scheduling optimization and equipment maintenance disposal suggestions is generated, so that potential problems can be found in time, and the stability and reliability of energy system operation are improved.

The invention belongs to the technical field of intelligent energy control, and particularly relates to an energy equipment control method based on regional people number distribution, which comprises the following steps: acquiring real-time people number distribution data, environmental parameter data, historical energy consumption data and historical people flow characteristic data, and predicting regional people number distribution data in different time periods; based on historical energy consumption data, historical people flow characteristic data, real-time people number distribution data and environmental parameter data, a dynamic energy demand model is constructed, regional people number distribution data in different time periods are combined, a people number density thermodynamic diagram is generated, regional energy equipment load change is predicted, and a regional energy demand model is established according to the load change and the people number density thermodynamic diagram. A differential control strategy is generated, and operation parameters of the energy equipment are dynamically adjusted; and regulating and controlling the energy equipment according to the differentiated control strategy, synchronously collecting regulated and controlled regional environment feedback data and equipment energy consumption data, and generating an energy efficiency evaluation report. Therefore, the problems of high response delay, limited parameter matching precision and the like in the prior art are solved.

The invention discloses an urban carbon emission peak prediction method, and particularly relates to the technical field of carbon emission prediction. A carbon emission data set with high time resolution is constructed, and first K influence factors with the highest contribution degree to a carbon emission peak value are screened based on an adaptive weight attribution mechanism, including extraction of heating system response time and energy scheduling hysteresis characteristics, construction of a carbon emission peak value prediction model, and when a prediction value exceeds a set threshold value, calculation of the carbon emission peak value prediction model is carried out. The method comprises the following steps: automatically adjusting the influence weight of a key factor, calculating a peak confidence interval on a test set, evaluating and optimizing the model performance, finally deploying an optimized model to an urban carbon emission monitoring system, and dynamically updating in combination with a real-time data stream to ensure the accuracy and stability of prediction; according to the method, the accuracy of urban carbon emission peak prediction is remarkably improved, the capability of responding to sudden energy demand increase in extreme weather is enhanced, and scientific support is provided for carbon neutralization policies, energy regulation and control and environment management.

The invention discloses a production process control system for functional food, and belongs to the technical field of production equipment control systems, and the system specifically comprises a parameter collection module which is used for collecting raw material characteristic parameters of functional food raw materials, and the raw material characteristic parameters comprise rheological characteristic parameters, thermal stability parameters and structural strength parameters; the process adaptation module is used for constructing a dynamic process adaptation model and mapping the raw material characteristic parameters into an equipment control instruction set; dividing energy demand levels according to raw material processing stages, and generating an on-demand energy supply tree structure; the equipment adjusting module is used for monitoring operation state data of the production equipment in real time; triggering a process parameter dynamic compensation instruction based on the equipment operation state data; the equipment feedback module is used for comparing the deviation between the actual product characteristics of the production line and the target characteristics through the closed-loop verification unit, and iteratively optimizing the dynamic process adaptation model; precise production, energy conservation and efficiency improvement of functional food are achieved through the multi-module cooperation technology.

The invention provides a ship shaft power generation and lithium battery coordinated multi-working-condition energy optimization management system, which is applied to the technical field of ship power systems and energy management and comprises a data acquisition module, an energy optimization processing module and a power regulation and control module, wherein the energy optimization processing module is provided with a prediction unit, a working condition identification unit, a loss evaluation unit and a decision-making unit, the prediction unit is used for predicting a ship load demand in a preset period, the working condition identification unit is used for identifying a ship operation working condition, and the loss evaluation unit is used for determining a life loss factor by combining a prediction result and an energy storage unit state; the decision-making unit generates a regulation and control instruction based on the life loss factor and the state of the power generation unit to realize power distribution of the power generation unit and the energy storage unit; according to the system, multi-working-condition energy consumption requirements of the ship are met, meanwhile, comprehensive optimization of fuel consumption cost and energy storage unit service life loss cost is achieved, the energy utilization rate is increased, the service life of the energy storage unit is prolonged, and the full-life-cycle operation cost of the ship is reduced.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.

