108 results about "Grid scheduling" patented technology

The invention provides a virtual power plant energy storage system co-scheduling and control method and system, and relates to the technical field of power management, and the method comprises the steps: obtaining power grid scheduling data, calculating the charge and discharge income, collecting the real-time parameters of a battery, analyzing the performance attenuation law through deep reinforcement learning, determining the initial working parameters, predicting the system state based on a rolling time domain, and obtaining the real-time parameters of the battery. An optimal scheduling scheme is calculated through mixed integer linear programming and model prediction control in combination with a power distribution strategy and is corrected in real time, operation is executed according to a time-phased scheduling instruction and is monitored in real time, and an emergency strategy is started when necessary, so that the economic benefit and the safety of an energy storage system are improved, and the service life of a battery is prolonged.

The invention is suitable for the technical field of short-term power load prediction and intelligent scheduling, and provides a short-term power load prediction method based on improved sparrow search algorithm (ISSA) optimization. The method comprises the following steps: constructing a multi-scene prediction task according to the time resolution and regional seasonal characteristics of a load; local features are extracted in combination with a convolutional neural network (CNN), time sequence dependence is modeled by a long short-term memory (LSTM) network, and an attention mechanism is introduced to strengthen key features; meanwhile, an ISSA is adopted to optimize a model network structure and hyper-parameters, the number of layers, the learning rate and the batch size of the CNN and the LSTM are adjusted in a self-adaptive mode, and the search efficiency and convergence performance of the ISSA are improved through Latin hypercube sampling, cosine annealing, dynamic spiral search and a Levy flight strategy. Simulation results show that the method can maintain high prediction precision under different time resolutions and regional and seasonal conditions, the generalization ability and cross-scene adaptability of the model are enhanced, and a stable and efficient load prediction scheme is provided for power grid dispatching optimization.

The invention discloses a block chain-based distributed power grid dispatching cooperation method and system, and relates to the technical field of block chains, and the method comprises the steps: calculating the total expected cost through an ADMM distributed optimization method, obtaining a consistency error through the total expected cost, obtaining a consensus priority based on an optimal strategy set and the consistency error, and carrying out the optimization of the consensus priority. Priority nodes are obtained based on the consensus priorities, and time sensitive weights are calculated through the consensus priorities and the priority nodes. A multi-scene generation method based on probability modeling is introduced, the depiction capability of photovoltaic power generation and load demand uncertainty is improved, a node strategy space is constructed, a power utility ratio and revenue function joint optimization is introduced, deep fusion of energy efficiency evaluation and strategy game is realized, an ADMM distributed optimization method and a block chain consensus priority mechanism are combined, and energy efficiency evaluation and strategy game optimization are realized. Centralization dependence is broken, and the dispatching intelligence level and the operation stability of the distributed power grid in a complex environment are improved.

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

The invention relates to the technical field of intelligent scheduling of photovoltaic power stations, in particular to a management method for improving the power generation capacity of a photovoltaic power station, and the method comprises the steps: analyzing a power regulation and control policy text, meteorological early warning data and electricity price historical fluctuation data, and generating a three-dimensional dynamic constraint condition set; a basic generated output prediction sequence is generated by using an LSTM time sequence model, and a three-dimensional constraint generation policy is superposed to adapt to the schedulable capacity; screening adjacent reference power stations based on a digital twinborn model, and correcting to obtain meteorological correction schedulable capacity; starting in-station compensation when the generated output of the target power station is insufficient; if compensation is insufficient, screening the auxiliary power station and generating an incremental power generation instruction set; storing the instruction and the result by using the alliance chain; and cross-power-station cooperative training is realized through federal learning. The power generation capacity of the photovoltaic power station and the power grid dispatching efficiency can be effectively improved, and the problems of power generation loss and low cross-station cooperation efficiency caused by meteorological fluctuation are solved.

The invention relates to a photovoltaic power generation power prediction method, which comprises the steps of collecting and processing real-time multi-source meteorological data to obtain a meteorological feature set; according to historical photovoltaic power generation data, time sequence information is obtained, dynamic time features are extracted, and a time dimension feature set is obtained; splicing and fusing the meteorological feature set and the time dimension feature set to obtain a fused feature set; the fused feature set is input into a power prediction model, a photovoltaic power generation power prediction value is obtained, the power prediction model is constructed through a double attention mechanism and an LSTM network and is obtained through training of a training set, and the training set comprises historical multi-source meteorological data, photovoltaic power generation data and corresponding photovoltaic power generation power values. Through the multi-source data fusion and deep learning method, the accuracy and stability of photovoltaic power generation power prediction are effectively improved, and important support is provided for efficient operation of a photovoltaic power station and power grid dispatching.

