1001 results about "Optimal scheduling" patented technology

The invention discloses a multi-energy-storage thermal power generating unit load prediction and optimal scheduling method and system, and relates to the technical field of multi-energy-storage thermal power generating units, and the method comprises the steps: collecting the operation parameters and external environment parameters of a thermal power generating unit in real time, and constructing a real-time state parameter matrix; constructing a load prediction model based on the historical state parameter matrix, importing the real-time state parameter matrix into the load prediction model, outputting a load trend prediction curve, and triggering an early warning signal through a secondary discrimination mechanism; identifying a load disturbance value based on the load trend prediction curve, obtaining a load disturbance sequence, and decoupling the load disturbance sequence into a plurality of components; inputting the plurality of vectors into a preset decision network, dynamically correcting a constraint condition built in the decision network in combination with the early warning signal, introducing an improved dragonfly algorithm for optimization iteration, and generating an optimization scheduling instruction; according to the method, the adaptability of optimal scheduling and high-precision prediction of the load trend are improved.

The invention discloses a large model calculation network scheduling method based on task combination automation, and relates to the technical field of data processing, and the method comprises the steps: constructing a task factor flow graph; simulating resource linkage between nodes by using a cascade pulse propagation mechanism, generating a pulse propagation topological graph and a pulse intensity matrix, and constructing a computing force field situation awareness network in combination with a pre-trained pulse graph neural network; predicting the resource matching degree of the task factor and the computing power node by using a preset space-time convolution predictor, and generating an affinity tensor; and constructing a joint strategy space, searching and generating a fusion strategy in the joint strategy space by using a multi-target equalization algorithm, and embedding the fusion strategy into the deep reinforcement learning framework to generate an optimal scheduling strategy. By constructing the computing power field situation awareness network, continuous tracking and awareness of the load evolution process, the resource coupling relation and the performance bottleneck dynamic migration of each computing power node are realized, and the self-adaptive decision-making capability and the resource matching efficiency of a scheduling system in a multi-source heterogeneous environment are improved.

The invention discloses a multi-scene adaptive AGV scheduling management system and a path planning method. Comprising a multi-scene perception and environment modeling module, a multi-AGV task scheduling and resource allocation module, a dynamic path planning and optimization module, a scene adaptive decision module, a multi-sensor fusion obstacle avoidance module, a heterogeneous communication and data synchronization module and a whole-process monitoring and fault diagnosis module. According to the invention, full-process adaptive control from environment modeling to decision optimization is realized. An SVM classification algorithm and dynamic matching degree calculation are adopted, so that the system can accurately identify characteristics of heterogeneous scenes such as warehousing, workshops and logistics hubs, and an optimal scheduling strategy is automatically switched; and a multi-dimensional cost function including path length, obstacle risk, smoothness and congestion degree is constructed, and seamless connection of global path pre-planning and local dynamic adjustment is realized.

The invention discloses a k8s heterogeneous resource scheduling method, system and device based on intelligent sensing and a medium, belongs to the technical field of cloud computing and resource scheduling, and aims to solve the technical problems that a traditional scheduler is weak in sensing capacity, extensive in scheduling decision, low in resource utilization rate and high in resource utilization rate in a heterogeneous environment. According to the technical scheme, the method comprises the steps that a multi-dimensional resource sensing layer is constructed, specifically, real-time collection and convergence of heterogeneous hardware dynamic performance indexes are achieved by deploying an expanded monitoring equipment plug-in, and fine-grained runtime data of hardware including a CPU, an FPGA and an AI acceleration card are abstracted into a standardized index data set in a unified mode; constructing a node dynamic resource portrait: constructing the dynamic resource portrait based on the standardized index data set through a feature fusion and modeling technology, and generating a quantitative capability evaluation vector for each computing node in the cluster; and intelligent scheduling decision making: through a decision engine based on reinforcement learning, obtaining an optimal scheduling target according to the resource demand characteristics of the Pod to be scheduled and the dynamic resource portraits of the nodes.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention relates to the technical field of low-carbon dispatching of a power system, in particular to a carbon footprint optimization dispatching method and system of a hydrogen-electricity collaborative power distribution network, electronic equipment and a medium. The method comprises the steps of firstly obtaining multi-source operation parameters and market data; constructing a carbon footprint tracking model for realizing dynamic allocation of node carbon emission according to the multi-source operation parameters and the market data; constructing a representative scene set according to set historical data; integrating the carbon footprint tracking model and the representative scene set to construct a multi-period optimization scheduling model for collaborative optimization of economic cost and carbon emission cost; and solving the multi-period optimization scheduling model by adopting a decomposition coordination algorithm, and outputting an optimal scheduling scheme. Through the mode, the technical problem that multi-target collaboration is difficult in the low-carbon transformation process of the hydrogen-electricity collaborative power distribution network is solved, and the carbon emission control precision, the operation robustness and the comprehensive decision-making capability of the system are improved.

