268 results about "Grid security" patented technology

The invention discloses an optical storage charging and discharging integrated power station vehicle network interaction method considering demand side response, and aims to improve the bidirectional interaction capability of a power station and a power grid and realize dynamic matching between the demand side response of the power grid and user charging and discharging behaviors. A high-precision photovoltaic output prediction model, an energy storage system SOC dynamic model and an electric vehicle charging load probability distribution model are constructed, and a joint output feature library is formed. A dynamic charging and discharging priority division method is put forward, and the charging and discharging power distribution weight is dynamically adjusted in combination with the vehicle state, the user electricity price sensitivity and the power grid load curve. And designing a composite demand side response excitation mechanism combining time-of-use electricity price and capacity compensation, and guiding the user to participate in peak regulation and valley filling of the power grid. An edge computing and cloud cooperative control framework is adopted, and a dynamic security check module based on model predictive control is adopted, so that potential out-of-limit risks are evaluated in advance, and the security of a power station and a power grid is guaranteed. And efficient operation of the light storage charging and discharging integrated power station and efficient utilization of new energy are realized.

The invention provides an intelligent power grid relay protection reliability evaluation and lifting control method and system, and relates to the technical field of intelligent power grid relay protection, and the method comprises the steps: collecting the operation data of a relay protection device, carrying out the preprocessing and multi-domain feature extraction, and inputting a deep hybrid neural network to obtain a comprehensive feature vector; a multi-stage fault diagnosis model is utilized to identify a fault mode, a collaborative anomaly and a reliability level at a device level, an interval level and a station level respectively, and a fault propagation path and influence are predicted based on a causal reasoning network. And finally, constructing a hierarchical cooperative control strategy, and through multi-objective optimization, distributed game optimization and hierarchical rolling optimization, realizing coordinated optimization of a protection fixed value and improving the reliability of a relay protection device, thereby reducing the fault propagation probability and ensuring safe and stable operation of a power grid.

The invention relates to the technical field of power grid self-healing control, and discloses a power grid self-healing control intelligent optimization method and system, and the method comprises the steps: obtaining the topological structure information of a power grid, dividing n power grid toughness evaluation regions according to the topological structure information of the power grid, calculating a hierarchical toughness evaluation index, and forming a comprehensive toughness evaluation index according to the hierarchical toughness evaluation index; a self-healing control intelligent optimization model is constructed based on the comprehensive toughness evaluation indexes, a self-healing control optimal solution is obtained by solving the self-healing control intelligent optimization model, the self-healing control optimal solution is dynamically calibrated, and a self-healing control strategy is generated; according to the method, the topological structure, the historical fault data and the real-time operation state of the power grid are comprehensively considered, a layered evaluation and dynamic calibration combined mode is adopted, the intelligent level and adaptability of self-healing control of the intelligent micro-grid are improved, the toughness and recovery capacity of the micro-grid for coping with complex faults are enhanced, and safe, reliable and efficient operation of the power grid is guaranteed.

The invention provides a power grid safety supervision method and system, and relates to the technical field of information, and the method comprises the steps: carrying out the grade classification and hidden danger rating of hidden dangers according to a preliminary hidden danger analysis result, a construction site business rule, and the severity of the hidden dangers, and obtaining a hidden danger grade classification result; hidden danger grades are judged according to the hidden danger grade classification result, the hidden danger grades are divided into grade I (major hidden danger), grade II (larger hidden danger), grade III (serious hidden danger) and grade IV (general hidden danger), and a corresponding response instruction sequence is issued to a construction equipment controller based on the hidden danger grades; generating a summary report of power grid supervision according to the fused multi-modal feature vectors; and the generated standardized construction report is stored in a database and pushed to related management personnel, so that the management personnel can know the construction site and the power grid state in time. The risk of safety accidents is reduced, and powerful support is provided for construction safety management.

