422 results about "Power system scheduling" patented technology

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention relates to the technical field of power system scheduling, and discloses a power distribution network simulation scheduling optimization method and system based on artificial intelligence, and the system comprises a data fusion module, a digital twin modeling module, an intelligent prediction module, a strategy optimization module, and a visual scheduling module. The whole scene of the power distribution network is simulated through the digital twin model, the operation state and fault influence of equipment are accurately simulated, a scientific basis is provided for making a maintenance plan, blind maintenance is avoided, and the maintenance and repair cost of the equipment is reduced; meanwhile, by optimizing a load transfer path and distributed power supply output, the network loss rate is reduced, and the utilization efficiency of electric power resources is improved; in addition, the knowledge graph and the LSTM deep learning algorithm are fused, the distribution network topology entity relation network is constructed, and multi-source data are trained, so that the fault prediction accuracy is improved, the power failure risk can be early warned in advance, the conversion from passive first-aid repair to active prevention is realized, and the power failure frequency outside a plan is reduced.

The invention relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The invention discloses a virtual power plant global optimization scheduling method, system and device based on cloud edge collaboration, and a storage medium, and belongs to the technical field of power system scheduling. The method comprises the following steps: based on a cloud edge coordinated regulation and control framework comprising a cloud layer, an edge layer and an end side layer, taking minimization of the total operation cost of a system as a target, comprehensively considering a power balance constraint, a main network interaction constraint, a distribution network transmission constraint, a distributed resource operation constraint, an energy storage equipment constraint and a renewable energy consumption constraint; establishing a global optimization scheduling model; edge collaborative optimization is realized by adopting an alternating direction multiplier method, a global coupling problem is decomposed into local optimization sub-problems and a cloud coordination problem of each region, and aggregation power information is sent to the cloud after the local optimization sub-problems are solved in parallel in each region; and the cloud performs global coordination optimization to generate an optimal scheduling strategy, and issues a scheduling instruction to the edge layer to control the actual operation of the distributed power supply, the energy storage equipment and the controllable load, thereby realizing the collaborative optimization scheduling of the virtual power plant. The problems that in virtual power plant large-scale distributed resource coordination optimization, calculation complexity is high, communication burden is heavy, and real-time performance and global optimality are difficult to consider at the same time are effectively solved.

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention discloses a multi-energy-storage thermal power generating unit optimal scheduling method, which belongs to the technical field of power system scheduling, and specifically comprises the following steps: acquiring operation data and power grid load data of a multi-energy-storage thermal power generating unit; constructing a load prediction model based on the collected data to obtain a load prediction result; taking the minimum comprehensive cost as a target, and combining a thermal power generating unit output constraint, an energy storage equipment charging and discharging constraint and a power grid security constraint to construct an optimal scheduling model; solving the optimal scheduling model by adopting an improved intelligent optimization algorithm to obtain an optimal scheduling scheme; performing real-time scheduling on the multi-energy-storage thermal power generating unit according to the optimal scheduling scheme; the method can improve the scheduling flexibility of the multi-energy-storage thermal power generating unit, effectively deals with the load fluctuation of the power grid, and is suitable for the optimal scheduling task of the multi-energy-storage thermal power generating unit in a complex power grid environment.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

The invention provides a distribution network scheduling pre-order ticket issuing method and device, a storage medium and computer equipment, and relates to the technical field of power system scheduling, and the method comprises the steps: analyzing distribution network scheduling information, matching a corresponding target template in a scheduling pre-order ticket template library, filling the target template, and obtaining a scheduling pre-order ticket draft; a structured grammar rule base is used for conducting grammar verification on a scheduling pre-order ticket draft, and the structured grammar rule base is constructed on the basis of the natural language processing technology by extracting scheduling terms from distribution network scheduling standard text terms, analyzing a grammar structure and converting an operation logic relation; when the grammar verification of the scheduling pre-order ticket draft is passed, calculating the text similarity between the scheduling pre-order ticket draft and the historical scheduling pre-order ticket; and when the scheduling pre-order ticket draft similarity verification is passed, obtaining the scheduling pre-order ticket, and issuing the scheduling pre-order ticket after confirming that the scheduling pre-order ticket passes the verification. Therefore, the method effectively improves the timeliness and accuracy of the scheduling instruction.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

