Implications of capacity expansion under uncertainty and value of information: The near-term energy planning of Japan

In this paper, we present the near-term analysis of capacity expansion under various uncertainties from the viewpoints of the decisionmaking process on the optimal allocation of investment and the value of information. An optimization model based on two-stage stochastic programming was developed using real data to describe the Japanese energy system as a case study. Different uncertainty parameters were taken into consideration by a disaggregate analysis of a bottom-up energy modeling approach, including end-use energy demands, plant operating availability and carbon tax rate. Four policy regimes represented as energy planning or policy options were also studied, covering business as usual, renewable energy target, carbon taxation and nuclear phase-out regimes. In addition, we investigated the role of various energy technologies and the behavior of the value of information with respect to the probability function of the worst-case scenario. This value of information provides decision makers with a quantitative analysis for the cost to obtain perfect information about the future. The developed model could be regarded as an applicable tool for decision support to provide a better understanding in energy planning and policy analyses.