Elite Genetic Algorithm Based Self-Sufficient Energy Management System for Integrated Energy Station
Lize Liu, Xiaoling Su, Laijun Chen, Shuai Wang, Jiawei Li, Siwei Liu
Abstract
In order to solve adverse effects caused by disorderly hydrogen refueling behavior of hydrogen fuel cell vehicles (HFCVs) and output power uncertainty of renewable energy sources on integrated energy stations, this article proposes a self-sustained energy management system based on elite genetic algorithm for integrated energy stations (IESs) to support the low carbon and economical operation of self-sustained highway transportation energy system. First, a detailed IES model is developed to simulate dynamic interaction between utility grid, hydrogen network and transportation network. Second, a two-layer energy optimization management system is established. The upper layer forms user behavior modes of HFCVs to reduce its peak-valley difference and hydrogen refueling cost. The lower layer gives energy management strategies and feeds back to the upper layer based on HFCVs refueling behavior patterns to improve self-sustain rate and reduce carbon emission costs of IESs, meanwhile IESs provide auxiliary services according to their operating status. The simulation results verify the feasibility and correctness proposed IESs model and its self-sustained energy management system.