The invention discloses a distributed power grid dispatching management and optimization method, and relates to the technical field of power grid automation, and the method comprises the steps: collecting multi-dimensional power grid data in a system, carrying out the data preprocessing of the multi-dimensional power grid data, carrying out the electrical load prediction of the initial multi-dimensional power grid data through an LSTM neural network, and carrying out the optimization of the electrical load. Performing energy storage SOC state real-time evaluation on the electrical load data based on a DTW dynamic time warping load identification model, performing state judgment on power grid equipment according to an evaluation result, constructing a multi-target optimization model by using mixed integer second-order cone programming, performing power grid constraint adjustment by using the multi-target optimization model, and performing power grid constraint adjustment on the basis of a power grid constraint result. And performing real-time power generation plan adjustment by using an improved particle swarm algorithm to obtain target electric power output of the power grid, and performing power grid energy storage control optimization according to the target electric power output. And the prediction accuracy of the new energy output and the load demand is obviously improved.

The invention discloses a micro-grid group operation optimization method and system based on a multi-target dragonfly algorithm, and the method comprises the steps: constructing a system model of a micro-grid cluster, and enabling the system model to comprise a plurality of micro-grids; taking the connection state of the plurality of micro-grids as a decision variable to represent the communication mode between the micro-grids; a dynamic grouping mechanism is introduced, and the connection relation of the multiple micro-grids is adjusted according to actual requirements; establishing a multi-objective optimization model, and designing operation constraint conditions; combining decision variables and constraint conditions, designing a structure-scheduling joint optimization strategy, and carrying out collaborative optimization on a micro-grid group structure and a scheduling strategy; and according to an optimization result, outputting a Pareto optimization scheduling solution set covering different operation preferences, and obtaining a low-carbon economic multi-energy-flow cooperative operation scheme of the micro-grid group. According to the method, the problems of local optimum and low convergence speed during multi-objective optimization in the micro-grid dispatching process can be solved.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention provides a multi-type micro-grid cross-layer collaborative scheduling method based on multi-agent reinforcement learning, and relates to the technical field of power system and micro-grid scheduling, and the method comprises the steps: firstly constructing a multi-type micro-grid layered collaborative architecture, a multi-agent reinforcement learning scheduling model, and a centralized coordination layer where global agents are deployed in a platform layer; and the local agents are deployed at each distributed micro-grid node of the platform layer, a cross-layer cooperative training mode is adopted to train the model, and finally, cooperative decision results of the global agents and the local agents are integrated to generate a global-local cooperative scheduling scheme. According to the method, global optimization and local flexibility can be considered, and the hierarchical multi-agent architecture adopts a multi-type micro-grid hierarchical collaborative architecture, so that the method can be suitable for multi-type micro-grid operation scheduling of a novel power system, and multi-type micro-grid collaborative scheduling considering global optimization and local flexibility is realized.

The invention provides a microgrid scheduling method, and the method comprises the steps: constructing a first optimization model which comprises a plurality of objective functions and a plurality of constraint conditions; converting the first optimization model into a second optimization model in a QUBO form; the second optimization model is solved through a mixed quantum approximate optimization algorithm to obtain a micro-grid scheduling strategy, the mixed quantum approximate optimization algorithm comprises a quantum approximate optimization algorithm and a classical algorithm, the quantum approximate optimization algorithm is used for solving the second optimization model through quantum evolution in the multi-round iteration process, and the classical algorithm is used for solving the second optimization model through quantum evolution in the multi-round iteration process. The classical algorithm is used for adjusting optimization parameters of the quantum approximate optimization algorithm based on candidate solutions generated by quantum evolution in each iteration process, parallel exploration of multiple potential solutions is achieved, and therefore the probability of finding an optimal solution is improved within feasible time.

