3537 results about "Power demand" patented technology

The invention discloses a computing power load stabilizing and energy-saving management system based on a flywheel energy storage system, and belongs to the technical field of energy storage and management, and the system comprises a data collection module which is used for collecting and calculating a real-time current harmonic spectrum of a cluster and server rack vibration data to generate an electric energy quality characteristic spectrum; the prediction modeling module is used for establishing a prediction model output computing power demand dynamic distribution matrix based on multi-dimensional space-time correlation; the optimization control module is used for analyzing a peak load phase in the power demand dynamic distribution matrix to generate a dynamic coordination control strategy; and the power execution module is used for executing the dynamic coordination control strategy to adjust the phase parameter of the reverse electromotive force of the flywheel so as to offset the voltage harmonic component of the server bus. Harmonic phase compensation is realized through dynamic reverse electromotive force modulation, the calculation load prediction precision can be effectively improved, and the energy utilization efficiency and the equipment operation reliability are remarkably improved.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention relates to the technical field of electric power energy storage management, in particular to an independent electrochemical energy storage integrated management system, which comprises a load prediction analysis module, a charge and discharge response control module, a dynamic load threshold adjustment module, an energy storage response optimization module, a PID (Proportion Integration Differentiation) adjustment module and an abnormal state monitoring module. According to the method, the change rate and fluctuation amplitude of the load are adjusted in real time, the power demand change is accurately predicted, the stability and response speed of the system are improved, the fluctuation period and sudden change value of the load are analyzed, the demand trend is accurately predicted, the charging and discharging rate is adjusted, the actual demand is matched, the energy utilization efficiency is improved, the service life of equipment is prolonged, and the response threshold value is dynamically adjusted; the operation time interval is optimized, so that the system is maintained in an optimal state under variable loads, the energy consumption and the operation and maintenance cost are reduced, power output and equipment states are monitored in real time, abnormities are handled in time, safety and stability of the system are ensured, and the overall efficiency and reliability of the power system are remarkably improved.

The invention relates to the technical field of intelligent power grid dispatching, in particular to a power grid dispatching method and system, and the method comprises the steps: collecting environment data, equipment health data and economic and market data, and generating fusion data through edge computing equipment; a multi-model integrated deep learning model is adopted to predict power demand and supply, a reinforcement learning algorithm is combined to simulate a power grid dispatching scene, and an optimal dispatching strategy is generated; and dynamically adjusting the output power of the power station and the power grid node load distribution, and updating the scheduling strategy in real time. According to the method, the prediction precision is improved through multi-model integration, a scene simulation strategy is optimized by utilizing reinforcement learning, and coordinated optimization of global and local states is realized in combination with multi-level feedback analysis, so that the stability and economy of power grid operation are remarkably improved, meanwhile, carbon emission is reduced, and the method is suitable for complex scenes of modern intelligent power grid dispatching.

The invention discloses a source-grid-load-storage integrated optimal scheduling method and system for a micro-grid, and relates to the technical field of smart grids and micro-grids, and the method comprises the steps: firstly collecting multi-source data in a specified micro-grid, and carrying out the optimization storage of the multi-source data in a time sequence database; constructing a time sequence prediction model, predicting generating capacity and load demand according to historical data, generating a preliminary scheduling strategy according to the generating capacity and load demand, realizing comprehensive cost minimization, analyzing the strategy, evaluating deviation and abnormity, and finally correcting the strategy according to an evaluation result; comprising an intelligent data aggregation module, a time sequence data prediction module, a preliminary scheduling generation module, a preliminary scheduling strategy analysis module and a real-time optimization feedback module, influences of various data sources are considered, the model can be adjusted in real time, variable power demands and power generation conditions in a micro-grid environment can be better coped with, and the real-time optimization feedback is realized. The accuracy of a prediction result is greatly improved, so that a powerful basis is provided for scheduling decision making, and risks possibly caused by prediction deviation are avoided.

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

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

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention discloses an edge computing power sharing method, an edge computing power sharing device, edge computing power sharing equipment and a medium, and the edge computing power sharing method comprises the following steps: connecting edge nodes through a decentralized P2P network, constructing a network connection module, and realizing computing power resource sharing among the nodes; collecting and monitoring computing power resource state data of each edge node through a computing power resource management module, judging nodes in an idle state through an evaluation algorithm, and adding computing power resources of the nodes into a shared pool; the node matching module is used for screening out an optimal matching node from the shared pool by utilizing a preset screening algorithm according to task demand information released by a computing power demand side; and the distribution execution module sends the task instruction to the selected optimal node from the cloud, and after the task is executed, the node releases the computing power resource and performs expense settlement through the charging module. According to the method, the utilization efficiency of computing power resources is optimized through decentralized node management and an intelligent matching mechanism, so that edge computing resources are more easily integrated and utilized.

