226 results about "Energy cost" patented technology

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 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 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 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 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 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.

This application relates to the field of energy dispatching, and in particular to a method and system for distributed energy storage dispatching based on multi-energy collaboration. The method includes: acquiring a set of factory order information; based on this, extracting and fusing multi-dimensional production value features to generate a dynamic production value assessment information set; based on this, combined with preset energy cost constraints, calculating dynamic electricity value thresholds and making global energy dispatching strategy decisions to generate an energy dispatching strategy information set; acquiring a set of energy supply influencing factors; based on the energy dispatching strategy information set and the energy supply influencing factors set, performing multi-timescale rolling optimization and global preset energy allocation to generate an energy allocation execution instruction set; based on this, issuing and executing dispatching instructions and dynamic production compensation for production lines and energy storage layers, generating and outputting a multi-energy collaborative dispatching report. This application can improve the economic efficiency of factories during energy dispatching.

The invention relates to the technical field of facility agriculture and micro-grid cooperative control, and particularly discloses a greenhouse micro-grid dynamic scheduling and photosynthetic efficiency cooperative optimization control method and system. The method comprises the following steps: constructing an energy management scheduling model with maximization of accumulated net photosynthetic products of crops in a scheduling period and minimization of net energy cost of a micro-grid as a collaborative optimization target; collecting real-time environment parameter data in the greenhouse and operation data of a micro-grid wind and light storage system and a load; a multi-objective optimization algorithm is adopted to solve the model online, and a joint optimization strategy including a micro-grid dynamic scheduling instruction and a greenhouse environment coordination control instruction is output; and executing the joint optimization strategy, and dynamically regulating and controlling the power output distribution of the micro-grid, the energy storage charging and discharging state and the operation process of the environment regulation and control equipment. According to the method, the multi-target collaborative optimization model with'photosynthetic product accumulation maximization 'and'net energy cost minimization' as the core is constructed, so that comprehensive maximization of energy benefits and agricultural production benefits is realized.

Energy aware data compression is disclosed. A storage array may receive data from a client to be compressed. The storage array may request information from a compression awareness engine that is configured to estimate compression times in the context of energy source and energy cost. The storage array makes a decision to compress the data based on the estimates or response received from the compression awareness engine. The data is then compressed and stored in the storage array.

The application provides a suspension mechanism of a mower and the mower. The suspension mechanism comprises a fixing frame, a sleeve fixed on the fixing frame, a first supporting column, one end of which is located in the sleeve, a second supporting column, one end of which is located in the sleeve and below the first supporting column, and an elastic member, two ends of which are connected with the first supporting column and the second supporting column respectively and located in the sleeve, and the elastic member is deformed along the horizontal direction, and the elastic member at least comprises a first deformation stage and a second deformation stage. In the first deformation stage, the elastic member is deformed until the elastic member and the top end of the sleeve abut together, and in the second deformation stage, the elastic member is limited by the sleeve, and the part of the elastic member located outside the sleeve continues to be deformed. The application can obtain the maximum vibration energy absorption with a small energy cost, thereby improving the safety and stability of the mower during operation.

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 utility model provides the drying storage rack, the drying storage rack is introduced on an automatic thick film production line, the continuous production of different products is realized, and the full-load operation of a sintering furnace in a subsequent process can be ensured through the production mode, so that the electric energy is obviously saved, and the energy consumption cost is reduced. And the drying storage rack adopts a multi-layer structural design, so that the space utilization rate is effectively improved, and the turnover process of materials in a production workshop is reduced. According to the improvement, the production layout is optimized, the product reject ratio caused by environmental factors in the material turnover process is reduced, and the product quality is remarkably improved. Products can be directly conveyed to a sintering furnace or a material storage layer through the material storage frame lifting machine assembly, automatic production is achieved, frequent transfer between a jig and a turnover trolley is reduced, jig management is unified, manual operation links are reduced, manual operation time is converted into production time, the production efficiency is further improved, and the production cost is reduced. And the whole production process is optimized.

