688 results about "Power system scheduling" patented technology

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

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.

Disclosed is a method and system for quickly identifying and responding weak point-associated power flow based on a sensitivity factor, relating to the technical field of dispatching automation of power system. The method includes: parameter data is acquired, and a transfer factor (SF) matrix is established; consumption and accommodation of new energy of a power distribution network is evaluated, and a line with power flow exceeding a limit is evaluated; and flexible load in the power distribution network is dispatched based on the sensitivity factor, and safety verification is carried out on the result. The present invention identifies a blocking line of the power distribution network, the situation of out of limit of the line is eliminated, so that the safety of the power gird is ensured, and the new energy is consumed and accommodated.

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 technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

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 power dispatching optimization method and system based on deep reinforcement learning. The method comprises the following steps: modeling and representing the state of a power system by using a deep neural network model; defining an action space of power scheduling based on the state representation of the power system; constructing a reward function of a power dispatching strategy by taking economic cost, carbon emission and system stability as targets, training an intelligent agent by using a deep reinforcement learning algorithm, and optimizing the reward function based on the state and action space of a power system to obtain the power dispatching strategy; dynamically adjusting and optimizing a power dispatching strategy according to real-time data and environment change information through a self-adaptive adjustment mechanism; and testing and verifying the adjusted and optimized power dispatching strategy in a simulated power system environment, and applying the power dispatching strategy to power system dispatching after verification is passed. According to the scheme, the intelligent level of power dispatching is improved, and the optimal balance of economy, environmental protection and stability of a power system is achieved.

The present invention relates to the technical field of power system dispatching automation. Disclosed are a rapid identification and response method and system for a weak point-associated power flow based on a sensitivity factor. The method comprises: acquiring parameter data, and establishing a transfer factor matrix; assessing renewable energy integration of a distribution network and assessing a power flow over-limit line; and dispatching flexible loads in the distribution network on the basis of a sensitivity factor, and carrying out safety verification on a dispatching result. According to the rapid identification and response method for a weak point-associated power flow based on a sensitivity factor provided by the present invention, a weak point in new energy integration of the distribution network is identified, to obtain a blocking line that hinders new energy integration, providing a foundation for subsequent precise dispatching control for flexible loads. Sensitivity analysis is carried out on a weak point-associated power flow and flexible load user nodes on the basis of the sensitivity factor, and the line over-limit condition is eliminated by dispatching flexible loads of corresponding nodes, so that the power grid safety is ensured and new energy integration is implemented.

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 the technical field of new energy grid connection, in particular to a new energy grid connection flexibility resource prediction model training method based on artificial intelligence, and aims at solving the problems that a traditional prediction method is insufficient in precision and poor in adaptability when coping with new energy grid connection flexibility resources. By collecting and processing multi-dimensional historical data, adopting machine learning and deep learning algorithms and combining artificial intelligence, effective features can be extracted from the data, and new energy grid-connected flexibility resources are accurately predicted. Through data expansion and optimization, the problems of data scarcity and uneven distribution are effectively solved, the generalization ability and prediction precision of the model are improved, more scientific and accurate decision support is provided for power system scheduling, and stable operation and flexible scheduling of a new energy grid-connected system are promoted.

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 relates to a power system migration reinforcement learning scheduling method based on flexible telescopic network reconstruction, and the method comprises the steps: determining a state space, an action space and a target reward function of a target power system scheduling model in a normal topological operation scene of a power grid, establishing a target power system scheduling model according to the state space, the action space and the target reward function; based on a preset depth deterministic gradient algorithm, training a target power system scheduling agent corresponding to the target power system scheduling model; and in a power grid topology change scene, migrating the target power system scheduling agent based on a preset migration learning strategy of flexible telescopic network reconstruction to generate a migration reinforcement learning adaptive scheduling strategy. Therefore, the problems that the adaptability of a scheduling decision model is limited under various operation scenes of a power grid, and the scheduling efficiency and reliability of a power system are difficult to improve in the prior art are solved.