The invention relates to a reservoir dispatching management method and system based on digital twinning, and the method comprises the following steps: carrying out the real-time parameter collection of a target reservoir, and obtaining a reservoir characteristic parameter matrix; performing three-dimensional space modeling based on the reservoir characteristic parameter matrix and the reservoir hydrogeological environment in the target reservoir to obtain a reservoir digital twinborn model; performing drainage basin rainfall-runoff dynamic response analysis on the reservoir digital twin model to obtain a drainage basin hydrological response characteristic curve group; performing prospective water balance calculation on the watershed hydrological response characteristic curve group to obtain a multi-period reservoir flow prediction sequence; and formulating an optimal scheduling instruction set of the target reservoir based on the multi-period reservoir inflow prediction sequence, and regulating and controlling a reservoir gate control system in the target reservoir based on the optimal scheduling instruction set. And the method is difficult to adapt to hydro meteorological conditions and multi-target scheduling requirements which change in real time.

The invention discloses a material scheduling optimization method based on a dynamic programming and genetic algorithm hybrid strategy. The method comprises the following steps: establishing a material scheduling task model; constructing a dynamic planning state space and a state transition relation; a dynamic planning method is adopted to solve the scheduling problem of the material scheduling task model in stages, and a plurality of initial feasible solutions are obtained; constructing a genetic algorithm initial population on the basis of an initial feasible solution obtained by dynamic programming; executing genetic algorithm evolution operation based on the genetic algorithm initial population; a local dynamic planning mechanism is embedded in the genetic algorithm evolution process, and fine adjustment repair is carried out on part of individuals so as to improve the individual fitness; judging whether a preset convergence condition is met or not; and outputting an optimal or approximately optimal scheduling result. According to the method, the local optimization capability of dynamic planning and the global search capability of the genetic algorithm are fused, the quality and the solving efficiency of the scheduling optimization result are remarkably improved, and the method is suitable for various material scheduling scenes such as manufacturing, logistics and construction.

The invention provides a cross-day two-stage random scheduling method for an industrial park integrated energy system, and the method comprises the steps: fitting the output fluctuation characteristics of new energy power generation equipment in a cross-day time scale based on historical data, and constructing an uncertainty scene set containing a new energy prediction error; establishing a cross-day two-stage stochastic programming model; according to the two-stage model, an optimal scheduling scheme is solved so as to minimize the overall operation cost, and the operation cost comprises the demand electric charge, the electricity purchase electric charge, the new energy power abandoning cost, the unit start-stop cost and the standby penalty cost; and on the basis of tie line power constraint and dynamic response characteristics of the multi-energy coupling equipment, feasibility verification and rolling optimization adjustment are performed on the scheduling scheme. According to the method, the new energy consumption capability of the industrial park integrated energy system under the cross-day time scale can be effectively improved, the total operation cost is reduced, and collaborative scheduling of demand cost optimization and spot market participation is realized.