The invention relates to the technical field of feed efficiency optimization, in particular to a feed efficiency optimization system based on a growth model, and the system comprises a data collection unit which is used for collecting and associating individual identities, individual weight data, time-phased feed intake data, environment data and environment forecast data; the model calibration unit is used for calibrating individual energy conversion efficiency parameters, generating an environment dynamic correction factor by using the environment data and generating a group classification label based on historical individual performance data; and the decision and execution unit operates a double-cycle cooperation mechanism based on pre-judgment and execution. By dynamically calibrating the individual energy conversion efficiency and combining with the environment forecast, real-time calculation and differentiated feeding regulation and control of the individual energy demand can be realized.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

The invention discloses a virtual power plant optimal scheduling method based on carbon-green certificate transaction, and relates to the technical field of electric power, and the method comprises the steps: S1, building a carbon-green certificate joint transaction model according to a stepped carbon transaction mechanism and a green certificate transaction mechanism; s2, establishing a virtual power plant low-carbon model according to the P2G-CCS coupling model, the hydrogen fuel cell operation model and the hydrogen-doped gas unit model, and establishing constraint conditions in combination with system energy demand response; s3, determining an optimization target according to the carbon-green certificate combined transaction model and the virtual power plant low-carbon model, and constructing a virtual power plant optimization scheduling model; s4, a CPLEX solver is adopted to process the virtual power plant optimization scheduling model based on optimization constraints, and an optimization scheduling result is obtained; the energy utilization efficiency and the energy consumption efficiency in the operation process of the virtual power plant are improved, meanwhile, low-carbon operation of the virtual power plant is considered, and the problem that in conventional P2G-CCS coupling operation, the virtual power plant cannot give consideration to the energy utilization rate, the energy consumption efficiency and the low-carbon operation at the same time is solved.

The invention relates to the technical field of power system regulation and control, in particular to a park multi-resource intelligent flexible control method and device considering energy and carbon consumption collaboration, and the method comprises the steps: obtaining historical energy consumption data and historical carbon emission data in a target park; training the historical energy consumption data and the historical carbon emission data based on a feature-level constraint preference optimization algorithm to obtain a park multi-energy prediction model based on feature-level constraint preference optimization so as to predict energy demands in a future time period; constructing a park multi-resource optimization scheduling model according to the energy demand in the future time period; and based on a preset energy storage equipment charging and discharging strategy and a preset flexible load control strategy of the target park, generating a multi-resource energy-carbon cooperative flexible control strategy of the target park according to the park multi-resource optimization scheduling model. Therefore, the problem that an existing cooperative control method cannot meet the comprehensive requirements of the modern park in the aspects of energy conservation, carbon reduction, cost optimization and system stability is solved.

The invention discloses a demand management control method and system based on real-time rolling prediction and decision analysis, and relates to the field of energy scheduling, comprising load prediction of a demand gateway line and power demand trend analysis of a controlled object. Determining a power demand control strategy by using a load prediction result and a power demand trend analysis result and through gate demand periodical rolling prediction and optimization calculation; constructing a sensitivity matrix between the power variation between the demand gateway and the load line and the power variation of the load line; using the sensitivity matrix to provide a demand out-of-limit value of a demand gateway line and support construction of a demand out-of-limit analysis control strategy; and performing power calculation by combining an equipment energy efficiency model and line load characteristics, and dynamically adjusting an energy distribution strategy of each piece of equipment. According to the invention, accurate prediction and dynamic adjustment of energy demands can be realized, energy distribution can be optimized, and operation cost can be reduced.

