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Abstract
Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry.
Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives
Data availability statement
The data that support the findings of this study are available. Restrictions apply to the availability of these data, which were used under license for this study.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Yunzhi Chen
Yunzhi Chen is a Chemical Engineering PhD candidate at the University of Utah and the Assistant Lead of the Intermountain Industrial Assessment Centre. Her research is in process system engineering, focusing on process modelling, simulation, control and optimisation, techno-economic analysis, and life-cycle emission analysis of complex, integrated energy systems. She received her B.S. and M.S. in Chemical Engineering from Harbin Engineering University and the South China University of Technology, respectively.
Blake W. Billings
Blake W. Billings is a Chemical Engineering PhD candidate at the University of Utah and the Lead Student of the Intermountain Industrial Assessment Centre. His current research focuses on energy system modelling and optimisation, grid-responsive smart manufacturing, and grid management utilising renewable sources. His previous positions include experience with the Federal Energy Regulatory Commission (FERC) and within private manufacturing. He holds a B.S. and M.S. in Chemical Engineering from Brigham Young University and the University of Utah, respectively.
Kody M. Powell
Dr. Kody M. Powell is an Associate Professor of Chemical Engineering and Adjunct Associate Professor of Mechanical Engineering at the University of Utah and the Director of the Intermountain Industrial Assessment Centre. His research focuses on using the tools of process systems engineering (such as simulation, automation, optimisation, and machine learning) to help operate complex energy systems in intelligent ways. Applications of this research work include energy storage, solar energy, renewable natural gas, nuclear energy, thermal power plants, and hybrid energy systems. Dr. Powell's research mission is to use intelligent and automated systems to better enable clean energy while maintaining system reliability and affordability.