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

A multi-scale coordinated control method of an integrated energy system for green hydrogen metallurgy, comprising the following steps:

• S1:
• 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;
• S2:
• 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;
• S3:
• 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;
• S4:
• 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 S2, the method of performing the equivalent modeling based on the heterogeneous links to obtain the regulation external-characteristic equivalent model specifically comprises:
• S21:
• 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;
• S22:
• selecting a corresponding deep neural network according to different regulation characteristic data of the various devices;
• S23:
• 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 S3 comprises the following substeps:
• S31:
• 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;
• S32:
• 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
• S33:
• 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 S31 comprises the following substeps:
• S31-1:
• 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;
• S31-2:
• 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;
• S31-3:
• 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 S32 comprises the following substeps:
• S32-1:
• analyzing energy-mass demand characteristics of each production process of the production tasks in the integrated energy system for green hydrogen metallurgy;
• S32-2:
• 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 S33, 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:
• E
• elec
• (
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• =
• h
• ⁡
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• E
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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.