Multi-scale coordinated control method of integrated energy system for green hydrogen metallurgy

Abstract

The present application discloses a multi-scale coordinated control method of an integrated energy system for green hydrogen metallurgy, and belongs to the field of energy system control technology. A detailed analysis is performed on various flexible and adjustable resources in the integrated energy system for green hydrogen metallurgy, and the regulation characteristics of these resources at different timescales are summarized. The cross-link regulation characteristics of these resources in multi-energy and mass interactions are then explored, with particular attention paid to the time-delay phenomenon in the transmission and conversion process of multi-energy and mass. An equivalent modeling method for heterogeneous links is proposed to provide support for the multi-link coordinated control of the system. Finally, the influence of different production tasks on the multi-energy and mass flow allocation is studied, and the dynamic association of different production processes on the multi-energy and mass flow allocation is analyzed.

What is claimed is: 
     
         1 . A multi-scale coordinated control method of an integrated energy system for green hydrogen metallurgy, comprising the following steps:
 S 1 : analyzing multi-timescale regulation characteristics of flexible and adjustable resources in the integrated energy system for green hydrogen metallurgy to form a regulation response timescale matrix;   S 2 : analyzing cross-link regulation characteristics in a multi-energy and mass interaction according to the regulation response timescale matrix, and further performing equivalent modeling based on heterogeneous links to obtain a regulation external-characteristic equivalent model;   S 3 : analyzing influence of different production tasks in the integrated energy system for green hydrogen metallurgy on allocation of multi-energy and mass flows based on dynamic association analysis, and establishing an extended resource-task network model representing an association between the production tasks and the multi-energy and mass flows;   S 4 : predicting random variables involved in a production process at a future moment based on a task-resource allocation result obtained by the extended resource-task network model, inputting the random variables into a multi-model prediction mechanism based on a local model combination and a multi-controller combination, and outputting an energy-mass flow control strategy at a current working condition to achieve the coordinated control of the integrated energy system for green hydrogen metallurgy; wherein   in the step S 2 , the method of performing the equivalent modeling based on the heterogeneous links to obtain the regulation external-characteristic equivalent model specifically comprises:   S 21 : collecting historical operation data of various devices in the integrated energy system for green hydrogen metallurgy; wherein   the historical operation data is regulation characteristic data of the various devices under different working conditions;   S 22 : selecting a corresponding deep neural network according to different regulation characteristic data of the various devices;   S 23 : training the corresponding deep neural network according to the historical operation data and dynamically updating to obtain regulation external-characteristic models corresponding to different regulation characteristics of the various devices;   the step S 3  comprises the following substeps:   S 31 : performing dynamic association analysis on production task types and the multi-energy and mass flow allocation in the integrated energy system for green hydrogen metallurgy, and constructing a dynamic association model for production tasks;   S 32 : performing dynamic association analysis on production task processes and the multi-energy and mass flow allocation in the integrated energy system for green hydrogen metallurgy, and constructing an inter-process energy-mass flow coordinated control model; and   S 33 : constructing an extended resource-task network model according to the constructed dynamic association model for production tasks and the inter-process energy-mass flow coordinated control model and by introducing dynamic characteristics of energy-mass flows in time and space;   the step S 31  comprises the following substeps:   S 31 - 1 : classifying the production tasks in the integrated energy system for green hydrogen metallurgy according to production demands, and determining demand characteristics of various production tasks on energy-mass flows at different time periods;   S 31 - 2 : fitting historical data corresponding to the demand characteristics of various production tasks on the energy-mass flows at different time periods, and establishing dynamic demand curves of the various production tasks;   S 31 - 3 : according to the dynamic demand curves of the various production tasks, dynamically generating a plurality of association models of different production task types to obtain a dynamic association model for production task; wherein   the production task types comprise a continuous type production task and a discrete type production task;   the step S 32  comprises the following substeps:   S 32 - 1 : analyzing energy-mass demand characteristics of each production process of the production tasks in the integrated energy system for green hydrogen metallurgy;   S 32 - 2 : adjusting an execution sequence of the production task processes and energy-mass flow allocation in real time through a dynamic planning or optimization algorithm according to the energy-mass demand characteristics corresponding to different production processes, so that the coordinated scheduling among the production processes is optimal, and further obtaining the inter-process energy-mass flow coordinated control model; wherein the inter-process energy-mass flow coordinated control model takes time scheduling cost among the production processes, dependency relationships among the production processes and resource limitation as constraint conditions;   in the step S 33 , the extended resource-task network model is constructed as a model for dynamically adjusting resource supply and task demand in the integrated energy system for green hydrogen metallurgy according to time series data;   when the extended resource-task network model is configured to perform coordinated control on resources related to a plurality of production tasks, multi-association processing is performed on the resources, an optimization model with constraint conditions is established, and multi-energy and mass supply coordinated matching with production task demands are achieved; wherein the constraint conditions comprise a resource supply capacity, dynamic allocation of energy-mass flows and time series dependency of production tasks;   when the extended resource-task network model is configured to allocate resources and tasks, a dynamic optimization algorithm is adopted to dynamically adjust an execution sequence of the production tasks and an allocation strategy of the energy-mass flows according to current energy-mass flow allocation in the integrated energy system for green hydrogen metallurgy;   the extended resource-task network model is defined as:   
       