The invention provides a power grid security protection system and method based on a large language model. The system comprises a multi-source data acquisition module, a large language model enhancement analysis module, a security evaluation module and an interaction and output module, wherein the large language model enhancement analysis module generates a multi-stage attack chain; determining a corresponding risk level according to the importance index of the power grid equipment related to the generated attack chain and the real-time alarm data, and generating a dynamic defense response strategy according to the risk level; the security evaluation module calculates a power grid equipment security risk index based on the dynamic security scoring model according to the power field big language model, and determines a vulnerability repair strategy corresponding to the power grid equipment according to the power grid equipment security risk index; the interaction and output module generates a visual security report according to the multi-stage attack chain; and the dynamic defense response strategy and the vulnerability repair strategy are pushed to the dispatching center in real time, so that the reliability of power grid security protection is effectively improved.

The invention relates to the technical field of power grid resource management, in particular to a power grid resource dynamic threshold early warning method and system based on multi-source data. The method comprises the following steps: performing dynamic baseline value weighted combination on standard historical multi-source power grid load data to obtain power grid load dynamic baseline value data; carrying out adaptive load early warning threshold processing on the power grid load dynamic baseline value data to obtain dynamic load early warning threshold curve data; constructing a power grid load prediction model; performing short-term load prediction by using the power grid load prediction model to obtain short-term load prediction data; and performing threshold load difference calculation on the short-term load prediction data through the dynamic load early-warning threshold curve data, and performing multi-stage early-warning trigger processing to obtain a power grid multi-stage early-warning trigger strategy. According to the invention, by dynamically evaluating the load change of the power grid, real-time monitoring and self-adaptive early warning are realized, false alarms and missing alarms are effectively reduced, and the safety management level of the power grid is improved.

The invention relates to a reconfigurable battery energy storage system grid-connected and off-grid seamless switching control method and device. The method comprises the following steps of: sensing a real-time power grid state, pre-judging a system operation mode, and acquiring a millisecond-level panoramic information flow of the power grid state through a high-speed sampling voltage sensor, a high-speed sampling frequency sensor, a high-speed sampling phase sensor and a high-speed sampling communication interface. And pre-judging the stability and disturbance trend of the power grid through a deep learning algorithm, executing a power distribution and voltage support algorithm in grid-connected and off-grid modes, and calculating an optimal switching opportunity point and a system reconstruction target topology scheme. Meanwhile, the harmonic distortion rate and the voltage sag depth are calculated in real time through discrete Fourier transform, so that a mode pre-judgment mechanism is automatically triggered. After switching, key parameters such as voltage, frequency and power are automatically corrected through real-time sampling and observation, and zero-sensing switching is achieved. The method can effectively improve the response speed and stability of the battery energy storage system when the power grid fails, and guarantees the safe operation of the power grid.

The invention discloses a substation dispatching data network security communication method based on quantum tunnel encryption, relates to the technical field of large-scale power grid security assurance, and aims to solve the problems of endpoint security loss, boundary solidification, weak quantum threat resistance and inflexible deployment of a dispatching data network. According to the method, the establishment of a secure session and the encapsulation, encryption and restoration of data are realized by integrating quantum key distribution and a post quantum cryptographic operation module through quantum secure communication terminals deployed in a dispatching master station and a transformer substation. According to the method, application layer encryption and signature are carried out on telecontrol protocol data, a double-layer encryption tunnel is constructed, and transmission of various physical network media is supported. By means of the scheme, end-to-end protection is achieved, quantum attacks are effectively resisted, double encryption is provided, and networking flexibility and smooth evolution are improved.

The invention relates to the technical field of power grid safety management, in particular to a digital twinning-based power grid control safety system and method.The digital twinning-based power grid control safety system comprises a power grid model unit, an intelligent recognition unit, a strategy making unit and a block chain cooperation unit. The method improves the operation safety and reliability of a power grid, optimizes the resource configuration of the power grid, reduces the energy consumption, provides detailed safety reports and risk assessment for the operation of the power grid, dynamically recognizes potential safety threats and abnormal behaviors, automatically generates a response strategy in real time, improves the overall safety of the system, and improves the safety of the system through the introduction of a block chain technology and the combination with digital twinning. According to the method, transparent information sharing and collaborative management among each level and each region of the power grid are realized, and through decentralized data storage and security guarantee, the power distribution networks in different regions can be ensured to be efficiently collaborative when facing network attacks or faults, and the response efficiency is improved.

