64 results about "Economic scheduling" patented technology

The invention relates to the technical field of electric power system regulation and control, and particularly discloses an electric power active balance control method for coupling multi-element energy storage of a coal power unit, which is used for solving the problem that the existing static active safety margin evaluation method cannot evaluate the safety of the coal power unit under the complex working conditions of high wind power penetration, large disturbance, system inertia decline and the like. The method solves the problems that multi-stage frequency response cannot be accurately described, online self-adaptive power distribution and continuous smooth scheduling are lacked, and consequently frequency drop is large, recovery is slow and output suddenly changes, and comprises the steps of building a collaborative characteristic model, calculating dominant characteristics on line, optimizing scheduling in a segmented mode and updating parameters in a closed loop. According to the method, by constructing a hierarchical inertia and multi-stage frequency modulation collaborative model, and adopting multi-scale online feature extraction and adaptive power distribution, segmented gradient optimization scheduling and closed-loop online parameter updating, quick response, smooth connection and accurate optimization of frequency support and economic scheduling in a wind power high-permeability scene are realized.

The invention provides a new energy power grid prospective scheduling method and device, and relates to the technical field of electric power system intraday economic scheduling. The method comprises the following steps: firstly, constructing an opportunity constraint optimal power flow model of prospective scheduling, analyzing the influence of new energy uncertainty on opportunity constraint, establishing a constrained Markov decision process of prospective scheduling, and then utilizing a risk evaluator network fitting risk function probability distribution and an actuator network considering extreme scene performance to determine the opportunity constraint optimal power flow model of prospective scheduling. The processing capability of the intelligent agent on a prospective scheduling scene containing a new energy extreme climbing event is enhanced; and finally, the training of the intelligent agent is accelerated by utilizing an imitation learning technology in a power grid prospective scheduling off-line simulation environment. According to the method, the solving speed and the strategy robustness and safety of the double-layer robust optimization model of the look-ahead scheduling can be considered.

The invention discloses a virtual power plant collaborative optimization scheduling method and system based on deep reinforcement learning, and the method comprises the steps: building a multi-time scale dynamic model covering second-level frequency response and hour-level economic scheduling through collecting the operation data of a virtual power plant, constructing a mixed sensing network, and taking the multi-time scale dynamic model as the input, extracting multi-scale time sequence features by combining a multi-head hierarchical attention encoder, obtaining equipment dynamic association features through a dynamic heterogeneous graph neural network, and generating comprehensive feature representation; constructing a hierarchical deep reinforcement learning control framework based on comprehensive feature representation, optimizing energy storage charging and discharging power, a load regulation instruction and a unit output strategy by adopting a deep reinforcement learning algorithm based on an experience playback mechanism, and constructing a multi-target reward function; and finally performing training-deployment process and simulation verification. According to the method, the response time of a protection mechanism can be shortened to be within 0.1 second under the working condition that the SOC is lower than 20%, the comprehensive operation cost of the system can be reduced under the normal working condition, and the signal-to-noise ratio of input signals of a control layer is remarkably increased.

The invention relates to the technical field of power system resource optimization scheduling, in particular to a deep scheduling method and system for deep peak regulation of a coal-fired unit, and the method comprises the steps: collecting multi-dimensional operation parameters of the unit, a deep peak regulation instruction and a carbon price signal of a carbon emission permit trading market, and achieving timestamp alignment; constructing a five-dimensional security constraint model associated with the economic cost boundary; taking peak regulation income maximization as a target, combining fire coal purchase cost, carbon transaction cost and equipment maintenance cost to construct a target function, adopting a dynamic planning algorithm to solve an economic dispatching scheme, and executing a cross-system cooperation strategy; and on the basis of LSTM-based equipment life prediction and digital twin simulation, triggering a cross-system correction strategy. According to the method, the commercial cooperation problem of economy, environmental protection and equipment reliability in deep peak regulation is solved, and the peak regulation income maximization of the coal-fired unit in an electricity market environment is realized.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention discloses an industrial micro-grid group double-layer optimization scheduling method for electricity-hydrogen shared energy storage, and aims to make full use of energy consumption complementation characteristics of different parks. A refinement model of various devices, electricity-hydrogen shared energy storage components and the like in the polycrystalline silicon micro-grid is constructed by considering a hydrogen recovery system. Based on a Stackelberg game theory, a double-layer optimization scheduling model with shared energy storage as a leader and a plurality of polycrystalline silicon micro-grids as followers is constructed. The upper layer model is an EHSES multi-target optimization scheduling model considering economy, environmental protection and sustainability, the lower layer model is an economic scheduling model of each polycrystalline silicon microgrid, and iterative optimization of the electro-hydrogen price of the EHSES and the polycrystalline silicon microgrid is realized. And solving the model by using a multi-target myxomycete algorithm based on reverse learning and a reference point and a CPLEX solver. Simulation results show that the model can effectively improve the economy, environmental protection and sustainability of the polycrystalline silicon micro-grid cluster, and the effectiveness of the model is verified.

