90results about How to "Optimizing Scheduling Strategy" patented technology

The invention relates to the technical field of ore conveying, and discloses an intelligent mine ore conveying management system, which comprises a deep reinforcement learning scheduling module used for realizing optimal scheduling through a deep reinforcement learning algorithm based on an ore conveying equipment state, a conveying path condition and ore queue information; the digital twinning system is used for creating a digital twinning model of the conveying equipment and carrying out multi-physics field simulation, real-time data synchronization and state prediction so as to manage the full life cycle of the equipment; and the edge calculation module is used for carrying out data acquisition, real-time processing and lightweight AI reasoning on a mine site so as to realize equipment anomaly detection and early warning. By introducing deep reinforcement learning scheduling, digital twinning, edge computing and block chain data management technologies, adaptive optimization scheduling, accurate state monitoring and fault prediction of the ore conveying system are realized, an efficient safety protection and intelligent user interface is provided, and the operation efficiency, reliability and safety of the system are improved.

The invention relates to the technical field of traffic management, and discloses a road obstacle clearance rescue enterprise automatic scheduling method comprising the following steps: S1, through multi-source data fusion and a dynamic convolutional neural network, generating a three-dimensional road model, and predicting accidents and congestion; s2, dynamically adjusting the priority of the rescue task by using a multi-target genetic algorithm in combination with real-time data; s3, path planning and cooperative scheduling are optimized through deep reinforcement learning and game theory algorithms; s4, dynamically correcting the path by using a vehicle-mounted sensor, and optimizing the flow and the energy consumption; s5, in an emergency, an emergency module based on reinforcement learning starts a plan and adjusts resources; and S6, after the task, analyzing an optimization strategy through collaborative learning, and carrying out self-improvement. According to the method, multi-source dynamic data fusion is carried out, a dynamic convolutional neural network is used for processing data in real time, a high-precision three-dimensional road environment model is generated, accidents and congestion are predicted, and it is ensured that a rapid and accurate scheduling decision can be made in a complex and changeable road environment.

The invention relates to the field of data visualization, in particular to a visualization method and system based on a vehicle service supply chain. The method comprises the following steps: acquiring whole-process information of a vehicle service supply chain; carrying out supply chain main body identification on the whole flow information of the vehicle service supply chain, carrying out spatial topology distribution analysis, and constructing a supply chain main body topology distribution map; obtaining supply chain main body interaction logic data based on the plurality of supply chain main bodies; performing topology interaction logic connection on the supply chain main body topology distribution graph by utilizing supply chain main body interaction logic data, and constructing a supply chain topology interaction network; performing intra-period path transportation frequency calculation on the whole-process information of the vehicle service supply chain to generate logistics demand trend characteristics; and performing operation behavior analysis of each link on the whole-process information of the vehicle service supply chain to obtain the capacity characteristics of each link. According to the invention, through visualizing the vehicle service supply chain, the resource scheduling efficiency and flexibility of the supply chain are improved.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The invention relates to the technical field of supply chain scheduling, and discloses a multi-supply chain scheduling method and system based on a global Critic multi-agent algorithm, and the method comprises the steps: constructing a multi-agent supply chain environment; introducing a global Critic network, performing reinforcement learning on the multi-agent supply chain environment by using a multi-agent depth deterministic strategy gradient algorithm, and constructing a multi-agent decision model; and inputting supply chain demand data into the multi-agent decision-making model to carry out optimization scheduling processing, and generating a scheduling strategy for maximizing the overall profit of the supply chain. The global Critic network is introduced to reduce multi-agent competition, and the overall performance is optimized. A decision model is constructed by using a multi-agent depth deterministic strategy gradient algorithm to adapt to a complex supply chain environment. And the problems of low efficiency and income are solved by taking overall profit maximization as a target. The environment has high adaptability, and scheduling problems of different scales can be solved. And through continuous learning optimization decision, the reliability is improved, and the efficiency and income are remarkably improved.

The invention relates to the field of data processing, in particular to an instruction optimization scheduling method based on a large model and a related device. The method comprises the following steps: for a to-be-processed target model, carrying out dynamic and static analysis on the target model to convert the target model into a calculation task graph; determining a scheduling priority of each instruction in the target model based on a calculation task graph; the scheduling priority is associated with the importance and timeliness of the instruction path of each instruction in the calculation task graph; dynamically adjusting an instruction scheduling strategy in the calculation task graph based on the scheduling priority; and dynamically optimizing the instruction scheduling strategy according to the register pressure state monitored in real time, and executing the optimized instruction scheduling strategy. According to the method, instruction scheduling optimization can be achieved, the resource utilization efficiency is improved, the calculation requirement of a large model is met, and the overall model performance is improved.