The invention relates to the technical field of ship power systems and energy management. The invention provides an energy optimization management method and system for an extended-range hybrid power ship. The method comprises the following steps: collecting ship multi-source heterogeneous data in real time, and constructing a multi-source heterogeneous data set; based on the multi-source heterogeneous data set, constructing a load prediction model based on a time sequence convolutional network and long and short term memory fusion, and dynamically outputting propulsive power demand probability distribution in a future navigation period; a multi-objective optimization model is established, and an improved multi-objective genetic algorithm is adopted to generate a Pareto frontier solution set; and on the basis of a rolling time domain optimization framework, in combination with real-time navigation situation awareness data, carrying out online correction on the Pareto solution set, and generating and executing an optimal energy distribution instruction set. The problems of an existing extended-range hybrid power ship energy management technology in the aspects of working condition adaptability, multi-energy coupling efficiency, real-time state sensing and predicting capacity and emission and economical efficiency balance are solved.

The embodiment of the invention provides an intelligent dynamic load regulation and control method and system for an intelligent power grid. Wherein power parameters of key nodes are collected in real time through time sequence analysis and a deep learning algorithm, future power demand fluctuation is predicted, and a power grid topological structure model is constructed to identify the key nodes and a potential influence range; a genetic algorithm, particle swarm optimization and other multi-objective optimization algorithms are adopted to generate specific operation instructions for different power utilization areas or devices, and optimization objectives comprise minimization of energy cost, maximization of renewable energy utilization rate and guarantee of power supply stability; converting the strategies into guidance suggestions, pushing the guidance suggestions to a terminal user, collecting feedback information, generating a preference model by applying a reinforcement learning algorithm, and selecting an optimal strategy for implementation based on user acceptance and feasibility evaluation; risks and abnormal conditions are detected through the Bayesian network, and the strategy is adjusted in time. According to the technical scheme provided by the embodiment of the invention, the efficient distribution of power resources and the energy utilization efficiency in the smart power grid are improved.

Disclosed in the present invention are a method and system for full-range participation of energy storage charging and discharging in power grid frequency regulation. The method comprises: collecting operating power and frequency data of a power grid, so as to construct a full-range droop coefficient; setting constraints for an upper limit value and a lower limit value of a frequency dead band; obtaining an actual output power instruction value by means of a constraint function constructed on the basis of a theoretical power instruction value and a charging / generated power rated value, and controlling the power grid to operate on the basis of the actual output power instruction value; setting a hysteresis range on a power instruction of an energy storage system, and when a power demand of the power grid exceeds an upper limit threshold value of the hysteresis range, switching the energy storage system from a charging mode to a discharging mode; and when the power demand of the power grid is lower than a lower limit threshold value of the hysteresis range, switching the energy storage system from the discharging mode to the charging mode. In the present invention, when the frequency of the power grid is increased, generated power is reduced until power is absorbed from the power grid, and when the frequency of the power grid is reduced, the absorption of power from the power grid is reduced until electrical power is delivered to the power grid, thus achieving full-range participation of energy storage in power grid frequency regulation control, and satisfying regulation requirements of power grids.

A charging method based on intelligent dynamic power allocation. The method comprises: acquiring the current SOC, a real-time power demand, a rated power and infrared position detection information of an electric vehicle; on the basis of the current SOC, the real-time power demand, the rated power and the infrared position detection information of the electric vehicle, and in combination with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold value incentive strategy and an overall-power utilization maximization strategy, performing power allocation and generating a corresponding control instruction; and controlling the output of a charger topology switch on the basis of the control instruction. Further disclosed is a multi-gun charger apparatus using the charging method. The charging method includes a moderate power allocation strategy and takes into account the demands of both a charger control system and users, and can effectively improve the overall charging efficiency, reduce the charging time, and improve the utilization rate of charging piles.

The invention discloses a power supply circuit, a server and a time sequence control method, relates to the field of server power supply, and solves the problem that the demand of loads such as a GPU (Graphics Processing Unit) and a CPU (Central Processing Unit) on short-time peak power is increasingly increased at present. According to the power supply circuit, the long-time power supply module and the peak value compensation module are introduced to work cooperatively, the long-time power supply module provides stable energy conversion, and the long-term stable power supply requirement of a load is met; and the peak compensation module dynamically adjusts power supply according to the load demand and the working state of the long-time power supply module, provides or recovers energy, and meets the instantaneous peak power demand, thereby avoiding the defect that the peak current cannot meet the requirement or needs a large-scale redundancy and high-cost capacitor row in the traditional scheme, and improving the reliability of the system. According to the design, power supply stability can be guaranteed, system cost and size are reduced, and overall power supply efficiency and response capability are improved.

