562 results about "Load Shedding" patented technology

The invention discloses a power distribution network power supply reliability evaluation method and system based on data analysis, and relates to the technical field of power distribution network detection, a meteorological station, a GIS and an equipment monitoring terminal collect temperature, rainfall, wind speed, load and fault record data, and after time sequence alignment and interpolation correction, a unified environment data set is generated to provide an accurate basis for model construction; secondly, using historical and online data, calling a nonlinear mapping function and an equipment function to train a meteorological-fault rate model, outputting the equipment fault probability in real time, and then calculating a system risk index and a line risk value in combination with confidence-weighted weather forecast and equipment load coupling operation to accurately identify a high-risk area; priority scheduling, load transfer and graded load shedding are carried out on the high-risk area, fault information is transmitted back, closed-loop control is achieved, and the operation efficiency is improved.

The invention discloses an AGV scheduling system optimization method based on dynamic priority adjustment and real-time load balance, and the method comprises the steps: dynamically monitoring the emergency degree and arrival time of cargos, enabling a system to adjust the task priority in real time, guaranteeing the priority processing of emergency cargos, and giving consideration to the scheduling efficiency of common cargos; information such as current positions, loads and electric quantity of the AGVs is collected, weighted load values are calculated, weight factors are dynamically adjusted according to task types, load balance is achieved, and the situation that part of the AGVs are overloaded and part of the AGVs are insufficient in load is avoided; and finally, scheduling optimization is carried out by comprehensively considering factors such as the adaptation degree between the task and the AGV, the path distance, the predicted completion time and the load balance, and the overall performance of the AGV system is fully exerted. The flexibility, efficiency and resource utilization rate of the AGV scheduling system are effectively improved, and the AGV scheduling system is suitable for various scenes such as logistics storage and production and manufacturing.

A system for dynamically scaling microservices in cloud environments, including: a predictive scaling module that uses machine learning algorithms to analyze historical and real-time workload data and predict resource requirements; an auto-scaling decision engine configured to determine and execute dynamic scaling actions based on predictions and current system performance; a cloud orchestration module that deploys and manages microservice instances in cloud environments to ensure optimal resource allocation; an intelligent load balancer that dynamically redistributes incoming requests across microservice instances to avoid bottlenecks and maintain service reliability; and a real-time monitoring module configured to continuously collect system performance data, detect workload fluctuations, and provide feedback for adaptive scaling.

The invention discloses an online efficient load shedding control method based on a forecast wind field. The method comprises the following steps: firstly, acquiring a load attack angle and a sideslip angle which are calculated in real time on a rocket; amplitude limiting processing is carried out on the load attack angle and the sideslip angle, an attack angle control instruction is generated after gain control network calculation is carried out, and an attitude control system carries out direct attack angle control in a load sensitive area in combination with the load shedding requirement and the guidance deviation correction capacity; and pitching and yaw feedback control instructions are generated, active load shedding control is implemented, and errors generated by attack angle control are compensated. The on-line active and passive load shedding mode covers the real deviation or interference influence during flight, the generated load shedding instruction is better matched with the actual flight, the universality is good, the implementation is easy, and the load shedding effect is better.

The invention relates to the field of new energy system dynamic load shedding, in particular to a new energy system dynamic load shedding method based on real-time load analysis. Comprising the following steps: acquiring power grid real-time load data and new energy system output, and performing time domain analysis on the power grid real-time load data to obtain a load fluctuation rate and a load change acceleration; predicting and standardizing a load fluctuation rate at a future moment to obtain a normalized load characteristic parameter at the future moment; predicting the output of the new energy system at the future moment, and calculating the deviation between the output of the new energy system at the future moment and the load power; introducing a normalized load characteristic parameter at a future moment, calculating an initial output constraint factor, and carrying out amplitude limiting processing to obtain an output constraint factor; and the adjusted new energy system output is calculated through a dynamic load shedding adaptive adjustment algorithm. The technical problem that the analysis result is distorted due to the fact that low-frequency sampling is mostly adopted to obtain load data in the traditional technology and the rapid change characteristic of the load is difficult to capture is solved.