The invention discloses an electric energy and inertia combined clearing method and system considering new energy virtual inertia, and relates to the technical field of electric power system dispatching automation, and the method comprises the steps: obtaining data, forming a calculation scene, evaluating a system inertia demand and a droop factor demand based on the calculation scene, and obtaining a clearing result according to an evaluation result. The method comprises the following steps: constructing an all-time electric energy and inertia combined clearing model, solving electric energy bid-winning output and inertia bid-winning quantity of each time period, dynamically adjusting a demand value, establishing a single-time electric energy and inertia combined pricing model based on a solving result of the all-time combined clearing model, and solving node prices of electric energy and inertia of each time period. And outputting the unit output, the on-off state, the inertia standard, the line blocking condition and the node price of each time period. According to the invention, by acquiring multi-source data to form a calculation scene, evaluating inertia and droop factor requirements, constructing a full-time joint clearing model and establishing a single-time pricing model, collaborative optimization configuration and pricing of electric energy and inertia resources are realized.

The invention provides a global adaptive power supply adjustment method and system under the condition of local load increase, and relates to the technical field of power system dispatching. The method comprises the following steps: acquiring regional power grid topology, equipment parameters and real-time operation data; historical power consumption data of classified users in recent three years are collected and preprocessed; generating a reference load curve based on real-time data and a historical rule, calculating a deviation ratio, predicting a load in future 2 hours, and outputting a risk level; the regional energy storage equipment is called to dynamically adjust the discharge power when the risk is medium or low, and the adjacent regional resources are evaluated and cross-regional line switching and power supply support are executed when the risk is high or the energy storage is insufficient. Through layered adjustment and a dynamic response mechanism, rapid identification and accurate control of sudden load increase are realized, power supply stability and resource utilization efficiency are improved, and the method is suitable for local load fluctuation response of scenes such as residential areas and industrial parks.

The invention provides an electric power system source-load coordinated optimization scheduling method considering the flexibility of a steel production process, and relates to the technical field of electric power system scheduling, comprising the following steps: carrying out operation characteristic analysis on the production load of a steel enterprise, and respectively establishing an electric arc furnace model, an air separation system model and a steel rolling line model; constructing an adjustable capacity model under the coupling of the power supply system and the production process; based on the operation cost of the iron and steel enterprise power system, constructing an iron and steel enterprise flexibility day-ahead scheduling model; the electric arc furnace model, the air separation system model, the steel rolling line model, the adjustable capacity model and the iron and steel enterprise flexibility day-ahead scheduling model are integrated into a mixed integer linear programming scheduling model, the mixed integer linear programming scheduling model is solved through a solving tool, and an optimal scheduling scheme of system source-load coordination is obtained. The flexibility of a power system can be remarkably improved, and the problem of supply and demand imbalance caused by wind power output fluctuation is effectively relieved.

The invention relates to the technical field of power system dispatching automation control, and discloses an AGC load distribution control method and system based on a multi-region greedy hydropower station, and the method comprises the steps: generating dynamic boundary thresholds of a high-efficiency region, a medium-efficiency region and a low-efficiency region based on a real-time performance curve and a dispatching set value of a hydroelectric generating set; judging whether to start a cross-interval distribution strategy or not by utilizing the generated dynamic boundary threshold value; when the number of the units participating in distribution is lower than a set scale, an enumeration algorithm is automatically switched to carry out global optimization, and generation of an optimal solution under a small-scale unit combination is ensured; calculating a deviation value between the actual total load and a target value based on the obtained cross-interval distribution strategy; and preferentially adjusting the output of the high-efficiency area unit through a dynamic compensation mechanism. The system comprises an operation interval dynamic calibration module, a region selection and load pre-distribution module and a deviation compensation and adaptive adjustment module. The power grid frequency adjusting precision and the overall operation efficiency of the hydroelectric system are remarkably improved.

The invention relates to the technical field of electric power system dispatching, in particular to a multi-microgrid collaborative optimization dispatching method based on an improved ADMM, and the method comprises the steps: constructing a microgrid operation cost minimization objective function based on a microgrid mathematical model, and carrying out the operation constraint of each microgrid; iteratively updating a local variable, a global variable and a Lagrange multiplier by using an ADMM model to solve the operation cost of the micro-grid; and income distribution is carried out based on the asymmetric Nash negotiation model. According to the method, the problem that the convergence speed and the calculation complexity need to be further improved when an existing model is used for multi-microgrid dispatching optimization is solved.

