Understanding the Supply and Demand of Critical Materials for Clean Energy Technologies: An Agentbased Modeling Approach
Abstract
With the rapid development of clean energy technologies, various bottlenecks on supplies of related critical materials emerged. Since supply chains of critical materials often involved with multiple layers of markets with different characteristics, to better identify bottlenecks and increase critical material availability, it is vital to have better understanding and projection on these markets. Agent-based modeling is a bottom-up approach that can imitate heterogenous objects in a changing environment. Therefore, it is an excellent tool to simulate markets with fierce competition and fast revolution. This work demonstrates the application of agent-based modeling by discussing three different topics related to critical material demand and supply induced by clean energy products. The first application focused on LED residential lighting market. LED lighting market grew rapidly and introduced potential demand on several critical materials including indium. The work modeled consumers as heterogenous and irrational agents in network purchasing new bulbs based on their own preferences towards different technologies. Projections of LED market were made based on different assumptions reflecting possible policies and events. The second model explained the indium refining market. Indium is an important by-product metal in LCD display and CIGS photovoltaics manufacturing. Refineries competition on indium supply market was modeled based on agent-based modeling and game theory. Since indium is a by-product metal, facilities capacities and expansions were also taken into consideration. Multiple uncertainties in the market were modeled as scenarios. The last work dedicated to end-of-life electric vehicles recycling market. Spent EV batteries contain valuable critical materials and are usually sold to recyclers by end-use consumers. However, a large portion of EOL EV batteries were sold to illegal recyclers with cost advantages. This work established an agent-based model utilizing biding mechanics to identify cost gaps between legal and illegal recyclers. Several scenarios representing uncertainties and possible policies were explored.
Degree
Ph.D.
Advisors
Zhao, Purdue University.
Subject Area
Alternative Energy|Condensed matter physics|Electrical engineering|Energy|Optics|Physics|Sustainability|Transportation
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.