415 results about "Energy cost" patented technology

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention relates to the technical field of high-performance computing resource management, and discloses a computing resource allocation method for a distributed supercomputing center. The method comprises the following steps: on the basis of obtaining real-time computing task and supercomputing center resource data and uniformly quantifying, integrally predicting resource requirements of future tasks; constructing a mixed integer linear programming model with the minimization of the total operation cost as a single target, wherein the total operation cost is the sum of the energy cost, the carbon emission cost, the data transmission cost and the SLA default penalty cost; solving the model by taking the time-varying electricity price, the green energy ratio, the resource capacity and the network parameters of each center as constraint conditions to generate an optimal resource allocation scheme; and then, by dynamically monitoring the resource state and the task progress, the model is triggered to resolve when the resource utilization rate is detected to be unbalanced or default risks, so that self-adaptive adjustment is realized. According to the invention, global collaborative resource allocation across super computing centers is realized, and operation economy, environmental sustainability and service reliability are considered.

The present disclosure discloses a system (106) and a method (400) for energy management in a network (108). The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The invention discloses a photovoltaic power generation and rod pumped well group off-peak power utilization coupling control method, and relates to the technical field of oilfield development and new energy utilization. The method comprises the following steps of: acquiring data such as photovoltaic power, irradiance, temperature, wind speed, an indicator diagram of a pumping unit, electrical parameters, a working fluid level and stroke frequency, and constructing a multi-modal data set comprising external meteorological characteristics, a shaft operation time sequence and a well group space relationship; a time sequence model combining reversible normalization and cross-variable attention is adopted to predict photovoltaic output, and a multi-mode liquid supply state recognition model is utilized to obtain the liquid supply sufficiency level of each well. On the basis, a peak shifting power consumption optimization model which takes minimum energy consumption and light abandoning quantity as targets and meets the requirements of yield, power grid capacity, start-stop times and liquid supply constraint is constructed, reinforcement learning is introduced to generate well group start-stop and stroke frequency strategies, and dynamic matching of photovoltaic output and pumping unit load is realized according to real-time data rolling updating, so that the optimal power consumption is realized. The photovoltaic utilization rate is improved; and the energy cost is reduced.

The invention relates to a photovoltaic low-carbon park smart energy management system and method based on digital twinning. Accurate mapping and simulation analysis of a park energy system are realized by constructing a digital twinborn model, dynamic optimization scheduling of energy equipment is realized by adopting a multi-agent cooperative control algorithm, an energy optimization strategy is formulated by taking low carbon as a target, the health state of the equipment is predicted by utilizing machine learning, and an intelligent operation and maintenance plan is generated. And decision support is provided through energy big data analysis. The system integrates multi-source data, and efficient utilization and low-carbon operation of park energy are realized through the steps of digital twin modeling, intelligent agent collaboration, low-carbon scheduling, equipment prediction, big data analysis and the like. Practical application shows that the system can improve the photovoltaic efficiency by 8%, reduce the energy cost by 18%, reduce the carbon emission intensity by 30%, and significantly improve the park energy management level and the low-carbon degree.

The present disclosure discloses a system (106) and a method (400) for energy cost (EC) optimization based on one or more beamforming actions in a network (108) The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The invention discloses a side plate type heat exchanger performance testing system and method, and relates to the technical field of heat exchanger testing, and the system comprises a computer which is used for constructing a digital twinborn model, generating multiple groups of dynamic working condition data, inputting the multiple groups of dynamic working condition data into the digital twinborn model to obtain dynamic performance data, screening according to the sensitivity coefficient of each piece of dynamic performance data to obtain sensitive working condition data; the test equipment executes the sensitive working condition data and collects actual measurement state data of the side plate type heat exchanger; and the computer judges whether the performance of the side plate heat exchanger meets the design requirement according to the actually measured state data. Physical modeling is carried out on the heat exchanger in a digital twinning mode, heat exchange simulation is carried out on the heat exchanger, invalid test working conditions are deleted from a parameter set, only sensitive working conditions are reserved, and time, manpower and energy cost needed by testing is greatly saved.