The invention discloses a vertical farm integrated control system and method and a computer readable storage medium, and the system comprises a sensor group which is used for collecting environment and crop growth data; the quality prediction module is used for predicting future quality indexes of crops; the joint optimization module is used for constructing a multi-objective function containing quality indexes and energy cost and calculating first control input; the security shielding module defines a security set based on the control barrier function, and projects the first control input into the security set to generate a second control input when the first control input violates a security constraint. And the actuator group is used for adjusting the farm environment according to the second control input. According to the invention, through establishing the quality prediction model and performing joint optimization with the real-time energy cost, the system can actively use the electricity price valley period to adjust the environment on the premise of ensuring the target quality, the energy cost is significantly reduced, and the economic benefit is maximized.

The present application provides a kind of unmanned aerial vehicle path planning method and related equipment based on ecological sensitivity, obtain the multiple predicted paths between the current node and the next node of unmanned aerial vehicle;Unmanned aerial vehicle corresponding total power consumption and ecological sensitivity operator are calculated in advance when unmanned aerial vehicle flies along the predicted path;Based on the total power consumption and ecological sensitivity operator corresponding to unmanned aerial vehicle at each time point when unmanned aerial vehicle flies along the predicted path, the energy cost value corresponding to the predicted path is determined.In the case where the total power consumption is the same, the greater the ecological sensitivity operator, the lower the energy cost value, the predicted path with the minimum energy cost value is selected as the main path between the current node and the next node of unmanned aerial vehicle, so as to induce the path to deviate from sensitive area.

The invention discloses a cooperative control method for a hydrogen-electricity energy storage unit cluster in a hydrogen energy supported micro-energy network, and belongs to the technical field of new energy power system control. According to the cooperative control method, the SOH of PEMFC / EC can be considered to be directly related to the health state of the whole hydrogen-electricity energy storage unit; the purpose of the invention is to ensure efficient, long-life and economical operation of a hydrogen-electricity energy storage unit. PEMFC / EC is taken as a main body, SOH of PEMFC / EC equipment individuals, SOH consistency (HSH) of PEMFC / EC similar equipment clusters, overall energy cost (COE) of hydrogen-electricity equipment and complementary adjustment between the hydrogen-electricity equipment are considered, high-proportion renewable energy consumption can be better promoted, and interconnection and intercommunication of hydrogen-electricity heterogeneous energy flows are achieved.

Systems and methods are disclosed herein for managing electric vehicle charging control that provide the reduction of the operational cost to charge an electric vehicle by an energy provider and the reduction of load power consumption. In some examples, a charging control unit can include at least one processor, at least one memory, and an energy cost program stored in the memory to provide calculations for one or more optimized charging windows based on the operational cost of providing electricity by the energy provider. The energy cost information can be acquired by performing a day-ahead web scrape of a publicly available energy cost aggregator for the day-ahead settlement point cost of electricity and the market price of electricity. The charging control unit can provide charge control commands to the EV via a vehicle application programming interface.

Base station cell activation / deactivation may be based on energy cost. A base station may communicate with at least one other base station to coordinate deactivation of a cell and / or offloading one or more wireless devices in the cell to at least one other cell. Determination of additional energy cost and / or capability for serving the offloaded wireless device(s) may be made to calculate overall energy saving and / or to ensure uninterrupted connectivity in view of the deactivation. Base station communication may improve energy efficiency by performing deactivation / activation of one or more cells based on energy usage.

The invention relates to a coupling type coal-fired unit flue gas carbon dioxide capture system, which can realize switching between power supply by a unit generator and power supply by a renewable energy source field station in a system variable load operation process, and ensures the power supply stability of the system. The fused salt heat storage and supply unit is used for achieving cross-time decoupling between steam extraction and heat supply of a unit steam turbine and heat consumption of the carbon dioxide capture unit, and the safety and stability of the system under the variable load operation working condition are improved. The energy storage device and the fused salt heat storage and supply unit can reduce the output of the coal-fired unit during the off-peak period of the power grid load, improve the output of the coal-fired unit during the peak period of the power grid load, and improve the wide-load peak regulation operation capability of the unit, thereby obtaining higher peak regulation subsidy and capacity electricity price; the electricity and heat of the carbon dioxide capture unit can be supplied by cheap green electricity provided by direct connection of the renewable energy field station, so that the comprehensive energy consumption cost of the carbon capture system is reduced.

