94 results about "Grid security" patented technology

The invention relates to the technical field of power system security, and particularly discloses a ground wire management operation system based on ground wire cabinet monitoring, which comprises a physical sensing layer, an edge computing layer, a block chain consensus layer and an application service layer. The physical sensing layer collects the identity, the in-place state and the electrical parameters of the grounding wire; the edge calculation layer carries out local data processing and event judgment; the block chain consensus layer realizes data credible evidence storage and cross-domain sharing through an intelligent contract and a distributed account book; and the application service layer provides asset panoramic display, cooperative verification and risk early warning services. Through the above architecture, unified management and safety collaboration of data of the whole life cycle of the grounding wire are realized, and the safety operation and maintenance level of a power grid is improved.

The invention discloses a main-distribution-micro-cooperation fault processing method and system, and belongs to the technical field of power system dispatching and fault handling, and the method comprises the steps: carrying out the fault perception and risk analysis of main-distribution-micro-cooperation after a fault is triggered, and generating an event fault event; through grid identification and power-loss monitoring, analyzing a power-loss condition and determining a power-loss influence level; generating an alarm event based on the event fault event and the grid identification and power loss monitoring result; and according to the alarm event and the power loss condition analysis result, generating a main-distribution-micro cooperative processing scheme, and supporting visual layered display. According to the method, the defects of fragmentation, low cooperation efficiency and scattered disposal schemes of main and distribution micro fault disposal in the prior art are overcome, the fault positioning accuracy, provincial and regional cooperation efficiency and the scientificity of the disposal schemes are remarkably improved, and the influence of faults on safe operation of a power grid and power supply of users is effectively reduced; the method is suitable for a main and distribution micro-grid fault handling scene containing distributed resources.

The invention provides an intelligent scheduling method and system based on source network load storage efficient collaboration, and relates to the technical field of source network load storage systems.The method comprises the steps that operation data of all nodes in a power grid are collected in real time through Internet of Things equipment, integrity verification and preliminary evaluation are conducted on the operation data, and an initial operation data set is formed; performing data cleaning and integration on the initial operation data set to form a unified operation data set; performing feature extraction and state analysis based on the operation data set to obtain a multi-dimensional feature vector; according to the multi-dimensional feature vector and an artificial intelligence technology, constructing a source network load storage collaborative energy management model; and obtaining a power grid operation state and load demand prediction result based on the model. According to the method, a full-process closed loop from data acquisition, processing, strategy generation to execution feedback is constructed, so that source-grid-load-storage efficient collaborative scheduling under data driving is realized, and finally, the goals of safe, stable and economic optimized operation of the power grid are achieved.

The invention relates to the technical field of power system dispatching, and discloses a power grid multi-target coordinated dispatching optimization method, device and equipment and a medium, and the method comprises the steps: constructing a dispatching target function which at least comprises a power abandoning penalty term and a safety out-of-limit risk penalty term; establishing a security constraint set of power grid operation; scheduling scheme solving is carried out based on the security constraint set, and when it is detected that power abandoning is caused by relaxation constraints, self-adaptive relaxation is carried out on the corresponding relaxation constraints according to a preset relaxation priority sequence; in the relaxed and updated feasible region, carrying out optimization solution by adopting a hierarchical reinforcement learning framework, and outputting an optimal scheduling instruction; wherein the upper layer of the hierarchical reinforcement learning framework is used for generating a scheduling feasible region meeting safety requirements, and the lower layer of the hierarchical reinforcement learning framework is used for performing rolling optimization of unit output based on the scheduling feasible region. According to the invention, on the premise of guaranteeing the safety of the power grid, the clean energy power abandonment rate is reduced and the optimization solution speed is improved.

The invention discloses a power grid analysis method, device, equipment and medium based on a time sequence text heterogeneous graph, and the method comprises the steps: constructing a time sequence text attribute heterogeneous graph based on power grid data, and storing the time sequence text attribute heterogeneous graph in an attribute graph database, the time sequence text attribute heterogeneous graph comprises multiple types of nodes and edges, the nodes and / or edges carry text attributes, and the text attributes are stored in the attribute graph database; the topology and the attribute of the time sequence text attribute heterogeneous graph are dynamically changed along with time; based on the attribute graph database, exporting an event sequence from the attribute graph database according to a time sequence, or aggregating operation state data of the power grid equipment according to a time window to generate a window snapshot and exporting the window snapshot; and performing operation state analysis, fault detection or intelligent scheduling decision on the power grid according to the exported event sequence or window snapshot. According to the technical scheme provided by the embodiment of the invention, the comprehensiveness of power grid operation state analysis, the accuracy of fault detection and the scientificity of intelligent scheduling decision are remarkably improved, and a powerful guarantee is provided for safe, efficient and economical operation of the power grid.

