The authors’ previous assessment indicated that the marginal electricity cost in 2050 in Japan is more than doubled in an energy system based on a 100% renewable power supply compared to the cost-optimal system. However, some assumptions may be conservative given the recent developments, including the cost of variable renewable energy (VRE) and energy storage technologies and the availability of dispatchable renewable power generation (such as biomass-fired). Therefore, to test the robustness of the previous assessment, this study conducts a sensitivity analysis with a focus on these factors, using an energy system optimization model with a detailed temporal resolution. Simulation results imply that the high marginal electricity cost in the “100% renewable power system” is partially due to the costs of managing VRE’s seasonality. Low-cost energy storage and dispatchable renewable power plants can curb the marginal electricity cost. However, the results also suggest that the marginal cost in these sensitivity cases remains high compared to the cost-optimal system, still posing economic challenges to the system based on a 100% renewable power supply.

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Site selection of onshore wind farms involves various evaluations. Evaluation of business feasibility includes calculating annual energy production based on wind conditions. Recent contracts for new connections to the power grid have been on a non-firm basis, which requires power plants to curtail their power when grid congestion occurs. Site selection in the future should consider power curtailment caused by grid congestion. Multi-area Regulation program (MR) is a simulator that can consider the supply-demand balance and the balancing capacity. The latest update of MR enabled the calculation considering grid congestion. This paper calculated the power curtailment of onshore wind farms in Hokkaido in FY2030 with MR. The power curtailment of onshore wind farms varied from node to node, and the curtailment rate around the Nishi-Nayoro substation was 34-38%, higher than any other node. The evaluation with supply-demand analysis is informative for the site selection of onshore wind farms.

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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.

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Since the Paris Agreement in 2015, there has been growing interest in global warming around the world, and accelerated measures to global warming are required to realize a decarbonized society by declaring the goal of carbon neutrality. As one of the measures, Demand Response (DR) are being actively introduced and provided. DR is a system whereby electric power companies pay incentives through transactions to consumers who cooperate in saving electricity during peak periods of electricity demand, thereby reducing peak electricity demand. Electricity demand for individual buildings is easily affected by seasonal fluctuations, the presence or absence of events, and other factors, and the occurrence of electricity demand peaks tends to be irregular, so highly accurate electricity demand forecasting is needed. In this study, we focus on the educational facilities such as University campus. The objective of this study is to use machine learning to construct a highly accurate forecasting model for not only the steady electricity demand in daily life, but also the characteristic electricity demand during events.

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In Japan, the active introduction of renewable energies is encouraged in order to achieve a decarbonized society. Among renewable energies, photovoltaic power generation, which can be introduced relatively easily in buildings and houses, is being used, and its further introduction is desired. Therefore, there is a need for technology to accurately predict the amount of electricity generated at potential sites for photovoltaic power generation facilities. In this study, we tried various machine learning methods for predicting the amount of electricity generated by photovoltaic power generation without using the information of the solar radiation meters, and examined the effect of the training period of machine learning on the accuracy of the estimation.

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