RETRACTED: Optimum operation of energy hub by considering renewable resources by considering risk tolerance and risk taking with Teaching–learning-based optimization

Abstract

Amidst growing concerns over climate change, the conventional energy landscape faces critical challenges associated with fossil fuel usage. In response, this study explores alternative approaches by investigating an energy hub system leveraging wind turbine-generated power. This hub integrates seamlessly with electricity and natural gas grids, offering a comprehensive energy supply encompassing electricity, natural gas, and thermal loads. The research strategy encompasses a multi-pronged approach. A Time-of-Use (TOU) load management program optimizes the distribution of electrical and thermal loads. To address the unpredictability of wind speed, the information gap decision theory (IGDT) is introduced to manage risk and deviations effectively. The central objective involves optimizing the energy hub's operations, focusing on minimizing operational costs and emissions. To address this intricate challenge, the study employs the Teaching–Learning-Based Optimization (TLBO) method. This advanced technique concurrently optimizes cost and environmental impact, exemplifying its efficacy in the energy hub context. The research culminates in demonstrating the TLBO algorithm's swift convergence, highlighting its aptitude for addressing complex cost and emission optimization dynamics. In sum, this study offers innovative insights into sustainable energy systems, where economic and environmental considerations converge harmoniously.