The invention discloses a multi-energy system closed-loop prediction control method, system, equipment and medium, and the method comprises the steps: carrying out the decomposition, dimension reduction and nonlinear modeling of the time sequence characteristics and environmental influence factors of photovoltaic output through a photovoltaic power prediction model, and outputting a future multi-period photovoltaic power prediction sequence; establishing a state equation and an output equation, integrating equipment operation constraints, and constructing a hydrogen-containing energy storage state space model; inputting local load power and a future multi-period photovoltaic power prediction sequence into the hydrogen-containing energy storage state space model, and solving an objective function through a rolling optimization algorithm to obtain a future multi-period optimal scheduling scheme; and applying a first hour control instruction of the optimal scheduling scheme to an actual system, and proportionally superposing the deviation between an actual measurement value and a historical prediction value to a photovoltaic power prediction sequence of a next period through a feedback correction item to realize closed-loop control. According to the method, the photovoltaic local consumption rate can be improved, and the power grid fluctuation rate is reduced.

The invention belongs to the technical field of energy management, and particularly relates to a light storage direct current flexible load self-evolution cooperative scheduling method, system and equipment, and the method comprises the steps: constructing a parameterized energy utility curve, quantifying the comprehensive utility of flexible load response in energy efficiency, comfort and equipment loss, and calculating the unit power marginal utility as the flexibility; establishing a multi-target collaborative scheduling model considering the time-varying carbon intensity, the electricity price and the utility curve, and solving by adopting a model predictive control and reinforcement learning mixed strategy; static and dynamic data are fused to construct a knowledge graph, and flexibility is predicted and cross-scene migration is realized through a sequence diagram neural network; and cooperatively optimizing a knowledge graph prediction result and a scheduling instruction through a Lagrangian relaxation method to form a self-evolution closed-loop control system. According to the method, flexible load refined modeling, carbon perception economic optimization scheduling and system adaptive learning are realized.

The invention discloses a micro-grid optimization scheduling method based on an improved Loueer fox algorithm, relates to the technical field of micro-grid operation, and realizes collaborative optimization scheduling of a distributed power supply, an energy storage device, a load and the like in a micro-grid system by simulating various perception and behavior strategies of the Loueer fox. Aiming at the demand of distributed power supply system micro-grid optimization scheduling, the micro-grid system operation economy is taken as a main target, a function corresponding to a micro-power supply scheduling scheme is constructed, improvement is carried out based on an RFO algorithm, and a quantum fractal annealing disturbance strategy, a hyperbolic tangent self-adaptive olfactory disturbance strategy and a Riemannian metric accumulation disturbance strategy are introduced, so that the optimal scheduling of the micro-power supply system is realized. Through the fusion of the triple innovation strategies, the defects of poor population diversity, easy premature convergence, insufficient balance capability, low precision and the like when the original RFO algorithm and the traditional intelligent algorithm are used for solving the complex optimization problem are solved, and the efficient solution of the micro-grid optimization scheduling scheme is realized.

An energy-conscious workflow planning system based on LLM with dynamic resource allocation, consisting of: a workflow input interface configured to receive workflow-directed acyclic graphs (DAGs), energy budget constraints, system performance constraints, and natural language requests from human operators; a large language model (LLM) logic module connected to the workflow input interface and configured to analyze the workflow specifications and system constraints in natural language, generate energy-conscious planning recommendations based on the analyzed workflow specifications, and provide explainable planning rationales in natural language; a reinforcement learning-based scheduling unit connected to the LLM reasoning module and configured to: receive scheduling recommendations from the LLM reasoning agent, fine-tune task-resource assignments by dynamically adapting to runtime variations, and perform online resource redistribution under runtime variability; an energy monitoring unit configured to: continuously monitor CPU and GPU utilization in heterogeneous clusters, track power consumption and thermal limits per node, and generate energy profiles for system components; a multi-objective optimization engine configured to: perform a Pareto-optimal scheduling analysis that balances energy consumption, lead time and reliability, apply statistical and AI-supported trade-off analyses and ensure optimal resource allocation based on Pareto frontier analysis; a dynamic resource allocation unit configured to: use predictive models that incorporate LLM inferences and feedback from reinforcement learning, reassign tasks between nodes and clusters while minimizing energy consumption and improving system throughput based on the predictive models; a performance optimization module configured to optimize scheduling decisions using multi-criteria optimization analysis; and a user interface that allows human operators to override and refine planning strategies in real time based on verifiable planning reasons.