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training / datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and / or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and / or to the CL(s). In certain embodiments, e.g. solar PV RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

The invention discloses a virtual power plant source load interaction optimization scheduling model based on low-carbon response and a solving algorithm, and belongs to the technical field of power system optimization scheduling. A low-carbon scheduling framework containing a distributed power supply, energy storage, a flexible load and a carbon transaction mechanism is constructed, the carbon emission intensity of each link is quantified to form a carbon flow scheduling signal, and a dynamic carbon emission factor and energy cost are coupled. A multi-objective optimization model is established, a complex function is processed by piecewise linearization, and a hybrid algorithm of an improved genetic algorithm and a commercial solver is designed to improve the solving efficiency. The prediction error is dynamically corrected through a'prediction-optimization-feedback 'closed loop, and the strategy is adjusted. According to the scheme, low-carbon and economic collaborative optimization is realized, renewable energy consumption and system stability are enhanced, user satisfaction and real-time scheduling are considered, and a solution is provided for low-carbon intelligent operation of the power distribution network.

The invention provides a carbon powder production line energy consumption optimization method and system, and is applied to the technical field of energy consumption optimization in the production and manufacturing field, and the method comprises the steps: obtaining a production order, decomposing the production order into subtasks and time-of-use electricity price information, dividing a time slot, calculating the energy cost, constructing a mathematical optimization model according to the energy cost, and solving the mathematical optimization model. And a group of optimal decision variable values are found, so that the total energy cost is minimum, and all production constraints are met. Through the steps, the method integrates the production task, the equipment capability, the dynamic energy price and the production constraint into an optimization framework, and finds the production scheduling scheme with the lowest energy cost through the solving model, so that the technical problem of reducing the energy consumption cost while meeting the production requirement is solved, and the method has the advantages of reducing the energy consumption and improving the production efficiency. And the production plan is optimized.

The invention provides an optimal control method and system for power system carbon emission, and relates to the technical field of carbon emission optimal control, and the method comprises the steps: collecting a real-time load, a power grid carbon emission factor and time-of-use electricity price, employing a clustering algorithm to automatically divide a power utilization period, carrying out the modeling of the dynamic change of the carbon emission factor at different time periods, and carrying out the optimal control of the power system carbon emission. An energy storage charging and discharging path is optimized according to the principle of peak period discharging, smoothing and valley period charging, and energy loss and the health state of an energy storage unit in the charging and discharging process are included in carbon emission accounting; and meanwhile, a multi-target collaborative optimization model is constructed to realize tradeoff optimization of the total carbon emission and the power consumption cost, so that the time sequence characteristics of the actual production load and the power grid carbon emission factor are accurately matched, the carbon emission collection precision and the scheduling optimization level are improved, and the carbon emission and the energy consumption cost in the production cycle are effectively reduced.

The invention provides a method and device for determining steelmaking-continuous casting production multi-process collaborative demand response, and relates to the technical field of manufacturing, and the method comprises the steps that target parameters in the steelmaking-continuous casting production process are obtained; based on a production plan, each work starting moment, each work ending moment, each unit energy consumption and time-of-use electricity prices of different time periods included in the target parameters, by taking the minimum energy cost and the minimum waiting duration as targets and based on an MILP method, establishing a heat coordinated optimization material flow mathematical model in the steelmaking-continuous casting production process and solving the heat coordinated optimization material flow mathematical model; and a coordinated optimization demand response strategy is obtained. According to the method, the coordinated optimization demand response strategy in the steelmaking-continuous casting production process is quickly obtained by solving the heat coordinated optimization material flow mathematical model, so that the determination efficiency of the heat distribution strategy is improved, the internal flexibility adjustment potential of iron and steel enterprises can be mined by the obtained coordinated optimization demand response strategy, and the economic benefit of the enterprises is improved. And friendly interaction between iron and steel enterprises and a power grid is promoted.