The invention relates to the technical field of energy storage configuration, in particular to an energy storage configuration method and system for photovoltaic power generation. The method comprises the following steps: acquiring information data of a photovoltaic power generation energy station; performing distributed synchronous energy storage network construction on the photovoltaic power generation energy station information data to obtain a distributed synchronous energy storage network; performing real-time power generation data acquisition on the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; performing photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; performing historical electricity load data acquisition on the distributed synchronous energy storage network to obtain historical electricity load data; and performing load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data. According to the invention, through real-time photovoltaic power generation prediction, load demand dynamic analysis and adaptive energy storage strategy optimization, the accuracy and flexibility of photovoltaic energy storage configuration are improved.

The invention provides an SPS trolley material distribution method and system based on digital twinning, and the method comprises the steps: installing a plurality of sensors in a factory environment, carrying out the preprocessing of real-time operation data, and constructing a digital twinning model of a physical factory and an SPS trolley system; constructing a scheduling model based on reinforcement learning according to the digital twinborn model, and optimizing the material delivery path and strategy of the SPS trolley in real time; analyzing and modeling historical data and real-time data by using a graph neural network, and predicting material requirements of each production node; according to the predicted material demand of each production node, constructing a multi-agent collaborative scheduling model dynamic adjustment scheduling strategy based on the SPS trolley; sharing and collaborative optimization are carried out on data among a plurality of factories or production lines; and constructing a block chain network to manage the material distribution data, and recording the material distribution of each SPS trolley. According to the invention, efficient management and optimization of the material distribution process are realized, and intelligent and digital development of the manufacturing industry is promoted.

The invention provides a scheduling control method for an industrial integrated energy system. The scheduling control method comprises the steps that S1, the electricity purchase price and the electricity purchase quantity of the integrated energy system are acquired; s2, acquiring a gas purchase price and a gas purchase amount of the integrated energy system; s3, acquiring a hydrogen purchase price and a hydrogen purchase amount of the comprehensive energy system; s4, the wind curtailment punishment price and the wind curtailment power of the integrated energy system are obtained; s5, calculating the carbon transaction cost of the integrated energy system; s6, calculating a translational power variable quantity and a replaceable demand response power variable quantity of the integrated energy system; and S7, establishing a scheduling control model of the integrated energy system, and calculating the total operation cost minimization of the system according to the data. By comprehensively and systematically analyzing the carbon emission process of the comprehensive energy industry park, deeply discussing the source and the flow direction of carbon emission and accurately estimating the capacity of IES by adopting a comprehensive carbon emission measurement method, the energy utilization efficiency is improved to the maximum extent, and meanwhile, the carbon emission is reduced to the maximum extent.

The invention discloses a distributed photovoltaic short-time output dynamic prediction method and system. The method comprises the following steps: acquiring micro-meteorological data, a cloud picture image and historical output data of a target area; calculating a building shadow shielding coefficient, and generating shadow influence factor time sequence data; obtaining cloud image occlusion index time series data based on the cloud image and a pre-constructed cloud image occlusion prediction model; fusing the data, and constructing and training a short-time output dynamic prediction model based on a long short-term memory network; predicting photovoltaic output by using real-time data and the trained model, and adjusting the model according to a prediction error and a confidence interval; according to the method provided by the invention, various factors such as microclimate, building shadow and cloud layer shielding are comprehensively considered, and the complex nonlinear relationship between the factors and the photovoltaic output is captured by using the deep learning model, so that the accuracy and robustness of distributed photovoltaic short-time output prediction are improved.

The invention relates to the technical field of smart power grids, in particular to a virtual power plant real-time regulation capability assessment method and system, and the method comprises the steps: obtaining real-time power consumption data and historical power consumption data of each power device in a virtual power plant, determining a power minimum interval unit, correcting data abnormity, and the like. Accurate evaluation of the real-time adjustment capability of the virtual power plant is realized; the method specifically comprises the following steps: acquiring power consumption data and historical data of power equipment, generating a load curve, determining a power minimum interval unit based on the historical data, and further performing anomaly correction on the data; on this basis, the maximum and minimum real-time adjustment capability of each power device is evaluated, and the overall adjustment capability of the virtual power plant is comprehensively evaluated; through the implementation of the method, the precision and efficiency of virtual power plant scheduling can be improved, and the stability and flexibility of a power grid under different load conditions are ensured; the scheme has high real-time performance and accuracy, and is suitable for scheduling optimization of a modern intelligent power grid.