A method for managing an energy system having one or more renewable energy sources, one or more energy storage devices, one or more loads, and a grid connection for connecting at least temporarily to an external energy distribution grid is disclosed. The method generates a prediction of energy demand of the loads using historical energy demand data, and a prediction of renewable energy availability from the renewable energy sources using weather forecast data. An amount of energy to be obtained from the distribution grid is determined in dependence on the prediction of renewable energy availability and the prediction of energy demand. An energy conservation strategy is generated using the predictions and determined energy amount, and energy supplied to one or more of the energy storage devices and / or one or more of the loads is adjusted automatically according to the energy conservation strategy.

The invention relates to the technical field of power systems, in particular to a wind-solar-thermal storage integrated comprehensive energy system and a multi-objective optimization method. The thermal power plant is optimized by connecting the thermal power plant with the carbon capture device and the steam extraction energy storage device, carbon dioxide generated during operation of the thermal power plant is captured through the carbon capture device and stored or utilized, and therefore the carbon dioxide entering the atmosphere is reduced, and the influences of the greenhouse effect and the climate change are relieved; the amount of carbon dioxide which can be captured by the carbon capture device is increased, excess heat generated in the energy production process is stored in a steam extraction mode through the steam extraction energy storage device, then the stored heat energy is supplied to heating or driving mechanical equipment in the peak period of energy demand, the utilization efficiency and sustainability of energy are improved, and the energy utilization rate is increased. The heat storage capacity of the system is improved while carbon emission is reduced, the energy storage cost is reduced, and low-carbon economical operation of the system is achieved.

The invention discloses a medium and long term planning method for a low-carbon park integrated energy system. The method comprises the steps of park basic data collection, energy demand prediction, energy resource evaluation, energy supply system construction, energy storage planning, carbon emission accounting and emission reduction strategy making, economic and environmental benefit evaluation, planning scheme optimization and determination and the like. According to the method, through a multi-objective optimization algorithm, an optimal planning scheme is solved by taking the lowest energy system cost, the lowest carbon emission and the highest energy supply reliability as objectives. According to the invention, optimization objectives of energy system cost minimization, carbon emission minimization and energy supply reliability maximization can be realized, so that low-carbon development and environmental benefit improvement of a park are promoted.

The invention belongs to the field of heat supply, and discloses a heat supply system and power grid interaction method, device and equipment and a storage medium, and the method comprises the steps: obtaining the real-time electricity price, the dynamic threshold electricity price and the operation data of a phase change heat storage system, inputting the obtained data into a pre-established Markov decision process model, the Markov decision process model comprises a heat storage process sub-model and a heat supply process sub-model; according to the real-time electricity price, the dynamic threshold electricity price and the thermal load, determining that the moment attribute of the current moment is a heat storage moment or a heat supply moment, and based on the determined moment attribute, selecting a target sub-model from two sub-models of a Markov decision process model; and solving the target sub-model according to a DQN algorithm, and controlling the operation of the phase change heat storage and supply system according to a solving result. Through the method, the building energy demand can be accurately met, the user energy cost is reduced, the real-time supply and demand balance of a power system is promoted, and the power supply pressure at the power peak is relieved.

The invention discloses a control method of a multi-split air conditioning system, the multi-split air conditioning system and a computer readable storage medium. The method comprises the steps of determining a first energy demand request of a current room according to a user set temperature parameter and a first environment parameter of the current room; and determining a second energy demand request of the current room according to the second environment parameter. Determining a third energy demand request of the current room according to the number of running air conditioner indoor units of the multi-split air conditioning system, the running mode of each air conditioner indoor unit, the calibrated temperature parameter of each air conditioner indoor unit, the user set temperature parameter of each air conditioner indoor unit, the third environment parameter and the power grid state information; the operation mode comprises a refrigeration mode and a heating mode. And according to the first energy demand request, the second energy demand request and the third energy demand request, the target energy demand of the current room is determined so as to control the multi-split air conditioning system. Therefore, individual requirements of users are met, comfort and energy conservation are considered, and the balance of comfort and energy conservation is achieved.