The present invention provides a method, device, system and electronic equipment for managing photovoltaic storage and charging energy, which are applied to the technical field of photovoltaic storage and charging energy management. The method dynamically adjusts the photovoltaic output, charging power and energy storage output at the next moment according to the electricity price period by obtaining the current electricity purchase price, electricity selling price and load power. The energy storage is discharged at the maximum during high-price periods, charged at the maximum during low-price periods, and the power is balanced during medium-price periods to ensure operation within the preset load limit, and the confirmed parameters are sent to the device side to achieve efficient energy management. The present invention comprehensively considers the real-time dynamic changes in the purchase price and the selling price of electricity, and adjusts the photovoltaic output, charging efficiency and energy storage output in combination with the electricity price period, thereby optimizing energy use, improving energy utilization and reducing overall energy costs.

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 provides a passenger compartment air conditioner control method based on model predictive control. The method comprises the steps that a passenger compartment model is constructed, and an original data set containing environment parameters, air conditioner control parameters and passenger compartment response parameters is obtained through experimental design; preprocessing the data; a neural network model is trained through the preprocessed data and serves as a prediction model to learn the nonlinear mapping relation among the air conditioner operation parameters, the environment parameters and the passenger compartment states; calling the prediction model to execute MPC rolling prediction, and generating a future state variable sequence; constructing a target function containing a state tracking error and control energy cost, obtaining an optimal control sequence through optimization solution, and acting the first control quantity on an actuator; and dynamically adjusting a prediction model or a control parameter according to real-time feedback. The technical problems that physical modeling is difficult due to high nonlinearity of a passenger compartment air conditioning system, and a traditional control strategy cannot give consideration to high-precision prediction and multi-working-condition adaptability can be solved.

The invention discloses a micro-grid-heating ventilation air-conditioning coordinated optimization method based on deep reinforcement learning, and relates to the field of building energy system management and scheduling, and the method comprises the following steps: S1, constructing a refined model of a heating ventilation air-conditioning system and a micro-grid system, which considers the comfort level of people and the building energy consumption cost; s2, aiming at the refined model constructed in the step S1, adopting a double-layer optimization control strategy to construct a micro-grid energy management system framework integrated with the heating ventilation air conditioning system; s3, based on the step S2, constructing a multi-energy coupling model of the micro-grid-heating ventilation air conditioning system, converting a time sequence optimization problem into a Markov decision process MDP, and performing mathematical representation; and S4, training the intelligent agent by adopting an improved priority experience playback depth deterministic policy gradient algorithm PER-DDPG. According to the method, a collaborative scheduling optimization framework of the micro-grid and the heating ventilation air conditioning system is constructed, and the energy cost of a building system can be effectively reduced under the condition that the comfort level of personnel is effectively guaranteed.

The invention discloses a production line arrangement method and device based on the industrial Internet of Things, a terminal and a medium, and the method comprises the steps: building a target function and a constraint condition corresponding to the target function with the balance of workshop energy consumption and workshop production efficiency as a target according to environment data, size data and basic data; generating a plurality of second production line arrangement schemes based on the plurality of first production line arrangement schemes, a production efficiency prediction value, an energy consumption prediction value, the objective function and the constraint condition; and screening out a target production line arrangement scheme from the plurality of second production line arrangement schemes based on the enterprise information and the market energy cost information corresponding to the factory building. The invention aims to comprehensively consider the actual condition of an enterprise and the current energy cost, and accurately generate the arrangement scheme of the production lines in the plant so as to balance the production energy consumption and the production efficiency of the plant.