The invention provides a distributed computing platform which is oriented to a big data scene and is based on a Serverless framework. The platform comprises an elastic computing resource pool composed of multiple nodes, and computing power resources can be supplied as required by combining a dual capacity expansion and contraction mechanism and intelligent resource analysis scheduling. The platform adopts a Serverless task operation model, simplifies big data application deployment, and realizes performance isolation in a multi-tenant scene through uniform resource management. In addition, the visual monitoring platform is assisted by intelligent analysis, and decision support is provided for operation and maintenance management. Compared with traditional static resource scheduling, the method has the advantages that the resource utilization rate can be remarkably increased, the operation and maintenance complexity is reduced, powerful resource isolation and fairness guarantee are provided in a multi-tenant environment, and technical innovation and application expansion of distributed computing in the big data era are effectively supported.

The invention relates to the technical field of automatic dispatching and management of a power system, and discloses an automatic dispatching control system based on photovoltaic power generation, which comprises a photovoltaic power generation unit used for converting solar energy into electric energy and monitoring power generation power and power generation efficiency in real time; the energy storage unit is electrically connected with the photovoltaic power generation unit and is used for storing redundant electric energy and intelligently releasing the electric energy according to the load state of the power grid; the meteorological monitoring module is used for collecting and predicting meteorological data in real time, the meteorological data comprise sunlight intensity, temperature, wind speed and the like, and generating a short-term weather forecast; and the demand response management system is electrically connected with the power grid and the user side equipment, and the system intelligently identifies non-critical load equipment by monitoring the load of the power grid in real time. The intelligent scheduling module is combined with meteorological prediction and real-time data, so that the system can dynamically adjust the output power of the photovoltaic power generation unit, ensures that the power generation capacity is matched with the power grid demand, reduces the power waste, and maximizes the utilization of renewable energy sources.

The invention discloses a robot intelligent scheduling and management and control system and method for hospital delivery services, and relates to the technical field of realizing material delivery services by using robots. The scheduling platform determines the execution category according to the attribute of the transport work order, and when the execution category is robot execution, the scheduling platform assigns the transport work order to the robot according to an order insertion, sequential and / or proximity strategy; a shortest-time-consumption planning strategy is adopted, and the path of the robot assigned to convey the work order is planned; and when executing the work order, the robot interacts with the scheduling platform in real time and reports the task completion condition. The scheduling strategy of the transport work order is further optimized, the transport efficiency and the load rate of the transport robot are improved, and the scheduling platform can master the information of each node in the transport path in real time, so that reference is provided for subsequent planning of the transport path, and the transport efficiency of the transport robot is further improved.

The invention relates to the technical field of intelligent buildings and elevators, and discloses an intelligent elevator passenger flow analysis and optimization system which comprises a flow monitoring module used for monitoring the passenger flow of an elevator waiting area in real time and obtaining passenger flow data; the data processing module is used for analyzing the passenger flow data and predicting the passenger flow in a future time period; the elevator dispatching module optimizes the dispatching strategy of the elevator according to the prediction result of the data processing module, and improves the efficiency of passengers to get on and off the elevator; and the energy efficiency optimization module adjusts the acceleration and deceleration process of the elevator according to the running state of the elevator so as to reduce the energy consumption of the elevator. By monitoring the passenger flow in the elevator waiting area in real time and conducting accurate analysis, the passenger flow in the future time period can be predicted, the elevator dispatching strategy is optimized, the waiting time of passengers is shortened, the waiting efficiency is improved, and intelligent dispatching is conducted on the elevator based on the shortest waiting time and the minimum energy consumption target.