The invention discloses a hybrid sequential network power consumption prediction method for reinforcement learning dynamic calibration, and the method comprises the steps: collecting multivariable power consumption time sequence data, completing the data preprocessing through resampling, feature engineering, normalization and sliding window technologies, generating a supervised learning sample set, and dividing the supervised learning sample set into a training set, a verification set and a test set; constructing a hybrid prediction model comprising a dynamic capture module, a long-term dependence modeling module, a regression prediction module and a reinforcement learning dynamic fine tuning module; training and optimizing by adopting a staged training strategy to obtain a hybrid prediction model; multivariable power consumption time sequence data are collected in real time and preprocessed, the preprocessed data serve as input, real-time prediction of future total consumption is conducted through the mixed prediction model, and a final prediction result after dynamic fine adjustment is output. According to the method, accurate and efficient prediction of the power grid load can be realized, and a reliable technical solution can be provided for power system scheduling optimization, demand side management, market transaction and other scenes.

The invention relates to the technical field of power system dispatching, in particular to a distributed storage system for source-network-load-storage collaborative dispatching, which comprises a distributed data acquisition module, a regional collaborative sensing module, a distributed energy storage evaluation module, a dispatching instruction generation module and a dispatching instruction distribution module, wherein the distributed data acquisition module is used for acquiring real-time operation data of source, network, load and storage nodes in parallel; the regional collaborative sensing module is used for calculating a real-time power deviation value; the distributed energy storage evaluation module is used for generating an energy storage adjustment instruction set; and the scheduling instruction generation module is used for generating a final refined scheduling instruction. According to the invention, through constructing a cooperative scheduling mechanism of the source network load storage full chain, accurate identification of power deviation and optimal distribution of energy storage resources are realized, and scheduling response efficiency and operation stability of the system are significantly improved.

The invention discloses a power system scheduling optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent power grid optimization scheduling. Comprising the following steps: receiving and synchronizing real-time operation data, meteorological data, equipment health indexes and renewable energy output data; constructing a power grid graph and generating a node time sequence matrix, and encoding the node time sequence matrix into multiple space-time embedding vectors; generating a short-term output predicted value and an uncertainty index based on the meteorological and renewable energy output data, and converting the short-term output predicted value and the uncertainty index into compensation factors; calculating a risk score according to the equipment health index and the meteorological data and mapping the risk score into a dynamic weight; inputting the multiple space-time embedding vectors, the compensation factor and the dynamic weight into a deep reinforcement learning model to generate a scheduling strategy, and performing feasibility verification; and if the verification is passed, issuing execution is carried out, and a result is returned for online model updating. According to the invention, by fusing multi-source data and a risk perception mechanism, the security and robustness of power system scheduling are effectively improved.

The invention discloses a new energy consumption-oriented power grid digital twinborn optimization scheduling method and system, and relates to the technical field of power system scheduling, and the method comprises the steps: constructing a digital twinborn model of a power grid; embedding a multi-time-space nested prediction model, establishing new energy output fluctuation characteristics under multiple time scales, performing dynamic evolution correction on each virtual power grid evolution branch according to the new energy output fluctuation characteristics, and establishing an uncertainty evolution trajectory; performing a multi-agent game in the sliding time window; and establishing a scheduling strategy by using a multi-agent game result, and performing optimal scheduling management according to the scheduling strategy. According to the method, the technical problems of low new energy consumption efficiency and high power grid operation safety risk caused by difficulty in accurately coping with the uncertainty of new energy output and lack of a multi-subject benefit cooperation mechanism in new energy consumption scheduling in the prior art are solved, and the purposes of realizing new energy consumption optimization scheduling and improving the power grid operation safety risk are achieved. And the new energy consumption efficiency and the power grid operation safety are improved.

The invention provides an electric power system source network load high-order uncertainty cooperative scheduling method integrating scene generation and schedulable flexibility, and relates to the technical field of electric power system scheduling optimization. Comprising the following steps: generating a typical working condition by using a joint probability model, and forming an uncertainty scene data set through Monte Carlo extension; determining robustness set data according to scheduling feasibility and risk constraints; constructing a unified flexibility model, correcting the boundary of the unified flexibility model, and forming a deliverable flexibility set and a rescheduling strategy; and establishing a source-network-load multi-side collaborative optimization model, and solving an optimal operation scheme in combination with multi-scene disturbance, thereby realizing risk-controllable and flexible coordinated scheduling of multi-type adjustable resources. The problems that in the prior art, source network load high-order uncertainty description of an electric power system is insufficient, deliverable modeling is not uniform, and multi-dimensional cooperative scheduling lacks closed-loop connection are solved.