The invention discloses a micro-grid scheduling strategy generation method and device, equipment, a medium and a product, and belongs to the technical field of micro-grid scheduling. The method comprises the following steps: constructing a micro-grid optimization scheduling physical model, wherein the micro-grid optimization scheduling physical model at least comprises a flexible load response model; based on the flexible load response model, establishing a multi-objective optimization function including a power supply reliability objective and an operation cost objective; system operation constraint conditions of the multi-objective optimization function are set, and the system operation constraint conditions at least comprise distributed power supply output constraints; and carrying out optimization solution on the multi-objective optimization function by adopting an optimization algorithm to obtain a micro-grid scheduling strategy, and carrying out cooperative control on various resources in the micro-grid based on the micro-grid scheduling strategy. According to the micro-grid scheduling strategy generated by the embodiment of the invention, the efficiency and robustness of micro-grid scheduling can be effectively improved.

The invention discloses a micro-grid intelligent scheduling method based on multi-energy collaborative optimization, and relates to the technical field of micro-grid scheduling, and the method comprises the steps: obtaining the subunit operation state information of an energy storage unit, and constructing a basic state parameter set for representing the current operation condition of the energy storage unit based on the subunit operation state information; the scheduling state of the energy storage unit in the current scheduling period is judged based on the basic state parameter set, a scheduling state identifier is generated, and the scheduling state identifier is a unique value in a single scheduling period and is used for limiting that the energy storage unit is only allowed to participate in one scheduling behavior type in the scheduling period. According to the method, the basic state parameter set is constructed and the state parameters representing the current operation conditions are extracted, so that the power capability, the energy boundary and the operation limitation of the energy storage unit in the current scheduling period can be accurately described, and a credible basis is provided for subsequent scheduling behavior identification and parameter selection.

The invention relates to the technical field of energy management, calculation and prediction, in particular to an energy storage micro-grid dynamic collaborative scheduling system based on photovoltaic prediction, a photovoltaic prediction system comprises a data acquisition module and a prediction model, and a micro-grid scheduling system comprises a load management module, a grid interaction module and a real-time response control module. The energy storage system comprises an energy storage battery pack, a charging and discharging control module and an SOC monitoring module. The photovoltaic prediction system is in communication connection with the micro-grid scheduling system, the micro-grid scheduling system is in communication connection with the energy storage system, photovoltaic and load prediction is fused, a multi-target optimization algorithm and a closed-loop feedback mechanism are introduced, and regulation and control perspectiveness, multi-target collaborative scheduling and whole-process system linkage are achieved. And the requirements of various types of micro-grid systems on controllability and stability of photovoltaic power generation are met.

The invention provides a micro-grid multi-energy coordinated scheduling method and device, and the method comprises the steps: building a multi-energy coordinated scheduling optimization model containing time-varying constraints through obtaining the data of a photovoltaic power generation system, an energy storage system and a load system in a micro-grid, and carrying out the optimization solving through employing an improved particle swarm optimization algorithm, and designing a gridding vector current control strategy to realize fault ride-through control, executing multi-energy coordinated scheduling control, and realizing stable operation of the micro-grid through hierarchical execution. According to the method, the problem that a traditional micro-grid dispatching method is insufficient in stability and economical efficiency in the face of renewable energy source uncertainty and power grid faults is solved, and the reliability and economical efficiency of micro-grid operation are improved.

The invention discloses a multi-target cost optimization scheduling system for a wind-light-gas storage micro-grid, and relates to the technical field of micro-grid scheduling, and the system comprises an equipment health parameter collection module which collects the operation parameters of wind-light-gas storage equipment through a sensor, and transmits the operation parameters to a health state evaluation module after preprocessing; the module constructs a multi-dimensional model to calculate a health index and predict an attenuation trend; an aging loss cost quantification module constructs a dynamic model to calculate the cost; the multi-target optimization scheduling module sets multiple targets and searches an optimal strategy; the strategy execution and feedback module executes the instruction and returns data; according to the invention, by constructing a linkage mechanism of the equipment health parameter acquisition module and the health state evaluation module, real-time perception and quantitative analysis of the state of the wind-light-gas storage micro-grid equipment are realized, and a multi-dimensional stress-fatigue accumulation model is adopted to calculate the health index of the equipment and divide the health level. The problems that in a traditional method, health assessment is high in subjectivity, and stress accumulation influences cannot be quantified are solved.