The invention relates to the technical field of power distribution management, in particular to an intelligent UPS power distribution management method and system based on dynamic load balance, and the method comprises the steps: monitoring the energy consumption parameters of each load in real time, and generating a multi-dimensional energy consumption data set based on the energy consumption parameters; inputting the multi-dimensional energy consumption data set into a preset power prediction model, and predicting a power demand prediction result of each load in future time; dynamically generating a load priority queue based on the power demand prediction result of each load and the real-time capacity state of the UPS, and adjusting the power distribution strategy of the UPS to each load in real time; and acquiring working data of the UPS system under the current power distribution strategy, acquiring abnormal data based on the working data of the UPS system, and updating the load power distribution strategy according to the abnormal data. According to the invention, the energy efficiency of the UPS power distribution system is improved, the stability of the system is enhanced, and the output power distribution of the UPS is dynamically adjusted.

The invention provides a dynamic self-adaptive low-frequency load shedding method and system for a high-proportion distributed new energy power grid, and the method comprises the following steps: processing the data of a multi-source power system through self-adaptive time sequence sampling and multi-dimensional anomaly detection, and extracting voltage class features; analyzing voltage grade correlation characteristics and dynamic influence characteristics, and evaluating a voltage grade coupling degree; inertia estimation and trend analysis are carried out based on frequency response characteristics; respectively extracting distributed and centralized new energy features and predicting output of the distributed and centralized new energy features; performing multi-scale decomposition and feature fusion on the system inertia and the new energy features; determining load removal priority and power distribution based on multi-dimensional risk assessment; and a hierarchical control method is adopted to generate a control instruction and dynamically optimize a control strategy. According to the invention, the dynamic characteristics of the system can be accurately identified, accurate load removal control is realized, and the stability and reliability of the system are improved.

The invention relates to the technical field of power grids, in particular to a load shedding anti-overspeed control method during power grid decoupling of a compressed air energy storage system. Acquiring rotating speed data of the compressed air energy storage system when the power grid is decoupled, and monitoring the rotating speed data; a load shedding mode is preset, and block valves and exhaust valves at the front ends of all stages of expansion machines are controlled; and executing a load shedding mode according to the change of the rotating speed data, and controlling the rotating speed of the system to be reduced to a preset range. The load shedding mode is divided into two stages, wherein in the first load shedding stage, rotating speed control is conducted by closing block valves at the front ends of all stages of expansion machines; and in the second load shedding stage, the residual working medium is discharged by opening the exhaust valve. A system standby recovery strategy is established, and stable transition of the system from an emergency state to a normal operation state is realized. According to the method, the problems of inaccurate rotating speed control, insufficient residual working medium treatment and the like during power grid decoupling of the CAES system in the prior art are effectively solved, and the operation safety and reliability of the system are improved.

The invention discloses a distribution network-microgrid toughness improvement robust planning method and device considering natural disasters, and the method comprises the steps: constructing operation models of a distribution network and a microgrid under the natural disasters respectively, and carrying out the integration to obtain a distribution network-microgrid collaborative optimization operation model considering the influence of the natural disasters; generating a random interruption scene of a distribution line, a micro-grid line and a traffic road under the natural disaster; searching a scene which enables the operation cost of the power distribution network to be maximum, and obtaining a most serious natural disaster scene; considering that the distribution network-micro-grid encounters the most serious natural disaster, a distribution network-micro-grid toughness improvement robust planning model is constructed by taking minimization of the total investment cost of distribution network planning as a target, and an optimal toughness improvement scheme is solved. According to the method, on the premise of considering economy, a pre-disaster toughness improvement scheme can be planned for the distribution network-microgrid, and the load shedding amount of the distribution network under natural disasters is effectively reduced.

The invention relates to the technical field of load balancing optimization, in particular to an intelligent computing center load balancing and energy-saving optimization method and system, and the method comprises the following steps: obtaining task execution duration and resource state data, calculating a multi-dimensional index, carrying out the weighted analysis, dynamically outputting a regulation and control scheme, and achieving the load balancing and energy-saving optimization of an intelligent computing center. According to the method, response time comparison based on a service level protocol is introduced, a task execution duration distribution structure is clearly recognized, the abnormal task recognition precision is improved, and the judgment accuracy of an overload trend is enhanced by comparing resource utilization rate changes of adjacent periods in the aspect of node state monitoring; in the task allocation process, the available memory of the node and the resource adaptation degree are combined, real performance load performance is obtained through energy efficiency and throughput data cross analysis, task allocation better conforms to the actual energy consumption level, the resource waste risk is effectively reduced, the response flexibility of a scheduling strategy to system load changes is enhanced, and the task allocation efficiency is improved. And the task execution efficiency and the energy efficiency distribution accuracy are improved.

