Investment decisions under uncertainty: An agent-based modelling approach

Kurzfassung

The energy transition requires coherent and consistent investment in new technologies and infrastructure. However, investment decisions are neither pre-planned nor centrally organized. Instead, they are done by a large set of heterogenous companies, each with its own interests and conditions. How these companies react to policy interventions and what combined effect they have on the energy system is still poorly understood. Traditional optimization methods of determining the capacity expansion for the energy system do not account for this, leaving policy makers without clear guidance. We present an agent-based model (ABM) of capacity expansion in the electricity sector to study the most important features of this complex system. The model assumes companies with different levels of information, capital resources, and risk tolerances. Given the imperfect foresight regarding future developments of the electricity market and policies, the companies rely on uncertain forecasts to inform their decision making. Importantly, the long-term electricity price forecasts differ between companies. Each forecast is based on the assumptions of a specific company regarding parameters like carbon and fuel prices or regulations. After the capacity expansion, the dispatch of the electricity system is modeled in AMIRIS, an agent-based electricity market model. Plants compete on a market whose technology mix changes over time with effects on price formation and profitability. We assess the actual profitability of each investment and shut down plants which underperform. Given this setup, our model allows to study 1) the impact of uncertain assumptions, 2) possible emergent behaviors, and 3) the long-term implications and transition pathways on the electricity market. Test cases verifying the model logic are presented. To conclude, we show that agent-based modeling can offer a fresh perspective on the effect of uncertainty on transition pathways.