The invention discloses an optical storage and charging micro-grid scheduling method and system, and the method comprises the steps: obtaining photovoltaic power generation data, energy storage state data, charging demand data and power grid operation data, carrying out the data cleaning and feature engineering processing, and obtaining a standardized feature vector; constructing a neural network search space by using an architecture embedding and transfer ranking method to obtain an optimal network structure for different prediction tasks; performing dependency modeling on the classification variables and the continuous variables by adopting a multi-head self-attention layer to obtain a mixed variable optimization model; and an improved NSGA-III algorithm is adopted to carry out multi-objective optimization solution, and a scheduling strategy of the optical storage and charging microgrid is obtained. According to the method, prediction precision is improved through framework automatic optimization, interaction among mixed variables is effectively processed, collaborative optimization of energy balance, power grid support and charging requirements is realized, and the method has good practicability and economic benefits.

The application provides a micro-grid scheduling control method and device based on policy optimization, equipment and medium, belonging to the power grid scheduling technical field, the method: collecting real-time operation data of new energy power generation equipment, power load equipment and energy storage system; receive user power generation and power consumption plan, combine real-time operation data and historical operation data, extract multi-resource dynamic response characteristics through LSTM model, output adjustment capacity evaluation matrix; receive power grid scheduling plan, based on the adjustment capacity evaluation matrix, match the optimal operation mode from the knowledge base, generate the scheduling instruction set; issue the scheduling instruction set to the target equipment and execute, collect the execution data in real time and calculate the operation deviation; when the operation deviation is over the threshold, the instruction weight is updated by using the online Q learning algorithm, and the optimization strategy is fed back to the knowledge base. The application realizes fine scheduling of micro-grid through real-time data collection and intelligent optimization algorithm, improves resource utilization and operation flexibility, and reduces operation cost.

The invention relates to the technical field of micro-grid dispatching, in particular to a micro-grid collaborative optimization dispatching method based on a layered bee foraging algorithm, which comprises the following steps of: constructing a dispatching model containing multiple targets of economy, environmental protection and stability, and designing a layered optimization algorithm consisting of a global exploration layer, a local development layer and a collaborative decision-making layer. The global exploration layer adopts a self-adaptive step length mechanism and chaotic mapping to initialize a food source, and the search breadth is improved; the local development layer performs neighborhood optimization through a self-adaptive crossover mutation operator to enhance the local search capability; a multi-subject benefit coordination mechanism is introduced into the collaborative decision-making layer, scheduling schemes with subject satisfaction degrees larger than 0.8 are screened from Pareto optimal solutions based on Nash equilibrium, fair and efficient unification is achieved, and the algorithm effectively balances the operation cost, the emission level and the system stability of the micro-grid through dynamic step length adjustment, chaos initialization and multi-target collaborative decision-making. And the scheduling precision and the convergence speed are improved.

The application provides an AC / DC hybrid micro-grid multi-objective scheduling method and system based on digital twinning, relates to the technical field of micro-grid scheduling, and comprises the following steps: establishing an AC / DC hybrid micro-grid digital twinning model and dynamically integrating grid related data; and establishing a comprehensive total cost model of a small AC / DC hybrid micro-grid system; the application combines photovoltaic power generation, photo-thermal power generation and wind power generation, and an energy storage device, establishes a device dynamic efficiency model based on the influence of local load, external grid environment and other factors on the efficiency of the AC / DC hybrid micro-grid equipment, obtains various twinning data such as local load and external grid environment through the digital twinning model, takes economic benefits and the health degree of the energy storage device as the target, proposes a multi-objective particle swarm optimization algorithm based on an improved file maintenance strategy and a fuzzy decision method, and optimizes the output of the energy storage device in real time, thereby improving the multi-energy complementation and conversion capacity of the system.

The invention provides a micro-grid scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of micro-grid scheduling. The method comprises the following steps: generating an initial scheduling scheme set of the micro-grid according to a plurality of operation data and a plurality of preset constraint conditions of the micro-grid; for any scheduling scheme in the initial scheduling scheme set, obtaining a fitness value of the current scheduling scheme according to the plurality of operation data and a preset objective function; iteratively executing the following steps until the number of iterations reaches a preset number of iterations; generating an initial scheduling scheme subset according to the fitness value of each scheduling scheme in the initial scheduling scheme set; determining a latest initial scheduling scheme set according to the initial scheduling scheme set and the initial scheduling scheme subset; and obtaining a final microgrid scheduling scheme from the newest initial scheduling scheme set. According to the method, the actual requirements of multiple targets are met at the same time, the scheduling accuracy of the micro-grid is ensured, and the requirements are met.

