Investment decisions in the electricity sector under uncertainty - An agent-based modelling approach

Kurzfassung

Motivation 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 [1]. Traditional optimization methods of determining  the capacity expansion for the energy system do not account for this [2], leaving policy makers  without clear guidance [3]. Approach We present an agent-based model (ABM) of capacity investment decisions 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.  Methodology The long-term electricity price forecasts are calculated with a combination of models parametrized according to the company’s assumptions. A fundamental energy system model estimates the  capacity expansion in the electricity sector over several decades. The resulting technology mix is used  for an initial dispatch simulation with the agent-based market model AMIRIS [4]. Plants that  represent possible investments are included in the model. The dispatch simulation yields the  expected power price and the optimal operation profile of the potential plants. These values are used to calculate the net present value (NPV) for each investment, a central metric for the investment decision. Companies make their individual investment decisions in annual rounds. The  sum of all investments, the capacity expansion, contributes to the new technology mix of the  electricity system. The dispatch of the electricity system with the agent-based capacity expansion is modeled in a  second AMIRIS run with hourly resolution. This is repeated for each simulated year. Plants  compete on a market whose technology mix changes over time. This has effects on price formation  and profitability. We track and assess the actual profitability of each investment. Plants can be shut down if they underperform and generate losses, e.g. because the realized power price is different from the forecasted one. 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. To conclude, we show that agent-based modeling can offer a fresh perspective on the effect of  uncertainty on transition pathways. With our open-source software, we strive to provide the tools to  spur this line of research forward.