         
           
             
               
                 
                   
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         wherein t is time, x is spatial position, and functions h and k describe dynamic allocation of different energy-mass flows in time and space dimensions. 
       
     
     
         2 . The multi-scale coordinated control method of the integrated energy system for green hydrogen metallurgy according to  claim 1 , wherein in the step S 1 , the method of analyzing the multi-timescale regulation characteristics of the flexible and adjustable resources comprises:
 analyzing regulation response characteristics of energy supply, demand, energy conversion and storage links in the integrated energy system for green hydrogen metallurgy on different timescales respectively, wherein the regulation response characteristics comprise energy supply regulation characteristics, demand side regulation characteristics, energy conversion link regulation characteristics, and storage link regulation characteristics.   
     
     
         3 . The multi-scale coordinated control method of the integrated energy system for green hydrogen metallurgy according to  claim 1 , wherein in the step S 2 , the method for analyzing the cross-link regulation characteristics in the multi-energy and mass interaction specifically comprises:
 analyzing influence of mutual conversion and interaction between different energy-mass flows on overall scheduling of the integrated energy system for green hydrogen metallurgy by constructing an energy-mass flow coupling model and analyzing a time-delay effect in a cross-loop energy-mass interaction process; wherein   the time-delay effect comprises cross-link energy-mass transmission delay and conversion delay.   
     
     
         4 . The multi-scale coordinated control method of the integrated energy system for green hydrogen metallurgy according to  claim 1 , wherein in the step S 4 , the multi-model prediction mechanism based on the local model combination refers to:
 constructing a local model for achieving energy-mass flow allocation prediction under different working conditions, and switching to an optimal local model according to the operating state of the integrated energy system for green hydrogen metallurgy to perform corresponding energy-mass flow allocation prediction based on a conditional model switching mechanism;   the multi-model prediction mechanism based on the multi-controller combination refers to:   on the basis of an output energy-mass flow allocation strategy predicted by a multi-model prediction mechanism of local model combination, designing corresponding independent controllers according to different energy-mass flow characteristics, and introducing a global objective function to optimize a control target of each independent controller by adopting a constraint optimization-based coordinated mechanism according to different working conditions and energy-mass flow control requirements, so as to obtain an overall optimal energy-mass flow control strategy for the system; wherein an output of each independent controller is associated.   
     
     
         5 . The multi-scale coordinated control method of the integrated energy system for green hydrogen metallurgy according to  claim 4 , wherein the step S 4  comprises the following substeps:
 S 41 : performing time series characteristic analysis on the random variables involved in the production process based on task-resource allocation result obtained by the extended resource-task network model; 
 S 42 : constructing a discrete-time prediction model according to a time series characteristic analysis result of the random variables, and predicting the random variables involved in the production process at the future moment by using the discrete-time prediction model; 
 S 43 : inputting the predicted random variables into a multi-model prediction mechanism based on a local model, predicting energy-mass flow allocation strategies in different operating states, and inputting the energy-mass-flow allocation strategies into the multi-model prediction mechanism based on the multi-controller combination; and 
 S 44 : in the multi-model prediction mechanism based on the multi-controller combination, on the basis of ensuring scheduling consistency among independent controllers, selecting a corresponding independent controller for energy-mass flow control according to the involved energy-mass flow characteristics, and outputting the energy-mass flow control strategy at the current working condition to achieve the coordinated control of the integrated energy system for green hydrogen metallurgy.