The invention relates to the technical field of transient frequency and emergency control, in particular to a power grid transient frequency emergency control method, device and equipment based on reinforcement learning, and the method comprises the steps: obtaining the current operation data of a power grid if the power grid is in a fault state; and inputting the current operation data to a target transient frequency emergency control model, so as to obtain an actual emergency control strategy from a plurality of emergency control strategy tables in the target transient frequency emergency control model through matching, the target transient frequency emergency control model being based on a power grid fault data sample, training by using a preset improved depth deterministic strategy gradient algorithm; and performing emergency stability control on the transient frequency of the power grid according to the actual emergency control strategy. Therefore, the problem of frequency fluctuation caused by large-scale power grid disturbance is solved, the reliability and economical efficiency of stable operation of the power grid are improved, generator tripping and load shedding online real-time emergency control strategies are quickly and accurately generated, and quick recovery of transient frequency stability after a fault is achieved.

The invention discloses a low-frequency load shedding method and system based on zero crossing point real-time frequency tracking, and the method specifically comprises the steps: setting a dynamic time window according to the power frequency characteristics of a sensing signal, and removing an abnormal zero crossing point exceeding the dynamic time window; matching a pre-stored harmonic feature library, establishing a mapping relation between a harmonic mode and a phase offset, and performing adaptive phase compensation on a pseudo zero crossing point of the sensing signal; a sensing signal, PMU phasor data and meteorological parameters are input into a three-dimensional convolutional neural network, a cross-modal incidence matrix is calculated through a self-attention mechanism, a future frequency change trajectory is predicted in combination with an LSTM unit, and an optimal load shedding strategy is output; and based on the optimal load shedding strategy, in combination with a forward acceleration criterion and reverse fault locking, executing emergency load shedding according to load importance grading. The technical bottleneck of a traditional low-frequency load shedding device is effectively solved, and the performance and reliability of frequency stability control of the power system are remarkably improved.

The present application describes a system and method for utilizing a tunnel in a networking routing protocol to provide a network segment access to additional servers when certain load balancing trigger events are detected.

The invention relates to the field of intelligent power grid and energy management, and discloses a park distribution box peak-valley scheduling monitoring method, system and device and a storage medium, and the method comprises the following steps: collecting and uploading the power data of a park distribution box in real time; on the basis of historical power data and meteorological information, predicting future power demands and identifying wave crests and wave troughs by using a deep learning model; generating a load scheduling strategy by adopting a reinforcement learning algorithm in combination with the prediction result and the real-time data; optimizing a scheduling strategy by using a particle swarm optimization algorithm, and outputting an optimal load distribution scheme; adjusting the load of the distribution box according to an optimization result, and balancing power resources; and when abnormity is detected, starting an emergency response mechanism to guarantee key load power supply. Through deep learning, reinforcement learning and a particle swarm optimization algorithm, accurate prediction and scheduling optimization of the park power load are realized, power resources are effectively balanced, the load response capability is improved, and key load power supply under abnormal conditions is guaranteed.

The invention discloses an electrical integrated energy system toughness planning method considering an extreme event tail risk, and relates to the technical field of integrated energy system optimization, and the method comprises the steps: firstly, considering the massive load shedding risk caused by a small-probability extreme event, and constructing a pre-disaster defense-post-disaster emergency response electrical integrated energy system toughness planning frame; secondly, comprehensively considering toughness planning cost, emergency scheduling cost and tail risk loss in a small-probability extreme scene, and establishing an objective function of a planning model; then, for a disaster stage, establishing disaster prevention resource deployment related constraints of the toughness planning model; then, considering operation constraints of the post-disaster power distribution system and the natural gas system, and establishing post-disaster emergency response related constraints; and finally, performing linearization processing on the established planning model, and calling a commercial solver for solving.

