463 results about "Load model" patented technology

The embodiment of the invention provides a chip platform resource dynamic scheduling method, which comprises the following steps of: deploying a resource monitoring probe to acquire a chip platform running state, constructing a resource use index database to record CPU utilization rate and memory bandwidth data, analyzing a time sequence characteristic and a change rule of the running state, establishing a load model, setting a resource threshold value based on the system bottleneck identification model, formulating a scheduling rule through a strategy generator, and creating a resource configuration scheme according to the resource threshold value and the scheduling rule; analyzing a system state through a system bottleneck identification model, selecting a corresponding optimization strategy according to a scheduling rule, constructing a feedback acquisition queue, evaluating a scheduling effect of an execution instruction by using a data analysis technology, inputting the scheduling effect into an evaluation model for verification, and updating the scheduling rule based on a verification result of the evaluation model; according to the invention, the resource management efficiency and performance of the system can be effectively improved, and more flexible cross-platform support is realized.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention discloses an electric power marketing management information platform daily electric power fitting method and related equipment, and relates to the technical field of electric power data management, and the method comprises the steps: obtaining historical load data, external environment data and equipment operation state data of a target region; constructing an initial load fitting model based on the historical load data; extracting a date characteristic factor according to a preset time classification rule, and dynamically correcting the initial load fitting model based on the date characteristic factor to generate a corrected load model; generating a multi-source fusion feature based on the external environment data and the equipment operation state data; and inputting the multi-source fusion features into the corrected load model, and outputting a target load prediction result.

The invention relates to the technical field of data processing, and discloses a load demand prediction method and system for a distributed new energy power generation system, and the method comprises the steps: obtaining historical data corresponding to power generation equipment and a load cluster in the distributed new energy power generation system, extracting feature parameters from the historical data, and carrying out the clustering analysis, dividing the power generation equipment and the load cluster into different subsets; processing the historical data corresponding to the power generation subsets and the load subsets to obtain a power generation output curve and a power utilization load curve; by analyzing the relevance between historical power generation and consumption data, obtaining a relevance index so as to fuse a power generation output curve and a power consumption load curve into a power generation load fusion model; and a parameter identification method is used to optimize the power abrupt change rate and the load change rate corresponding to the historical power generation data and the historical power utilization data, so that the power generation load fusion model is integrated into a generalized load model structure, a fusion equivalent model is obtained, and the load demand of the distributed new energy power generation system is accurately predicted.

The invention provides a computing-electric-thermal coupled data center integrated energy system collaborative optimization method, and relates to the field of data center energy-saving management and control, and the method comprises the steps: firstly obtaining real-time data collected by a data center; secondly, a data center load model is constructed based on the relation among real-time data quantification calculation power, electric power and heating power; constructing a collaborative optimization model according to the data center load model, and solving the collaborative optimization model to obtain an optimization result; and finally, adjusting calculation task distribution, a power supply strategy and cooling system operation parameters according to an optimization result to form a calculation-electricity-heat coupled data center integrated energy system collaborative optimization strategy. According to the calculation-electricity-heat coupled data center comprehensive energy system collaborative optimization method, collaborative optimization is carried out on the data center energy system through multi-energy complementation, and energy conservation and efficiency improvement of the data center are achieved.

The invention discloses a virtual power plant intelligent aggregation optimization control method for multi-type flexible resources, and the method comprises the steps: constructing a dynamic characteristic model of distributed resources, wherein the dynamic characteristic model comprises a photovoltaic output probability prediction model, an energy storage SOC-life coupling model, an electric vehicle behavior chain model, an adjustable load constraint model, and an industrial interruptible load model; an edge agent node calculates an adjustable potential interval of a resource cluster in real time and uploads the adjustable potential interval to a cloud end, a global optimization target is solved on the cloud end based on an improved sparrow search algorithm (ISSA), after a scheduling instruction is generated, model parameters are corrected in a rolling mode according to actual output deviation calculated in real time, and a scheduling result is obtained. And triggering a resource fault emergency strategy for prediction deviation and resource fault problems occurring in the operation process of the virtual power plant. Through an edge-cloud collaborative architecture and a multi-stage optimization strategy, accurate modeling, optimization aggregation and intelligent scheduling of distributed resources are realized.

