Adapting to uncertainty: Modeling adaptive investment decisions in the electricity system

The electricity system is undergoing a rapid transformation, with the decisions of investors significantly shaping not only the future supply mix of the system, but also dictating the pace of this transition. Given the sector's inherent complexities and uncertainties, investors are actively adapting their strategies to respond to evolving investment conditions. Effective policy design for low-carbon transition hinges on an understanding of these investment decisions. Traditional energy system models, however, often default to a simplistic view of static investment behavior, falling short of capturing the dynamics of adaptive decision-making. In response to these challenges, our study underscores the necessity of integrating adaptive investment decisions into energy system modeling. We introduce a novel approach to model investment decisions that accommodate the dynamic nature of hurdle rates, the uncertainties tied to the economic performance of various power plant technologies, and differences in investors' levels of loss aversion. While conceptual, the model's scale is comparable to the electricity market of a country such as Germany. Our findings underscore the differences in investment decisions among adaptive and non-adaptive investors. In adaptive scenario, agents initially invest more in wind and solar technologies, but less in later years compared to the no-adaptive case. Furthermore, adaptive agents with less aversion to losses show higher equity values, but also face increased bankruptcy risks. By enhancing our modeling approach to incorporate heterogeneity in adaptive investment decisions, this study aims to contribute to the ongoing discourse on low-carbon energy transition and further development of energy system models.