The invention discloses a novel power system energy storage optimization and load loss evaluation method in extreme weather, and relates to the technical field of power systems and energy storage optimization. In order to solve the technical defect of lack of a comprehensive extreme weather scene construction method in the existing power system and energy storage optimization technology in the prior art, the technical scheme provided by the invention comprises the following steps: establishing an extreme weather scene; establishing a power distribution network digital model, and simulating a power distribution network operation scene under an extreme meteorological condition; constructing a fault rate model of the electrical element, and generating a comprehensive time-varying fault probability model; obtaining operation parameters of the power distribution network, and analyzing response characteristics of the power distribution network under different operation conditions; calculating a load loss probability and a power shortage expected value, and comprehensively evaluating the load loss risk of the power grid; and the configuration and scheduling strategy of the energy storage equipment is optimized. The method can be applied to planning, operation scheduling and emergency management work of a power system under extreme weather conditions.

The invention discloses a distributed emergency frequency control method based on multi-agent near-end strategy optimization. The method comprises the following steps: establishing a frequency response model representing frequency dynamic characteristics and physical constraints of a power system under disturbance; each emergency power support station and each low-frequency load shedding station in the power system are modeled as independent agents, a distributed multi-agent cooperative control model is constructed, each agent makes a decision based on a local observation vector, and the action output of the agent is a continuous power regulation variable. The cooperative control model has a global reward function including frequency security and control cost; performing centralized cooperative training on the cooperative control model based on a multi-agent near-end strategy optimization algorithm; on the basis of the trained strategy network of each intelligent agent, online distributed execution is carried out, in the online distributed execution process, each intelligent agent does not need to communicate with each other, each intelligent agent can obtain a better cooperative control strategy only based on local observation, and the engineering adaptability and robustness of emergency frequency control are remarkably enhanced.

The invention provides an electric power system power supply capacity insufficiency evaluation method considering new energy uncertainty, and the method comprises the steps: building the probability distribution of electric quantity prediction results at different time scales in a to-be-evaluated time period through Gaussian mixture distribution, and obtaining the probability distribution of the electric quantity prediction results in a total time period; constructing a joint probability density function according to the prediction error distribution of the generated power of the plurality of wind power plants and the prediction error distribution of the generated power of the plurality of photovoltaic power stations, and obtaining a space joint probability density function; establishing the probability density distribution of a wind-solar joint prediction error band in the to-be-evaluated time period according to the probability distribution of the electric quantity prediction result in the total time period and the spatial joint probability density function; calculating to obtain load shedding probability distribution through probability density distribution of a day-ahead operation domain and a wind-solar combined prediction error band; therefore, the risk of insufficient power supply is determined. And flexible assessment of the short-term scale power supply capacity insufficiency risk in the power system with short-term uncertainty in the new energy is realized.

The invention belongs to the technical field of electric power information physical system security, and discloses an urban power grid information physical system security situation early warning method based on cross-space risk propagation. A power grid physical layer and information layer coupling model and a cellular space false data injection attack model are constructed, and a fault cross-space propagation mechanism is simulated through an event-driven cellular automaton theory; an integrated kernel extreme learning machine model is adopted to predict the operation state of the power grid, multi-dimensional data fusion is realized through a radial basis kernel function, and an integrated structure is adopted to fuse a plurality of model prediction results so as to improve the model prediction precision; and establishing an early warning system including voltage out-of-limit, line overload and load loss indexes, and dynamically distributing weights and dividing early warning grades in combination with an entropy weight method. According to the method, the problem of low precision of urban power grid security situation early warning under multivariate disturbance is solved, and the accuracy and robustness of security situation early warning can be effectively improved.

The invention discloses a dynamic electric power-traffic coupling system optimal pricing method considering differentiation V2G, which fully considers electric vehicle user V2G differentiation factors on the background of traffic flow simulation of a dynamic traffic distribution model, and sets an optimal electricity price to realize optimal scheduling of an electric power-traffic coupling system. Firstly, a dynamic traffic distribution model considering traffic flow is established, and differentiated behaviors of users for V2G and changes of vehicle cost at any time and space movement are considered in the model. And then establishing a second-order cone DistFlow-based power distribution network optimal power flow model in the power distribution network to describe a power flow distribution condition, realizing coupling with a traffic network through a charging station and a V2G station, constructing a dynamic power-traffic coupling system optimal pricing method considering differentiated V2G, and guiding charging flow and V2G flow distribution through formulating an optimal charging electricity price and V2G reward so as to realize optimal pricing of the dynamic power-traffic coupling system. Load shedding loss caused by overload in the peak period of the power distribution network is reduced, and flexibility and reliability of the power distribution network are enhanced.

