Event-Triggered Online Scheduling for Industrial-Integrated Energy System

Sound scheduling and allocation of multienergy media is of paramount significance for reducing energy consumption and improving operation efficiency in an industrial-integrated energy system of a SIP, 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 metacognitive 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 characteristics described by granularity. Furthermore, an adaptive interval type-2 fuzzy system based 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.