The invention discloses a rope net ladder data production scheduling and traceability system based on big data storage, and particularly relates to the field of data scheduling and traceability, comprising the following steps: the system obtains total factor data of equipment, process, quality and the like through a data perception and digital acquisition module and standardizes the total factor data; the integrated storage and quantitative analysis module constructs functions such as efficiency evaluation and quality risk to quantify production indexes; the adaptive decision optimization and scheduling module generates an optimal scheduling scheme and a dynamic rescheduling instruction based on a multi-target comprehensive decision function; the full-link tracing and self-feedback optimization module realizes full-link bidirectional tracing and drives process parameter and model self-feedback optimization; the system realizes the intelligent control of the whole production process, balances the efficiency, quality, cost and delivery target, improves the resource utilization rate and production stability, and is suitable for the precise production scene of structural members such as rope net ladders.

The invention discloses a virtual power plant load regulation method and system based on a deep learning scheduling strategy, and relates to the technical field of power plant load regulation, and the method comprises the steps: obtaining multi-source heterogeneous data, and obtaining a feature tensor through preprocessing; the feature tensor is input into an LSTM-Transform hybrid network model, and a context code is output; inputting the context code into the time sequence convolutional network model, outputting an ultra-short-term prediction result, and further obtaining a short-term prediction result; constructing a multi-objective optimization model, and modeling constraint conditions; solving the multi-objective optimization model through a preset layered architecture to obtain an optimal scheduling strategy, and generating a scheduling instruction; and issuing the scheduling instruction to distributed resources in the virtual power plant, and executing and converting the scheduling instruction into an equipment action. The method solves the problems that in the prior art, the optimization target is single, the dynamic adaptive capacity is lacked, and the real-time fluctuation response speed of the power grid is limited to a certain extent.

The invention discloses an electric vehicle charging station energy storage scheduling method based on a multi-agent system. The method comprises the following steps: obtaining and standardizing operation basic data of a plurality of new energy vehicle charging stations; setting five types of agents, defining observation variables and action space, and constructing a multi-agent system model; constructing a global collaborative scheduling network, and executing strategy evaluation and strategy generation; setting a constraint boundary, and constructing a linear programming scheduling model; constructing a training sample, and performing offline training and updating of a global collaborative scheduling network; solving a local optimal scheduling amount based on the real-time operation basic data, and analyzing to generate a control instruction; and issuing an energy storage control instruction and a computing power unit control instruction, acquiring a cooperative scheduling result to generate a final scheduling result set, and submitting the final scheduling result set to an upper-layer scheduling system. According to the method, a multi-agent collaborative scheduling system is constructed, strategy optimization and linear programming are fused, and efficient, stable and executable global collaborative scheduling of energy storage and computing power tasks of the charging station is achieved.

The invention discloses an optimal scheduling method based on a multi-energy complementary micro-grid and an active power distribution network, which belongs to the technical field of power grid scheduling and comprises the steps of S1, existing scheduling problem identification including multi-energy system systematic defect analysis, source load fluctuation adaptability defect quantification, distributed energy consumption bottleneck identification and economy-reliability constraint conflict modeling; according to the optimal scheduling method based on the multi-energy complementary micro-grid and the active power distribution network, the defects of insufficient multi-energy coordination, poor source load fluctuation adaptation and the like are diagnosed firstly, and then an energy storage multi-space-time coordination, wind-light output prediction and flexible load response model is constructed; through hierarchical optimization, multi-energy flow-power distribution network linkage, multi-target weight dynamic adaptation and dynamic safety margin adjustment landing, the distributed energy consumption rate and the multi-energy cooperation efficiency can be effectively improved, the power supply reliability, economy and environmental protection performance are balanced, the toughness of an electric power system to deal with uncertainty is enhanced, and the power supply efficiency is improved. And an efficient scheduling scheme is provided for novel power system construction.

