Double-Layer Optimization of Industrial-Park Energy System Based on Discrete Hybrid Automaton

Abstract

Combing advanced measurement infrastructure (AMI) and information and communications technology (ICT), the Internet of Things (IoT) has been widely used in the energy system. The continuous state modeling method cannot effectively deal with the various operation scenarios in the park multiple energy system. This paper proposes a hybrid state system modeling method based on the discrete hybrid automaton (DHA). The DHA-based model judges the equipment mode through the logical discriminant and describes the energy transmission process of the equipment with a switched affine system. And a doublelayer optimization structure for the multiple energy system is proposed, which combine the long-term forecasting data and short-term forecasting data. Based on the advanced measurement infrastructure, the forecasting system predicts the system state and determines the scheduling plan of each equipment. This paper uses a typical industrial park case with multiple energy system to verify advantages of the DHA-based modeling method, calculates the economic benefits brought by the optimization and evaluates the maximum carbon emission reduction capacity of the industrial park.