The invention discloses a micro-grid dispatching method, device and equipment and a storage medium, and relates to the technical field of micro-grid management and control, and the method comprises the steps: obtaining an energy operation model corresponding to target energy equipment in a target micro-grid; performing mixed integer programming operation based on the energy operation model to obtain a target operation curve; and performing power scheduling on the target energy equipment according to the target operation curve. According to the method, the mixed integer programming operation is performed according to the energy operation model pre-established for the target energy equipment in the micro-grid, so that the target operation curve corresponding to the optimal overall energy operation condition of the micro-grid is predicted, and power scheduling of the target energy equipment is performed based on the target operation curve. According to the invention, each energy device in the micro-grid is ensured to adaptively operate in an optimal state according to an actual condition, so that the effect of each energy device in a power system is exerted to the greatest extent, the overall economic benefit of the micro-grid is improved, and the energy consumption cost of the micro-grid is effectively reduced.

The invention relates to the technical field of power systems and renewable energy scheduling, and discloses a small hydropower station cluster planning and grouping method and device and a medium, and the method comprises the steps: obtaining multi-source data of each small hydropower station, and constructing a small hydropower station cluster feature space model; constructing a mathematical effectiveness index and a field rationality index, and establishing a k-means clustering algorithm multi-dimensional evaluation index system; executing an improved k-means + + clustering algorithm, and generating clustering schemes and evaluation results under different K values; constructing a derivative evaluation index based on an error sum of squares, distributing an index weight, calculating a comprehensive score corresponding to each K value, and determining an optimal clustering k value; and outputting a small hydropower station cluster grouping scheme corresponding to the optimal clustering number. According to the method, adaptive grouping of small hydropower station clusters is realized through a clustering algorithm driven by a small amount of data, and a small hydropower station cluster division scheme with reasonable spatial distribution, unified basin attributes and coordinated operation characteristics is provided for regional power grid dispatching.

The application provides a highway micro-grid scheduling method, system, electronic equipment and medium, and belongs to the field of micro-grid day-ahead scheduling. The method comprises the following steps: establishing an electric operation vehicle operation constraint according to a highway topology structure, a minimum maintenance time and a maximum number of carried batteries of each electric operation vehicle; establishing a highway micro-grid operation constraint according to an electric vehicle battery replacement load and a renewable energy unit predicted output based on the battery carrying constraint; based on the electric operation vehicle operation constraint and the highway micro-grid operation constraint, taking the sum of highway micro-grid power generation loss, electric operation vehicle operation loss, wind and light abandonment penalty and electric vehicle waiting time for battery replacement as the target, establishing a highway micro-grid scheduling model, and solving to obtain a micro-grid day-ahead scheduling strategy. The application considers the energy mutual aid effect of the electric operation vehicle, combines the unit output to schedule the highway micro-grid, and improves the operation efficiency and flexibility of the highway micro-grid.

The invention relates to the technical field of unit scheduling of a gravity energy storage system, in particular to a unit scheduling optimization method, system and equipment of the gravity energy storage system and a medium, and the method comprises the steps: obtaining historical scheduling demand data of a power grid and real-time capacity parameters of each unit in the gravity energy storage system; based on the historical scheduling demand data and the capacity parameters, solving a unit combination scheme meeting the power grid demand through a combination optimization algorithm; layering the units according to the capacity, selecting the unit combination scheme with the highest proportion of the middle capacity layer as a first target scheduling instruction, and outputting the first target scheduling instruction to the execution end. The method has the advantages that intelligent decision support can be provided for power grid dispatching, the system operation cost is reduced, the dispatching flexibility and reliability are improved, the overall performance of an energy storage system is improved, resource waste is reduced, and the power grid stability is improved.

