2023 年 44 巻 3 号 p. 135-144
Increasing numbers of distributed energy resources (DERs), such as photovoltaics (PVs) and electric vehicles (EVs), are being integrated into power grids worldwide. Consequently, distribution system operators (DSOs) must consider the long-term effects of DERs on grid planning. Some DSOs have begun to develop new grid-planning methods with long-term DER adoption forecasts. Because the development is still in the early stages, simple deterministic methods are mainly used for now. However, a stochastic method is more appropriate because the forecasts entail large uncertainties. In this study, we use Bayesian inference to covert the deterministic Bass diffusion model into a stochastic model for forecasting the diffusion curve of new distributed energy technologies. The benefits of this method are the following. 1. Socioeconomic and demographic data that are difficult to obtain at the local level are unnecessary. 2. The model is automatically updated as the adoption data accumulate. 3. This model can be applied to any type of DER. Real residential rooftop PV and EV adoption data from California are used to evaluate this method against other models. The results show that the accuracies of the forecasted values and probability distributions obtained using the proposed model are better than those of other models.