A multi-agent reinforcement learning framework for managing energy transactions in microgrids that includes three layers of agents, each pursuing different objectives. The first layer, including prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon emission impact while balancing the consumption and production of both renewable and conventional energy. The framework takes into account fluctuations in energy demand and supply due to household supplied energy from renewable energy sources and energy storage levels in household energy storage devices.

The invention discloses a Transform-based fuel cell system life optimization method, and relates to the field of fuel cells, and the method comprises the following steps: S1, collecting multi-dimensional data of a conventional driving behavior; s2, constructing a model for predicting the attenuation condition of the fuel cell system and the residual life of the electric pile; s3, collecting multi-dimensional data of a specific driving behavior, analyzing an energy demand mode and an energy consumption mode of the vehicle, and training the model; s4, collecting operation data in real time, and predicting the attenuation condition of the current fuel cell system and the residual life of the electric pile; s5, an optimization control strategy is generated, and real-time optimization adjustment of the operation parameters is carried out; and S6, continuously collecting operation data, and carrying out updating training on the model. By predicting and optimizing control and decision of the fuel cell system, the performance and the service life of the fuel cell system are improved, dynamic optimization and global optimization of the system are realized through a data-driven optimization strategy, so that the stability and the reliability of the system are improved, the service life of a galvanic pile is prolonged, and the maintenance cost is reduced.

The embodiment of the invention discloses an electricity-hydrogen-heat energy system construction method for a multi-type energy demand scene. The method comprises the following steps: constructing a system simulation model corresponding to an electricity-hydrogen-heat energy system, and determining a target limiting condition corresponding to the system simulation model in a target energy demand scene based on a target cascade analysis method; and finally, determining system target equipment parameters of the electricity-hydrogen-heat energy system under the target limiting condition according to the annual total construction resource function, and constructing a target electricity-hydrogen-heat energy system according to the system target equipment parameters. Through the construction method of the target electricity-hydrogen-heat energy system, the optimal system equipment parameters are automatically and correspondingly determined while the multi-type energy requirements in different scenes are met, the system construction cost is reduced, heat recovery of the hydrogen fuel cell is achieved by introducing the heat storage tank, the renewable energy utilization efficiency of the system is improved, and the energy utilization efficiency of the system is improved. And the use satisfaction of the user is improved.

The invention relates to the technical field of energy and power engineering, and discloses an efficient thermoelectric decoupling system based on phase change energy storage, which comprises an energy storage unit module, a regulation and control algorithm module and a scheduling optimization module. The energy storage unit module adopts a nanotechnology novel phase change material, so that the energy storage density and stability are improved; the regulation and control algorithm module realizes accurate control of charging and discharging through a multi-stage regulation and control strategy; and the scheduling optimization module predicts energy demand and production by using a machine learning algorithm, and dynamically adjusts an energy distribution strategy. The problems that a traditional heat storage material is low in energy storage density and poor in stability and lacks an intelligent scheduling mechanism can be solved, the energy utilization efficiency and operation reliability of a thermoelectric system are improved, and the energy cost is reduced.

The invention discloses a hospital energy resource intelligent management method and related equipment. The method comprises the following steps: predicting the output of renewable energy and the energy demand of each department in a future time period by using an LSTM-Attention model; on the basis of a dynamic mixed AHP analytic hierarchy process, the department energy consumption priority is evaluated by comprehensively considering the emergency state, the number of patients, the equipment operation state, historical energy consumption data and multi-dimensional factors of special medical tasks; and formulating an energy scheduling strategy through a multi-objective optimization model by combining the real-time energy consumption demand and the renewable energy source yield of the hospital according to the department energy consumption priority evaluated by the dynamic hybrid AHP method. The problems that due to insufficient application of clean energy and energy storage technology in hospitals, hospital operation cost is high, carbon emission is increased, a traditional system is difficult to dynamically adapt to complex energy consumption requirements, and stable energy supply of key areas cannot be guaranteed can be solved.