The invention relates to the technical field of industrial ovens, in particular to an energy-saving industrial oven control system. A central control module receives and fuses multi-source input information such as real-time temperature data of each temperature zone fed back by a partition temperature control module, heat recovery efficiency parameters fed back by a waste heat recovery module and real-time energy cost and availability information fed back by a multi-energy scheduling module, and target collaborative decision is optimized based on system global energy efficiency and cost. And generating three types of corresponding control instructions. The three kinds of instructions respectively regulate and control the partition temperature control module to achieve accurate temperature control of all temperature zones, the waste heat recovery module adjusts the working state of the device to recover high-temperature waste gas heat energy, the multi-energy dispatching module completes dynamic distribution and switching of multiple kinds of energy, feedback dynamic optimization collaborative decision logic is executed by combining all the modules, and a closed-loop control system is formed. The whole process of temperature zone control, waste heat recovery and energy allocation is comprehensively planned through collaborative decision making of the central control module, and the operation energy efficiency utilization rate, the temperature control accuracy and the energy cost management and control reasonability of the industrial oven are improved.

The invention relates to the technical field of resource planning, and discloses a smart city resource flow simulation and planning system based on a city metabolism model, and the system comprises a parameter collection module which determines a time boundary parameter and a scale parameter; the scale parameterization module is used for calculating metabolic integral scale elasticity; the effective budget generation module is used for calculating to obtain an elastic weighted effective budget; the resource allocation solving module is used for solving the allocation amount of each type of source; the scheduling curve mapping module is used for calculating to obtain a scheduling curve; and the planning result output module outputs the allocation list. According to the method, the robust budget is constructed through the scale parameterization model and the metabolic integral scale elasticity, and the planning deviation under different statistical granularities is reduced. The objective function is fused with the energy cost, the source cost and the scale robust item, the balance of cost control and scale adaptability is realized, and the execution deviation caused by purely pursuing the optimal cost is avoided.

This invention relates to a comprehensive energy optimization method employing an automatic allocation model, comprising: acquiring energy data; analyzing and processing the acquired energy data; acquiring energy status information; analyzing the energy status; automatically allocating energy based on the analysis results; and adjusting the energy supply based on the allocation results to optimize the energy supply system. This invention also discloses an energy optimization system. By real-time acquisition of response-sensitive and price-sensitive data from energy data, this invention uses a genetic algorithm to calculate optimal values, thereby guiding the allocation of scarce, urgently needed, and other energy sources. This achieves comprehensive energy supply optimization and scientific and rational allocation, reduces energy costs, and improves energy response speed and energy utilization rate.

This invention belongs to the field of building energy consumption monitoring technology and provides an anomaly detection method based on ARIMA residuals. It utilizes an ARIMA model to extract the residual sequence after the linear trend of data, learns normal patterns using LSTM-AE, and identifies anomalies. Simultaneously, it combines correlation coefficient matrices and anomaly propagation matrices to analyze fault propagation paths, featuring high detection accuracy and strong interpretability. This invention improves anomaly detection accuracy and effectively reduces false alarm and false negative rates. It also effectively handles sparse data, making it suitable for scenarios such as building energy consumption monitoring. By constructing correlation coefficient matrices and anomaly propagation matrices, it analyzes the propagation paths and impact mechanisms of anomalies in multi-parameter systems, providing a reliable basis for anomaly tracing and system optimization. Through practical application, this invention helps enterprises promptly identify and resolve energy waste problems, save energy costs, optimize energy management systems, and improve energy utilization efficiency.