The invention relates to the technical field of power system monitoring, and provides a power grid data fault diagnosis method and device based on an intelligent power monitoring instrument, and the method comprises the steps: collecting the operation data of a power grid through the intelligent power monitoring instrument, and generating a fault feature vector containing the multi-dimensional feature parameters of voltage, current and power factor; matching and comparing the feature vector with a preset fault criterion; determining a specific fault type based on a comparison result of the characteristic parameters and a threshold value, including identifying voltage abnormity through a voltage deviation rate, identifying harmonic exceeding through a current harmonic total distortion rate, and identifying a three-phase imbalance fault through a three-phase imbalance degree; for judgment of the three-phase unbalance degree, mean square error statistic calculation is adopted, and a safety coefficient is introduced for threshold comparison; and the fault influence priority is evaluated by quantifying the degree of deviation of each characteristic parameter from the threshold. The fault diagnosis efficiency and reliability can be improved, and intelligent decision support is provided for safe operation of a power grid.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention discloses an intelligent scheduling system for power grid safety guarantee based on multi-source data analysis, and the system comprises a space-air-ground sensing layer which is used for collecting meteorological data, equipment state data, and power grid operation data; the multi-source data fusion engine is connected with the air-space-ground sensing layer and is used for carrying out space-time alignment and feature extraction on the multi-source heterogeneous data and generating a power grid panoramic state matrix; the extreme weather decision closed-loop module deduces a disaster propagation path through federal learning based on the power grid panoramic state matrix, and outputs a scheduling control instruction; the power grid immune engine is used for receiving the power grid panoramic state matrix, simulating a hidden fault chain through a digital twinborn body and dynamically generating a protection device correction strategy; and the intelligent execution terminal is used for executing the scheduling control instruction and the protection device correction strategy and feeding back the physical power grid state to the digital twinborn body. Through multi-source data fusion and an intelligent algorithm, power grid panoramic perception, disaster pre-judgment, dynamic protection and strategy optimization are realized, and the safety guarantee capability is improved.

The invention discloses an electric power artificial intelligence model security protection method and system based on a zero-trust architecture, and belongs to the technical field of power grid security protection. The method comprises the following steps: constructing an encryption service request according to identity recognition information and environment information of an access user; verifying the request by adopting a multi-factor identity verification method, and generating an authentication credible score; performing weighted calculation by combining the user historical behavior score, the equipment safety score and the environmental factor score to obtain a dynamic comprehensive credibility score; and generating a permission configuration strategy based on the score to realize fine-grained access control. The system comprises a user request access module, an information collection module, a credibility evaluation module and an access control module, and through continuous verification and dynamic permission adjustment, safe access of the electric power artificial intelligence model is ensured. The defect that a traditional security technology depends on static authentication and boundary protection is overcome, data leakage, model tampering and illegal access are effectively prevented, and the overall security and stability of a power system are improved.

The invention discloses a large power grid security check calculation method suitable for multiple time scales, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining the calculation content of a multi-user security check task, evaluating the complexity of the calculation content, and selecting a parallel calculation mode based on an evaluation result; s2, based on a parallel computing device pool set in a parallel computing mode and a job scheduling strategy, extracting computing sub-tasks of the multi-user security check task, and dynamically distributing the computing sub-tasks to corresponding computing nodes by adopting a load balancing mechanism; and S3, processing the calculation subtasks of the plurality of calculation nodes by adopting a parallel algorithm of a CPU + GPU hybrid architecture, merging calculation subtask results of the plurality of users, and generating a final security check report. According to the scheme, the computing resource utilization rate of the resource pool is improved, and the large-scale power grid safety checking efficiency is remarkably improved.