The invention relates to the technical field of electric power system control, in particular to an electric power resource intelligent control system based on digital twinning, which comprises a data acquisition module, a digital twinning model module, an electric power dispatching module, a fault early warning module and a three-dimensional visualization module. According to the method, the load demand prediction model is established based on the hybrid RNN, the RNN and full-connection network hybrid neural network model is constructed, the historical time sequence, the time feature, the historical statistical feature and the similarity feature are learned, and the problem that the power load demand is difficult to predict due to renewable energy source grid connection and power demand increase is solved; a power dispatching decision is made through a multi-objective function optimization algorithm based on an improved particle swarm, the particle swarm optimization algorithm is improved by adding a variation strategy for high-fitness particles, the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the practice process is solved, and therefore the problem that power dispatching of multi-objective optimization is difficult to achieve is solved.

The invention relates to the technical field of power management, and provides a power distribution method based on BMS power control and related equipment thereof. The method comprises the following steps of: performing multi-mode synchronous acquisition and self-adaptive preprocessing on detected battery pack global state data to obtain a battery state tensor, and performing unsteady-state signal decomposition and time-space feature extraction on a received load power demand signal to obtain a power component matrix; performing multi-constraint dynamic planning on the battery state tensor and the power component matrix to obtain an initial power distribution strategy, performing dynamic correction in combination with a deep reinforcement learning model to obtain an adaptive distribution strategy, and performing multi-layer circuit equalization optimization on the adaptive distribution strategy to obtain a power regulation instruction set; and finally, performing multi-physics field coupling verification and fault-tolerant reconstruction on the power regulation instruction set to obtain an execution signal sequence. According to the invention, through data fusion, demand prediction, strategy optimization and deep learning, power distribution is accurately regulated and controlled according to the battery state and the load power demand.

The invention provides a source network load electricity storage quantity distribution method based on multiple targets of prediction and reinforcement learning. The method comprises the following steps: firstly, by collecting and preprocessing multi-modal data, constructing a GRU prediction model based on an attention mechanism so as to improve the prediction precision of power demand and supply; then, designing a multi-objective optimization particle swarm resource allocation model based on reinforcement learning, and optimizing a resource allocation strategy by adaptively adjusting particle swarm parameters and introducing a reinforcement learning component; and finally, a resource allocation scheme is corrected and adjusted through a dynamic adjustment mechanism, and cost minimization and system operation efficiency maximization are realized while system constraints are ensured to be satisfied. According to the method, the response speed and the scheduling precision of the power system can be improved, the stability and the economical efficiency of power supply are ensured, the resource configuration is optimized, the operation cost is reduced, the adjustment capability of the load side can be improved, and medium-and-long-term time-phased transaction can be better carried out.

The invention relates to the technical field of electric power regulation and control, in particular to a method and system for controlling an electric power energy storage system, and the method comprises the following steps: obtaining the voltage and current when energy storage equipment is charged, analyzing the voltage fluctuation and current change trend, recognizing a fluctuation point, and calculating the fluctuation amplitude and frequency. And judging the deviation degree between the fluctuation data and the normal operation data of the equipment to obtain an equipment stability index. According to the invention, by monitoring the voltage and current of the energy storage device during the charging period and deeply analyzing the fluctuation characteristics of the electrical parameters, the evaluation accuracy of the stability of the device, the accurate fluctuation point identification and the calculation of the fluctuation amplitude and frequency are significantly improved, and the behaviors of the device in different charging and discharging periods are allowed to be more accurately judged. And the residual capacity of the energy storage equipment is matched with the actual power demand, so that the power distribution efficiency is effectively improved.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention relates to the technical field of power dispatching, in particular to a power demand real-time sensing and responding system and method based on Internet of Things driving, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting power utilization power and power utilization time data at a user side, and carrying out the preprocessing of the data; constructing an electricity utilization power curve, and extracting electricity utilization characteristics; using a K-means clustering algorithm to carry out clustering analysis on the user power consumption characteristics, and dividing the users into k power consumption categories; a power consumption demand prediction model is constructed by training a long short-term memory (LSTM) network model, and the future power consumption power curve trend is predicted; and establishing a power utilization scheduling response mechanism based on the prediction result and the clustering-divided power utilization behavior categories in combination with a charging and discharging scheduling strategy of the power energy storage equipment, thereby realizing flexible scheduling response to the power utilization behaviors in different time periods in the future. According to the method, the power consumption management precision and efficiency can be improved, personalized power consumption requirements are met, and energy utilization is optimized.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention discloses an optimization method for a new energy ship energy management system, and the method comprises the steps: S1, obtaining a priority assumption condition related to the energy use of a new energy ship according to a pre-created new energy ship system; s2, constructing a ship energy management mathematical model about a new energy ship system based on priority hypothesis conditions; s3, according to the constructed ship energy management mathematical model, obtaining an objective function model and optimization constraint conditions of ship energy for quantitatively evaluating advantages and disadvantages of different energy use schemes; s4, ship operation energy consumption and historical navigation data are collected and obtained; s5, according to the historical data, obtaining a neural network prediction model for pre-estimating the photovoltaic generating capacity and the ship navigation power demand based on weather prediction data; and S6, according to the neural network prediction model, obtaining estimated data of the photovoltaic power generation amount and the ship navigation power demand, and based on the target function model of the ship energy and the optimization constraint condition, obtaining an optimal energy use scheme of a corresponding new energy ship energy management system according to an optimization algorithm. The problems that an existing ship energy management system and a calculation method thereof have remarkable defects in the aspects of energy efficiency optimization, multi-energy coordination, dynamic response, operation simplicity and convenience, intelligent management, safety, new energy compatibility, comprehensive optimization strategies and data processing capacity, and complex requirements and environmental protection requirements of modern shipping are difficult to meet are solved.

