Optimal stochastic day-ahead scheduling of multi-carrier energy hub integrated with plug-in electric vehicles

Abstract

Confronted with climate change, environmental pollution, and energy crisis, energy hubs (EH) are promising multi-carrier systems that could lead to a flexible, reliable, and clean operation. EH could be conceptualized as an aggregator for energy generation resources, storage, and coupling networks that aim to satisfy electrical, thermal, and cooling demands. This study investigates the optimal day-ahead scheduling of a multi-carrier EH system that incorporates renewable energy sources (RES), large-scale compressed air energy storage (CAES), battery energy storage (BESS), plug-in electric vehicle (PEV), and thermal energy storage (TES). The proposed model is a stochastic multi-objective framework that minimizes the operation cost and the emission generated. The effectiveness of the proposed stochastic framework for optimal day-ahead scheduling has been shown based on simulation findings and results.