The invention discloses a virtual power plant distributed collaborative optimization method and system for communication interruption, and the method comprises the steps: building a scheduling control model of a virtual power plant based on a communication network topology structure of distributed power generation nodes in a power grid system, and generating a cost optimization function according to the active output power of each distributed power generation node, generating an initial virtual reference signal based on the cost optimization function, smoothing the initial virtual reference signal, generating a smooth reference signal, generating a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal, and optimizing the scheduling control model based on the decentralized controller. According to the method, the distributed cooperative scheduling of the virtual power plant in a communication interruption scene is optimized, so that the dependence on continuous communication is effectively reduced while the economic scheduling precision is guaranteed, the scheduling continuity and the control response capability of the virtual power plant are improved, and the economic efficiency of the virtual power plant is improved. Coordinated control and stable operation of the virtual power plant under an unstable communication condition are effectively guaranteed.

The invention discloses a multi-micro-grid power distribution system two-stage robust collaborative optimization method and device, and belongs to the technical field of multi-micro-grid power distribution system optimization scheduling, and the method comprises the steps: inputting the obtained basic operation data and uncertainty characterization parameters of a multi-micro-grid power distribution system into a pre-constructed multi-micro-grid economic scheduling model, solving the model by adopting a column and constraint generation algorithm to obtain a day-ahead scheduling plan of the multi-microgrid power distribution system in the worst scene considering the space-time correlation; inputting the day-ahead scheduling plan, the obtained basic operation data of the multi-microgrid power distribution system and the tie line power coupling constraint between the power distribution network and the multiple microgrids into a pre-constructed double-layer collaborative optimization model, and solving the model in a distributed manner by adopting an alternating direction multiplier method, an intra-day optimization scheduling scheme meeting the requirement of good cooperative operation of the power distribution network and the multi-micro network friends is obtained; according to the method, the optimization result in the worst scene meets the robustness requirement, and meanwhile, the economical efficiency of the system is guaranteed.

The invention relates to the technical field of power systems and energy management, discloses an industrial and commercial energy storage system energy resource scheduling method and system, and aims to solve the problems that in the prior art, economy optimization and battery health management are separated, degradation model simplification causes life damage, and the adaptability to load and electricity price uncertainty is insufficient. Specifically, multi-source heterogeneous state data are fused; constructing and calibrating a battery digital twinborn model coupled with electrochemistry, a thermal field and an aging mechanism; and executing a three-layer cooperative strategy of long-term degradation track planning, medium-term random scheduling optimization and short-term model prediction control, enabling the battery degradation cost to be generated in an economic scheduling target, and achieving high-robustness real-time control through rolling optimization. The system comprises a data acquisition and fusion unit, a digital twinning module, a probability prediction module, a hierarchical scheduling engine and an instruction execution unit. According to the invention, the unification of full-life-cycle value maximization and operation reliability is realized.

The invention relates to the technical field of power systems, and particularly discloses a multi-park integrated energy system scheduling method based on personalized federal reinforcement learning, and the method comprises the steps: building a multi-park integrated energy system low-carbon economic scheduling model with the minimum daily operation cost as the target, the constraint conditions comprise park equipment power output upper and lower limit constraints, storage battery operation constraints, energy supply equality constraints and energy supply inequality constraints; solving the low-carbon economic dispatching model of the multi-park integrated energy system by adopting a personalized federal reinforcement learning method; and solving to obtain a scheduling scheme. The method has the advantages that the efficient low-carbon collaborative scheduling of the multi-park integrated energy system is realized on the premise of strictly protecting the data privacy of each park through a personalized federal reinforcement learning framework.