The invention discloses a user side resource aggregation scheduling method and system and a related device, and belongs to the technical field of power system scheduling, and the method comprises the steps: determining a security domain of a power distribution network under the condition of not violating any operation constraint conditions, and obtaining a flexible resource adjustment model combined with the operation constraint boundary of the power distribution network; a convex polyhedron is used to describe the precise security domain of a single resource, then a sino polyhedron is used to describe the precise security domain aggregation user side flexible resource of the single resource, and based on the flexible resource potential model under the power distribution network operation constraint requirement and the flexible resource adjustment model combined with the power distribution network operation constraint boundary, the power distribution network operation constraint boundary is adjusted. Constructing a user side resource aggregation model considering the operation security domain of the power distribution network; and solving the user-side resource aggregation model considering the operation security domain of the power distribution network through a sino polyhedron, and carrying out user-side resource aggregation scheduling based on a solving result. According to the method, the problem of concurrent scheduling of numerous small-scale distributed resource clusters can be solved.

The invention discloses a photovoltaic power generation power prediction and power system scheduling method and a system for realizing the method. The method comprises four steps of data acquisition and preprocessing, similar day selection and training set construction, GA (Genetic Algorithm)-fuzzy RBF (Radial Basis Function) neural network modeling and power scheduling plan generation. The method comprises the following steps: selecting a meteorological variable with high correlation degree through a Spearman rank correlation coefficient, and constructing a time sequence feature; similar days are utilized to construct training samples, and the generalization ability of the model is improved; a fuzzy RBF network optimized by GA is adopted to enhance the nonlinear modeling precision; and inputting a prediction result into the power system simulation model, and generating an optimal scheduling instruction by adopting dynamic programming. According to the method, the photovoltaic power prediction accuracy and scheduling efficiency can be remarkably improved, the power grid stability is enhanced, and the new energy consumption capability is promoted.

The invention discloses an electric power allocation method and device based on an electric vehicle electrical load and electronic equipment, and relates to the technical field of electric vehicle charging management and electric power system scheduling or other related fields, the method comprises the following steps: collecting travel behavior data of N electric vehicle users, and obtaining road network topology data, N being a specified numerical value; simulating road network driving conditions of the N electric vehicles in the target time period based on the travel behavior data and the road network topology data to obtain simulated travel information; determining charging demand information of each electric vehicle based on the simulated travel information; and summarizing the charging demand information of all the electric vehicles to obtain the spatial and temporal distribution demand of the electrical load, and generating an electric power allocation strategy based on the spatial and temporal distribution demand of the electrical load. The technical problem that the time-space allocation of electric power and electric energy is difficult to match the power demand of a user due to low accuracy of charging load analysis of the electric vehicle in the related technology is solved.

The invention provides a cogeneration unit peak regulation capability evaluation method, which comprises the following steps: firstly, constructing a thermodynamic system mathematical model, solving system coupling parameters under a deep peak regulation working condition through a multi-layer iterative loop architecture, and generating a multi-working-condition data set which covers a full working condition and meets physical consistency; secondly, deducing a linear qualitative relation equation of the power, the main steam flow and the heat supply steam extraction flow, preprocessing a data set, training a pure data driving model as a performance reference, introducing the linear qualitative relation equation as a physical constraint item into a loss function, and constructing and training a PINN; and finally, the rated boundary condition of the peak regulation capacity is determined, a thermal characteristic curve is obtained through PINN simulation, the peak regulation capacity range and the real-time peak regulation margin under different heat supply requirements are calculated, and the result is transmitted to a power system dispatching platform. According to the method, the all-working-condition peak regulation evaluation precision and reliability can be improved, and the flexibility and universality of the model are enhanced.