The invention belongs to the technical field of large-scale power grid safety guarantee, and particularly relates to a secondary virtual loop intelligent checking method and system based on a substation configuration file. According to the method, an electrical information fusion graph fusing primary wiring and secondary functions is constructed by analyzing a substation configuration file; secondly, performing reverse tracing to form an independent virtual loop causal chain aiming at an execution outlet node; calculating a full-link time sequence deviation of each virtual loop causal chain; and finally, performing weighted summation on respective time sequence deviations of a plurality of causal chains which are gathered at the same outlet and have mutually exclusive starting end function logics to obtain comprehensive risk weighted time delay, and judging whether virtual loop configuration conflicts exist or not according to a comparison result of an index and a preset threshold value. According to the method, accumulated time delay risks can be found, and hidden deep logic configuration conflicts among different functions can be identified and quantified, so that the substation configuration file checking depth and precision are remarkably improved, and the safety of large-scale power grid operation is ensured.

The invention discloses a power grid transient stability evaluation system and method based on an enhanced physical information neural network. The technical problem that an existing power grid transient stability evaluation system cannot meet the requirements of power grid online scheduling and real-time early warning is solved. The system comprises a power grid data acquisition module, a preprocessing module, a core evaluation module and an output and decision support module. The power grid data acquisition module is used for acquiring running state data of a power grid; the preprocessing module is used for preprocessing the operation state data to obtain a power grid operation state feature vector; the core evaluation module is used for performing transient stability reasoning according to the power grid operation state feature vector by adopting a target enhanced physical information neural network model based on a domain decomposition strategy, and outputting a power grid transient stability evaluation result; and the output and decision support module is used for outputting a power grid safety decision according to the power grid transient stability evaluation result and visualizing the power grid transient stability evaluation result and the power grid safety decision.

The application discloses a kind of AI coordination edge computing's distributed power grid monitoring system, it is related to the technical field of distributed power grid monitoring based on AI model;Including distributed data acquisition module, acquisition multiple operating data, support multi-protocol access;Edge node preprocessing module, local processing original data, classified storage;AI collaborative analysis module, edge and cloud model cooperation, iteration updates model;Abnormal intelligent identification module, constructs rule base and identifies multiple abnormalities, generates alarm;Hierarchical data transmission module, hierarchical transmission data, with link redundancy;Visual management module, show power grid state, support query analysis;Security protection module, encrypted data, hierarchical authorization, detect attack, double backup.The application eliminates power grid monitoring blind area, improves data processing real-time and transmission efficiency;AI collaborative analysis improves the accuracy of abnormal identification, hierarchical disposal improves fault efficiency;Adapt to multiple scenarios, reduce operation and maintenance cost, help power grid safe operation.

The embodiment of the invention provides a power grid evaluation index system construction method and device, electronic equipment, a computer readable storage medium and a program product, and relates to the technical field of power grid monitoring. According to the method, a multi-dimensional index system covering safety coverage, risk weighting and safety toughness is constructed by obtaining a long-time-sequence safety statistical table and operation parameters, and the stable operation capacity of a power grid under the long-time scale can be comprehensively reflected; a safety coverage index is used for evaluating basic safety, a risk weighting index is combined with a fault weight and an operation parameter to quantify risk strength, a safety toughness index is used for describing continuity and aggregation of an unsafe state, and the three indexes cooperate to form a power grid evaluation index system. Under the condition that manual intervention is not needed, systematic, differentiated and dynamic evaluation of the safety level of the power grid is automatically achieved, and a technical basis with higher guiding value is provided for operation optimization, planning decision making and emergency response.

The invention discloses a power grid operation instruction verification method, a power grid operation instruction verification device and a readable storage medium, and belongs to the technical field of power grid control. According to the method, the execution priority of each operation instruction in the operation instruction queue is determined, and the operation instructions with the front priorities are executed in sequence according to the execution priorities, so that the power grid operation instructions can be subjected to dynamic safety verification and priority ranking, and timely execution of high-priority instructions is facilitated; the problem that the current power grid static protection cannot accurately identify the power protection action of the equipment so that the security protection of the power grid has loopholes is solved.