The invention discloses a multi-time-scale park integrated energy system distribution robust optimization scheduling method, which comprises the following steps of: constructing an electric heating collaborative system model taking a combined heat and power generation unit as a core, and introducing a carbon transaction mechanism; constructing a confidence set in combination with a 1-norm and an infinity-norm, and respectively making a robust start-stop plan and a flexible operation strategy in day-ahead and intra-day two-stage scheduling; a column and constraint generation algorithm is adopted to decompose the constructed day-ahead and intra-day two-stage model into a main problem and a sub-problem for repeated iterative solution, and an optimal scheduling scheme with both economical efficiency and robustness is obtained. According to the method, the uncertainty of new energy prediction is fully considered, and the coping of the park to the randomness of the new energy can be better played through processing of different time scales.

The invention discloses a multi-power-grid cooperative regulation and control method and system based on multi-level resource aggregation decoupling, and belongs to the technical field of intelligent power grid operation control. The method comprises the following steps: determining a time-varying feasible boundary of a microgrid in a power regulation domain of a common connection point by acquiring multivariate state information of heterogeneous resources of the microgrid; determining a power bidirectional interaction boundary constraint between the micro-grid and the power distribution network through the common connection point, and constructing a micro-grid power distribution optimization model according to the power bidirectional interaction boundary constraint and the electric energy characteristics of the power distribution network; dividing resource adjustment levels corresponding to the heterogeneous resources to obtain a multi-level adjustment sequence; when the micro-grid power distribution optimization model outputs a scheduling instruction, the scheduling instruction is decomposed into optimal scheduling strategies corresponding to the levels according to the multi-level adjustment sequence, and the optimal scheduling strategies are issued to the levels in sequence, so that the adjustment instruction issued by the power distribution network is decomposed in sequence according to the adjustable characteristics of heterogeneous resources, and the optimal scheduling strategies are obtained. The resource adjustment efficiency is improved; and the response cost is optimized.

The invention relates to the technical field of intelligent window scheduling, in particular to a multi-objective optimization intelligent window dynamic scheduling method and system combined with business rules. The method comprises the following steps: acquiring reservation queuing data, customer demand characteristics and window state data associated with each service window in real time; based on real-time collected data, a multi-objective optimization function including global average waiting time minimization, overall service efficiency maximization of all windows and global resource utilization balance degree maximization is constructed; solving the multi-objective optimization function based on a sorting genetic optimization algorithm; selecting an optimal scheduling strategy through a multi-factor fuzzy decision maker; and dynamically allocating window service resources according to the optimal scheduling strategy, and adjusting a window service queue in real time. By constructing the multi-objective optimization function, the resource waste problems of partial window congestion and partial idle windows caused by traditional fixed rule scheduling are effectively avoided, and the overall rationality and efficiency of window scheduling are improved.

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

The invention discloses a virtual power plant source load interaction optimization scheduling model based on low-carbon response and a solving algorithm, and belongs to the technical field of power system optimization scheduling. A low-carbon scheduling framework containing a distributed power supply, energy storage, a flexible load and a carbon transaction mechanism is constructed, the carbon emission intensity of each link is quantified to form a carbon flow scheduling signal, and a dynamic carbon emission factor and energy cost are coupled. A multi-objective optimization model is established, a complex function is processed by piecewise linearization, and a hybrid algorithm of an improved genetic algorithm and a commercial solver is designed to improve the solving efficiency. The prediction error is dynamically corrected through a'prediction-optimization-feedback 'closed loop, and the strategy is adjusted. According to the scheme, low-carbon and economic collaborative optimization is realized, renewable energy consumption and system stability are enhanced, user satisfaction and real-time scheduling are considered, and a solution is provided for low-carbon intelligent operation of the power distribution network.

