1730 results about "Optimal scheduling" patented technology

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

The invention discloses a virtual power plant scheduling method based on a large language model and deep reinforcement learning, and belongs to the technical field of virtual power plant scheduling. Comprising the following steps: constructing a virtual power plant multi-agent cloud edge collaborative scheduling framework based on large language model driving; predicting wind power, photovoltaic power and load power based on a large language model; constructing a mathematical model of virtual power plant optimization scheduling; converting the virtual power plant optimization scheduling model into a Markov game process in combination with a large language model; performing initialization training on the strategy network of the edge layer intelligent agent by adopting imitation learning to obtain a pre-trained edge layer intelligent agent strategy network; and based on the pre-training strategy network of the boundary layer intelligent agent, combining with a large language model and adopting an improved multi-agent near-end strategy optimization algorithm to solve a scheduling strategy.

The invention discloses an automatic scheduling method and system for ship unloading equipment, and relates to the technical field of port automation. According to the method, a high-precision digital twinborn model for ship unloading operation is constructed, physical equipment is abstracted into a digital intelligent agent with an autonomous decision-making capability, real-time multi-dimensional data and historical data are utilized to perform deep fusion to drive system synchronization, and a future multi-step scheduling strategy is deduced in a parallel simulation manner in a virtual space based on rolling time domain control, so that the real-time multi-dimensional data and historical data are subjected to real-time multi-dimensional data synchronization driving system synchronization is realized. Dynamic evaluation and optimization are carried out by adopting a multi-objective evolutionary algorithm combined with a cooperative game mechanism, and conflicts and cooperation among equipment are effectively coordinated by defining an individual utility function and introducing cooperative game negotiation and a meta-controller to dynamically adjust target weights, so that system-level global optimal scheduling is realized under multiple objectives of efficiency, energy consumption, safety and the like, and the scheduling efficiency is improved. The intellectualization, the self-adaptability and the comprehensive operation benefit of port ship unloading operation are comprehensively improved.

The invention provides a micro-grid group-containing AI active distribution network scheduling optimization method, a medium and a system, and belongs to the technical field of power grid scheduling. The method comprises the following steps: firstly, constructing a micro-grid group and main and distribution network interaction model, and determining boundary constraints; predicting key parameters of the micro-grid group by using deep reinforcement learning; constructing an active distribution network power balance equation and topology constraint conditions; solving by adopting mixed integer programming to obtain power flow distribution of the distribution network; constructing a power grid dispatching optimization objective function based on a quantum evolutionary algorithm; processing multi-source data by using an MGPN deep neural network to output an optimal scheduling strategy; monitoring a running state verification effect in real time through a state estimation technology; updating the strategy in real time by applying a rolling optimization mechanism; and establishing an evaluation system to dynamically optimize neural network model parameters, realizing efficient collaborative scheduling of the micro-grid group and the active power distribution network, and solving the technical problem of low distributed energy consumption rate in the collaborative scheduling optimization process of the micro-grid group and the active power distribution network.

The invention relates to a big data-based passenger-roll transport demand prediction and ship intelligent scheduling method and system. The method comprises the steps of obtaining multi-source shipping data for a target area; inputting the multi-source shipping data into the spatial-temporal feature mining model, and predicting passenger rolling transportation demand information of the target area; acquiring ship real-time position, passenger carrying capacity, energy consumption data and port real-time operation state in the target area, and dynamically generating an optimal scheduling scheme by adopting a shipping scheduling model in combination with the predicted passenger transport demand information; the shipping scheduling model is obtained by interacting a decision scheduling model with an intelligent agent corresponding to the passenger roller transportation system and performing iterative training by adopting a reinforcement learning algorithm; and converting the optimal scheduling scheme into visual information, pushing the visual information to operation terminals of the ship and port workers so as to start corresponding shipping scheduling operation, and monitoring the execution effect of the shipping scheduling operation in real time.

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

The invention relates to the technical field of power distribution network dispatching. By providing a power grid load dynamic prediction and optimal scheduling method, device, equipment and medium, the method comprises the following steps: performing multi-source heterogeneous fusion processing on meteorological parameters, historical load curves and new energy output data to generate a dynamic load prediction map; constructing a dynamic network model, and performing power flow distribution simulation processing based on the dynamic network model to obtain a stability margin calculation result and a preset safety threshold boundary; performing stage decomposition processing on the global scheduling target to generate a progressive scheduling stage sequence; performing matching processing on the response characteristics of the power generation equipment to generate a self-adaptive progressive scheduling instruction sequence; and executing an adaptive progressive scheduling instruction sequence, performing feedback processing on the real-time state of the power grid, and generating a dynamic adjustment instruction so as to realize multi-dimensional data association modeling, stability margin quantitative analysis and dynamic instruction optimization, thereby improving the load prediction precision and reducing fault diffusion and transient oscillation.