The invention discloses an energy storage scheduling method and system based on a service area load and renewable energy power generation prediction, and the method carries out the prediction of the load demand of a service area based on a long short-term memory (LSTM) network, and combines the renewable energy power generation prediction with a power grid electricity price signal to dynamically adjust the charging and discharging strategy of an energy storage unit. By predicting the fluctuation of the load in the service area and the power generation condition of the renewable energy source, the optimal energy storage scheduling scheme can be automatically generated in different time periods, and the method is particularly suitable for scenes with large traffic flow fluctuation and complex power demand in the service area. According to the method, the dependence of the service area on the external power grid is effectively reduced, and the stability and economic benefits of system operation are improved.

The invention belongs to the technical field of computing power scheduling, and particularly relates to a computing power distribution scheduling method and system for an edge computing platform, and the method comprises the steps: generating a business pool based on a historical resource request task; constructing a business computing power demand prediction model based on XGBoost, adding the influence factors into the prediction model, generating resource request business prediction output of each node device, updating the business pool according to the prediction output, evaluating the computing power scale required by the resource request business in the updated business pool, and obtaining the computing power demand; according to the computing power demand obtained by each node device, taking energy consumption minimization and response time minimization as targets, carrying out multi-target solution based on an improved firefly algorithm, and obtaining a computing power distribution scheme which enables the energy consumption and the response time to reach the optimal; and allocating schedulable computing power resources in the edge computing platform based on the computing power allocation scheme. According to the method, overload of part of the data centers can be avoided, meanwhile, the overall calculation efficiency is improved, and calculation power resources are reasonably utilized.

The invention relates to the technical field of power grid optimization, in particular to a regional power grid reactive power optimization control method and system, and the method comprises the steps: building a dynamic response matrix according to key characteristic parameters of reactive power compensation equipment in a regional power grid; dynamically tracking the reactive power flow of the power grid according to the dynamic response matrix and the regional power grid line power flow operation state data to obtain a reactive power demand change trend; determining a target output adjustment value of each reactive compensation device according to the reactive demand change trend; generating an output adjustment control instruction according to the target output adjustment value; and adjusting the reactive power output of each reactive power compensation device according to the output adjustment control instruction, and dynamically adjusting the output limit of the reactive power compensation device according to the device operation state and the reactive power data of the reactive power compensation device in the reactive power output adjustment process of the reactive power compensation device. By dynamically adjusting the output limit of the reactive power compensation equipment, accurate control and optimal adjustment of the output of the reactive power compensation equipment are realized, and the reactive power compensation efficiency of a power grid is improved.

The invention relates to the technical field of computing power resource scheduling, and particularly discloses a green data center computing power demand prediction and energy consumption control method, system and device. Firstly, computing power information and environmental parameters of a data center are collected in real time based on edge equipment; afterwards, a time sequence trend is predicted by using an LSTM neural network, a CNN network is fused to extract computing power features, an LSTM-CNN hybrid model is constructed, repeated training is performed through an Adam optimization algorithm, and the model can accurately pre-judge a future computing power demand; and in combination with analysis of historical energy consumption and real-time monitoring data, an energy consumption analysis model is established to evaluate power consumption conditions under different computing power requirements. Based on the prediction result and the energy consumption analysis, the working state of the server cluster is dynamically adjusted, the load is scheduled, the energy use is optimized, the energy efficiency ratio of the data center is continuously improved by verifying and adjusting the energy consumption strategy, the overall operation cost and the energy consumption are remarkably reduced, and the development requirements of green energy conservation are met.