1386 results about "Smart grid" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

The invention discloses a power grid equipment intelligent operation and maintenance method based on a large language model and a knowledge graph, and relates to the technical field of intelligent power grid operation and maintenance, and the method comprises the steps: carrying out the operation and maintenance of power grid equipment through a power grid operation and maintenance knowledge graph constructed through a large language model and a knowledge federation technology, the power grid equipment operation and maintenance comprises one or more of health state evaluation, fault risk prediction and early warning, intelligent operation and maintenance strategy generation and health degree dialogue query of the power grid equipment. According to the invention, a power grid operation and maintenance mode can be effectively promoted to be transformed and upgraded from a traditional manual experience type and a passive maintenance type to a data-driven, intelligent and active preventive maintenance mode. Key intelligent operation and maintenance technical support is provided for building a novel electric power system with new energy as a main body, the novel electric power system is assisted to achieve the development goals of being safer, more efficient, cleaner and lower in carbon, and important industry strategic significance and social contribution are achieved.

The invention discloses an energy storage system operation and maintenance strategy optimization method based on digital twinning, and belongs to the technical field of electric energy storage and intelligent power grids. Establishing a digital twinning synchronous model of the energy storage system and calibrating the digital twinning synchronous model; generating prediction data at the current moment based on the digital twinborn model, performing residual analysis on the prediction data and real-time data, and generating a quantitative diagnosis index; according to the quantitative diagnosis index and the fault mode, adjusting parameters of the digital twinning synchronization model, and ensuring that the model and the actual state of the energy storage system are kept synchronous; inputting real-time data into the adjusted digital twinborn model, calculating a future operation index of the energy storage system, and generating simulation operation data; and generating a non-periodic operation and maintenance strategy according to the simulation operation data, the fault mode and the operation and maintenance rule base. According to the method, the adaptive calibration digital twinborn model is adopted, real-time diagnosis and prospective optimization can be fused, and the operation and maintenance efficiency and reliability of the energy storage system are remarkably improved.

The invention discloses a power distribution management system of an intelligent charging pile, and belongs to the technical field of electric vehicle charging facilities and intelligent power grids. The load prediction module carries out space-time alignment fusion on historical data and real-time monitoring data to generate a power distribution demand prediction value, and the edge calculation controller executes a model prediction control algorithm based on multi-source data to generate a real-time control strategy containing relay time sequence parameters and a capacitance compensation scheme. And the dynamic power distribution adjustment module executes strategy parameters through the solid-state relay array and the parallel compensation capacitor bank. Self-adaptive adjustment of the power distribution network is achieved through a real-time monitoring-prediction-optimization closed-loop control mechanism, the harmonic content of the power grid is effectively reduced, the energy distribution efficiency of the charging pile group is improved, and the method is suitable for intelligent electric energy management of public charging stations and other scenes.

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention relates to the technical field of intelligent power grids, and provides an intelligent operation centralized control center intelligent scheduling interaction method and system, and the method comprises the steps: generating a heat distribution gradient through environment noise filtering, reflecting the temperature field change of equipment, obtaining a voltage amplitude envelope through fundamental wave and harmonic wave separation, and monitoring the power quality; a current fluctuation coefficient is extracted by combining segmented deviation analysis to evaluate a line load state, and frequency fluctuation parameters are captured by using time domain statistics to track system stability. By constructing a dynamic association map, heterogeneous data such as thermodynamics, electrical quantity, load characteristics and synchronous states are mapped to power grid topological nodes, and a cross-dimension association relationship is established to realize whole-network state coupling analysis. An overload risk, a voltage out-of-limit probability and a system instability threshold are quantified through a hierarchical evaluation mechanism, a multi-target optimization constraint condition is formed, and finally a scheduling strategy considering equipment safety, economy and power supply reliability is generated. When the power system operates, intelligent scheduling is carried out according to the operation state of the power grid.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The invention relates to the technical field of communication, in particular to an underground power distribution room communication system and method based on heterogeneous network and multi-modal fusion, and the system comprises a three-dimensional heterogeneous network cooperation module, a cross-modal feature fusion module, a dynamic routing decision engine module, a self-adaptive interference suppression system and a cross-layer cooperation optimization module. The three-dimensional heterogeneous network cooperation module comprises a 4G / 5G wireless communication sub-layer, a power line carrier communication sub-layer and an edge computing management sub-layer; the cross-modal feature fusion module comprises a multi-modal encoder group, a shared feature projection layer, a twin network structure and a cross-modal attention fusion module; and the dynamic routing decision engine module comprises a state space monitoring unit, a routing optimization unit and a millisecond-level path switching strategy. Through the arrangement, the reliability, the real-time performance and the energy efficiency of communication of the underground power distribution room are systematically improved, and a high-robustness communication infrastructure is provided for an intelligent power grid.

