Real Option Analysis for Renewable Energy: A Systematic Review

Renewable energy projects suffer from deep uncertainties associated with volatile market conditions, unstable policy regimes and changing technological landscapes. Traditional valuation tools like Net Present Value (NPV) are increasingly being accepted as insufficient to capture the managerial flexibility needed to deal with this complex environment. As a result, a powerful alternative investment framework, Real Options Analysis (ROA), has been proposed, in which the possibility of strategic adaptability under uncertainty is valued explicitly for renewable energy investment. This paper reports a systematic review between 2000-2025 of research works on ROA application in the renewable energy sector. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, 288 peer-reviewed studies were identified from twelve major academic databases (Scopus, IEEE Xplore, and Wiley Online Library). Each study was reviewed in terms of key dimensions: renewable technology type, real option category, modelling technique, dominant sources of uncertainty and geographical focus. The results show the dominance of the decision to defer (timing option) as the most important strategic flexibility for all technologies, emphasising the key problem of optimal investment timing. Methodologically, the field has transitioned from basic analytical models to complex simulation-based models, with binomial lattices and Monte Carlo models dominating the scene, followed by a significant move to hybrid, fuzzy, and AI-enhanced models after 2015. The analysis also reveals clear regional patterns in the types of uncertainties modelled with European studies focusing on market and policy risks, Asian studies on resource availability and work in the Americas taking into account technical risks. However, a serious underrepresentation in Africa, especially in Nigeria, is also revealed, which constitutes a major gap in the research. This review concludes that while the methodological foundations of ROA are well established, its practical application remains limited, particularly outside developed countries. Expanding the use of ROA could better support the global energy transition, but achieving this requires addressing barriers such as computational complexity, limited modeling expertise, and regulatory reliance on deterministic valuation methods. Greater integration of these flexible decision-making tools into policy design and project appraisal, especially in high-risk and underrepresented regions, is therefore necessary.