The Real Option Approach to Investment Decisions in Hybrid Renewable Energy Systems: A Systematic Literature Review

In recent years, the global energy crisis, concerns about energy security and grid parity, and the pressure to develop policies for reducing the environmental impact of anthropogenic activities have accelerated investments in renewable energy. A growing body of literature applies the real options approach (ROA) to renewable energy projects, recognizing its value in capturing irreversibility and flexibility under uncertainty. The present work provides a detailed state-of-the-art analysis on the adoption of real options to evaluate mixes of energy technologies for power generation, with a special emphasis on investments in hydropower and solar photovoltaics. The objective is to assess current applications, identify knowledge gaps, and outline priorities for advancing decision-making tools in this domain. We performed a systematic literature review following the PRISMA protocol, identifying 38 papers from the Scopus database up to February 2024. Eligible studies were peerreviewed articles in English applying the ROA to power generation, following a technology selection process; policy evaluation or research and development studies were excluded. The selected papers were analyzed to identify trends over time and space, adopted energy technology, types of real options with valuation methods, and sources of uncertainty. The present paper also discusses the main findings and emerging gaps, providing an overview of hybrid renewable energy systems. Our analysis suggests that, despite the significant advances achieved in this area, further research is needed to exploit the potential of the ROA in investment decisions for combined renewable energy technologies, especially in cases where internal uncertainty and community perspectives need to be explicitly considered. By linking the ROA to the challenges of mixed renewable energy projects, this study enhances understanding of investment decision-making under uncertainty and identifies pathways toward more robust and adaptive project evaluation.