The invention provides a power plant steam extraction energy storage and power generation optimization method and system based on fused salt heat storage, and relates to the technical field of electric power. The extracted steam is introduced into a multi-stage tandem type steam-molten salt heat exchanger group; when the load of the power grid is higher than a preset dynamic threshold value, conveying the high-temperature fused salt to the cascade fused salt-water heat exchanger group by adopting a multi-path parallel fused salt conveying system; in the fused salt-water heat exchanger group, a structure of combining countercurrent flow heat exchange and a spiral heat exchange pipe is adopted, so that the heat exchange efficiency is improved, and superheated steam is generated; the generated high-temperature and high-pressure steam is conveyed to the improved steam turbine with the variable air inlet area, the air inlet area is dynamically adjusted according to the power grid load requirement, and the efficiency of the steam turbine is optimized; the intelligent power grid technology and the demand side response strategy are utilized to realize bidirectional interaction between the power generation end and the power utilization end, and power supply and demand are dynamically balanced.

The invention discloses a mobile energy storage pre-layout and dynamic scheduling system and method oriented to toughness improvement of a power distribution network, and belongs to the technical field of power systems and intelligent power grids. In the pre-disaster stage, a two-stage robust optimization model fusing photovoltaic output uncertainty is constructed, and the configuration cost of mobile energy storage, the load reduction risk and the traffic accessibility of key nodes are comprehensively considered to generate an optimal deployment scheme; in a post-disaster stage, based on a traffic network state and a multi-source output characteristic, a multi-source collaborative dynamic scheduling model is constructed, a migration path, a charging and discharging strategy and charge state control of mobile energy storage are optimized in real time, and power supply loss of a key load is minimized. The system realizes rapid power supply recovery and safe operation in the island power grid by cooperatively controlling distributed resources such as mobile energy storage, electric vehicles, photovoltaic and diesel generators and the like. According to the invention, the quick response capability and recovery efficiency of the power distribution network under extreme disasters are significantly improved.