The invention relates to the technical field of solid waste sorting scheduling, in particular to a real-time monitoring optimization method and device for a solid waste collection, transportation and sorting ERP system. The method comprises the following steps: obtaining a real-time solid waste sorting log based on a solid waste collection, transportation and sorting ERP system; solid waste carrying vehicle tracking is carried out on the real-time solid waste sorting log, and a carrying track distribution visual view is constructed; acquiring an omnibearing monitoring image of a real-time sorting site; equipment sorting behavior visual identification is carried out on the real-time sorting site omnibearing monitoring image, dynamic sorting evolution is carried out, and a dynamic sorting twinborn model is constructed; carrying out multi-time-point solid waste sorting simulation on the dynamic sorting twinborn model, carrying out sorting load demand prediction, and generating a sorting load demand prediction value; and carrying out vehicle load calculation on the sorting load demand prediction value one by one, and then carrying out dynamic vehicle scheduling decision making to obtain a dynamic vehicle scheduling strategy. According to the invention, efficient and real-time solid waste sorting scheduling optimization is realized.

The invention provides an energy optimization scheduling method and system based on data analysis, and the method comprises the steps: collecting power generation data, load demands and meteorological data of solar energy, wind energy and an energy storage system in real time, and recognizing a power generation mode and a load change trend through a data analysis technology; based on an analysis result, a double-layer energy scheduling model is constructed, the main target is to maximize system reliability and scheduling flexibility and minimize carbon emission, and the secondary target is to minimize scheduling cost; introducing the uncertainty of wind and light output and the multi-joint constraint of information gap decision; and performing optimization solution by adopting a non-dominated sorting genetic algorithm NSGA-II in combination with a crowding distance and an elitist strategy, and outputting a Pareto leading-edge solution set to obtain an optimal scheduling strategy. According to the method, the timeliness and accuracy of scheduling decision making are ensured, the utilization efficiency of renewable energy sources is effectively improved, carbon emission is reduced, and powerful support is provided for an intelligent power grid and sustainable development.

The invention relates to the technical field of power grid demand response, and discloses a power grid dispatching demand response system and method.The system comprises a state vector generation module, a state sequence generation module, a demand factor extraction module, a factor correlation analysis module, a weighted feature generation module and a resource dispatching module; the method comprises the following steps: generating a state estimation value of a power grid, carrying out vector coding to obtain a state vector, carrying out splicing and dimension reduction to obtain a state sequence, extracting demand factors from demand data, calculating a relationship among the demand factors through correlation analysis, weighting the demand factors according to factor correlation to obtain demand weighting characteristics, and finally, obtaining a state estimation result. And a load management strategy is generated based on the characteristics and the state sequence, so that resource scheduling is performed on the power grid according to the load management strategy, and the accuracy of power grid scheduling demand response is improved.

The invention discloses a load demand-based cross-period dynamic hydrogen storage planning method, which relates to the technical field of energy utilization and comprises the following steps of cross-period hydrogen storage system modeling, typical day selection and feature extraction, comprehensive energy system planning model establishment, random-robust two-stage optimization method solving and objective function design. By designing the introduction and modeling of the IHS, the energy transfer function is realized, the problem that energy storage equipment in a traditional power system cannot store energy across seasons is solved, the technical challenge of unbalanced power supply and demand is overcome, the permeability of renewable energy sources is improved, wind and light abandoning phenomena are reduced, and carbon emission is reduced. And the realization of low-carbon and green energy targets is promoted.

The invention relates to a power grid investment planning scheme rationality evaluation method considering a low-carbon target, and the method comprises the steps: collecting related data and indexes related to power grid investment planning, and carrying out the region division of a power grid load prediction region based on the related data and indexes, the method comprises the following steps: decomposing load data of a power system into three component parts by using a seasonal decomposition method of a time sequence, constructing a prediction model for different components to obtain a load prediction result, clustering annual daily wind and light output and load data by using a k-means algorithm, and selecting a typical day of each quarter to perform short-term operation simulation to obtain a load prediction result. A planning scheme evaluation model with the minimum operation cost as the target is constructed, the matching relation between output and load curves of various power supply types is simulated, and the peak regulation capacity and economical efficiency of the system and the investment and planning scheme of the power system are optimized; and solving the multi-target model through a marine predator algorithm, and if the planning schemes all meet the peak regulation requirements of the system, considering that the planning result is reasonable.

The invention provides a model training method and device, electronic equipment and a storage medium, and relates to the technical field of distributed computing. The method comprises the steps of dividing a context into context tasks based on structural analysis of a to-be-trained model; performing dependency relationship analysis on the context task to determine a task dependency graph; configuring priorities for the context tasks according to the task dependency graph, distributing the context tasks with the configured priorities to corresponding computing nodes, training according to an execution sequence, and obtaining load information and task execution information of the computing nodes; and according to the load information and the task execution information of the computing nodes, scheduling context tasks among the computing nodes, adjusting the execution sequence of the context tasks in the computing nodes, and outputting a training result. According to the real-time load condition of each node, task allocation is adjusted, load balancing of the computational nodes is realized, the utilization efficiency of computational resources is improved, a context task scheduling strategy is optimized, the utilization rate of the computational resources is improved, and the training time is shortened.