The application discloses a power distribution network scheduling method and system, and a computer program product, and belongs to the technical field of smart grids, and comprises the following steps: dividing a power distribution network into multiple power distribution network levels according to voltage grades, collecting data of each power distribution network level, setting a minimum total cost of virtual power plant output as a target function, and constructing an optimization model; starting from a lower power distribution network level, sequentially and independently calculating power flow of each power distribution network level, equivalently taking first-end injected power of the lower power distribution network level as corresponding node load of an upper level, outputting power flow operation conditions of each power distribution network level, establishing a power flow checking constraint model, judging whether the safety operation checking constraint is met, if yes, solving the optimization model by using an optimization algorithm to obtain the minimum total cost and corresponding output values of an output unit group, and if not, reassigning data to perform a new round of optimization. The application considers inverse-level dynamic coupling modeling of grid connection of new power generation units, and provides an integrated solution for dynamic safety and economic scheduling of the power distribution network.

The application provides a kind of multi-microgrid collaborative economic dispatching method based on blockchain, comprising: based on microgrid group multi-group architecture model, construct microgrid group dispatching architecture model based on blockchain;Based on objective function submodel, constraint condition submodel and incremental cost submodel, an economic dispatching model is constructed;Wherein, economic dispatching model includes: microgrid layer economic dispatching model and microgrid group layer economic dispatching model;Based on microgrid group dispatching architecture model, intra-group scheduling is carried out on microgrid layer economic dispatching model, and inter-group scheduling is carried out on microgrid group layer economic dispatching model, to obtain multi-microgrid collaborative economic dispatching result.The application constructs microgrid group system distributed dispatching architecture, designs system economic dispatching model with comprehensive economic and environmental protection targets, and proposes a kind of intra-group optimization and inter-group optimization economic dispatching method based on blockchain.In the case of ensuring good expansibility, the economic efficiency and fault tolerance of microgrid group are improved.

The invention relates to the technical field of power dispatching, and provides a digital twin power energy dispatching system based on distributed control, and the system comprises a data collection layer which is used for collecting the operation data of a plurality of energy nodes distributed in a target power grid in real time and the environment data of the environment where each energy node is located; the digital twinborn layer is used for updating a pre-constructed digital twinborn model based on each operation data and each environment data so as to enable the updated digital twinborn model to be mapped with each energy node in real time, and performing analogue simulation and state prediction of power grid operation based on the digital twinborn model to generate simulation prediction data; and the distributed scheduling layer is used for calculating a scheduling instruction set by adopting a distributed optimization algorithm based on the simulation prediction data so as to perform power energy scheduling on the target power grid based on the scheduling instruction set. Safe and economic dispatching of the power grid is achieved, and the intelligent level and the operation economical efficiency of the power grid are remarkably improved.

The present application relates to the technical field of power system resource optimization scheduling, and particularly relates to a deep scheduling method and system for deep peak regulation of coal-fired units, which comprises the following steps: collecting multi-dimensional operation parameters of the unit, deep peak regulation instructions and carbon price signals of the carbon emission trading market, and realizing timestamp alignment; constructing a five-dimensional safety constraint model associated with the economic cost boundary; taking the maximization of peak regulation income as the target, constructing an objective function by fusing the coal purchase cost, carbon trading cost and equipment maintenance cost, solving the economic scheduling scheme by using a dynamic programming algorithm, and executing a cross-system collaborative strategy; based on the LSTM, predicting the equipment life and digital twin simulation, and triggering the cross-system correction strategy. The present application solves the business collaboration problem of economy, environmental protection and equipment reliability in deep peak regulation, and realizes the maximization of peak regulation income of coal-fired units under the power market environment.

The application provides a low-carbon optimization scheduling method for an electric carbon system considering device variable working condition characteristics, relates to the field of low-carbon optimization scheduling of comprehensive energy systems, and comprises the following steps: a variable working condition efficiency model of an energy conversion device is established, linearization processing is performed on the variable working condition efficiency model, and the linearization processing result is mapped to a carbon emission state to construct an improved carbon emission flow model; a carbon emission flow model of an energy storage device is established; the carbon emission flow model of the energy storage device contains a carbon charging rate used for representing a carbon storage state of the energy storage device; based on the improved carbon emission flow model and the carbon emission flow model of the energy storage device, a comprehensive energy optimization scheduling model under multi-market coupling is constructed; and based on the comprehensive energy optimization scheduling model, a distributed robust optimization method is adopted to determine a scheduling scheme of the comprehensive energy system with the minimum total system cost as an optimization target, so that the collaborative optimization of accurate carbon emission measurement and low-carbon economic scheduling can be realized under the condition of uncertain wind and light source load.