The application discloses a regional building carbon emission optimization system and method, aiming at solving the problem of multiple optimization targets of regional building balanced carbon emission, economic cost and user comfort. The system includes a perception layer, a transmission layer, a platform layer and an application layer. The perception layer collects multi-source data such as building energy consumption, environmental state and regional energy. The energy load prediction module of the platform layer predicts future load based on historical data. The multi-objective collaborative optimization module constructs a target function with the minimum regional total carbon emission, the lowest total energy cost and the highest average user comfort as the target. The non-dominated sorting genetic algorithm is used to solve the optimal regional energy scheduling strategy. Finally, the strategy is executed through the regional energy hub module. Through data-driven collaborative scheduling, the application scientifically balances the contradiction between emission reduction, economy and comfort, and significantly improves the comprehensive energy efficiency and low-carbon operation level of the regional building group.

The utility model discloses a speed reducer structure, belongs to the technical field of photovoltaic equipment, and particularly relates to a photovoltaic panel tracking system, which comprises a motor, a rotary assembly, a first driving assembly, a second driving assembly and a mounting plate, the rotating assembly is fixedly installed in the middle of the installing plate, the first driving assembly and the second driving assembly are symmetrically installed on the installing plate, located on the two sides of the rotating assembly and connected with the rotating assembly, and the motor is fixedly installed on the rotating assembly and drives the rotating assembly to rotate; the rotating mechanism can rotate around two shafts at the same time, and is suitable for high-altitude and high-latitude areas with good sunlight irradiation conditions. The system has the advantages of higher light energy utilization efficiency and better adaptability, is suitable for a large-scale photovoltaic power station, can improve the power generation efficiency, reduces the power generation cost of the photovoltaic power station, and realizes higher energy utilization efficiency and lower energy cost.

The present application relates to electric bus charging joint scheduling technical field, provide electric bus fixed and dynamic wireless charging joint scheduling method and system. Electric bus fixed and dynamic wireless charging joint scheduling method includes that fixed and dynamic wireless charging coordination problem is modeled as limited time domain constraint Markov decision process, and then the power of fixed and dynamic wireless charging is determined as charging scheduling strategy based on system state, and the optimization objective function is constructed as minimizing cumulative expected energy cost within the set operation period; the optimization objective function is solved by using the rolling time domain model predictive control method, and the terminal SOC shortage cost is decomposed into the expansion and contraction of the stage cost by using the convex potential function, while keeping the optimal charging scheduling strategy, the end of the day requirement is internalized in the rolling time domain of the rolling time domain model predictive control method.

We provide a scheduling system and scheduling program that create an energy usage schedule that can reduce energy costs without changing the lifestyle of consumers. [Solution] The system includes an estimation task 161 that estimates the lifestyle 3 of customer P based on the amount of electricity used measured at predetermined intervals by a smart meter 2 installed at customer P's home, a pricing plan 5 in which the energy usage charges fluctuate according to the time of day in conjunction with the market price in the wholesale electricity market 4, and a scheduling task 162 that creates an electricity usage schedule based on the lifestyle 3, such that at least a portion of the time of day when customer P uses electricity is in line with the lifestyle 3 and is a time when usage charges are low.

The utility model discloses a cumene oxidation reaction system and relates to the technical field of cumene production systems. The cumene oxidation reaction system is composed of a first preheater, a second preheater, a first oxidation reactor and a second oxidation reactor. Cumene is introduced through a first inlet of the first preheater, sequentially passes through the first preheater and the second preheater, then enters the first oxidation reactor and then flows to the second oxidation reactor, and materials after reaction are conveyed to a next treatment unit. Meanwhile, circulating materials of the second oxidation reactor and the first preheater form a loop, and the second preheater introduces a heat source and is communicated with the exhaust unit. According to the system, isopropylbenzene is preheated step by step through the first preheater and the second preheater, so that an ideal reaction temperature is achieved when materials enter the oxidation reactor, and the reaction efficiency and the product quality are remarkably improved; circulating materials of the second oxidation reactor are introduced into the first preheater, so that reaction heat is effectively recycled, external heat source consumption is reduced, and energy cost is reduced.