The invention discloses a CAN (Controller Area Network) bus test experiment system, which relates to the technical field of data processing and comprises a data acquisition and processing module for acquiring data frames from a CAN bus, adding timestamps and executing tasks such as data analysis, synchronization, filtering and caching; the control and display module is used for generating and sending a control instruction according to the vehicle state data and obtaining and updating the state information of the vehicle; the experimental report generation unit is used for sorting experimental data such as bit error rate, time sequence analysis and waveform detection into an experimental report; a plurality of test functions and experiment modules are integrated, and comprehensive support is provided for teaching and research of the CAN bus.

The invention provides an active distribution network scheduling optimization method comprising a micro-grid group, a medium and a system, and belongs to the technical field of active distribution network scheduling, and the method comprises the steps: obtaining an active distribution network parameter and a micro-grid parameter of each micro-grid in an active distribution network; calculating the similarity between any two micro-grids by using a preset micro-grid similarity function; establishing an active power distribution network stable scheduling equation set considering the power distribution network parameters and a micro-grid stable scheduling equation set considering the micro-grid parameters; a double-layer optimization model based on a grey wolf hunting optimization algorithm and a genetic algorithm is constructed, the grey wolf hunting optimization algorithm is used for active power distribution network optimization, and the genetic algorithm is used for microgrid optimization; when the grey wolf population is initialized, setting an initial population of a genetic algorithm for each micro-grid to represent various possible scheduling schemes in the micro-grid; by not executing outer-layer optimization and inner-layer optimization, the problem that the characteristics of the whole active power distribution network and the interior of the micro-grid are difficult to consider in the prior art is solved.

The invention belongs to the field of hydrological forecasting, and discloses a reservoir level prediction method based on a graph convolutional neural network (GCN). The method aims at solving the reservoir level prediction problem under the rainfall condition, and effective support is provided for reservoir dispatching. Rainfall image data are trained mainly through a graph convolutional neural network (GCN), and a bidirectional long-short term memory network (BiLSTM) is combined to extract time sequence features of the reservoir water level, so that a model capable of predicting the reservoir water level through rainfall images is constructed. Specifically, the GCN is used for extracting spatio-temporal features of a rainfall image and fusing the features by using a multi-head attention mechanism. In BiLSTM, an input time series data sequence is processed through LSTM networks in two directions, so that past and future information is captured at the same time. By combining the hidden states in the two directions, the model can understand the internal law of the time series data more deeply, so that the prediction precision of the reservoir level is improved.

The invention relates to the technical field of tracking management, in particular to an electric power building engineering material tracking and management system, which comprises a material identification module, a material management module, a material management module and a material management module, and is characterized in that all materials entering and exiting from a warehouse are identified and tracked in real time by using an RFID technology to ensure the traceability of material flows; the database management module is used for storing material related information; the material scheduling module is used for automatically analyzing project requirements and optimizing material distribution and scheduling strategies based on a scheduling algorithm; the environmental adaptability analysis module is used for evaluating the influence of different environmental factors on the material performance; the data analysis and prediction module is used for evaluating a material demand trend and price fluctuation by adopting a big data analysis technology; and the material compatibility and life cycle evaluation module is used for evaluating compatibility among different materials. According to the method, the high traceability of the material flow is ensured, the material distribution and scheduling strategy is optimized through the artificial intelligence algorithm, the resource waste is remarkably reduced, and the material use efficiency is improved.

The invention provides an AI-based zero-carbon park energy management method and system, and a medium, and aims at achieving the purposes of efficient utilization and zero-carbon emission of energy in a park through an intelligent scheduling strategy. According to the method, energy consumption demand estimation, carbon emission optimization, multi-energy system management and other technologies are utilized to carry out real-time monitoring and optimal scheduling on various energy use conditions of the park. Specifically, the system collects the power use condition in the park, and performs energy consumption demand estimation and carbon emission calculation through an AI model. And then, according to a demand estimation result and a carbon emission optimization model, an optimal energy scheduling strategy is selected, real-time feedback is obtained after scheduling execution, and a decision model is further optimized. And finally, realizing collaborative optimization of an environmental protection target and economic benefits by adaptively adjusting a discount factor and a weight balance mechanism.