The invention provides a wind-light-storage integrated transformer network scheduling method and system, and belongs to the field of transformer distributed scheduling, and the method comprises the steps: designing a distributed optimization strategy, and guaranteeing the minimum operation cost of a transformer network in a normal operation state. Firstly, an economic dispatching problem of a transformer network is modeled into a class of distributed optimization problems; secondly, aiming at the optimization problem, constructing a class of Lagrange functions, and establishing a first-order optimal condition of an optimal value point based on a convex optimization theory; and finally, on the basis of the obtained first-order optimal condition, a class of continuous time distributed primal dual strategies are designed in combination with a primal dual strategy and a consistency strategy. When the strategy is operated, each transformer makes a decision only by using local information. Therefore, the strategy can protect the privacy of the transformer network, reduce the network communication cost, improve the robustness of the network, and ensure the efficient operation of the network.

The invention discloses a fuzzy logic-based rolling optimization integrated energy system energy scheduling method, which comprises the following steps of: improving an evolutionary fuzzy reasoning system, and solving a plurality of fuzzy decision parameter sets by a plurality of possible scenes generated in a day-ahead stage; selecting the fuzzy decision parameter set obtained in the day-ahead stage by using a model predictive control framework in the intra-day stage, and making a decision on the current time period through a fuzzy reasoning system corresponding to the selected fuzzy decision parameter set, thereby realizing economic scheduling of the integrated energy system; a rolling optimization energy scheduling method based on fuzzy logic is applied to economic scheduling of the integrated energy system, and the loss of an energy storage unit and other energy costs are fully considered. According to the method, the problem of negative influence caused by prediction error accumulation in the real-time prediction process is effectively solved through fuzzy logic, rolling prediction is performed by using the model prediction framework, the overall operation cost of the integrated energy system is reduced, and economical scheduling of the integrated energy system is realized.

The invention belongs to the technical field of energy scheduling, and particularly relates to an electricity-hydrogen-garbage multi-energy system collaborative scheduling method based on phase change material heat storage. Comprising the steps that firstly, a phase change material heat storage coupling alkaline electrolytic cell waste heat recovery and heat pump synergistic multi-stage heat management system is constructed to achieve space-time translation storage of hydrogen production waste heat, and a heat pump is combined to establish a multi-heat-source complementary garbage drying system; secondly, by providing a flue gas treatment method which comprises a flue gas storage device and is firstly purified and then separated, space-time decoupling of waste incineration power generation and flue gas treatment is achieved; and finally, on the basis of a model prediction control theory, constructing a two-stage optimization scheduling strategy combining day-ahead economic scheduling and intra-day rolling, and verifying the feasibility and superiority of the proposed system architecture and scheduling strategy through a simulation example. According to the system provided by the invention, the economy and flexibility of the system are remarkably improved through multi-dimensional cooperation of waste heat recovery, garbage drying and a flexible scheduling mechanism.

The present application relates to a kind of CMIES multi-time scale optimization scheduling method considering carbon-green certificate transaction and CCS-P2G combined operation, belong to mining area energy comprehensive utilization technical field.The present application comprehensively considers the energy characteristics of CMIES, the market characteristics of carbon-green certificate transaction mechanism and the low-carbon characteristics of CCS, two-stage P2G, and proposes a CMIES low-carbon economic scheduling method considering carbon-green certificate transaction and CCS-P2G combined operation under multi-time scale.The economic target is to minimize the energy purchase cost, energy abandonment penalty cost, carbon-green certificate transaction cost, equipment operation cost, carbon sequestration cost and load calling cost, and a CMIES day-ahead and day-ahead rolling optimization scheduling model based on MPC is established.The present application can better cope with the load and mining area multi-energy output fluctuation under different time dimensions, and can be adjusted according to the flexibility of CMIES internal equipment and the multi-time scale characteristics of demand response resources, which can effectively improve the wind, light, associated energy consumption capacity, CMIES low carbon and economy.