The invention discloses an intelligent load management and allocation method for an electric meter box, relates to the technical field of intelligent power grid management and energy optimization, and solves the problems that the adaptability of load management in an existing electric meter box is insufficient, the prediction accuracy is influenced by data prejudice, and the dynamic adjustment capability is limited. According to the method, diversified data are collected and analyzed through a real-time data interface and a gradient boosting tree technology, a power consumption prediction model is constructed in combination with a long-short term memory (LSTM) network, and meanwhile, prediction performance is optimized by adopting a data enhancement algorithm and an adaptive enhancement algorithm; fast response and dynamic adjustment are realized by using a sliding window prediction method based on Kalman filtering, a load distribution strategy is optimized through an adaptive depth deterministic strategy gradient algorithm, and collaborative distribution scheduling and management are performed on cross-regional electric meter boxes through a distributed collaborative scheduling system; according to the invention, the stability and efficiency of the power system are greatly improved, and the energy utilization efficiency and economic benefits are remarkably improved.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention discloses a fan inertia support evaluation and control method considering power grid inertia space-time distribution. Based on wide area measurement system data, a power grid inertia space-time distribution model is constructed, inertia contribution characteristics of different areas are analyzed, and the influence of extra-high voltage direct current transmission on inertia distribution is considered. And calculating the inertia response power of the fan, quantifying the support contribution of the virtual inertia of the fan based on the frequency change rate and the regional equivalent inertia, and establishing an inertia support efficiency evaluation index system. A dynamic feedback control strategy is put forward, a reinforcement learning or adaptive gain adjustment method is adopted in combination with the real-time distribution condition of the inertia of the power grid, the virtual inertia control parameters of the fan are optimized, and the response accuracy is improved. Based on a wide-area coordination optimization method, a fan inertia support scheduling strategy is optimized, and the system frequency stability is improved. The method can effectively improve the frequency supporting capability of the new energy power grid, improves the new energy consumption level, enhances the operation safety of the power grid, and is suitable for a high-proportion new energy grid-connected smart power grid.

The invention discloses an energy storage efficiency improvement method and system combined with energy management, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting the state data, environment data and load data of an energy storage unit through a sensor network, obtaining the information of a power grid through the Internet, and carrying out the real-time processing; constructing a prediction model based on the multi-level memory network, and predicting future load demands and electric energy supply conditions; according to the prediction result, combining the real-time state of the energy storage unit and the power grid information, generating a preliminary charging and discharging instruction, and sending the instruction to an edge node; performing preliminary decision on the preliminary charging and discharging instruction at the edge node, generating a local charging and discharging plan, and uploading the local charging and discharging plan to a cloud server; the cloud server gathers the local charging and discharging plans, carries out global optimization, generates a final charging and discharging instruction, and issues the final charging and discharging instruction to the energy storage unit; through the prediction model constructed based on the multi-level memory network, the prediction precision is remarkably improved, and meanwhile, through a physical constraint embedding mechanism, the credibility of a prediction result is ensured.

The invention discloses a non-contact magnetoelectric composite current sensor with magnetism gathering and magnetic shielding. A sensor core is composed of a magnetoelectric sensing element, a magnetofluid layer, a magnetism gathering structure and a magnetic shielding layer. The magnetoelectric sensing element is made of manganese-doped lead magnesium niobate and amorphous alloy film composite materials, the outer side of the sensing element is coated with a magnetofluid layer made of soft magnetic composite materials, the magnetism gathering structure is made of permalloy materials with high magnetic conductivity and is divided into an upper part and a lower part, the upper part accommodates the sensing element and the magnetofluid layer, and the lower part accommodates the magnetic gathering structure. And the lower part is provided with a standardized groove for placing a conductor to be tested. The outermost layer is a magnetic shielding layer composed of multiple layers of nanocrystalline alloy strips, the vertical split type design is also adopted, and it is ensured that the magnetic shielding layer is tightly matched with the magnetic gathering structure through precision machining. The upper magnetic shielding layer and the lower magnetic shielding layer are detachably connected, and field installation is facilitated. The overall structure is compact, a circuit does not need to be disconnected during installation, and the device is especially suitable for on-line current monitoring requirements in the fields of smart grids, industrial automation and the like.

The invention provides an electric energy metering system operation state comprehensive evaluation method and system, and the method comprises the steps: receiving a real-time energy consumption data stream, carrying out the processing through an adaptive threshold anomaly detection algorithm and a Bayesian inference method, and generating a real error range estimation corresponding to each data point; generating a target prediction result by using the time sequence prediction model in combination with historical same-period data, seasonal factors and a reinforcement learning mechanism; comparing the target prediction result with the real-time energy consumption data flow to obtain a comparison result, and generating a comprehensive performance fluctuation evaluation result based on the comparison result and the multi-dimensional state space model; constructing a comprehensive evaluation index system by using an integrated learning algorithm and a fuzzy logic algorithm, and obtaining a comprehensive score corresponding to each ammeter by using the comprehensive evaluation index system; according to the method, the prediction accuracy of the electric energy metering system, the comprehensiveness of health condition evaluation and the scientificity of maintenance decision are improved, and support is provided for efficient management and optimal scheduling of an intelligent power grid.