The invention relates to an Internet of Things equipment deployment and control management method, in particular to a building Internet of Things equipment deployment and control management method. Comprising the following steps: deploying various sensors including an infrared sensor, a camera and an air quality sensor in a building, and collecting personnel flow and environment change data in real time; through data analysis and scene classification, the data are converted into scenes including office peaks, night modes and emergency evacuation of the building; training a scene recognition model by using a random forest classifier; a time sequence prediction model is adopted to predict scene change in a future short period, and a corresponding equipment control instruction is generated; the method comprises the following steps: establishing an incidence matrix of use frequency, spatial position and function interaction between devices by using sensor data, and identifying a device cooperation mode; dynamically generating an equipment deployment scheme through a graph theory algorithm based on the incidence matrix; issuing the generated deployment scheme to each equipment control node; and performing fine adjustment according to real-time data feedback.

The invention provides a mobile energy storage scheduling method and device based on a knowledge constraint depth expectation Q network, and studies charging and discharging decisions taking mobile energy storage as a main load support component, including emergency resource space-time coupling, uncertainty of an extreme meteorological scene, energy storage charging and discharging and time-space transfer constraint. Establishing a load recovery-oriented mobile energy storage elastic optimization scheduling model; based on a mobile energy storage elastic optimization scheduling model, integrating emergency resource space-time coupling, uncertainty of an extreme meteorological scene, energy storage charging and discharging and time-space transfer constraints into mathematical derivation of machine learning preprocessing so as to establish an energy storage Markov decision process covering optimization constraint knowledge fusion; and based on an energy storage Markov decision process, establishing an elastic scheduling process training agent based on a depth expectation Q network algorithm so as to optimize a scheduling strategy of the mobile energy storage system. According to the method, the elasticity of the system is enhanced, the calculation efficiency is ensured, and the problem of uncertainty is effectively solved.

The invention discloses a multi-stage coordinated regulation and control method and system for a virtual power plant, and the method comprises the steps: carrying out the quantitative description of an adjustable space of various distributed devices in the virtual power plant through a unified modeling method, and building an evaluation model of real-time output and response capabilities; aiming at the problem of online coordinated scheduling between the virtual power plant and the power distribution network, a centralized-distributed hierarchical scheduling structure is adopted, and the scheduling efficiency of the system and the response capability of equipment are effectively improved through unified modeling and hierarchical optimization. According to the method, the total operation cost of the virtual power plant can be effectively reduced, the problem of comprehensive information sharing needed by traditional centralized scheduling is avoided through privacy protection design, efficient interaction and information safety between the virtual power plant and the power distribution network are achieved, the requirement for real-time economic scheduling in a complex scene can be met, and high expandability and adaptability are achieved. And meanwhile, the method accords with the development direction of optimal dispatching of the current power distribution network, and plays a crucial role in promoting energy structure transformation and accelerating renewable energy consumption.

The invention discloses a photovoltaic hydrogen production-based hydrogen-electricity coupling hydrogen production scheduling system and an electric energy scheduling optimization method. The system comprises a photovoltaic power generation unit, a hydrogen production unit and a load unit, the photovoltaic power generation unit comprises a photovoltaic cell panel and a fuel cell, the photovoltaic cell panel is electrically connected with the direct current bus through an AC / DC converter, and the fuel cell is electrically connected with the direct current bus through a DC / DC converter; the hydrogen production unit comprises an electrolytic cell, a compressor, a hydrogen storage tank, a hydrogenation machine and a hydrogenation vehicle, the electrolytic cell is electrically connected with the direct current bus through a DC / DC converter, the electrolytic cell is connected with the hydrogen storage tank through the compressor, and the hydrogen storage tank is connected with the hydrogenation machine and the hydrogenation vehicle; the load unit comprises a charging pile and an electric vehicle. The charging pile is directly connected with the DC bus. The problem of optimal energy scheduling in the hydrogen production system integrating photovoltaic power generation is solved, and the optimal scheduling strategy of the hydrogen-electricity coupling hydrogen production scheduling system is obtained by constructing the scheduling strategy of the hydrogen-electricity coupling hydrogen production scheduling system and optimizing the scheduling strategy of the hydrogen-electricity coupling hydrogen production scheduling system.