The invention provides a time sequence coupling constraint embedded power resource scheduling method and system and a storage medium, and relates to the technical field of energy management, and the method comprises the steps: obtaining a classical security constraint economic scheduling model; recursive calculation is carried out on the classic security constraint economic dispatching model; wherein in the forward propagation process of the recursive calculation process of the neural network model, parameters of the neural network at all time points are kept consistent, and meanwhile, the output of the calculation unit of the current time step serves as the input of the calculation unit corresponding to the next time step so as to connect all the calculation units; embedding the constraint condition into an output end of a set neural network model, and constructing a gradient return information highway; and training a set neural network model by using the power training data, and adjusting neural network parameters to obtain a power scheduling model so as to output a power resource scheduling scheme. Through the coupling calculation unit, the parameter sharing and the recursive structure, the calculation efficiency is remarkably improved, and the power resource scheduling scheme can be quickly generated.

The application discloses a micro-grid economic dispatching method and device, relates to the technical field of micro-grid operation control, and solves the technical problem that the stability of the whole micro-grid operation is reduced due to the fact that the existing micro-grid economic dispatching method usually adopts fixed time intervals to make scheduling plans; the method comprises the following steps: generating a corresponding predicted power curve according to predicted environment data; generating a scheduling time period based on the predicted power curve; constructing a target function in an economic dispatching model corresponding to the micro-grid based on the scheduling time period; constructing a plurality of constraint conditions in the economic dispatching model corresponding to the micro-grid based on operation constraint data; the constraint conditions comprise power constraint conditions and basic constraint conditions; solving the target function in the economic dispatching model to obtain an optimal scheduling scheme; and scheduling power resources in the micro-grid based on the scheduling scheme; so that the economic dispatching is more targeted, and the stability of the whole micro-grid system is improved.

The application provides a multi-micro-grid system optimization method, device and storage medium, relates to the new energy power supply field, and the method comprises the following steps: determining the topological structure of the multi-micro-grid system in a shared energy storage mode, establishing an economic scheduling model based on the optimization scheduling process of the multi-micro-grid system, determining a source-load uncertainty model and a constraint condition, establishing a fuzzy opportunity constraint based on the source-load uncertainty model, constructing a game model to solve the economic scheduling model under the fuzzy opportunity constraint, and obtaining the optimization result of the multi-micro-grid system; and the device and the storage medium are used for realizing the method. The application has the beneficial effects that the source-load uncertainty in the multi-micro-grid system is considered, the scheduling precision is improved, and the application has important theoretical and application values for solving the multi-micro-grid system optimization problem.

The invention discloses a method for improving the generalization ability of a power grid real-time scheduling agent, and relates to the power grid control and artificial intelligence technology. The method comprises a reinforcement learning decision-making agent model construction technology for high-proportion new energy power grid dispatching, a hybrid energy power grid safety real-time dispatching method based on safety reinforcement learning and a power grid safety dispatching agent generalization technology oriented to multiple operation scenes. According to the invention, an action decision network and a scheduling self-evaluation network are established, a reinforcement learning decision agent model is constructed, and safe and economic scheduling for a high-proportion new energy power grid is realized; a reasonable agent decision correction algorithm is designed, safety factors are introduced into a reinforcement learning objective function, and the safety and reliability of scheduling are further improved; a multi-scene feature fusion method is designed, a reinforcement learning power grid dispatching scheme suitable for multiple scenes is provided, and safe and effective real-time decisions can be generated for different power grid operation scenes.

The invention discloses a carbon transaction and interruptible load micro-grid optimization scheduling method based on an improved flower pollination optimization algorithm, and provides the improved flower pollination optimization algorithm by improving the algorithm by adopting multiple strategies aiming at the defects of relatively low convergence speed and relatively low optimization precision of a standard flower pollination algorithm. An anti-alignment learning mechanism is adopted to generate an initial population to expand the range of an initial solution, so that the algorithm is not easy to be locally and optimally captured. The adaptive mechanism is introduced to dynamically adjust the conversion probability to balance the global search and local search capability, and the solution precision of carbon transaction and interruptible load microgrid optimization economic dispatching is high.