The invention relates to the technical field of power grid intellectualization, in particular to a digital power grid asset classification and evolution monitoring method, system and device based on self-supervised comparative learning and a storage medium. The method comprises the following steps: acquiring network flow data in a digital power grid, extracting multi-dimensional features such as a time interval, a direction, a data packet length, a protocol type and an address port, and constructing an asset behavior feature matrix; then constructing a self-supervised contrast learning model, taking the communication behavior sequences of the same asset in different time periods as a positive sample pair, taking the communication behavior sequences of different assets as a negative sample pair, and training an asset embedding model through an InfoNCE contrast loss function; then, asset communication behaviors are mapped to a semantic embedding space, and asset classification and identification are carried out based on embedded vectors; and finally, carrying out evolution monitoring on the assets by adopting a sliding time window mechanism, generating an asset evolution trajectory sequence, and carrying out abnormity perception early warning. Autonomous learning without manual annotation, high-precision asset identification and real-time state evolution monitoring are realized.

The invention relates to a load regulation and control method and system based on reinforcement learning, and the method comprises the steps: 1, decomposing a multi-time scale optimization problem into a plurality of layers in a source network load storage management and control scene through employing a layered optimization frame, enabling each layer to correspond to a different time scale, and carrying out the optimization; a model prediction control algorithm is adopted in the short-term optimization layer, a mixed integer linear programming algorithm is adopted in the medium-term optimization layer, and a reinforcement learning method is adopted in the long-term optimization layer to solve a multi-objective optimization problem; and 2, long-term load regulation and control decision making: controlling the power grid to make different load regulation and control decisions through an actually adjustable reward mechanism based on reinforcement learning, so that the intelligent power grid load regulation and control system adapts to continuously changing environments and targets, and the flexibility and adaptability of the system are improved. According to the invention, the reliability and stability of power supply can be ensured, fine management and regulation of power loads are realized, and the operation efficiency, reliability and safety of a power system are improved.

The invention discloses an old community photovoltaic building integrated reconstruction design method, and particularly relates to the technical field of building reconstruction design, and the method comprises the following steps: firstly, obtaining the three-dimensional structure data of a building through scanning equipment, and determining a suitable photovoltaic installation region based on a preset reconstruction region grade; calculating the bearing capacity of the photovoltaic module according to the transformation area, determining the total weight of the bearable photovoltaic module, and designing the initial distribution of the module; an inclination angle and an azimuth angle of a photovoltaic module are optimized through sunlight analysis, after installation is completed, an inverter is used for converting direct current into alternating current, the alternating current is connected to an intelligent power grid system, finally operation data are collected regularly, and a neural network is used for predicting faults and evaluating a health state. According to the invention, a feasible scheme is provided for large-scale popularization of renewable energy sources, the installation of the photovoltaic system can make full use of the idle space of old buildings, the utilization efficiency of solar energy resources is improved, the dependence on traditional energy sources is reduced, and the energy consumption of old communities is reduced.

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The invention discloses an intelligent power grid fault deduction method and device based on a knowledge graph and electronic equipment, and relates to the technical field of power grid fault diagnosis, and the method comprises the steps: receiving protection action alarm information and circuit breaker tripping alarm information when a target intelligent power grid breaks down; based on the protection action alarm information and the circuit breaker tripping alarm information, performing hypothesis reasoning by using a preset power grid topology knowledge graph to obtain a suspected fault equipment set; performing deductive reasoning on each suspected fault device in the suspected fault device set by using a preset protection configuration knowledge graph based on the protection action alarm information and the circuit breaker tripping alarm information to obtain observation event confidence corresponding to each suspected fault device; and evidence fusion reasoning is carried out on each suspected fault device based on each expected observation event confidence, and actual fault information of the smart power grid is obtained. The method can improve the accuracy of the fault diagnosis result of the smart power grid.