The invention discloses a virtual power plant optimal scheduling method and device of a micro-grid, electronic equipment and a medium, and is used for solving the problem that a traditional virtual power plant optimal scheduling method is difficult to balance interests of multiple parties and is difficult to deal with strong uncertainty of renewable energy output and market price. The method comprises the following steps: acquiring the external cost of the micro-grid, constructing an internal cost optimization model based on the external cost, and solving the internal cost optimization model through mapping to obtain the internal cost and a micro-grid scheduling result; constructing a micro-grid scheduling optimization model according to the internal cost, and solving the micro-grid scheduling optimization model through mapping to obtain a micro-grid scheduling optimization result; and constructing an energy storage scheduling optimization model of the virtual power plant based on the external cost, and solving the energy storage scheduling optimization model through mapping according to the micro-grid scheduling result and the micro-grid scheduling optimization result to obtain an energy storage optimization scheduling result of the virtual power plant.

The invention discloses a micro-grid scheduling optimization method and system based on a multi-objective parrot-shaped Arctic pole optimization algorithm, and the method comprises the steps: building a multi-objective optimization scheduling model of a micro-grid, and the multi-objective optimization scheduling model at least comprises an operation maintenance cost objective function and an environment protection cost objective function; a multi-strategy fused multi-target parrot-shaped north pole optimization algorithm is adopted to solve the multi-target optimization scheduling model, and a Pareto optimal solution set is obtained; and determining an optimal output strategy of each distributed power generation unit in the microgrid according to the Pareto optimal solution set. The space exploration capability of the algorithm in the early stage and the optimization precision of the algorithm in the later stage are ensured, and the optimization effect of micro-grid dispatching is improved.

The invention discloses a hierarchical optimization scheduling method. The method comprises the following steps: S1, acquiring micro-grid system parameters, electric vehicle parameters and power grid time-of-use electricity price information; s2, establishing an EV layer optimization model, and performing optimization solution by taking the user satisfaction degree of the electric vehicle user as a target function to obtain an optimal charging and discharging power plan of each electric vehicle; s3, transmitting the optimal charging and discharging power plan to a micro-grid layer; s4, establishing a micro-grid layer optimization model, taking the comprehensive operation cost of the micro-grid and the interaction power fluctuation between the micro-grid and the main grid as objective functions, and performing optimization solution by combining the optimal charging and discharging power plan to obtain a scheduling plan of distributed energy in the micro-grid; wherein the EV layer optimization model and the micro-grid layer optimization model jointly form a layered optimization framework. The scheduling method can promote the electric vehicle user to join the microgrid scheduling to a greater extent.

The embodiment of the invention discloses an island power grid dispatching method and device based on transfer learning, and the method comprises the steps: obtaining power grid parameter information, and constructing multi-dimensional feature data according to the power grid parameter information; performing time sequence segmentation and frequency domain decomposition on the multi-dimensional feature data to obtain a steady-state feature subset and a dynamic feature subset; respectively mapping the steady-state feature subset and the dynamic feature subset to a target island power grid feature space through a domain adaptive algorithm, and in the aligned feature space, establishing a topological constraint feature graph by using a graph neural network structure; performing small sample fine tuning on the source domain model based on the topological constraint feature map and historical operation data of the target island power grid to obtain a scheduling model adapted to the target island power grid; and generating a power grid dispatching scheme by using the dispatching model in combination with short-term load prediction and renewable energy output prediction. According to the invention, a reliable scheduling strategy is generated, and the stability and economy of emergency power supply are guaranteed.

The invention relates to the technical field of power grid collaborative optimization, in particular to a state-grid-provincial multi-level collaborative scheduling operation solving method and system based on ADMM, and the method comprises the following steps: constructing a multi-level collaborative balance model comprising a state scheduling level, a grid scheduling level and a provincial scheduling level based on an equivalent aggregation theory; setting a consistency constraint of the transmission quantity and the response quantity of each level, and introducing a primary penalty term and a secondary penalty term of the consistency constraint into a target function of a national scheduling level and a network scheduling level to obtain a multi-level collaborative scheduling optimization model; and performing hierarchical decoupling solution on the optimized multi-level collaborative scheduling optimization model by using an alternating direction multiplier method to obtain an operation scheme of each level. Layered decoupling solution is carried out on the national-network-provincial three-level collaborative scheduling optimization model while variable transmission is carried out, so that the effectiveness of model equivalence and the convergence of solution can be ensured.