The invention relates to the technical field of industrial intelligent manufacturing, and discloses a digital factory-oriented dynamic energy price response type production regulation and control system, which comprises a data acquisition module, a data processing module, a model establishment module, a scale regulation and control module, an instruction verification module, a visualization module and a closed-loop optimization module, the dynamic energy price is introduced into production scheduling, so that the comprehensive energy cost is reduced; a multi-agent reinforcement learning model is adopted for real-time response; an optimization strategy is decomposed into long-term, short-term and real-time instructions, macroscopic planning is carried out by using a cloud, and accurate control is executed by relying on edges; three defensive lines of safety, feasibility and stability are set for all control instructions, so that the harm of wrong instructions is effectively prevented; a decision view and a manual intervention interface are provided to ensure the reliability and credibility of the decision; the successful scheduling case is stored in a strategy library, online fine adjustment and offline training are carried out on the core model, and the continuous evolution ability is achieved.

The invention discloses an intelligent warehouse task allocation method and system for multiple types of AGVs, and particularly relates to the technical field of intelligent warehouse logistics automation, and the method comprises the steps: obtaining a to-be-allocated task through a docking warehouse management system WMS, carrying out the structural normalization processing, mapping the to-be-allocated task to a warehouse topological map, and generating a task execution element set; and meanwhile, the running state and event log records of each AGV are collected. Calling a task consumption model based on the environment feature vector to obtain a power consumption predicted value and a time consumption predicted value; constructing a health risk score based on the monitoring window and mapping the health risk score into a risk level; and generating an electric quantity safety margin by combining the residual electric quantity with the necessary return stroke or the nearby complementary energy cost, and forming a dynamic capability portrait. In the candidate set generation stage, sustainability constraint verification is carried out according to the health risk score, the risk level, the electric quantity safety margin and the maintenance window constraint, a rolling scheduling mechanism is adopted to generate a task allocation result, and a task instruction is issued to the corresponding AGV to be executed.

The invention discloses an optimal scheduling method for a comprehensive energy system, relates to the field of energy management, and is used for solving the problems that a traditional scheduling method is difficult to realize overall optimization, the traditional scheduling method is limited, the traditional scheduling method generally simplifies an energy system into a deterministic model, the uncertainty of energy supply and demand is ignored, and the energy consumption is low. And the scheduling scheme is poor in adaptability in actual operation. The method comprises the following steps of S101, data comprehensive acquisition and fine preprocessing, S102, precise system model construction, S103, uncertainty scene scientific generation, S104, optimal scheduling model elaborate construction, S105, model efficient solving and verification, and S106, scheduling scheme robust implementation and dynamic adjustment. The invention provides an optimal scheduling method for a comprehensive energy system, which can fully consider the energy supply and demand uncertainty and various energy coupling characteristic factors, realizes the economic and efficient operation of the comprehensive energy system, and reduces the energy cost and environmental pollution.

The invention discloses a learning and optimization-based power grid interaction type residential building online cooperative operation method, which belongs to the technical field of power grid interaction type residential building cooperative operation and artificial intelligence crossing, and comprises the following steps of: establishing a problem of minimizing the operation cost of a single residential building during a non-power grid service period; modeling a single residential building operation cost minimization problem as a Markov decision process; training a residential building energy management agent by adopting a cross normalization algorithm to obtain an energy management strategy; total power limitation set by a power distribution system operator is considered, and a resident building group double-layer collaborative operation cost minimization problem is established; solving the collaborative operation cost minimization problem based on the energy management strategy obtained by each residential building and optimization auxiliary binary search, and executing a decision; the power distribution system operator calculates economic compensation and issues the economic compensation according to the energy management decision of each residential building before and after collaboration, the energy cost can be effectively saved, and the higher power grid service capability is achieved.

The invention relates to the technical field of electronics, and discloses a management and control method, device and equipment of an integrated energy system, a medium and a program product, and the method comprises the steps: solving an optimization model through employing a preset solving algorithm and the demand information of different forms of energy under a target working condition, and obtaining the target installed capacity and target output power of each piece of equipment in the integrated energy system; and managing and controlling the integrated energy system based on the target installed capacity and the output power of each device in the integrated energy system. According to the method provided by the invention, the target installed capacity and the target output power of each device in the comprehensive energy system are obtained by solving the optimization model, and the operation of the comprehensive energy system is adjusted according to the energy quality coefficient, so that the optimal utilization of resources is realized, and the comprehensive energy system can achieve the optimal utilization of the resources on the premise of not wasting energy. And user requirements are met to the maximum extent, energy cost is saved, and environmental influences are reduced.