The invention relates to the field of load control, in particular to a non-fixed load accurate removal method based on load control fairness and effectiveness consideration. Comprising the following steps: acquiring the frequency of a power grid system, calculating the frequency change rate, judging whether to send a'preparation for load shedding 'instruction to all substations by monitoring the frequency of the power grid system and the frequency change rate, entering a load shedding amount calculation stage, and calculating the load amount needing to be cut off in each region through a dynamic weighted load precise cutting algorithm, calculating a total load shedding amount; based on the load capacity needing to be cut off in each area, the substation optimizes the cut-off combination of the load lines in the areas, after optimization is completed, the substation executes cut-off, and the master station monitors the frequency and the frequency change rate of the power grid system after cut-off and judges whether a locking condition is met or not so as to stop further cut-off. The technical problems that power grid frequency monitoring mostly depends on low-sampling-rate equipment, the rapidly-changing frequency dynamic state is difficult to capture, and instability detection lags or misjudgments are caused are solved.

A unit maintenance plan double-layer optimization method considering new energy grid connection comprises the steps that an upper-layer optimization model is established, and the minimum unit operation cost and the minimum unit conventional maintenance cost in the planned maintenance period serve as target functions; solving a unit start-stop plan during a maintenance plan period by using a genetic algorithm, and arranging a historical maintenance plan as an initial population; establishing a lower-layer optimization model, and taking the minimum unit fault risk cost and the minimum wind and light abandoning and load shedding expected cost in the maintenance plan period as a target function; according to the obtained unit start-stop plan, solving the optimal output and scheduling arrangement of the unit during the maintenance plan by using an improved particle swarm algorithm; and solving the total economic cost of the system according to the obtained optimal output and scheduling arrangement of the unit, and obtaining an optimal maintenance plan according to optimization iteration. According to the method, a basis can be provided for a maintenance plan of a power grid worker, the load loss amount is minimum, the economical efficiency is optimal, and specific guidance is provided for maintenance of equipment with different voltage grades in a power grid.

Methods and systems for load balancing requests from clients across server devices are disclosed. The method may include obtaining a request from one of the clients. The method may also include making a determination regarding whether a load signature for the one of the clients is available. When the load signature for the one of the clients is available, one of the server devices may be selected based at least in part on the load signature and the selected one of the server devices may be assigned to service the request from the one of the clients.

Systems and methods are described for providing an application-level gateway that allows computing devices to utilize an on-demand network code execution system. An on-demand network code execution system may allow users to submit code to be executed in a serverless environment, and may provide an interface for executing the user-submitted code on demand. The interface may require that users authenticate, provide input in a particular format, or meet other criteria when sending a request to execute the code. An application-level gateway may thus provide an interface that implements these functions, thereby allowing computing devices to interact with the code as though it were running on a server (e.g., by using the Hypertext Transport Protocol). The application-level gateway may also use on-demand code execution to provide load balancing for servers that are running the user-submitted code, and seamlessly provide access to code that runs on both server-based and serverless environments.

Disclosed in the present application are a configuration-free and maintenance-free method and system for relay protection and fault information substations, relating to the technical field of computer platform load balancing. The method comprises: acquiring all network node information by means of a relay protection and fault information master station, and constructing a network topology diagram; marking a newly added node in the network topology diagram; establishing communication with the marked network node, and uploading the original version of all data of the marked network node to a secure area I by means of a dispatching data network; performing account configuration on a plant side relay protection and fault information substation, and downloading configuration file data to the relay protection and fault information substation; parsing a packet at the relay protection and fault information master station; and establishing a protocol library at the relay protection and fault information master station, and performing protocol conversion and data parsing on the original version packet uploaded by the newly added node. The configuration-free and maintenance-free method for relay protection and fault information substations provided by the present application allows for the data received by the relay protection and fault information master station being completely consistent with the raw data of the plant side, thereby improving the working efficiency and reducing the working cost.