The invention belongs to the technical field of smart power grid security, and particularly relates to a power grid false data injection attack positioning method based on a self-adaptive space-time diagram neural network, which comprises the following steps: generating an optimized reconnection adjacency matrix, and realizing self-adaptive neighborhood selection; carrying out global feature fusion by utilizing an improved Transform module; the self-embedding of the nodes is input into the spatial-temporal feature fusion module, all the obtained nodes are embedded and spliced and input into the improved Transform module to fuse multi-source features, final node total embedding is obtained and input into the classification layer to judge node abnormity, and power grid false data injection attack positioning is achieved. Improving the expression of the GNN in the low-homogeneity graph data while considering the time dependence and the spatial relevance of the power grid data; adaptive neighborhood selection and spatio-temporal feature fusion are integrated, and the attack detection and positioning effects are improved.

The invention relates to the technical field of collaborative optimization operation of a power system, in particular to an electricity-carbon dispersion collaborative optimization operation method considering source side uncertainty, which comprises the following steps of: coping with fluctuation of wind and light energy in day-ahead scheduling based on a DRO model of a data-driven probability density Wasserstein distance; establishing an uncertainty scheduling model of the source side of the electricity-carbon cooperative system; establishing a DRO day-ahead scheduling model of the multi-zone power system by combining the DRO uncertainty model; the DRO day-ahead scheduling model is converted into a single-layer linear programming model through out-of-sample approximation and constraint dual conversion; and designing a power system electricity-carbon point-to-point transaction distributed coordinated optimization architecture containing grid security constraints, and solving the single-layer linear programming model by adopting an ADMM under a dynamic penalty factor updating strategy to obtain a coordinated operation scheme. According to the method, the data privacy of the multi-zone power system is protected on the premise of ensuring the calculation accuracy, and meanwhile, the solving speed is remarkably increased.

The invention discloses a power grid industrial control system security protection scheme based on a block chain, and belongs to the field of power grid automation security. Aiming at the problems of high centralized authentication risk, weak physical layer protection and the like of a traditional industrial control system, a trusted execution environment and intelligent contract response system is constructed through fusion of a block chain distributed account book and a hardware security module (HSM). According to the scheme, equipment physical identity binding and vibration monitoring are achieved through an HSM, the consensus efficiency is optimized through an improved PBFT algorithm, instruction legality, parameter compliance and execution logic rationality are dynamically verified in combination with an intelligent contract, a security baseline is generated in real time, and the instruction stream deviation degree is monitored. The method can dynamically balance the security and real-time performance of the instruction, reduce manual intervention, improve the abnormal response efficiency to a second level, is suitable for industrial control systems such as SCADA (Supervisory Control And Data Acquisition) and the like, remarkably improves the protection capability in scenes such as DDoS attack resistance and physical tampering detection, and provides an extensible intelligent protection normal form for power grid security.

The invention discloses a software supply chain security analysis method and system based on knowledge graph and GNN model fusion, a medium and a processor. The method comprises the steps of collecting power grid software supply chain safety related data, preprocessing and storing the data, constructing a knowledge graph, mining effective association rules by applying an Apriori algorithm, constructing a GNN model, introducing an attention mechanism for training and testing, and carrying out visualization and interactive design on the model. The system comprises an acquisition module, an atlas module, a mining module, a model module and an analysis module. A computer readable storage medium and a processor may execute the method. Compared with the prior art, the method has the advantages that scattered data can be integrated, association rules can be accurately mined, threats can be accurately identified by means of a GNN model, threat information can be conveniently checked and analyzed through a visual interaction interface, the capability and efficiency of power grid software supply chain safety analysis are comprehensively improved, and powerful support is provided for power grid safety protection.

The invention relates to the technical field of power grid safety guarantee, and particularly discloses a standardized ring main unit intelligent safety protection and early warning system, which comprises a data acquisition layer, a central processing layer and an execution control layer, and is characterized in that the data acquisition layer is responsible for real-time acquisition of operating parameters of a ring main unit; the central processing layer comprises a data preprocessing sub-module, a risk assessment sub-module and a decision optimization sub-module, and realizes data cleaning, feature extraction, risk assessment and strategy generation. Through hierarchical design, efficient and accurate ring main unit state monitoring and fault prevention and control are realized. The data acquisition layer comprehensively acquires electrical, insulating and state parameters in real time by means of various sensors, and provides an accurate and rich data basis for the system; and a data preprocessing, risk assessment and decision optimization sub-module of the central processing layer performs deep processing on the acquired data, realizes a complete process from data cleaning, feature extraction to risk assessment and strategy generation, and ensures that the risk judgment is accurate and the strategy is reasonable.