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 new energy user side multi-energy efficiency collaborative optimization method and system, and belongs to the technical field of power system dispatching, and the method comprises the steps: collecting the real-time operation parameters of photovoltaic equipment, energy storage equipment and electric equipment, carrying out the health degree evaluation of the equipment, generating an operation risk evaluation value, dividing the equipment into a quick response group and a slow compensation group, generating a hierarchical optimization instruction; and adjusting the hierarchical optimization instruction through a preset multi-objective optimization function, generating a control strategy, converting the control strategy into an equipment executable adjustment signal, and respectively sending the adjustment signal to the quick response group and the slow compensation group. According to the method, the real-time parameter acquisition and dynamic equipment grouping technology is adopted, the graded adjustment instruction is generated in combination with the multi-objective optimization function, collaborative optimization of economic cost, carbon emission and equipment loss can be achieved in a user side new energy scene, and the system regulation and control precision and comprehensive energy efficiency are improved.

The invention discloses a multi-scale electric energy flow control method and system based on deep reinforcement learning, and the method comprises the steps: S1, constructing prediction models of different time scales, and integrating the trained prediction models into a calculation platform; s2, the state space of the energy storage system optimization control algorithm comprises the charge state of the current energy storage system, real-time load data, predicted load and photovoltaic power generation power, real-time electricity price information and time characteristics; s3, the action of the energy storage system optimization control algorithm is the charging and discharging power of the energy storage system, and the action space is a continuous action space; s4, a reward function of the energy storage system optimization control algorithm considers electric power purchase cost, demand electric charge, energy storage loss, green energy consumption and real-time load fluctuation; and S5, training the energy storage system optimization control algorithm in the simulation environment to output an optimal energy storage control strategy. According to the invention, changes of photovoltaic power generation and real-time load are accurately predicted, and scheduling and optimization requirements of a power system are met.

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 electric power dispatching, in particular to an electric power optimization dispatching method and system based on reinforcement learning, and the method comprises the steps: obtaining the operation data, load prediction data and renewable energy output data of an electric power system; based on the operation data, the load prediction data and the renewable energy output data, constructing a power system scheduling optimization model; generating an optimization scheduling strategy based on the power system scheduling optimization model by using a deep reinforcement learning algorithm; calculating the scheduling cost, the environmental influence and the system reliability index of the power system according to the optimal scheduling strategy; and outputting the optimal scheduling strategy and the corresponding scheduling cost, environmental influence and system reliability indexes, so that the operation cost of the power system can be reduced, the utilization rate of renewable energy sources can be improved, and safe and stable operation of a power grid is ensured.

The invention discloses an electric power system dispatching man-machine cooperation model construction method based on a large language model, and relates to the technical field of new energy electric power system dynamic analysis. According to the method, multi-modal data fusion and natural language interaction capability of ChatGPT are utilized, and a prioritized experience replay (PER) technology is combined, so that bidirectional collaborative decision optimization of the dispatcher and the AI can be realized, and theoretical analysis is verified through an experimental result. A research result shows that after the LLM is introduced, the performance of the model on an active unit scheduling strategy is remarkably improved.

The invention discloses a wind power-containing electric power system source load collaborative scheduling method considering the participation of a smelting type high-energy load, and the method comprises the steps: analyzing the scheduling demands of a wind power-containing electric power system, and building a model considering the load of an electric arc furnace and the wind power output uncertainty based on the schedulable characteristics of the load of the electric arc furnace; a data driving method is adopted to generate a large-scale scene, the uncertainty superposition influence of the electric arc furnace load and the wind power output is analyzed, and the reserve capacity needed by the electric power system for coping with uncertainty fluctuation is determined; a smelting type high-energy load self-adaptive electricity price mechanism considering wind power output change and source load fluctuation is provided, and iron and steel enterprises are stimulated to participate in demand response; and establishing an optimal scheduling model giving consideration to interests of multiple parties, and solving an optimal scheduling scheme by adopting an artificial bee colony algorithm. According to the method, economic benefits and social responsibilities of smelting type high-energy load enterprises are considered, support is provided for the enterprises to participate in consumption of blocked wind power, and new energy consumption and economic and stable operation of a power system are facilitated.