The invention discloses an active power distribution network two-stage island division method and system considering flexible resource support. The method comprises the following steps: establishing a first resource allocation model considering energy storage and controllable load, the first resource allocation model comprises energy storage battery charging and discharging upper and lower limit constraints, an energy storage battery degradation cost model, a controllable load model and a controllable load related user protocol; obtaining parameter data of a target power distribution system, wherein the parameter data comprises a line parameter, a load level, a load level, a network topology connection relationship, a system operation voltage level and branch current limitation, a system fault position, a reference voltage and a reference power initial value; and according to the parameter data, establishing a mathematical model for two-stage island power supply recovery based on a first resource allocation model. The method for solving the island is simple and convenient, the island recovery amount can achieve the optimal recovery effect, meanwhile, the search time is greatly shortened, the solving efficiency is higher, and the rapid power supply recovery capacity of the power distribution network is improved.

The invention relates to the technical field of intelligent distribution, in particular to a power grid load distribution system and method based on intelligent control, and the system comprises a data collection and processing module which carries out the real-time collection and data processing of all node data and meteorological data of a power grid, and obtains the actual power grid load data; the communication network module is used for transmitting the actual power grid load data; the intelligent regulation and control module is used for constructing a rural power grid load model to obtain a load prediction value; the monitoring alarm module is used for monitoring the actual rural electricity consumption condition in real time according to the load prediction value; the system verification module is used for verifying the stability of the power grid after regulation and control configuration, verifying the resource utilization efficiency of the power grid according to the load rate of the transformer, and adjusting the regulation and control configuration; and the execution feedback module is used for performing control strategy command execution on the power grid equipment. The power supply and demand balance of the rural power grid is realized, and the utilization efficiency of power grid resources is improved.

The invention discloses an aggregation regulation and control system for demand side adjustable resources. The aggregation regulation and control system comprises an adjustable resource pool, a resource regulation and control management module and a resource dynamic interaction decision module, the adjustable resource pool is used for acquiring load node data of adjustable resource load nodes, clustering to obtain different types of adjustable resource loads, and constructing load models corresponding to the various types of adjustable resource loads; the resource regulation and control management module is used for constructing a load prediction model corresponding to each type of adjustable resource load, and then predicting the predicted load output of each type of adjustable resource load according to the load prediction model; and the resource dynamic interaction decision module is used for analyzing the adjustable power of each type of adjustable resource load in different scheduling scenes according to the predicted load output of each type of adjustable resource load, and scheduling the demand response of each type of adjustable resource load. According to the invention, aggregation regulation and control of demand-side adjustable resources can be realized.

The invention discloses a battery capacity performance testing method and device and a storage medium, and relates to the technical field of battery capacity performance testing. By constructing a dynamic load model and a multi-load test protocol and combining a high-precision data acquisition and filtering algorithm, adaptive data analysis and a machine learning regression model, the problems of difficulty in balancing precision and efficiency, large data acquisition error, fixed test protocol and the like in the traditional battery test are solved; by dynamically adjusting test parameters and environmental conditions, complex working conditions of the battery in actual use are accurately simulated, meanwhile, dynamic updating and optimization of a test protocol are achieved, the test precision, the data reliability and the test efficiency are remarkably improved, and a comprehensive, efficient and high-adaptability solution is provided for battery performance evaluation and health management.

The invention provides a computer hardware performance evaluation method and system based on an analogue simulation model, and relates to the technical field of computer performance evaluation. According to the method, hardware component models such as a processor, a memory and I / O are constructed by analyzing a system structure description file, semantic analysis is performed on an instruction stream or a parallel computing graph of a to-be-evaluated application, and a task load model is formed. The model is input into an event-driven scheduling module, simulation is executed on the hardware component model, task-resource mapping is generated, and task time delay, communication traffic and power consumption are recorded. And calculating average response time delay, data bandwidth and unit energy consumption according to the simulation data, and outputting an evaluation report compared with the performance baseline. According to the method, real load-oriented multi-dimensional performance prediction can be realized, and the method can be used for architecture optimization and scheme selection.

The invention relates to the technical field of intelligent temperature control, in particular to an industrial mainboard computer heat dissipation optimization method and system based on AI intelligent regulation and control, and the method comprises the steps: obtaining mainboard real-time temperature data, carrying out time sequence preprocessing, and generating distributed time sequence data; extracting thermal features and classifying and identifying the thermal features to obtain coordinate distribution of the high-energy-consumption area; inputting a classification model to output a classification result, and screening areas exceeding a preset threshold to determine a priority regulation label; performing spatial interpolation based on the label to generate a heat map, and extracting heat distribution boundary data; analyzing thermal resistance according to the boundary data and identifying a bottleneck region to obtain thermal resistance distribution; performing fan parameter optimization based on the thermal resistance to obtain an optimized heat dissipation strategy; inputting a load model, and outputting dynamic load adaptation regulation and control parameters; and real-time regulation and control and energy efficiency evaluation are executed according to the regulation and control parameters, and final heat dissipation operation parameters are screened and solidified. The method can solve the problem of heat dissipation control lag.