This application provides a two-layer prevention and control method and system for grid cascading faults considering the integration of new energy sources. Based on the probability density function and output power function of wind farms and photovoltaic farms, the Monte Carlo method is used to calculate stochastic power flow. Assuming an initial fault occurs on a line and it is disconnected by backup protection, for the remaining lines other than the line with the initial fault, an expression for whether a cascading fault occurs in the grid is established based on the calculated power flow value and its corresponding setting value. The node injection power combination is obtained based on the calculated power flow value, and a grid safety margin is established based on the node injection power combination. An inner-layer optimization model is constructed with minimizing the grid safety margin as the first objective function, and an outer-layer optimization model is constructed with maximizing the grid safety margin and minimizing grid loss cost as the second objective function. The two-layer prevention and control model for cascading faults is solved to obtain a cascading fault prevention and control strategy, thereby enabling early identification and precise and effective prevention and control of cascading faults.

The embodiment of the invention provides a quantum-enhanced power grid multi-agent cooperative authentication and session key generation method and system, and belongs to the technical field of power grid security. The method comprises the following steps: selecting a collaborative authentication main body and initializing the collaborative authentication main body; initiating an authentication request; generating a multi-subject collaborative challenge; generating a joint authentication response; judging a local authentication result; aggregating and judging collaborative authentication results; collaboratively confirming the session key; and feeding back an authentication result and executing a security policy. According to the method, the safety, reliability and credibility of the power grid system in a complex operation environment are improved.

This application discloses a collaborative fault early warning method for power communication networks and dispatch automation systems, relating to the field of power system fault early warning technology. This method collects multi-source data on equipment status, service operation, load, and digital twins. After time-series alignment and standardization, it uses a causal forest algorithm to extract dual-dimensional causal features related to long-term performance degradation and real-time operation, and dynamically allocates fusion weights. Combined with digital twin simulation, it dynamically calculates the fault impact level and handling priority, generates targeted handling instructions, and optimizes parameters through closed-loop feedback. It further enhances real-time performance and adaptability through an edge-cloud collaborative architecture and scenario adaptation mechanism, achieving deep fusion of multi-dimensional data. This method realizes the deep and organic integration of the full lifecycle status data of power communication network equipment, the dynamic load characteristics of dispatch automation system services, and the real-time simulated power grid operation status of the digital twin, enabling early warning several hours in advance and ensuring the safe and stable operation of the power grid.

The invention relates to the technical field of power grid dispatching, in particular to a power grid supply and demand balance dynamic evaluation method and system fusing multi-source data, and the method comprises the steps: collecting six types of heterogeneous data of power grid operation, new energy prediction and the like, carrying out the differential preprocessing, and then offsetting conflicts through a credibility weight model and a weighted voting method; the edge layer is used for improving Kalman filtering to output real-time supply and demand features, and the cloud layer is used for outputting prediction features through an attention mechanism multi-modal Transform model; scene adaptive adjustment is carried out on weight evaluation of safety, economy, environmental protection and power balance, and grading early warning and reinforcement learning are carried out to optimize regulation and control suggestions. The system adopts a four-layer architecture of perception, edge, cloud and application to realize the method. According to the method, the data conflict resolution rate is greatly improved, the real-time delay is relatively low, the prediction accuracy is effectively improved, early warning can be performed in advance, and safe, economical and environment-friendly operation of a power grid is guaranteed.

The invention relates to the technical field of EV scheduling, and discloses a multi-zone-area electric vehicle cluster scheduling optimization method and device and electronic equipment, which are used for solving the technical problem of poor scheduling effect of the existing EV cluster scheduling scheme. Comprising the steps that minimization of the sum of power grid operation cost and user comprehensive cost is taken as an optimization target, power grid security constraints, user demand constraints, adjustable potential constraints and power boundary constraints are taken as constraint conditions, and the adjustable potential of a single transformer area is combined to construct a day-ahead initial scheduling model; when a preset condition is triggered, generating an uncertain scene, and based on the uncertain scene, constructing a rescheduling model by taking minimization of rescheduling cost and conditional value-at-risk as an optimization target and taking an adjustment amplitude constraint, a power grid security constraint and a user demand constraint as constraint conditions; generating a two-stage stochastic optimization rescheduling model by adopting the day-ahead initial scheduling model and the rescheduling model; and solving the two-stage stochastic optimization rescheduling model to obtain a multi-zone-area electric vehicle cluster optimization scheduling scheme.