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 belongs to the field of electric power system scheduling, and relates to a multi-microgrid cooperative scheduling method based on a master-slave cooperative game and target cascade analysis, which utilizes the cooperative scheduling problem of electric power resources and carbon emission of a multi-microgrid system to construct a double-layer master-slave cooperative game framework. Power scheduling and operation strategies of the micro-grid alliance are planned as a whole; the micro-grids of the lower-layer micro-grid alliance realize power transaction and resource sharing through a cooperative game; based on a target cascade analysis algorithm, introducing an Anderson acceleration method to dynamically adjust tie line power, and combining an augmented Lagrangian penalty function to process constraints, optimizing an upper and lower layer decoupling iteration process and accelerating convergence; the problem of low efficiency of multi-agent collaborative optimization is effectively solved, stable system power and reasonable distribution of electric energy resources are ensured, and technical support is provided for operation of a multi-microgrid system.

The invention discloses a power system scheduling and optimizing method and system based on a multi-granularity tree diagram neural network. The method comprises the following steps: step 1, constructing a power grid state sample data set; 2, performing graph representation and feature extraction on the power grid data; step 3, constructing a hierarchical multi-granularity power grid information coarsening model; 4, constructing a power grid information graph disturbance model; step 5, constructing a tree neural network adaptive path selection model; and step 6, according to the multi-granularity tree neural network model, executing explanation and scheduling decisions of the power system. According to the method, graph network structure characteristics of a power system are considered, a power grid is modeled into graph data, and on the basis of an existing graph neural network model, a multi-granularity graph coarsening module, a learnable disturbance module and a self-adaptive path selection module are introduced to construct a tree-shaped model of a multi-layer structure; therefore, the state and representation of the power system of each granularity level, such as global and local scale in different ranges, are analyzed, and the prediction accuracy of the model and the interpretability of the decision are improved.

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 an electric power system safety and stability margin prediction method and device based on a graph neural network, and the method comprises the following steps: constructing an analysis model according to an electric power system, obtaining a key power transmission line according to the impedance of the power transmission line and the voltage level of the power transmission line, initializing the analysis model, obtaining the current active transmission power of the key power transmission lines under different operation conditions, carrying out fault presetting on each power transmission line to obtain the limit transmission power according to a stability criterion, and obtaining the safety and stability margin of the operation conditions according to the limit transmission power and the current active transmission power; and forming a graph data set based on the analysis model and the safety and stability margin value set, performing graph neural network training to obtain a graph neural network model, and analyzing the power system to obtain a safety and stability margin predicted value. The invention provides a rapid and accurate method for analyzing the state of the power system in real time, and provides a visual safety and stability index for dispatching control of the power system.

A power system dispatching method, applied to a virtual power plant. The virtual power plant comprises a plurality of resource devices. The method comprises: acquiring an operating model of a single resource device (102); acquiring a first feasible region space on the basis of the operating model, the first feasible region space being a feasible region space for operating parameters of the single resource device (104); acquiring a second feasible region space on the basis of the first feasible region space and the operating parameters of the single resource device, the second feasible region space being a feasible region space for operating parameters of total resource devices (106); and establishing a power system dispatching model on the basis of the operating parameters of the total resource devices and the second feasible region space, so as to dispatch power resources (108).

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 an improved TimesNet-based power data time sequence interpolation method and system. The method comprises the following steps of performing missing value identification, anomaly detection and standardized preprocessing on power data, and extracting time sequence features; according to the method, an improved TimesNet model is constructed, a 1D time sequence is converted into a 2D tensor, and features are extracted through a Transformer-CNN hybrid model: an axial attention branch establishes periodic internal and external dependencies in row and column directions, a multi-scale convolution branch captures local features, and a gating mechanism fuses output of the axial attention branch and the multi-scale convolution branch. A deformable convolution kernel is adopted to dynamically adjust a receptive field to adapt to a multi-scale time mode. And in training, time sequence enhancement and GAN are combined to generate data, so that the generalization of the model is improved. The method solves the problem that the traditional interpolation technology cannot effectively model the complex time sequence dependence of the electric power data, remarkably improves the missing value interpolation precision, and provides reliable data support for dispatching and prediction of an electric power system.