Review and analysis of investment decision making algorithms in long-term agent-based electric power system simulation models

Abstract Long-term electric power system planning models are frequently used to provide policy support in the context of the ongoing transition towards a low-carbon electric power system. In a liberalized market, this transition relies on generation company investment decisions. These decisions are shaped by both economic and behavioral factors. Agent-based modeling allows the incorporation of both these factors in the description of the investment decision making process. Nevertheless, there are several challenges associated with the design of agent-based models such as the definition of the model structure and its lack of transparency. In this study, we aim to increase the transparency of investment decision making algorithms by shedding light on how implicit assumptions of the price projection methods used in these algorithms impact model results. To achieve this goal, we developed a core long-term agent-based model to assess different investment decision making algorithms from the literature and we introduced a novel price projection method based on optimization modeling. Our results show that investment decisions vary enormously depending on the assumptions and parameters used in investment decision making algorithms. We also found that our proposed price projection method is robust to parametric deviations. Thus, the proposed investment decision making algorithm enables agent-based modelers to mitigate the potential impacts of hidden implicit assumptions.