The application discloses a kind of source network load system attack path tracking and security protection method, first, source network load system network attack monitoring collection technology is proposed, and power generation side, energy storage system, load side, power grid operation, environmental factors and network security data are collected.Then, whether the instruction or the information collected is subjected to network attack by verifying whether the instruction and the uploaded power consumption data are reasonable;Secondly, according to the monitored source network load system network attack behavior, based on the mechanism of infectious disease model, the path of the source network load system subjected to network attack is given by reverse tracking the uploaded data subjected to attack.Finally, based on the key network node in the source network load system attack path selected by the sea squirt colony algorithm, the security protection measures are configured, considering the deployment cost and the expected effect, and finally outputting the protection scheme to guide the source network load system to deploy defense measures at the key nodes of the attack path and improve the security of the power grid.

The invention discloses a full-cycle analysis and emergency guarantee method and system for unconventional events of a large power grid, and belongs to the technical field of safety and simulation analysis of the large power grid. The method comprises the following steps: constructing basic data for large power grid simulation calculation, and generating a power grid element fault probability of an unconventional event based on the basic data; based on pseudo-random sampling, generating a large power grid serious fault set according to the power grid element fault probability; based on the serious fault set of the power grid, executing whole-process dynamic simulation of the large power grid, performing safety scanning and instability judgment on the whole-process dynamic simulation, and outputting a scanning judgment result; and according to the scanning judgment result, generating a staged emergency control strategy, and when the large power grid is unstable, executing the emergency control strategy. Execution of the emergency control strategy has great engineering guiding significance for improving the disaster prevention and reduction capacity of a large power grid.

The invention discloses a large-model-driven charging operation multi-agent collaborative decision-making method and system, and relates to the technical field of electric vehicle charging management. The method comprises the following steps: receiving meteorological data, power grid SCADA data, user electric vehicle data and historical constraint violation cases, and constructing a multi-source heterogeneous data set; and according to the constructed data set, a Stiefel-LoRA fine tuning algorithm is adopted to carry out parameter fine tuning on the pre-trained large language model, an adapter matrix is optimized through Riemannian gradient projection and a QR contraction updating algorithm, a fine-tuned large language model adapted to a charging decision task is obtained, and the fine-tuned large language model outputs an initial charging decision scheme. According to the method, the large model efficiency is improved through Stiefel-LoRA fine tuning, a physical information perception neural network is utilized to ensure that a charging decision strictly meets power grid security constraints, multi-agent collaboration and lexicographical order optimization are realized based on a standardized protocol, the problems of scheme ineffectiveness and decision deadlock of a traditional method are avoided, and the method is suitable for large-scale popularization and application. And safe, reliable and economical intelligent charging operation is realized.

The invention discloses a power market transaction data traceability and compliance verification method and system, and relates to the technical field of data traceability. The method comprises the following steps of: splitting transaction data into public and private field sets by each node, calculating a hash value of the private field set, generating a zero-knowledge proof, performing digital signature by using a private key, forming a transaction data packet, verifying the data packet in parallel by the nodes of the whole network, storing the data packet into a memory pool, selecting a plurality of data packets, constructing a Merkel tree, and packaging the Merkel root of the block and the hash value and the timestamp of the previous block to a block head, packaging a transaction data packet to a block body, assembling to form a candidate block, performing consistency confirmation on the validity of the candidate block through a power grid security contract, and adding the candidate block to the block chain by each node. According to the invention, full-life-cycle credible traceability and automatic compliance supervision of power market transaction data from a power generation source to final settlement can be realized, and a set of complete credible data chain penetrating links of power generation, transaction, transmission and settlement is constructed.