The invention provides a power grid fault positioning and intelligent relay protection selective control method and system, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting the multi-point electrical data of a power grid, adjusting a sampling window and a filtering threshold value according to the operation state of the power grid, and carrying out the data processing; after eliminating the end effect, performing empirical mode decomposition to obtain a fault feature component; inputting the characteristic component into a fault diagnosis model, calculating a fault initial probability based on a Bayesian network, generating multiple groups of fault prediction data in combination with a time sequence evolution rule, and taking a position corresponding to a minimum information entropy value as a fault positioning result; determining a fault area according to a positioning result, establishing a local communication network of relay protection devices in the area, obtaining the operation state of each device, calculating a protection sensitivity coefficient, determining a protection range and a tripping time sequence, generating and sending a control instruction, and realizing selective isolation of the fault area.

The invention discloses a power transmission tower edge multi-device linkage method based on an ad hoc network and two-dimensional and three-dimensional fusion, and relates to the technical field of intelligent power transmission networks, and the method comprises the steps: deploying multiple devices, embedding a passive sensor, and employing an unmanned plane to carry a laser radar to generate a tower three-dimensional space reference model; an unmanned aerial vehicle autonomous inspection route is planned based on the iron tower three-dimensional space reference model, laser point cloud, infrared thermal imaging and backscattering data are synchronously collected, passive sensor deformation parameters are extracted by analyzing channel state information, and a multi-dimensional data set is constructed; fusing the multi-dimensional data set, generating a dynamic risk thermodynamic diagram and labeling risk levels; and when a high risk level is detected, triggering an equipment deployment strategy and recording operation data. According to the invention, by constructing a mechanical-thermal-geometric multi-field coupled power transmission tower dynamic monitoring system, a monitoring normal form with high-precision positioning, real-time response and extremely simple operation and maintenance is provided for an intelligent power grid, and intelligent and unmanned evolution of power transmission facility management is promoted.

The invention discloses a vehicle network charging and discharging optimization method and system based on a dynamic electricity price, and relates to the technical field of smart power grids and electric automobiles, and the method comprises the steps: S1, constructing a prediction model of the polarization degree of a battery through an electrochemical model and historical operation data, obtaining a quantification result of the polarization degree of the battery, and obtaining a prediction result of the polarization degree of the battery; s2, according to the quantification result of the battery polarization degree, combining the dynamic electricity price data and the user demand data, adopting a reinforcement learning algorithm, constructing a charging and discharging strategy optimization framework, outputting a preliminary charging and discharging strategy, determining the adaptability of the strategy in different scenes, and obtaining a smooth charging and discharging curve; according to the vehicle network charging and discharging optimization method and system based on the dynamic electricity price, intelligent management of the charging and discharging process of the battery is achieved, the service life of the battery is effectively prolonged, the energy utilization efficiency is improved, economic benefits are maximized, and technical support is provided for efficient operation of a battery energy storage system.

The embodiment of the invention discloses a multi-microgrid regulation and control method and device based on federal hierarchical reinforcement learning, and a medium, belongs to the technical field of smart grids, and solves the problem of low microgrid dispatching precision. Each microgrid uploads a model gradient parameter corresponding to the local scheduling model to a central server; the central server determines an aggregation weight according to the similarity between the micro-grid data distribution characteristics so as to update a global model; the central server dynamically divides each micro-grid into a plurality of micro-grid clusters according to model gradient similarity, and constructs a leader-follower game model in each micro-grid cluster; the central server outputs a first scheduling strategy corresponding to each micro-grid based on the updated global model, the plurality of micro-grid clusters and the leader-follower game model; and each micro-grid periodically performs secondary optimization on the first scheduling strategy based on the local feature data to obtain a second scheduling strategy corresponding to the current micro-grid.