The invention relates to a cooperative control method and system suitable for network construction type and network following type energy storage isolated networks. The method comprises the following steps: monitoring network construction type energy storage residual available capacity in real time; judging whether the active remaining available capacity of the network-building type energy storage is smaller than an active threshold value or not, further judging whether the active remaining available capacity of the network-following type energy storage is larger than the active threshold value or not, and if yes, adopting frequency deviation control on the network-building type energy storage and adopting a frequency additional control strategy on the network-following type energy storage; and judging whether the reactive residual available capacity of the network construction type energy storage is smaller than a reactive threshold value or not, further judging whether the reactive residual available capacity of the network following type energy storage is larger than the reactive threshold value or not, and if yes, adopting voltage amplitude deviation control on the network construction type energy storage and adopting a voltage additional control strategy on the network following type energy storage. According to the invention, cooperative control under the isolated network modes of network construction type energy storage and network following type energy storage in the micro-grid is realized, human intervention operation is reduced, and safe and stable operation of the power grid is ensured.

The invention discloses a micro-grid safety monitoring method and system based on attack detection data, and relates to the field of power grid safety monitoring, and the method comprises the steps: outputting a state vector of each node; obtaining a predicted attacked result of each node; outputting an estimated value set and a measured value set of the operation data of each prediction node; and obtaining an attack detection result of each prediction node and executing corresponding operation. According to the method, the data channel safety degree and the multi-dimensional evaluation parameters are fused, a state estimation and attack probability recognition mechanism combining extended Kalman filtering and a time sequence model is introduced, a hierarchical response mechanism is set according to attack levels, the elastic defense and active recovery capability of the system under different attack intensities is improved, and stable operation of the micro-grid is guaranteed.

The invention discloses a multi-element load resource cooperative regulation and control method and system in a power grid imbalance scene, and relates to the technical field of power regulation and control. The method comprises the following steps: S1, obtaining a future state section of a power grid, and carrying out balance analysis on the future state power grid to determine a balance gap when the power grid has an insufficient balance scene; s2, aggregating the multi-element load side resources accessed to the power grid, and constructing an aggregated resource pool; s3, screening load side resources meeting regulation and control requirements from the aggregation resource pool according to resource regulation characteristics and demand response characteristics, and generating a resource collaborative regulation and control strategy; and S4, adjusting the load side resources based on the regulation and control strategy to eliminate the balance gap of the future state of the power grid. According to the scheme, active pre-judgment and pre-control can be carried out on a possible power grid balance insufficiency scene in the future, the risk perception and disposal capability of a power system during power grid balance insufficiency is effectively improved, and safe operation of a power grid is guaranteed.

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 discloses a power grid simulation environment scheduling optimization method and system based on reinforcement learning. A multi-target reward function system and a double insurance mechanism combined with reward constraint limitation are designed. In the training process of the A3C algorithm, modification is carried out in combination with the actual condition of high-proportion new energy grid connection, safe and stable operation of a power system can be effectively maintained by introducing a power grid safety index, passive action screening is carried out to reduce the single-step action space dimension, the convergence speed of reinforcement learning is increased, and the method is suitable for large-scale popularization and application. And the real-time performance of the A3C algorithm in power grid dispatching optimization is enhanced. Then, by combining economic cost, output limitation of a generator set, capacity limitation of a power transmission line and the like, it is ensured that the algorithm can comprehensively consider and effectively process the constraint conditions in the training process; through the standardized constraint processing mechanism, the generated scheduling strategy not only meets the actual operation requirements of the power system, but also can be optimized under various complex constraint conditions.