The invention discloses a micro-grid endogenous toughness control method and device, electronic equipment and a medium, and relates to the technical field of micro-grid safety. The method comprises the following steps: through accurate mapping of IGCC and immune three-layer defense and topological adaptation of a graph convolutional network, autonomous detection, isolation and recovery of communication attacks are realized, challenges of malicious attacks of a communication layer and destruction of a cooperative mechanism can be effectively coped with, deep fusion and autonomous evolution defense of a biological immune defense mechanism and the graph neural network are realized, and the security of the communication layer is improved. The self-adaptability and the stability level of the micro-grid control system in a complex attack environment are improved, and the problems that a traditional defense method lags in response and lacks self-organization are effectively solved.

The present application relates to the technical field of large-scale power grid security, in particular to an optimal decentralized coordination control method for microgrid in new energy power system, comprising the following steps: based on the online obtained power grid topology and real-time line flow data, the virtual disconnection test is carried out on the key transmission line one by one, and the whole network cascade risk quantitative index is established. The present application realizes the simulation and analysis of line fault scene by real-time acquisition of power grid topology and line flow data, implementation of virtual disconnection test on key transmission line, establishment of whole network cascade risk quantitative index on this basis, and real-time comparison with safety threshold, identification of the initial fault line which is the biggest threat to system security, generation of vulnerable line risk contribution list, realization of positioning and advance prevention of high-risk line; further, the power adjustment amount required by microgrid group is directly calculated according to the risk contribution list, and the prospective flow diversion instruction set is formed.

The application discloses a physical information neural network-based island micro-grid safety analysis method, and steps include: step 1, establishing a micro-grid power flow analysis model; step 2, constructing a physical information neural network; step 3, constructing a static safety analysis model based on the physical information neural network, including: 3.1) data processing; 3.2) constructing a basic framework of a deep learning model; 3.3) constructing an overall framework of static safety analysis, and completing static safety analysis.The application belongs to the technical field of power system automation and artificial intelligence, and solves the problem that the existing PINN lacks a systematic method system in the application in the power system, and the evaluation efficiency and results are difficult to meet the requirements.

The invention relates to the technical field of power grid safety protection, and discloses a power grid safety protection method, system and device and a medium, and the method comprises the steps: effectively integrating a plurality of links such as risk assessment, strategy generation and protection, and forming a set of complete and efficient power grid safety protection system. And a comprehensive fourth safety assessment operation is performed on the third data set through the risk assessment module, so that the safety risk of each layer of the platform can be accurately identified, and a clear direction is provided for subsequent protection work. And the strategy generation module generates a targeted safety protection strategy based on the evaluated safety risk, so that the effectiveness and practicability of the strategy are ensured. And the protection module trains a sixth classification model by using a security protection strategy and related infrastructure data and security configuration data, so that the model can accurately predict whether the input data has security risks or not, thereby realizing accurate security protection of the power grid.

The invention discloses an intelligent power grid safety monitoring system, and relates to the technical field of electric power, and the system comprises a data collection module which is used for obtaining various kinds of key data in the operation process of an intelligent power grid in real time, and providing a basis for subsequent analysis and decision making; the communication transmission module is used for safely, reliably and quickly transmitting a large amount of collected data to a data processing center; the data processing module is used for efficiently processing mass data transmitted by the communication transmission module; the data analysis module is used for carrying out deep analysis on the collected data; and the monitoring display module is used for visually displaying results of the data processing module and the data analysis module to operation and maintenance personnel. The system is provided with the monitoring display module, operation and maintenance efficiency is improved through visualization, key support is provided for safe, efficient and economical operation of the power grid through cross-department cooperation and refined management and control, and the system is a core link of the intelligent power grid from technology integration to practical landing.

The invention discloses a power grid dispatching boundary operation scene generation method, which comprises the following steps of: acquiring an ultra-short-term point prediction value of a target time period and a corresponding prediction error covariance matrix, and constructing conditional Gaussian distribution for each future time period; sampling the conditional multivariate Gaussian distribution to generate an initial scene set; inputting the initial scene set into a pre-trained scene risk discrimination model, and outputting a business index prediction value of each scene; based on the business index prediction value, screening out a target scene meeting a preset requirement from the initial scene set; according to the method, the quality and practicability of boundary scene generation can be remarkably improved, and the active defense capability of a dispatching system on extreme risks is enhanced, so that safe and stable operation of a power grid is guaranteed.