The invention provides a power cooperative control method, device and system based on load removal and a medium. The method is applied to a micro-grid configuration flexible interconnection device for micro-grid fault protection. After the fault is positioned, a VSG low-voltage ride-through strategy is adopted to carry out current limiting, an importance quantification model is constructed for loads, and according to a real-time operation condition, a shearable load with the minimum influence on the system is screened to carry out priority removal; and dynamic power transmission adjustment of the flexible interconnection device is synchronously triggered, a micro-grid power flow path is reconstructed, and finally a fault is removed based on a current limiting and directional element protection method. According to the invention, system protection is realized by fusing intelligent load removal and flexible power adjustment, and the fault isolation speed and the stability of the microgrid system are effectively improved.

The invention relates to the technical field of charging piles, and provides a wireless communication system and communication method for a charging pile, and the method comprises the steps: obtaining charging request information, carrying out the signal analysis of the charging request information, obtaining load balance data, carrying out the matching analysis of the current available power data of the charging pile based on the load balance data, and obtaining a wireless communication scheduling parameter; and generating signal interference information and signal intensity distribution data based on the wireless communication scheduling parameters, and constructing a wireless signal scheduling scheme by using the signal interference information and the signal intensity distribution data. According to a wireless signal scheduling scheme constructed through signal interference information and signal intensity distribution data, interference in the communication process is reduced, the stability and efficiency of signal transmission are improved, and the problem that the communication efficiency is greatly improved under the conditions of multi-device concurrent connection, real-time transmission of a large amount of data and complex charging is solved. The problems of limited data transmission efficiency, network congestion and difficult elimination of communication interference exist in the prior art.

The invention relates to the technical field of intelligent control, in particular to a remote control method, device and equipment for a safe, energy-saving and environment-friendly intelligent terminal, and the method comprises the steps: obtaining historical power, rated power, actual power and actual temperature data of a historical time period and a current time period on a parallel branch; according to the difference among the historical power, the rated power and the actual power of the parallel branches, analyzing the load abnormal condition of the parallel branches, and screening the parallel branches to determine abnormal branches and balanced branches; according to the difference between the actual power of the balance branch in the current time period and the historical time period and the difference between the actual temperature data of the current time period and the historical time period, a load balance auxiliary index is obtained by combining the load abnormal condition and the power adjustment degree of the balance branch in the current time period; and performing balance control on the load on the parallel branch according to the actual power, the power adjustment degree and the load balance auxiliary index of all the abnormal branches in the current time period. According to the invention, the load balancing efficiency is improved, and the power utilization safety is ensured.

A fast calculation method for dynamic resilience region of an urban power system includes: constructing an urban power system three-stage model applicable for multiple types of public safety events by considering security constraints before, during, and after a disaster, to minimize a three-stage operation cost and a load shedding amount of urban power grid; based on the urban power system three-stage model, defining a boundary between a resilience region and a non-resilience region as a resilience cut line, and removing the non-resilience region from the outside to quickly obtain the dynamic resilience region; and based on the urban power system three-stage model, using the shortest distance from a current operating point to the boundary of the dynamic resilience region to represent a dynamic safety margin of urban power system under a current disaster prediction scenario.

The invention belongs to the technical field of electric digital data processing, and discloses a dynamic strategy configuration-based cancel-after-verification process automation system, which comprises the steps of obtaining cancel-after-verification interaction information, performing scene feature extraction and strategy element deconstruction, and forming a strategy element knowledge base; analyzing the voucher identification code, performing multi-dimensional voucher attribute deconstruction and type portrait description, and generating a voucher intelligent portrait; performing rule matching degree evaluation and strategy source tracing, and locking an applicable strategy rule set; performing strategy conflict harmonic analysis and rule priority ranking to generate a cancel-after-verification execution instruction chain; performing flow wave crest prediction and resource scheduling arrangement to form a load balancing strategy table; performing intelligent channel shunting scheduling, and establishing a cancel-after-verification process execution network; according to the invention, the intelligent automation of the cancel-after-verification process is realized, the system processing efficiency is improved, and the method can adapt to complex and changeable business scene requirements.