The invention discloses an underwater robot multi-target dynamic resource scheduling method, and relates to the technical field of robot resource scheduling. The method comprises the following steps: constructing an underwater robot resource scheduling problem model, and establishing a target optimization model which comprises the steps of minimizing task maximum execution time, minimizing total power consumption and balancing load; generating reference points and distributing the reference points in a target space; a population is coded and initialized, a scheduling scheme is represented by adopting multi-segment chromosome coding, the scheduling scheme comprises a task allocation sequence and tasks allocated by a robot, and an initial parent population is generated in a random mode; a non-dominated solution set is obtained through evolutionary iteration, and the non-dominated solution set is obtained through reference point niche selection and combined evolutionary iteration of crossover and mutation operation based on an improved NSGA-III algorithm; and obtaining an optimal scheduling scheme, and screening an optimal compromise solution from the non-dominated solution set as the optimal scheduling scheme. According to the invention, a data-driven intelligent scheduling scheme is provided for collaborative operation of underwater robots in a complex marine environment.

The invention relates to the technical field of cloud computing, and discloses an enterprise resource planning data efficient processing method based on cloud computing, and the method comprises the steps: collecting system data of finance, supply chain, production management and the like through a multi-source interface module, and carrying out the preprocessing through a hierarchical fusion framework, thereby obtaining a standardized data set; the method comprises the following steps of: selecting a multi-target scheduling model, inputting the multi-target scheduling model into a task decomposition model, generating task decomposition parameters based on a block processing mechanism and a service association rule, constructing the multi-target scheduling model aiming at the shortest processing duration and the minimum resource occupation, and outputting an optimal scheduling scheme by adopting a dynamic allocation algorithm. A hierarchical execution control model comprising a strategy layer, a coordination layer and an execution layer is established, the strategy layer performs global task planning, the coordination layer performs local resource adjustment, and the execution layer tracks tasks based on a load balancing algorithm and outputs a processing control instruction to realize efficient processing. The method improves the data processing efficiency and the resource utilization rate, and provides support for enterprise decision making.

The invention relates to an intelligent optimization method for an industrial waste transportation path, and the method comprises the steps: firstly collecting multi-source and multi-dimensional input variables, such as vehicles, roads, orders and waste attributes, achieving the dynamic simulation of a whole transportation process through digital twin modeling, and carrying out the structural processing of data through combining with space and environment labels; when an abnormal event is monitored, an abnormal correction action is dynamically generated and optimized through a multi-agent cooperation and game reasoning mechanism, and an optimal scheduling decision aiming at different working conditions is realized; and continuously monitoring and executing feedback by the system, returning the actual deviation to the model and the knowledge base, and driving the self-learning and continuous evolution of the anomaly correction strategy.

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention belongs to the technical field of iron phosphate preparation, and discloses an energy-saving control system for iron phosphate preparation based on an energy consumption scheduling model. The system is composed of a data acquisition module, an energy consumption sensing module, a preparation process modeling module, an energy consumption prediction module, an energy-saving scheduling module, an intelligent execution module, a feedback correction module, a man-machine interaction module and a remote operation and maintenance module. The energy consumption sensing module intelligently senses an energy consumption state, the preparation process modeling and energy consumption prediction module accurately predicts energy consumption, the energy-saving scheduling module generates an optimal scheduling strategy, the intelligent execution module accurately executes an instruction, and the feedback correction module realizes closed-loop adaptive regulation and control; all the modules cooperatively operate, process parameters are adjusted in real time according to actual working conditions of iron phosphate preparation, energy consumption in the preparation process is remarkably reduced, the energy utilization rate is increased, and energy-saving optimization of iron phosphate preparation is achieved.

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

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

The invention discloses a server resource dynamic scheduling method for dealing with video stream high concurrent access, and particularly relates to the technical field of computer network and intelligent scheduling. A user access behavior data set is constructed; a deep learning model is adopted to train an access hot spot prediction model based on the time sequence features to predict a future access hot spot area and a peak trend; acquiring response delay, CPU / GPU occupancy rate and bandwidth load information of the heterogeneous server group, and generating a resource state multi-dimensional parameter set; carrying out joint modeling on the model and the parameter set, constructing a resource scheduling priority model by adopting a graph neural network, and generating an optimal scheduling path graph based on an A * improved algorithm; task transfer, instance elastic expansion, cache preheating and other scheduling operations are executed according to the model; according to the method, the resource utilization efficiency and the service quality of the video system in a high-concurrency scene can be improved, and the method has the advantages of being high in real-time performance, intelligent in scheduling and high in self-learning capability.