The invention is suitable for the technical field of power system scheduling, and provides a new energy access provincial power grid multi-time scale intelligent scheduling method, which comprises the following steps of: obtaining related data of a provincial power grid, generating a new energy power point prediction curve of a day-ahead time scale, a new energy power fluctuation interval prediction result of an intra-day time scale, a new energy power point prediction curve of a real-time time scale and a new energy power fluctuation interval prediction result of the current moment by using the trained multi-time scale prediction model; processing by the day-ahead deep reinforcement learning agent, and outputting a day-ahead scheduling instruction; processing by the intraday deep reinforcement learning agent, and outputting an intraday adjustment instruction; and a real-time control instruction is output after the real-time deep reinforcement learning intelligent agent processes the real-time control instruction. According to the method, global optimization from day-ahead robust planning and intra-day active prevention to real-time multi-target cooperation is realized, and the cooperative scheduling control level of scheduling decision is improved.

The invention discloses a frequency safety target construction method, system and equipment for multi-element frequency modulation resource frequency response characteristic analysis, and a medium, and belongs to the technical field of power system scheduling, and the method comprises the steps: collecting the operation parameters of a thermal power generating unit, an energy storage device and a new energy inverter; establishing dynamic frequency response models including constraints such as response delay, power climbing, charge and discharge rate, energy capacity and virtual inertia, and uniformly coupling the models to form a system overall equivalent frequency response model; calculating the frequency offset, the frequency recovery time and the oscillation attenuation rate, constructing a frequency safety objective function in combination with an electric power system operation safety standard, inputting the frequency safety objective function into an electric power system dispatching optimization model, and realizing coordinated distribution of the thermal power generating unit, the energy storage device and the new energy inverter through optimization solution. According to the invention, collaborative optimization scheduling of multi-element frequency modulation resources is realized, the accuracy and safety of system frequency control are improved, and stable operation of a power system under a high-proportion new energy access condition is ensured.

The invention discloses a power distribution network scheduling method and device based on approximate dynamic programming and load aggregation and electronic equipment, and belongs to the technical field of power system scheduling, and the method comprises the steps: obtaining parameters such as the line, cost, topology and load prediction of a power distribution network, and the operation constraint of distributed loads; constructing an aggregation feasible region of the adjustable load cluster; establishing an optimal dispatching model of the power distribution network by taking minimization of the operation cost as a target; reconstructing the power distribution network optimization scheduling model into a Markov decision model; and solving the Markov decision model period by period under the aggregation feasible region and the physical constraint of the power grid. And in each time period, constructing a post-decision state value function by using the current state and a preset fixed slope parameter, solving an optimal scheduling scheme of the current time period, updating the state of the next time period, generating an all-day optimal scheduling scheme, and executing the all-day optimal scheduling scheme. By implementing the method and the device, the technical problem of poor practical operability of dispatching of the power distribution network in the prior art can be solved.

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 relates to the technical field of power system dispatching and control, in particular to a fire coal-fused salt energy storage dynamic characteristic decoupling method based on multi-time scale control, which comprises the following steps: constructing a multi-physical field coupling model of a coal-fired unit and a fused salt energy storage system; dividing a minute-level adjustment scale and a second-level response scale based on the coupling model; aiming at the minute-level regulation scale, designing a power distribution strategy based on model prediction control, matching a load trend in a power grid, and reserving a power regulation margin for second-level response; aiming at a second-level response scale, designing a compensation strategy based on sliding mode control, and correcting power deviation to track a real-time instruction of a power grid; and associating the minute-level strategy with the second-level strategy through a dynamic weight coefficient to form a closed loop to cooperatively complete dynamic characteristic decoupling. The problem of power supply and demand mismatch of the coal-fired unit and the fused salt energy storage system in coupling operation can be solved, the response speed, the adjustment precision and the operation stability of the system are improved, and technical support is provided for efficient operation of a novel power system.

The invention relates to the technical field of power system dispatching, in particular to a virtual power plant low-carbon economic dispatching method and system.The method comprises the steps that firstly, historical output data of all virtual power plants are obtained, and output prediction data considering uncertainty are obtained through prediction according to a time sequence; then constructing a carbon emission reduction responsibility distribution space, determining a carbon emission reduction responsibility weight of each virtual power plant through topological homeomorphic mapping, and solving a target function with the lowest carbon emission cost by combining a carbon emission constraint index to obtain an output optimal planning sequence; finally, a discrete-continuous hybrid dynamic model is constructed, cooperative scheduling of multiple energy storage devices is achieved, the situation that time differences of different scheduling periods are not integer multiples is processed, a carbon emission reduction responsibility dynamic allocation mechanism is constructed through the topological mapping theory, output prediction uncertainty is quantified through the probability measurement theory, and the probability prediction uncertainty is improved. And performing optimal scheduling on each virtual power plant.