The present application belongs to the field of optical storage system scheduling, and particularly relates to a kind of economic scheduling method of optical storage system fusing uncertainty information, to improve the intelligent level of photovoltaic energy storage system under uncertainty environment scheduling.Method includes obtaining photovoltaic array power generation and meteorological data through data acquisition module, after pretreatment, it is divided into training set and test set.Use training set to train time series convolution network, through time series convolution network, photovoltaic power day-ahead point prediction is carried out, and multiple confidence interval prediction results are generated based on prediction error distribution.Compression sensing and sparse coding method are used to convert interval prediction results into sparse connection weight matrix, complete reserve pool network initialization and carry out spectral radius normalization, based on the initialized reserve pool network, strategy network and value network embedded in proximal policy optimization algorithm are constructed.In the preset photovoltaic power station simulation environment, strategy network and value network are iteratively trained, and the day-ahead scheduling strategy that meets economic optimization is output.

The invention discloses an industrial park multi-energy dynamic complementary scheduling method and system based on multi-source data, and belongs to the technical field of energy scheduling. The method comprises the following steps: an acquisition module, a first determination module, a second determination module, a constraint determination module and a scheduling scheme determination module. According to the method, the power change rate is predicted in real time and quantified into the dynamic stability margin, then the dynamic stability margin is used as the rigid constraint to be embedded into the economic dispatching optimization model, the standby capacity is reserved in advance in the dispatching decision-making stage to prevent risks, the balance of economy and safety is converted into economic optimization within the safety boundary, and the economic optimization efficiency is improved. According to the method, the safety requirement is improved from a soft target to a hard constraint, active beforehand defense of an electric energy quality problem is realized, economic optimal scheduling is realized while the system stability is guaranteed, and the problem that voltage sag and frequency fluctuation are difficult to effectively suppress in renewable energy fluctuation processing in the prior art is solved.

The application discloses a comprehensive energy system Pareto optimal scheduling method based on multi-target weight variability. The method uses Markov decision to describe the low-carbon economic scheduling problem of the comprehensive energy system, builds an Actor-Critic network, including an Actor network and two Critic networks which are respectively focused on operation cost and carbon emission cost. The state of the environment of the comprehensive energy system is input, the optimal action is output through the Actor network, the reward vector and the next time system state are obtained after execution, and the five-tuple training sample is stored in the experience storage area. The advantage function of the two Critic networks is weighted and fused by using K sets of weights, the advantage function values of the networks under different weights are obtained, the network parameters are updated through the proximal policy optimization algorithm, and the optimal scheduling strategy is obtained. After completing the scheduled rounds of proximal policy optimization, the adaptive update of the advantage function weight is triggered, so that the intelligent agent learns the Pareto optimal decision.

The invention relates to the technical field of energy management, and discloses a source-network-load-storage multi-energy complementary collaborative optimization scheduling method and system, and the method comprises the steps: deploying collection points at a source end, a network end, a load end and a storage end, and collecting operation parameters in real time; carrying out denoising, abnormity correction, missing value interpolation and normalization processing on the data; extracting data key feature indexes; and formulating a scheduling strategy on the basis, including deciding energy storage charging and discharging according to a source-load power matching condition in a peak-valley period so as to realize economic scheduling of peak clipping and valley filling, and introducing a power supply side risk real-time evaluation and switching mechanism based on cosine similarity so as to realize economic scheduling of peak clipping and valley filling. And a power transmission line risk coefficient calculation and line switching protection mechanism based on a multi-parameter weighted fusion model. The system correspondingly comprises a data acquisition module, a data processing module, a data analysis module and an evaluation scheduling module which jointly realize safe, economical and optimized operation of the energy system.

The invention discloses a wind power generation prediction and scheduling method and system, and the method comprises the steps: firstly constructing a probability wind power generation prediction model based on a self-adaptive federal learning framework which integrates self-adaptive clustering and an elastic weight consolidation mechanism, generating a calibration prediction interval of quantization uncertainty, and carrying out the prediction of the prediction of the probability wind power generation; the data heterogeneity is overcome and the prediction precision is improved on the premise of protecting the data privacy of each power generation field. And then, taking generated power uncertainty information provided by the prediction interval as key input, embedding the generated power uncertainty information into a load side distributed optimization model based on an alternating direction multiplier method, converting the optimization problem into a standard form, and constructing an augmented Lagrange function to carry out distributed iterative solution. Therefore, decentralized economic dispatching of each load agent is realized, and abnormal load behaviors are identified in real time by monitoring the deviation between the state of each agent and the global consensus in the process. According to the method, a robust, efficient and safe intelligent power grid operation scheme capable of coping with high wind power permeability challenges is formed.