The invention discloses a metal formwork production full-process management and control system based on a cloud platform, and relates to the technical field of industrial manufacturing, and the system comprises an industrial cloud platform which is in communication connection with the following modules: a global element perception processing module, an industrial Internet-of-Things terminal used for combined deployment, and a cloud platform module. And multi-source heterogeneous data including equipment state, cutter service life information, material circulation information, personnel operation information and workpiece quality detection data are collected in real time. According to the method, the industrial Internet of Things terminal is deployed, multi-source heterogeneous data such as equipment state, cutter life and material circulation are collected in real time, a virtual production environment synchronized with a physical workshop is constructed in combination with a digital twinning technology, a production scheduling problem is converted into a path planning problem based on an ant colony algorithm, an optimal scheduling scheme is generated in real time, and the scheduling efficiency is improved. The problems that a traditional system is rigid in plan and slow in response are solved, and the flexibility and efficiency of production scheduling are remarkably improved.

The invention relates to the technical field of power grid load prediction, in particular to a power grid load prediction and dynamic scheduling optimization method based on big data. Comprising the following steps: preprocessing power grid multi-source data; constructing an attention enhanced load prediction model, extracting time sequence load features through an LSTM network, and screening key influence factor features through a random forest; generating a layered constraint improved whale optimal scheduling scheme; real-time closed-loop adjustment is carried out; and data archiving and tracing. According to the method, the attention enhanced load prediction model is constructed, the time sequence load features are extracted by using the LSTM network, the key influence factor features are screened by using the random forest, the attention mechanism is introduced to highlight the power consumption peak period feature weight, and the Adam optimizer training and precision verification are combined, so that the load change rules of different periods can be more accurately captured, and the load prediction accuracy is improved. And the adaptability of the load prediction result and the actual power grid operation condition is improved.

The invention discloses a GPU cluster scheduling strategy optimization system based on deep reinforcement learning, which comprises a simulation environment layer, a reinforcement learning layer and a strategy evaluation layer, and is characterized in that the simulation environment layer is used for providing a real and credible GPU cluster scheduling environment, reproducing a core mechanism of actual cluster scheduling and simultaneously providing controllable experiment conditions; comparative evaluation and iterative optimization of strategies are facilitated; the reinforcement learning layer converts a GPU cluster scheduling problem into a Markov decision process based on a deep neural network, and obtains an optimal scheduling strategy by applying a reinforcement learning strategy; and the strategy evaluation layer counts each key index, compares and analyzes a plurality of scheduling strategies, and visually displays an analysis result to realize optimization of the GPU cluster scheduling strategy. The system solves the time sequence short view problem of a traditional scheduler, so that the scheduling decision can consider the influence on the future, global optimization instead of local optimization is realized, and the overall resource utilization efficiency is improved.

The invention discloses a dynamic scheduling optimization method for a low-altitude logistics distribution network, and the method comprises the steps: a server side builds a multi-source sensing network through satellite remote sensing, an unmanned plane airborne sensor and ground traffic monitoring, fuses meteorological data, airspace control data and order distribution data which are collected in real time, and generates a four-dimensional space-time grid map; based on the four-dimensional space-time grid map, the server side adopts a TD3-GA hybrid intelligent algorithm to carry out path planning of the logistics distribution network; the edge calculation end generates an optimal scheduling scheme of the unmanned aerial vehicle group through a multi-objective optimization function based on the global path; and the server side performs security risk assessment on the optimal scheduling scheme by using a Bayesian network model, and dynamically adjusts a space-time routing strategy of the unmanned aerial vehicle cluster according to an assessment result. According to the method, in a large-scale unmanned aerial vehicle concurrent scheduling scene, the scheduling efficiency can be effectively improved, the response time delay is reduced, the risk prediction accuracy is improved, and the timeliness and safety of a low-altitude distribution network are remarkably improved.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant optimal scheduling system and method, and the system comprises a data obtaining module, an edge calculation module, a prediction module, a scheduling controller, a topology reconstruction module, and an intelligent terminal device cluster. According to the invention, the edge computing module carries out localization processing and prediction on the sensing data, so that rapid generation and issuing of a scheduling scheme are realized, and the problem of response delay caused by network transmission and centralized computing of a traditional centralized architecture is avoided, thereby supporting millisecond scheduling feedback and improving the scheduling efficiency. The real-time response capability under the sudden load fluctuation or fault condition is remarkably improved, a multi-dimensional perception and prediction mechanism is constructed based on an LSTM neural network prediction model, the recognition and trend prediction capability of the system on meteorological disturbance, equipment aging and operation abnormity is enhanced, the intelligent level of the virtual power plant system is improved, and the real-time performance of the virtual power plant system is improved. The system can dynamically generate an optimal scheduling strategy to ensure stable operation of the virtual power plant under various working conditions.

