Abstract
Sound scheduling and allocation of multi-energy media is of paramount significance for reducing energy consumption and improving operation efficiency in an industrial integrated energy system of a steel industrial park, and the online optimization solution of various scheduling events can be regarded as the prerequisite for such challenging tasks. Thus, a novel granular-driven extended particle swarm optimization with meta-cognitive component in terms of what-to-learn, how-to-learn, and when-to-learn, termed as MC-GEPSO, is proposed in this study, which were realized by the observation, learning and selection of operation mode described by granularity. Furthermore, an adaptive interval type-2 fuzzy systembased on autonomous fuzzy rule learning mechanism is reported for achieving the selection of granularity described by equipment operation performance, and the multiple-objective optimizations of online-scheduling can be solved by GEPSO. The performance of the proposed MC-GEPSO is experimentally validated by a number of industry study cases, where the proposed approach outperforms manual scheduling in aspect of operation cost, operation efficiency, and carbon emissions.
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