To provide a carbon dioxide recovery method in which energy cost can be reduced.SOLUTION: A carbon dioxide recovery method includes: an adsorption step of adsorbing carbon dioxide contained in air to an adsorbent in an adsorption device 2; a depressurization step of reducing pressure in the adsorption device 2 to below atmospheric pressure; a desorption step of heating the adsorbent A to desorb the carbon dioxide; and a cooling step of cooling the adsorbent. In the desorption step, while depressurizing the inside of the adsorption device 2 so that a saturated vapor pressure lower than a temperature of an inner surface of a wall portion of the adsorption device 2 that contacts outside air is obtained, saturated steam having a temperature higher than the temperature of the inner surface of the wall portion of the adsorption device 2 is introduced into the adsorption device 2.SELECTED DRAWING: Figure 1

The invention discloses a distributed transaction-driven power distribution network toughness improvement method and system, and relates to the technical field of demand side flexible resource regulation and control, and the method comprises the steps: obtaining public distributed energy resources, and carrying out the pre-construction of a distributed transaction-driven power distribution network toughness improvement model through the public distributed energy resources based on an upper layer and a lower layer; optimizing the distributed transaction-driven power distribution network toughness improvement model based on a VCG mechanism to obtain an optimized distributed transaction-driven power distribution network toughness improvement model; the distributed transaction-driven power distribution network toughness improvement model is solved through a distributed solution algorithm based on L (p)-Box ADMM, a power distribution network toughness improvement result is obtained, a producer and a consumer can be effectively stimulated to truly report own energy consumption cost and demand, and reasonable distribution of limited resources in an extreme scene is promoted.

The invention discloses a multi-target operation optimization method and system for an optical storage micro-grid, and relates to the technical field of new energy power system optimization, and the method comprises the steps: carrying out the collection and digital twinning mapping to form a health semantic graph as a state base; calculating an equilibrium priority index on the health semantic graph in a self-adaptive clustering manner to generate a health weight; dynamic weights are fused, charge and discharge power proportions are distributed according to clusters, and health constraint, aging suppression and load response collaboration are achieved; inputting the updated electric quantity vector and the predicted aging trajectory into a multi-target scheduling solver, and synchronously optimizing energy consumption cost, carbon emission estimation and frequency deviation penalty to form a planned power sequence; when the sudden change of the electricity price or the load is detected, the solver agent model is called to quickly recalculate the plan and write the plan back to the health semantic graph to ensure that the control strategy evolves along with the scene. Through mutual feedback and control interlocking of each layer of information, the overall gain of power grid frequency difference convergence and comprehensive operation cost reduction is realized.

The invention discloses a distributed energy power control method and system, and relates to the technical field of power control, and the method comprises the following steps: obtaining first data, constructing a multi-layer frequency deviation index based on the first data, and constructing a frequency response urgency feature; dynamically adjusting charging and discharging parameters based on a reinforcement learning model, and obtaining a power adjustment period according to the frequency response urgency degree characteristic; according to the power adjustment period, combining the residual electric quantity demand of the electric vehicle and the urgency of the charging deadline, quantifying the user demand constraint as a charging priority; a distributed optimization objective function is established according to the power grid frequency stability, the charging priority and the energy cost, and an optimal power distribution scheme is solved through a genetic algorithm; and synchronizing the state of each node based on the optimal power distribution scheme, and distributing the charging and discharging power of each electric vehicle in the region. The charging and discharging power of the electric vehicle can be quickly and reasonably allocated, the frequency deviation is effectively inhibited, and the stability and reliability of a power grid are remarkably improved.