A port collaborative scheduling method based on an information gap decision theory comprises the following steps: S1, establishing a port integrated energy system framework model for a port, and finally constructing a berth logistics system; s2, collecting equipment parameters in the port integrated energy system framework model; s3, establishing a logistics side minimum energy consumption cost target under berth-logistics-energy collaborative scheduling; s4, establishing a minimum energy consumption cost target of a berth side under berth-logistics-energy collaborative scheduling; s5, establishing a common coupling electric energy balance relation; s6, establishing and solving an overall target under berth-logistics cooperative scheduling; s7, performing modeling and solving under uncertainty based on an information gap decision theory; according to the method, the ship energy demand, the container throughput dynamic and the logistics demand are integrated, and meanwhile, the operation limitation of the port comprehensive energy system is considered. And an electricity-hydrogen coupling transportation method is adopted to dynamically optimize hydrogenation, battery replacement time windows, logistics operation and berth allocation under time-of-use electricity price.

The invention provides a self-adaptive regulation and control method for renewable energy of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: constructing a modular energy storage architecture of the renewable energy of the unmanned aerial vehicle; calculating an optimal energy conversion strategy of the unmanned aerial vehicle; determining an optimal energy distribution strategy of the unmanned aerial vehicle; based on an energy conservation principle and a task priority theory, utilizing a machine learning method to construct an empirical model of a task type and an energy consumption demand, and predicting an optimal energy storage switching strategy through the empirical model of the task type and the energy consumption demand; and based on the optimal energy conversion strategy, the optimal energy distribution strategy and the optimal energy storage switching strategy, performing energy regulation and control on energy equipment in the modular energy storage architecture of the renewable energy of the unmanned aerial vehicle. According to the invention, the most suitable energy module can be automatically selected for power supply according to the energy demand, and the energy utilization efficiency is greatly improved.

The invention discloses a multi-element flexible load feature extraction method based on hydrogen energy demand response, and relates to the technical field of hydrogen energy demand resource planning. In order to construct an optimization control model responding to a power grid signal, hydrogen production efficiency maximization and power utilization planning rationalization are realized. The method specifically comprises the following steps: obtaining new energy power generation power characteristic data, predicting daily average generating capacity data according to the data, obtaining all-day time-of-use electricity price characteristic data of a commercial power grid, and dividing the data into peak / flat / valley time periods; according to the multi-element flexible load feature extraction method and the control strategy, the electric energy use strategy and the hydrogen production and hydrogen supply equipment operation strategy suitable for the whole hydrogen energy supply project can be formulated according to the stirring filtering of the new energy generating capacity, so that the energy consumption is reduced on the premise that the stable and sufficient hydrogen supply amount is ensured. The power consumption of the hydrogen energy supply project in the peak period and the flat period of the power grid is effectively reduced, and the comprehensive energy efficiency and the green power consumption rate of the hydrogen energy supply project are effectively improved.

The invention relates to a wind-light-hydrogen integrated energy supply system based on a reversible solid oxide fuel cell, which is specially designed for special areas such as open sea islands, frontier sentries and the like. The system integrates a wind power generation module (1) and a photovoltaic power generation module (2), and converts wind energy and solar energy into electric energy. Redundant electric energy is used for producing hydrogen by electrolyzing water in an SOEC mode by the RSOC module (3) and is stored in the solid hydrogen storage module (4). And when the energy demand peak or the wind-light-electricity is insufficient, the RSOC module is switched to the SOFC mode, and hydrogen is converted into electric energy to be output. The system is further provided with a seawater desalination module (5), and the RSOC waste heat is used for desalinating seawater. And the ultrapure water production module (7) is used for producing industrial-grade ultrapure water by condensing the high-temperature steam. And the intelligent monitoring module (6) monitors in real time and controls the modules to operate cooperatively. The system has the advantages of efficient energy conversion, high environmental adaptability, multifunctional expansion and the like, and provides a stable, environment-friendly and economical energy solution for special areas.