The invention provides a virtual power plant scheduling method and system based on distributed resource operation domain description, and relates to the technical field of virtual power plant optimization scheduling, and the method comprises the steps: determining a virtual power plant distributed resource operation domain; considering the influence of demand response load active power and reactive power adjustment on the virtual power plant output model, and constructing a comprehensive load model; dividing a feasible region of a variable in a bilinear term in the comprehensive load model into several non-intersecting regions by adopting a segmented McCormick relaxation method, and converting the regions into a mixed integer linear programming form; the operation constraint condition of the virtual power plant is considered, a mixed integer linear programming form is combined, boundary points of the operation domain of the virtual power plant are solved, and the range of the distributed resource operation domain of the virtual power plant is depicted; and in the distributed resource operation domain range of the virtual power plant, constructing a virtual power plant optimization scheduling model by taking the sum of the total operation cost and the demand response compensation as a target, and applying the obtained optimal operation scheduling strategy to the virtual power plant for execution.

The invention discloses a virtual power plant load optimization method based on source load uncertainty, and the method comprises the steps: introducing discrete random variables based on the source load uncertainty, including the source load uncertainty based on normal distribution and the output and load of a photovoltaic power plant based on probability distribution; the probability distribution condition of the output and the load of the photovoltaic power station can be better described, and a foundation is laid for the construction of a photovoltaic output and load model; a virtual power plant load optimization scheduling model is established by taking maximization of energy consumption and minimization of operation cost as targets, virtual power plant load optimization scheduling is realized, high-efficiency operation of the virtual power plant can be ensured, a good peak clipping and valley filling effect on an electricity price peak-valley trend can be achieved, economic benefits are relatively considerable, and the method has high reliability and is suitable for popularization and application. Scientific guidance is provided for power demand side management, and the problem that an existing virtual power plant load optimization scheduling method is extremely prone to causing standby insufficiency under the condition that the source load is uncertain due to the fact that standby demands are not considered is solved.

The invention discloses a remote sensing satellite autonomous task planning method and system, and the method comprises the steps: constructing an orbit prediction model, an attitude maneuver model, a load model and other models, and calling the models in a planning process for task sorting, constraint checking and meta-task generation; only point / line / surface related information, load related parameters, task imaging intervals and other necessary task demand parameters need to be injected on the ground, path planning, task decomposition, task sorting, constraint check, autonomous instruction generation and other operations are carried out through an on-satellite autonomous task planning system, and an instruction sequence related to task execution is generated and distributed to a related subsystem. The load subsystem, the data transmission subsystem, the control subsystem, the satellite service subsystem and the like cooperate to complete tasks. After the autonomous task planning system receives task information injected on the ground, satellite pre-planning information can be fed back to the ground in time, and the satellite pre-planning information can be used for ground synchronous task situations.

The invention relates to the technical field of source network load storage intelligent scheduling, and discloses a source network load storage AI intelligent scheduling method and system, and the system comprises a data collection module, a fluctuation analysis module, a constraint calculation module, a load modeling module, an energy storage analysis module, a decision engine module, and a safety correction module. Output data of a photovoltaic power station, a wind power plant and a traditional power plant are collected in real time through an Internet of Things sensor, and a multi-source heterogeneous energy data pool is constructed; aligning the corrected source load data with the energy storage state and the power grid operation parameters according to a time sequence to generate a four-dimensional optimization combination matrix; matching an optimal scheduling algorithm through a bionic search strategy, and analyzing the data matrix through the deep reinforcement learning model to generate a three-section scheduling instruction; and finally, the power grid control system executes power generation adjustment, load regulation and control and energy storage charging and discharging instructions. According to the system, the edge computing gateway is adopted to realize data acquisition, the new energy consumption capability and the power grid stability are improved, and the risk of source-grid load-storage collaborative failure is reduced.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention provides a simulation calculation method of an urban rail transit traction power supply system, which comprises the following steps of: constructing a locomotive load model, establishing a locomotive running state information table with a unified time section, and combining the position of a locomotive with the position of a traction station in a line to obtain topological structure analysis of different time sections; constructing a traction power supply system model, and obtaining rated voltage, line length, line unit impedance and traction position; simulating that a plurality of locomotives start from a vehicle depot and run from the head end of a line to the tail end of the line, obtaining power and position values of the locomotives according to a locomotive load model, substituting the power and position values into a traction power supply system model to complete load flow calculation of a direct-current traction network, and obtaining current and voltage of the locomotives and bus voltage and feeder current values of a traction station; according to the invention, the power of the train in different operation states can be simulated, and the bus voltage and feeder current of each traction station of the traction power supply system can be calculated.