The application relates to the technical field of power system network security, and discloses a digital power grid security situation awareness prevention and control method based on artificial intelligence, which comprises the following steps: storing historical power flow feature vectors into a physically isolated static storage area and performing hierarchical version management; constructing a dynamic cache area in an independent memory space, extracting real-time feature vectors through an adaptive sliding window, and completing cache eviction by attaching a time decay label; adopting Jensen-Shannon divergence and cosine similarity linear weighting to calculate a global similarity matrix, and generating a feature importance decay compensation weight vector; constructing a main gradient flow and compensation gradient flow double-channel architecture in the model back propagation stage, updating the model parameters after gradient gate fusion; and dynamically adjusting the threshold based on the gradient flow contribution degree, so that system closed-loop adaptive optimization is realized. The application can relieve the model catastrophic forgetting problem, and improve the power grid security risk identification precision and the adaptive ability of the prevention and control system.

The invention discloses an island micro-grid optimization scheduling method and system considering a frequency response whole process, and belongs to the field of island micro-grid optimization scheduling, and the method comprises the steps: generating a photovoltaic output typical day scene through K-means clustering, and constructing an island micro-grid basic random optimization scheduling model; dividing the frequency response of the island micro-grid after disturbance into three parts; constructing an island microgrid disturbance power and mechanical inertia expression based on frequency response characteristics under generator N-1 off-grid disturbance; and aiming at the three parts of frequency response, linear frequency stability constraints are respectively constructed by coordinating multiple frequency modulation resources, and the linear frequency stability constraints are integrated into a basic random optimization scheduling model of the island micro-grid, so that the island micro-grid optimization scheduling method considering the whole process of frequency response is formed. According to the method, the stable frequency under the N-1 event is guaranteed, the source-load-storage response potential is mined, the operation cost and the dependence on the generator are reduced, and safe and efficient operation of the island microgrid is supported.

The invention discloses a power grid security situation awareness platform and method, and relates to the technical field of power grid awareness, and the method comprises the steps: collecting power grid topology, real-time measurement and meteorological data, and constructing a space-time aligned feature matrix; calculating a node power anomaly degree and a line meteorological vulnerability factor as dynamic characteristics; designing a space-time convolution kernel to construct a graph neural network, and extracting node association features in a layered manner; calculating a dynamic fault propagation factor through the fault permeability and the gradient field; calculating a path selection entropy based on the propagation probability matrix, and generating a risk thermodynamic diagram; and superposing the entropy thermodynamic diagram to a GIS system, and dynamically rendering a fault propagation path. Through a dynamic fault propagation factor algorithm, the early-stage accurate capture of the hidden fault of the power grid is realized, a weak link can be identified before the fault is dominated, and a longer early warning response window is provided for scheduling personnel.

The application discloses a power grid safety emergency early warning method and system based on multi-source data fusion, relates to the technical field of power grid public opinion monitoring, and sets the public opinion influence grade and the public opinion tolerance of each power construction area, screens each public opinion transmission node of each power construction area, then monitors and evaluates the transmission influence of each public opinion transmission node, obtains the public opinion monitoring result of each power construction area, and finally executes a corresponding public opinion safety emergency early warning scheme, so that the key node can be accurately locked, resource waste caused by generalized monitoring can be avoided, the monitoring efficiency is improved, the emotional change track of the netizen who generates an interactive behavior in public opinion transmission is tracked according to the public opinion guiding effect, so that the actual situation of public opinion guidance can be judged, potential public opinion risks can be effectively identified, a targeted and efficient response strategy can be made when public opinion occurs, and the enterprise image can be effectively maintained.

The invention relates to the technical field of power system optimization scheduling, and discloses a multi-time-scale optimization scheduling method considering key section power flow safety, which comprises the following steps: firstly, identifying a system partition map based on an adaptive GN algorithm, and establishing an optimization model considering power grid safety operation constraint conditions such as node power balance and line power flow balance; optimizing to obtain a key section tidal current safety limit value; establishing a day-ahead deterministic optimization scheduling model by taking the minimization of the total operation cost of the power system as a target, and further establishing a date two-stage distribution robust optimization scheduling model considering the wind power uncertainty and solving the date two-stage distribution robust optimization scheduling model; and considering a refined time scale, carrying out refined modeling on an intra-day load and a wind power output predicted value, establishing an intra-day rolling optimization scheduling model based on a model prediction control theory, and solving the intra-day rolling optimization scheduling model. According to the method, the robustness of a power system for dealing with short-term internal source load rapid fluctuation is improved, and the safety and reliability of power grid operation are remarkably improved.