Mitigating emissions: energy balancing in eco-industrial zones considering renewable energy and electric vehicle uncertainties

Abstract

Nowadays, the industrial sector stands as the major energy consumer globally, simultaneously holding a pivotal role as a significant contributor to greenhouse gas emissions. Therefore, energy system planning and management in these systems are under heightened scrutiny due to concerns over energy, economic, and environmental challenges. This study aims to develop a comprehensive optimal model that integrates renewable potential assessment and utilizes particle swarm optimization for accurate and cost-effective planning and operation of the energy system within an industrial zone. The research proposes a novel strategy for planning and operating industrial energy hubs, offering a robust and adaptable framework tailored to industrial zones. By integrating uncertain renewable energy sources and EVs, the framework effectively manages variability and uncertainty. It holistically connects electricity, heating, cooling, and transportation sectors, enabling cross-sectoral flexibility and enhancing system adaptability. The study compares four scenarios: BAU, BAU CO₂-Aware, CO₂-Blind, and CO₂-Aware, evaluating their impact on energy costs, investment, operational cost, and environmental benefits. The results show that the CO₂-Aware and CO₂-Blind scenarios reduce overall costs by approximately 15% and 10%, respectively, compared to the BAU. Additionally, the CO₂-Aware scenario achieves a 32% reduction in CO₂ emissions. Despite higher investment and operational costs, these alternative energy systems provide substantial economic and environmental advantages. Additionally, the implementation of this smart energy system within the industrial zone has addressed certain energy challenges in the studied region, such as mitigating electricity shortages during summer and alleviating natural gas shortages in winter.