Investment under uncertainty in the future energy system: The role of expectations and learning (InvestExL)

This project focused on the analysis of investment in the Norwegian and European energy system under uncertainty. EU's and Norway's ambitious climate and energy targets need to be fulfilled by making many small and large investment decisions. Each of these need to balance organizational objectives, profitability, and sustainability concerns, and will be made under considerable uncertainty regarding the future energy system. InvestExL developed knowledge that supports decision makers and policy makers in analysing investment processes in the energy system. We studied how learning about changes in framework conditions affect investment behavior, and how it should affect it. Learning is meant in a statistical sense, but also more broadly. The 5-year study was a co-operation between the Department of Industrial Economics and Technology Management at the Norwegian University of Science and Technology, Hydro Energi AS, Tilburg University (NL), the University of Illinois at Urbana-Champaign (US), the University of York (UK) and the University of Lisbon (P), and for a two-year period Nord University. During the course of the project we organized several project meetings with our scientific and industry partners and informal visits to collaborate on research both of theoretical and applied nature. In March 2018 we organized the PhD Winter School on Real Options and Commodity Markets in Tauplitz, Austria. 30 participants spent the week attending morning and afternoon sessions, in which 13 international lecturers conveyed both state-of-the art knowledge and recent advances on real options analysis and commodity finance with a special focus on energy markets. All international project partners contributed to the winter school as lecturers and were available for discussions with the participants throughout the week. Lecturers came from: NTNU, Tilburg University (NL), University of Glasgow (UK), University of Illinois at Urbana-Champaign (US), University of Toronto (CA), University of Lisbon (P), University College London (UK), Oklahoma State University (US) and the University of Brescia (I). Furthermore, we have been involved in the organization of the 15th Conference on Computational Management Science that took place at NTNU, 29-21 May, 2018. We launched a project website: https://www.ntnu.edu/investexl, where we introduce the project and present highlights of the project results. The project resulted in more than 30 publications, published in both peer-reviewed scientific journals as well as popular science outlays, and the development of workshops for industry. In the following we highlight some of our research results: We examined how several sources of uncertainty impact the investment decision with particular emphasis on learning through time. More specifically, the firm might learn about the true political or business climate by waiting yet waiting reduces the discounted expected cash flows. Our results illustrate how learning about policy commitment impacts investment decisions. In order to facilitate this analysis, we incorporate Bayesian learning into real options modelling. This can be understood as a firm’s interpretation of signals from regulatory agencies. In Dalby et al. (2018) we investigate how investment behaviour is affected by updating a subjective belief on the timing of a subsidy revision. More specifically, how retroactive downward adjustment of a common renewable energy subsidy (fixed feed-in tariffs) impacts the investment decision. Our results indicate that the effect from a high subsidy level can be significantly mitigated by a perceived unreliable government. In the same line of work, we study a firm with the option to engage in costly active learning, either through paying an upfront fee or a continuous learning cost in Hagspiel et al. (2021). We find that continuous learning results in an incentive to invest sooner in order to avoid further learning cost payments, especially if the learning rate is chosen to be large. Furthermore, our results indicate that clear communication on a future policy attracts investments from firms that are efficient learners. This is because firms that learn more efficiently, adjust their beliefs more rapidly than firms that learn poorly. Hence, efficient learners invest sooner given that we are in a good economic climate. However, these firms are also more sensitive to bad signals, thus less likely to invest in a bad economic climate.Firms might also learn by collecting additional data. Ødegaard et al. (2020) investigate how gathering snow measurements can facilitate learning about future inflow for a hydropower producer. In this study we find that for smaller reservoirs, where the probability of overflow is greater, snow measurements can add considerable value given that the measurements have high accuracy. We conclude that snow measurements might add between 0 and 10% in value for reasonable parameters.

Our main achievements have been: i) Training key researchers to PhD and professor level positions ii) Actively including master students at NTNU in our work. iii) Providing a meeting point between academics and industry. iv) Actively involving external stakeholders in all part of our research, and thereby enhancing the relevance and quality of our research. v) Presenting research results at several conferences and publishing in high?ranked, peer reviewed scientific journals. vi) Providing knowledge on how expectations and learning about energy market and policy uncertainty factors can affect investment decisions of renewable energy investors and how policy makers can design policies to incentivize renewable energy investments in an efficient manner. vii) Providing knowledge to support investment processes in practice.

This project focuses on analysis of investment in the Norwegian and European energy system under uncertainty. EU's and Norway's ambitious climate and energy targets need to be fulfilled by making many small and large investment decisions. Each of these need to balance organizational objectives, profitability, and sustainability concerns, and will be made under considerable uncertainty regarding the future energy system. InvestExL will develop knowledge that assists decision makers and policy makers, using active learning to support investment processes in the energy system. Learning is meant in a Bayesian sense, but also more broadly. We will at the same time deliver scientific contributions at the forefront of real options theory in the direction of learning, and improved formation of expectations, about energy market and policy uncertainty factors. Specifically, the project will (1) analyse how the potential to learn about key uncertainties affects investment behaviour, (2) give advice on how the potential to learn about extensions, revisions or termination of current government policies can affect investment behaviour in the long term and (3) study how private investors can learn about grid access uncertainty by anticipating the conflicting objectives of a welfare-maximizing transmission system operator. Additionally, the project will develop and hold executive workshops at the industry partner and introduce a novel real options model that is transparent and flexible enough to allow for increased penetration of practical use. These efforts will increase the competence in decision making under uncertainty in the Norwegian electricity industry. The 4-year study is a co-operation between the Department of Industrial Economics and Technology Management at the Norwegian University of Science and Technology, Hydro Energi AS, Tilburg University (NL), the University of Illinois at Urbana-Champaign (US), the University of York (UK), the University of Lisbon (P) and Nord University.