Modeling energy management of an energy hub with hybrid energy storage systems for a smart island considering water–electricity nexus

Abstract

Energy hubs (EHs) represent a pivotal paradigm in achieving optimal resource utilization across various energy domains. This paper presents an advanced framework for the optimal management of a smart island, leveraging the synergies within the water-electricity nexus. By integrating diverse resources, including electricity, water, heat, and hydrogen, the proposed EH model aims to meet the multifaceted demands of consumers on the island. To enhance operational efficiency, this study delves into the nuanced impacts of key EH components, elucidating their roles in meeting demand profiles and minimizing operational costs. Formulated as a mixed integer linear programming (MILP) model, the EH optimization problem is addressed using the GAMS optimization tool. The overarching objective is to fulfill consumer demand while concurrently optimizing resource utilization, considering factors such as storage degradation costs and emissions from fossil-fuel-based units. In addition to strategic optimization, this study pioneers a novel approach to stochastic parameter forecasting, integrating convolutional neural networks (CNNs) and long-short-term memory networks (LSTMs). By harnessing the capabilities of these advanced forecasting techniques, the EH model can anticipate dynamic changes in demand patterns with heightened accuracy and precision. The empirical results underscore the transformative potential of the proposed EH framework, showcasing significant reductions—up to 30%—in emission costs. Moreover, the study underscores the pivotal role of EHs as enablers for scaling up renewable energy penetration, offering a robust foundation for sustainable energy transitions in island communities and beyond. Additionally, implementing a load-shifting demand response program can lower total costs by approximately $257 per day, offering significant savings for EHs over extended periods.