The invention discloses a power system network abnormal data clustering vulnerability detection technology, and belongs to the field of power system network security. Aiming at the problems of insufficient high-dimensional data processing capability, poor dynamic adaptability, high false alarm rate and the like of a traditional anomaly detection method, a detection technology based on an improved FCM algorithm is provided. According to the technology, through a dynamic feature weight matrix design module, a time adaptive membership updating module, a physical constraint regularization module, an incremental density peak initialization module and the like, an intelligent clustering framework fusing spatial-temporal features and physical constraints is constructed. And extracting multi-dimensional features by using GCN and Transform, dynamically adjusting the weight in combination with an attention mechanism, and introducing a time decay factor and a fuzzy logic constraint to realize accurate clustering. Experiments show that when the technology is applied to a provincial power grid, the anomaly detection accuracy is improved to 94.3%, the decision generation time is shortened to 14.8 ms, the constraint violation rate is reduced to 2.1%, and the real-time performance and reliability of power grid safety protection are remarkably improved.

The invention relates to the technical field of power grid safety evaluation, in particular to a power system short-circuit parameter prediction method, system and device based on time-space relevance and a storage medium. A multi-dimensional state vector including a node voltage vector, a branch current vector, a short-circuit impedance matrix, spatial position information and a time feature vector is constructed, and a space-time correlation feature vector including a time-dimensional feature, a spatial-dimensional feature, a space-time coupling feature and a state feature is extracted. Wavelet transform, Fourier analysis and graph theory analysis are adopted to identify a parameter change rule and a propagation effect. A multi-level prediction model of a statistical prediction layer, a deep learning layer and a fusion layer is constructed, and comprehensive description of dynamic characteristics of short-circuit parameters is realized through spatio-temporal feature fusion. A staged training strategy is adopted, and comprises the steps of establishing a basic model through offline training, updating parameters in real time through online learning, and quantifying uncertainty through a probability prediction framework. And a prediction correction mechanism and a reliability evaluation system are established, and a complete prediction-evaluation-correction closed loop is formed.

The invention relates to a power grid security defense system based on artificial intelligence and a block chain, and the system comprises an AI defense layer, a block chain trust layer, a cooperative control layer, an attack traceability and attribution layer, a physical-information fusion verification layer, a scene adaptive configuration layer and a risk prediction module. Data credibility is guaranteed by reputation weighted consensus, dynamic defense is completed by means of intelligent contracts and cross-layer cooperation, and beforehand early warning and computing power adaptation are realized through a risk prediction module. The system effectively solves the problems that in the prior art, an AI model is prone to being attacked, block chain response lags behind, virtual and real data are not synchronous, multi-scene adaptability is poor, pre-risk early warning is lacked, and computing power dispatching lags behind, can be widely applied to scenes such as a power transmission network, a power distribution network, a micro-grid and energy storage grid connection, and has high practicability. The anti-attack ability of the power grid to false data injection, equipment backdoor attack, collaborative DDoS and other threats is significantly improved, and the operation stability and safety of the power grid are guaranteed.

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 discloses a day-ahead optimal scheduling decision method and device considering the climbing speed difference of a thermal power generating unit. The method comprises the following steps: expanding an optimal scheduling decision period of the thermal power generating unit; modeling peak regulation intervals of different climbing rates in the operation process of the thermal power generating unit by adopting a 0-1 variable; establishing a thermal power generating unit optimization scheduling objective function considering different state operation cost and deep peak regulation compensation cost, and thermal power generating unit operation constraint conditions reserved by considering different working condition start-stop, segmented climbing rate, start-stop process output and standby climbing capability; combining hydropower operation, system balance, new energy operation and power grid security constraints to construct a high-proportion new energy power grid unit combination optimization scheduling decision model; and accessing a planning day power grid topology model and operation boundary data, and generating a day-ahead unit commitment plan and an output plan based on the unit commitment optimization scheduling decision model. According to the invention, the power grid security risk caused by insufficient climbing ability can be reduced, and new energy consumption is promoted.