The invention discloses a modeling and scheduling method and device for a dynamic load and a shared distributed energy storage system under shared power conversion, and relates to the technical field of shared power conversion and distributed energy storage systems, and the modeling and scheduling method comprises the steps: building a multi-target optimization scheduling model based on user power conversion information and power conversion demand prediction data under a shared power conversion scene; and constructing a collaborative solving mechanism, solving the comprehensive scheduling model, and obtaining and selecting an optimal shared energy storage scheduling scheme. The optimal scheduling model comprehensively considers the economic cost of optimal scheduling and the operation and scheduling balance of the shared distributed energy storage system, so that the cost of configuring energy storage by a single user can be reduced, the operation coordination and load balance capability of the whole system can be optimized and improved, good resource sharing and system coordination effects are embodied, and the user experience is improved. The method is suitable for building a flexible energy system with a user side as a core in the future.

The invention discloses an electric vehicle collaborative optimization scheduling method based on a'network-station-vehicle 'layered architecture. The method comprises the following steps: constructing a double-layer distributed scheduling model; in the upper-layer model, modeling is carried out on aggregation flexibility of the charging stations, and an aggregation charging and discharging plan of each charging station is formulated by taking maximization of the total benefit of the system as a target; in the lower-layer model, the charging and discharging behaviors of the individual electric vehicle are scheduled by following an upper-layer plan instruction and taking the comprehensive satisfaction degree of a vehicle owner as a target; a second-order cone programming relaxation technology is adopted to convert a non-convex power flow constraint equation in the upper-layer model into convex constraint; decomposing the global optimization problem into a plurality of independent sub-problems which can be solved by each charging station in parallel by adopting an improved Lagrange relaxation dual method; and iteratively updating the Lagrangian multiplier through a sectional self-adaptive step length strategy until the algorithm is converged and a global optimal scheduling plan is generated. According to the method, the scheduling effect close to centralized optimization can be obtained while the privacy of the user is protected; and the problem of curse of dimensionality is effectively solved.

The invention discloses a light storage energy scheduling optimization recommendation method based on artificial intelligence, and relates to the technical field of energy management. According to the method, power generation side state parameters, energy storage side working data, load side demand characteristics and power grid side economic signals are collected in real time, and a comprehensive energy data set is formed through space-time alignment and standardized fusion; analyzing the data set by adopting a time-space two-dimensional prediction model, and outputting the power generation capacity, the power generation fluctuation ratio, the load and the risk probability distribution in a future time period; the predicted value is input into an intelligent decision engine, and an optimal scheduling strategy is deduced and generated in a virtual simulation environment through a strategy optimization algorithm; the strategy is converted into a control instruction through a northbound interface to be issued and executed, and execution deviation is collected to generate a verification report; strategy effect data is fed back to the cloud incremental learning platform, model parameters are updated online through distributed collaborative learning, and continuous evolution and scene self-adaptive optimization of the scheduling strategy are achieved. The method has the effect of improving the economic benefit of light storage energy scheduling.

The invention relates to the technical field of new energy management and edge computing, and particularly discloses a heterogeneous computing power aware new energy cluster collaborative scheduling method and system. According to the method, information such as static attributes and dynamic loads of various energy devices and control units in a cluster is collected through a lightweight probe, and a dynamically updated heterogeneous computing power resource portrait is constructed after processing; analyzing the structured scheduling request, and combining a cluster physical layout to construct a space-time-capability-dependency-computing power-energy consumption five-dimensional coupling adaptation degree model; searching an optimal scheduling plan through a hybrid optimization algorithm, wherein the optimal scheduling plan covers energy distribution, energy storage scheduling and transmission path planning; after the stability is verified by the digital twin sandbox, an instruction is issued; real-time monitoring is carried out during operation, and computing power-physical joint rescheduling is triggered. Intelligent perception, accurate prediction, simulation verification and dynamic collaborative optimization of the heterogeneous computing power resources of the new energy cluster are realized, and the overall efficiency, stability and adaptive capacity of an energy system are remarkably improved.