The invention belongs to the technical field of intelligent power grid construction, and relates to a source network load storage system operation optimization method and system based on digital twinning, and the method comprises the steps: a test entity which collects the information of a physical entity through various sensors, and inputs the information of the physical entity to a virtual entity; the virtual entity comprises a prediction model used for predicting the new energy power generation power according to the information of the physical entity; the multi-objective optimization model is used for optimizing and determining charging and discharging power of energy storage and tie line power of the source network load storage system and the power distribution network in real time; calculating a health index of the physical entity by a health index calculation model, and dividing health state grades according to the health index; and the digital twin model is used for mapping the physical entity according to the output result of the virtual entity. The prediction value can be compensated according to the similar day meteorological search algorithm, so that the final power prediction value of new energy power generation is determined, and the condition that prediction errors are gradually increased after long-term operation is avoided.

The invention belongs to the technical field of building heat supply and intelligent power grid demand response control, and particularly relates to an intelligent power grid-oriented heat pump dynamic preheating and demand response control method, which comprises the following steps of: identifying building thermal characteristic parameters, introducing real-time environment variables such as external meteorological parameters and the like and thermal resistance and thermal capacity characteristics of a building, excessive or insufficient preheating caused by only relying on a timer is effectively avoided, and the balance of accurate heat supply and energy conservation is achieved; by adjusting the preheating starting time and the peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or the power grid demand signal, and the peak load is translated or reduced, so that the dual purposes of economic benefits, energy conservation and emission reduction are achieved; and formulating a peak period control strategy and formulating a non-peak period control strategy. According to the control method, actual thermal characteristics and external meteorological conditions of a building are taken as cores, load reduction in the peak period of a power grid and indoor thermal comfort are taken as double targets, and preheating time self-adaption, demand response flexibility and system operation reliability are achieved at the same time.

The invention relates to the technical field of intelligent power grids and interaction between electric vehicles and power grids, and provides an electric vehicle intelligent charging and discharging control method and system based on vehicle-network interaction. The method comprises the following steps: acquiring charging pile related information, load data and charging resources of a to-be-regulated area; calculating a baseline load according to the load data, and calculating the adjustable capability of the to-be-regulated region according to the baseline load; constructing a user charging behavior analysis model according to the battery initial charging electric quantity, the battery end charging electric quantity, the time of arriving at the charging place and the vehicle taking time; according to the power grid regulation and control demand and the adjustable capability, the response capability of the vehicle network resources is judged, and a vehicle network resource first backup list is obtained through screening; matching the charging resource with a power grid regulation and control target, and performing secondary recommendation matching on the first backup list of the vehicle network resource based on a matching result to obtain a second backup list of the vehicle network resource; and formulating an ordered charging regulation and control scheme based on the vehicle network resource second backup list.

The invention belongs to the technical field of intelligent power grids, and discloses an intelligent power grid load balance control method and system. The method comprises the steps of dividing a power grid into a plurality of regions based on a predetermined rule; based on the regional power supply quantity and the regional power consumption quantity, identifying an overload region with insufficient power supply quantity; formulating and executing a load balancing strategy for the overload area by adopting a natural heuristic optimization algorithm, wherein the load balancing strategy is a load scheduling strategy taking scheduling time minimization as a target; calculating the priority coefficient of the electric equipment in the overload area, and supplying power to each electric equipment according to the priority coefficient; effective balance and optimal allocation of intelligent power grid loads are achieved, emergency power supply is guaranteed to the maximum extent in natural disaster time, and therefore the power grid operation level and livelihood guarantee capacity are improved, and the power supply reliability and the power utilization efficiency of the power grid are improved.