The invention discloses an optimal scheduling method for grid connection of a power distribution network and a distributed power supply, and particularly relates to the technical field of optimal scheduling, which comprises the following steps of: determining employment performance information during grid connection of the distributed photovoltaic power supply according to the difference between real-time and historical solar photovoltaic output models; based on charge state data of an energy storage system connected with the distributed photovoltaic power supply, determining energy storage performance information during grid connection of the distributed photovoltaic power supply, determining a grid connection strategy of the distributed photovoltaic power supply, and determining a grid connection strategy of the distributed photovoltaic power supply based on consideration of a node system topology connection relation of the power distribution network and related data of nodes and branches; possible risks existing in distributed photovoltaic power grid-connected nodes in the power distribution network are simulated, grid-connected risk information of the power distribution network is determined, the matching degree of output power and required power of the distributed photovoltaic power is considered, grid-connected load matching information of the power distribution network is determined, and the overall grid-connected performance of the power distribution network is quantified. According to the invention, stable operation of the power distribution network can be ensured.

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.

The invention relates to the technical field of virtual power plants, and provides a partition virtual power plant optimization scheduling method and device based on reinforcement learning, and a medium, and the method comprises the steps: determining a power partition set based on the resource information of a virtual power plant; constructing a global optimization scheduling model, and establishing global constraint conditions about the global optimization scheduling model; constructing a partition optimization scheduling model, and establishing a partition constraint condition set; determining a global optimization scheduling strategy based on a near-end strategy optimization algorithm, the global optimization scheduling model and a global constraint condition; determining a partition optimization scheduling strategy based on a heterogeneous proxy near-end strategy optimization algorithm, the partition optimization scheduling model and the partition constraint condition set; and performing joint training, generating a cross-region optimal scheduling strategy, and determining a target scheduling method of each partition. According to the embodiment, the scheduling efficiency and flexibility of the virtual power plant in the multi-partition power system are improved by combining the global and partition optimization scheduling strategy, the resource configuration is optimized, and the energy consumption and the cost are reduced.

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 multi-objective optimization-based enterprise resource coordination and distribution method, which comprises the following steps of: S1, acquiring multi-dimensional description information of enterprise task requirements, and constructing a requirement diagram; s2, based on the demand graph structure, performing clustering division on task nodes by adopting a self-organizing clustering algorithm, generating task clusters, generating a candidate resource demand set, and constructing a resource node set; s3, constructing a resource scheduling candidate topological graph; s4, defining a scheduling adaptability function containing a multi-target index, wherein the multi-target index comprises cost, period, resource utilization rate and resource scheduling distance; s5, extracting an optimal scheduling sub-graph from the candidate scheduling topology by adopting an improved heuristic aggregation algorithm; and S6, outputting a corresponding resource allocation scheme to complete resource allocation. According to the method, task structure modeling and multi-target scheduling optimization are fused, the precision and adaptability of resource allocation are improved, and the method is suitable for an enterprise resource management system in a complex scheduling scene.

The invention provides a virtual power plant group resource scene adaptive scheduling method and system, and a storage medium, and the method comprises the steps: building a typical external feature model of a virtual power plant based on the resource characteristics and core parameters of different types of distributed resources; generating a feasible region of the single equipment based on power constraint, electric quantity constraint and climbing constraint of the single equipment in the virtual power plant, and aggregating the feasible region of the single equipment to form an aggregated feasible region of the virtual power plant; based on a typical external feature model of the virtual power plant and different service scene requirements, dynamically adjusting response capability index weights in different service scenes, and based on an aggregation feasible region of the virtual power plant, constructing a virtual power plant dynamic aggregation model adapted to multiple scenes; and solving the dynamic aggregation model of the virtual power plant by taking minimization of the power generation cost of the virtual power plant as a target to obtain an optimal scheduling scheme of the virtual power plant.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

The invention discloses a distribution line load prediction and optimal scheduling method and system, and relates to the technical field of intelligent scheduling of power systems, and the method comprises the steps: generating a time-aligned multi-source fusion input data set; constructing a mixed time sequence load prediction model, and introducing a weighted quantile loss function in a model training process; constructing a joint probability distribution model of the renewable energy output and demand response participation rate, and sampling joint probability distribution; constructing a rolling time domain power distribution network optimization scheduling model; a two-layer mixed strategy is adopted to deal with uncertainty, an optimization problem is decomposed into a plurality of sub-problems, and an alternating direction multiplier method with adaptive penalty parameters is used for distributed solution. According to the method, a multi-objective optimization scheduling model is established in a rolling time domain, and dynamic closed-loop optimization is realized; and by introducing a two-layer hybrid solving strategy and an ADMM distributed algorithm with an adaptive penalty parameter, the calculation efficiency and expandability are remarkably improved while the global consistency is ensured.