Optimizing the flexible design of hybrid renewable energy systems

Abstract

Engineering systems often operate for a long period of time under varying conditions. The system should be designed based on the best available information at the time of the decision. Designers should also account for future uncertainties in the initial design of the system. The initial design may or may not change as the future evolves and conditions change. The goal of this study is to optimize the design of a hybrid renewable energy system (HRES) to deliver electricity under highly uncertain demand. This research explores designing the hybrid system while taking into account uncertainties over a long period of time (i.e., 20 years in this study). The objective is to minimize the expected discounted cost of the HRES during the next 20 years. A design solution may also be flexible, which means that the design can be adapted or modified in the future to meet new scenarios. This article incorporates flexibility or capacity expansion into engineering design under long-range uncertainty when the objective function is evaluated via a Monte Carlo simulation. The value of expanding capacity is measured by comparing the cost without capacity expansion and cost with capacity expansion.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was in part supported by the NSF Industry-University Cooperative Research Centers Program Center for e-Design.

Notes on contributors

Ramin Giahi

Dr. Ramin Giahi received his BSC and MSC in Industrial Engineering from University of Tehran, Iran (2009 and 2013), and a Ph.D. in Industrial Engineering from Iowa State University (2020). Dr. Giahi has received several awards and recognitions for his research, including the Best Paper Award from the Engineering Economy Division at the Institute of Industrial & Systems Engineers (IISE) in May 2019 and Research Excellence Award from Iowa State University in 2020. His Research interests are Decision-making under uncertainty, Reinforcement learning, Machine learning, and Optimization.

Cameron A. MacKenzie

Dr. Cameron A. MacKenzie is an Assistant Professor in the Industrial and Manufacturing Systems Engineering Department at Iowa State University. He specializes in decision and risk analysis and teaches courses in the subjects as well as in stochastic simulation and engineering economics. He received his BS and BA from Indiana-Purdue University at Fort Wayne (2001), an MA in International Affairs from The George Washington University (2003), an MS in Management Science & Engineering from Stanford University (2009), and a Ph.D. in Industrial Engineering from the University of Oklahoma (2012). He served as president of the Engineering Economy Division in the Institute of Industrial and Systems Engineers.

Chao Hu

Dr. Chao Hu received his B.E. degree in Engineering Physics from Tsinghua University in Beijing, China, in 2007 and his Ph.D. degree in Mechanical Engineering at the University of Maryland, College Park in Maryland, in 2011. He worked first as a Senior Reliability Engineer and then as a Principal Scientist at Medtronic in Minnesota from 2011 to 2015. He is currently an Associate Professor in the Department of Mechanical Engineering at Iowa State University and has a courtesy faculty appointment in the Department of Electrical and Computer Engineering. His research interests are engineering design under uncertainty, lifetime prediction of lithium-ion batteries, and prognostics and health management. Dr. Hu has received several awards and recognitions for his research, including the Best Track Paper Award at the IISE Annual Conference & Expo in 2019; the ASME Design Automation Young Investigator Award in 2018; the Highly Cited Research Paper 2012–2013 in the Journal of Applied Energy in 2015; the Star of Excellence Individual Award at Medtronic in 2014; and the Best Paper Awards at the ASME Design Automation Conference and the IEEE International Conference on Prognostics and Health Management in 2013 and 2012, respectively.