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention relates to a system for a user-side energy storage sharing framework. A power energy scheduling optimization method comprises: establishing a user load model, an energy storage battery model and a user-to-user capacity sharing model; with a minimum electricity cost of a whole community as an objective optimization function, resolving optimal scheduling based on the function using a MILP algorithm as an optimization technique for power energy scheduling of a user-side energy storage sharing framework; and performing power energy scheduling for users according to a resolved result. The invention determines a user cost model under the user-side energy storage sharing framework, establishes an electricity model of non-transferable loads and transferable loads and corresponding constraints, and obtains optimal values of adjustable variables to control electricity consumed by electric loads by resolving an optimal value of an objective function under the precondition of reducing user costs, thus further reducing electricity costs.

The invention relates to the technical field of electricity consumption prediction, in particular to an electricity consumption load prediction method based on an intelligent electric meter communication module. The method comprises the following specific steps: collecting historical power utilization load data and power utilization influence factors, and extracting power utilization feature vectors; clustering the electricity utilization characteristic vectors by using a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering method to obtain a group of clusters; performing local feature difference calculation and dynamic feature weight adjustment on the samples in each cluster to obtain a weighted feature matrix; and performing local consistency measurement and local consistency enhancement on the weighted feature matrix to obtain an enhanced matrix. And performing intra-cluster hierarchical division, hierarchical feature focusing and hierarchical fusion on the enhanced matrix to obtain a final clustering result, and evaluating the clustering result and the electrical load model. According to the method, the difference in each cluster in the clustering result can be reduced, and the effect of the clustering result is improved.

The invention discloses a load management system based on a load model in the plastic product industry. The load management system comprises a load time sequence model building module which classifies and marks equipment according to operation behaviors of the equipment to obtain a load time sequence model; the data acquisition and fusion module acquires power, current, voltage and start-stop state data of equipment in real time through a monitoring point position, and generates a real-time load data set in combination with real-time production state data; the load prediction and adjustability evaluation module generates a load prediction curve by using a long short-term memory network, generates an adjustability score by adopting a support vector machine model, and determines an adjustable process list in combination with real-time production state data; and the scheduling execution module determines candidate adjustment equipment according to the load prediction curve, the adjustability score and the adjustable process list, constructs a multi-objective optimization function through a multi-objective optimization algorithm, and generates a load adjustment strategy by using a particle swarm optimization algorithm to execute scheduling on the equipment. According to the invention, the refinement level of load management is improved.

Disclosed are a risk assessment method for a distributed power distribution network considering a line fault and a device thereof, belonging to the field of risk assessment of faults of a power distribution network. The method includes the following steps: establishing a wind generating set output model, a photovoltaic module output model and a load model, and generating a wind generating set output data sample, a photovoltaic module output data sample and a load sample; establishing a short-term fault rate model of a wind generating set, a photovoltaic module and a line, and generating a system state sample; establishing an objective function and a constraint condition, and reconstructing the power distribution network to obtain the reconstructed power distribution network topology; carrying out the risk assessment and an entropy weight method.

The invention discloses a method and a system for identifying a power imbalance state of a power system under climate and weather changes. The method comprises the following steps: determining climate and weather environment data of a target region; determining a current climate and meteorological environment event of the target region; if the climate and meteorological environment event meets the preset type, triggering an electric power imbalance state identification process of a target electric power system in the target region; constructing a source side output model matched with the power supply side, and constructing a load model matched with the load side; constructing a source-load coupling prediction model of the target power system; inputting real-time power operation data of the target power system into the source-load coupling prediction model to obtain power gap data; obtaining an identification result of the current state of the target power system; and generating an imbalance state identification log of the target power system. According to the method and the system for identifying the electric power unbalance state of the electric power system under the climate and weather change, the electric power unbalance state is comprehensively evaluated from the source side and the load side.

