Real options analysis of investment under uncertainty in the future energy system
Abstract
This thesis aims to explore the complex interplay between renewable energy investment, uncertainty, and policy risk, with the goal of providing insights that can contribute to the development of more effective investment strategies and policy design. In the first two papers, I use real options analysis to examine the trade-offs involved in renewable energy investment under uncertainty and policy risk. Real options analysis is a powerful tool that has gained widespread use in the valuation of investment decisions that involve uncertainty, flexibility, and irreversibility. In the third paper included in this thesis, I incorporate Bayesian learning into a real options model. Through the use of Bayesian learning, the decision maker can update their understanding of the risks that may disrupt or terminate a project by incorporating new information. By continuously updating its beliefs about the likelihood of an event terminating the project, Bayesian learning can help a decision-maker make more informed investment decisions under uncertainty.
Paper I examines investment under uncertainty and subsidy withdrawal risk with a capacity size decision, taking both the point of view of a profit-maximizing investor and a welfare-maximizing social planner. The results show that investment is done sooner under a larger subsidy withdrawal risk, but this goes at the cost of a lower investment size. In terms of welfare, a lump-sum subsidy can only increase welfare if the withdrawal risk is low. Paper II revisits the framework of Paper I, but investment is assumed to be done incrementally instead of lumpy. The welfare-optimal policy strongly depends on the time frame of the social planner, as there is a strong trade-off between welfare in the short and long term. Finally, Paper III proposes a framework to examine active learning in a project subject to termination risk, where the learning rate can be chosen and comes with a cost. The decision to invest in learning is driven by uncertainty, and not by expected revenue.
Has parts
Paper 1: Nagy, Roel; Hagspiel, Verena; Kort, Peter M.. Green capacity investment under subsidy withdrawal risk. Energy Economics 2021 ;Volum 98. s. © 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Available at: http://dx.doi.org/10.1016/j.eneco.2021.105259Paper 2: Nagy, Roel L. G.; Fleten, Stein-Erik; Sendstad, Lars H.. Don’t stop me now: Incremental capacity growth under subsidy termination risk. Energy Policy 2022 ;Volum 172. s. - © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Available at: http://dx.doi.org/10.1016/j.enpol.2022.113309
Paper 3: Nagy, Roel; Hagspiel, Verena; Sund, Sebastian; Thijssen, Jacco J. J. Investment under a disruptive risk with costly Bayesian learning. This paper is submitted for publication and is therefore not included.