The invention discloses a charging module energy efficiency optimization scheduling method based on big data prediction, and the method comprises the steps: collecting multi-source heterogeneous data related to a charging load and a power grid state, carrying out the preprocessing and feature fusion, and generating a fusion data set in time-space correlation; based on the fused data set, training and applying a big data prediction model, and outputting a regional charging demand prediction result and a power grid load prediction result in a future scheduling period; inputting the regional charging demand prediction result, the power grid load prediction result and the renewable energy output prediction data into a collaborative scheduling optimization model established for a plurality of charging modules; and solving the collaborative scheduling optimization model to obtain an optimal scheduling instruction sequence of the start-stop state and the output power of each charging module in a future scheduling period so as to execute start-stop control and power distribution on each charging module. According to the invention, the charging energy efficiency can be improved, the service life of equipment is prolonged, and the renewable energy consumption rate is improved.

The invention provides a micro-grid energy optimization scheduling system fusing digital twinning, and the system comprises a physical micro-grid layer which is used for collecting real-time operation data through a sensor network, and adjusting the operation state of equipment according to an equipment control instruction issued by an execution control layer; the data sensing layer is used for receiving, preprocessing and transmitting the real-time operation data; the digital twinborn platform layer is used for constructing and updating a digital twinborn model based on the preprocessed real-time operation data, performing real-time dynamic simulation, extreme scene simulation and operation state prediction based on the digital twinborn model, and outputting a simulation result and prediction data; the energy optimization decision-making layer is used for receiving the simulation result and the prediction data, solving through an optimization algorithm by taking low carbon and stability as a planning target, and generating an optimal scheduling scheme; and the execution control layer is used for converting the optimal scheduling scheme into an equipment control instruction and issuing the equipment control instruction to the physical micro-grid layer so as to better adapt to a complex micro-grid operation environment.

The invention discloses a coordinated regulation and control method for a comprehensive energy system to participate in multiple frequency modulation services of a power grid, and the method comprises the steps: collecting the operation state data of four resources, carrying out the preprocessing of the operation state data, obtaining the original data of the resources, constructing four resource virtualization energy storage models, and bringing the original data of the resources into the four models. The method comprises the following steps: respectively obtaining adjustable energy and power boundaries, constructing a virtual energy storage model, uniformly mapping four resources into a virtual energy storage data set through the virtual energy storage model, substituting the virtual energy storage data set into a multi-frequency modulation collaborative optimization model, obtaining an optimal scheduling result, and realizing model flexibility. And the coordination control between each subsystem in the IES and the power grid is realized, and the coordination operation mechanism of multiple auxiliary service markets is perfected.

The invention provides a textile flexible production line multi-material scheduling system, which comprises a physical execution layer and a multi-material scheduling layer, wherein the physical execution layer comprises reconfigurable textile production equipment; the intelligent sensing layer is arranged at key nodes of the physical execution layer; the central scheduling and control layer is in communication connection with the physical execution layer and the intelligent sensing layer; and the digital twin layer is a virtual mapping of the physical execution layer in an information space, and forms a closed loop with the central scheduling and control layer and the physical execution layer. According to the method, the perspectiveness and the global optimization capability of a scheduling decision are realized through a digital twinning technology, the system places an initial production scheduling scheme in a high-fidelity virtual production line model for millisecond-level concurrent simulation deduction, and potential process bottlenecks and quality risks during blending production of different materials are accurately predicted; and by taking the production efficiency, the energy consumption and the quality qualification rate as multi-target reward functions, completing iterative optimization of massive strategies in a virtual space, thereby obtaining an approximately globally optimal scheduling instruction before the physical production is put into use.