The invention discloses a micro-grid scheduling optimization method and system, a terminal and a storage medium. A user model of each user node in a micro-grid is established in advance; the user model comprises a plurality of energy models: a heating ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-power grid energy transaction model and an inter-user energy transaction model; determining a balance relation between energy loss and output according to the user model, and determining constraint conditions; establishing a user cost function of each user node according to the cost function of each energy model; and establishing an optimization problem according to the user cost function of each user node, obtaining an optimal solution according to the constraint condition and the optimization problem through an alternating direction multiplier algorithm, and determining an optimal scheduling scheme of the microgrid. All users are designed as peer-to-peer nodes, and a distributed scheduling algorithm is adopted, so that the autonomy of the system can be enhanced, the communication burden can be reduced, and meanwhile, the efficient privacy protection level can be improved.

The invention provides an optimized scheduling method for an integrated energy system of an electrolytic aluminum park. The optimized scheduling method comprises the following steps: constructing a refined electrolytic aluminum load model, and constructing a demand response model and a carbon market model in which electrolytic aluminum load participates; an improved CSP-CCHP unit model is constructed, and an original self-contained power plant of the electrolytic aluminum park is replaced with the improved CSP-CCHP unit; an electrolytic aluminum park comprehensive energy system optimization scheduling model is constructed and solved, optimization scheduling results are obtained, and the optimization scheduling results comprise park operation total cost, park operation carbon emission, various unit output conditions and various load optimization scheduling results. Compared with a model which only considers the simple characteristic of the load in the prior art, the optimal scheduling method for the integrated energy system of the electrolytic aluminum park, provided by the invention, has the advantages that the model is more refined and practical, and accurate scheduling support is provided for participation of the load of the electrolytic aluminum park in the power market and demand response.

The invention discloses a permanent magnet synchronous motor joint simulation method, which comprises the following steps of: firstly, establishing a permanent magnet synchronous motor finite element model in Maxwell, and defining material nonlinear characteristics and motion boundary conditions; a motor drive control circuit is constructed in Simplorer, the finite element model is imported through a Twinbuild tool, and an electromagnetic-circuit joint simulation environment is established; creating a motor multi-body dynamic model and an external load model in Adams, and respectively exporting the motor multi-body dynamic model and the external load model into an FMU format and an Adamssub format; and finally, establishing an electromagnetic-circuit-mechanism joint simulation platform in the Simulink, and executing joint simulation. Compared with traditional joint simulation, according to the method, through electromagnetic-circuit-mechanism three-field real-time interaction, the motor can sense the load sudden change working condition, then the torque and current of the motor are rapidly adjusted through the PI controller, the problems that in engineering practice, the external load of the motor is not real, and the abnormal load is detected are solved, and the reliability of the motor is improved. And the crossing from an ideal load to an actual load is realized.

The invention discloses a novel power system satellite-ground converged communication load modeling and situation awareness method and system, and the method comprises the steps: carrying out the classification of communication loads based on the load feature information of a satellite-ground converged novel power system, and constructing a communication load model according to a load classification result, generating a load demand regulation and control strategy according to the response characteristic information of the communication load, aggregating the communication load based on the communication load model and the load demand regulation and control strategy, performing dependency analysis on a communication load aggregation result, and obtaining space-time dependency information among the aggregated loads; the load situation of the power system is predicted according to the space-time dependency information, the communication load is aggregated, so that the analysis efficiency is improved, the analysis complexity is effectively reduced, the communication load prediction accuracy of the satellite-ground fusion novel power system is greatly improved through space-time dependency analysis on the communication load, and the communication load prediction accuracy is improved. The timeliness of the prediction result is ensured, and the communication quality of the power business is effectively guaranteed.

The invention discloses a distributed power supply load model establishment method and system, and relates to the technical field of load model establishment, after an establishment system obtains power system information through an API interface, a topological graph model of a power system is established, each component in the power system is abstracted as a node and an edge in the topological graph model, and the topological graph model is established; after simulation software is used for conducting simulation detection on the topological graph model, a target value of the topological graph model is calculated through an objective function, an optimal path between nodes is updated through a path algorithm, current distribution of edges is adjusted through a graph cut algorithm, power loss is reduced, overload is avoided, the steps are repeated for multiple iterations, and when convergence conditions are met, the optimal path is obtained. And outputting the final topological graph model as a comprehensive load model. The system can optimize the targets at the same time through a graph optimization algorithm, improves the overall benefits of the system, and overcomes the defect that the complexity of the power system cannot be comprehensively considered in single target optimization.