The invention belongs to the technical field of industrial process control, and particularly relates to a heating furnace energy consumption optimization method and system, and the method comprises the steps: constructing a digital twin model which is formed by combining a mechanism model established by a lumped parameter method and a data-driven deviation compensation model; a comprehensive energy efficiency cost function is defined, the deviation between the workpiece temperature and the process target is considered, and real-time energy price prediction and an actuator health state index are introduced; adaptive model predictive control is adopted to generate a heating strategy for collaborative optimization of power input and production takt, the predictive and control time domain of the heating strategy can be adaptively adjusted according to dynamic characteristics of the system, and dynamic replanning can be performed in response to disturbance; and updating the digital twinborn model according to the multi-dimensional weighted deviation of the measured value and the predicted value to realize continuous optimization. According to the invention, the energy cost and the equipment maintenance cost are reduced while the product heating quality is ensured, and the intelligent level and robustness of the system for coping with complex working conditions are improved.

The invention discloses a machine room energy scheduling method and system based on optical storage direct-current flexible coordination, relates to the technical field of machine room energy scheduling, and adopts a day-ahead scheduling and real-time scheduling double-layer architecture to realize machine room energy optimization scheduling in coordination with four elements of photovoltaic, energy storage, direct-current power distribution and flexible load. According to the invention, through cooperation of four factors of light storage, direct current and flexible and double-layer scheduling optimization, the photovoltaic consumption rate can be increased to 90% or more, and the light abandoning loss is greatly reduced; meanwhile, energy storage participates in daily scheduling, the utilization rate is greatly improved, and the return on investment period is greatly shortened. Based on an energy storage SOC dynamic equation and a direct current distribution power balance equation, accurate control of the energy storage SOC is realized, and over-charging and over-discharging are avoided; through linkage of multi-objective weighted optimization and time-of-use electricity price, the energy cost of a core machine room is greatly reduced, and the carbon emission of a green machine room is reduced; meanwhile, the air-conditioning load is adjusted as required, the auxiliary load is started and stopped discretely, and ineffective energy consumption is additionally reduced.

The invention discloses an electro-hydrogen coupling system configuration and energy management method, which comprises the following steps that a device model is obtained based on the operation characteristics of each module in a device, and the device model comprises the technical parameters of each module in the device; energy management strategy models of different operation modes are constructed on the basis of the mapping relation between the generated output and the electrical load of the device and scene characteristics of the hydrogen storage amount, and the operation modes comprise four modes including water electrolysis hydrogen production and storage, electricity abandoning after hydrogen storage is full, hydrogen fuel cell power generation and lack of power supply after the hydrogen storage amount is insufficient; the energy management strategy model is used for constraining technical parameters of each module of the device; a multi-target optimization model is constructed based on the parameter minimization of the leveling energy cost, the power shortage rate and the power abandoning rate; and inputting wind and light power generation resource data and power utilization load data, and obtaining a result of a configuration scheme of the device through a dung beetle optimization algorithm.

The invention discloses a cyclic resource scheduling method, system and device based on Kubernetes and a medium, and the method specifically comprises the steps: collecting resource use data of Pod and nodes in a Kubernetes cluster in real time, and predicting a load trend; dividing the resources into a hot resource pool, a cold resource pool and an idle resource pool based on the prediction results of the resource use data and the load trend; according to the electricity price and carbon emission data of each region, dynamically adjusting task distribution, and preferentially scheduling the task of the thermal resource pool to a region with a high renewable energy source proportion; for the cold resource pool and the idle resource pool, executing a progressive dormancy process by monitoring the power consumption change in real time; and training a reinforcement learning scheduling strategy by taking the queue length of the hot resource pool, the recovery time of the cold resource pool and the calling frequency of the idle resource pool as input. According to the invention, intelligent cyclic scheduling of Kubernetes cluster resources is realized, the resource utilization rate is improved, the energy consumption cost is reduced, the service continuity is guaranteed, and the intelligent level of scheduling decision is improved.