Forecasting electricity prices plays a key role for stakeholders to construct an electricity business strategy. In recent years, electricity power system structures have undergone changes because of the large-scale penetration of renewable energy sources and the change in consumer behavior. These system changes have led to increased uncertainty in the behavior of electricity prices. This behavior is also influenced by various other factors such as past prices, weather, and consumer lifestyle. This research focuses on the day-ahead forecasting of the most recent electricity spot market prices in the Japan Electric Power Exchange, and proposes an approach to identify the effects of important factors on the dynamic characteristics of the market prices by constructing sparse models. The concept of the sparse modeling is an attractive approach to identify the effects of variables mechanically based on the coefficients in constructed models. Additionally, the proposed approach selects variables of particular importance based on statistical testing using the coefficients. The proposed scheme was applied to a real-world price dataset and discussed in the context of representation errors and interpretability. The results show that the proposed scheme is an effective approach for representing the impact of variables on electricity market price.

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The massive deployment of PV and wind, which provides decarbonized energy supply, bring about various additional challenges, among those, the reduction of operational capacity of synchronous machines in a power system have been affecting stable operation of a power system. With the reduced system inertia, the system frequency deviates faster and more largely and the deviation sometimes results in instability of a power system operation. In this paper, we present the trend of system inertial reduction from 2030 to 2040 in Japan and quantify the cost increase when criteria of system inertia are applied. We further present the benefit of a PV PCS which can supply system inertia in terms of an improved system inertia profile and a reduction of annual operational cost in order to evaluate a strategy to maintain a system inertia.

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We examined differences in the potential installation of photovoltaics (PV) in non-residential buildings in Japan, published by The Ministry of Economy, Trade, and Industry (METI), The Ministry of Environment (MOE), and New Energy and Industrial Technology Development Organization (NEDO). We modeled procedures employed in these publications for calculating annual power generation by PV. The model was applied to 66 non-residential buildings ready for zero-emission (denoted as “ZEB Ready”). Results showed that the constant ratio of annual power generation to the installable area (Q) and the constant ratio of the installable area to total area (Z) were identified as sensitive to the large rate of change, while solar radiation (H_Am) depending on azimuth angles (ψ) were found insensitive. We assessed the method by NEDO-MIRI as the most elaborate among them, based on sensitivity analysis.

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In recent years, the trend to introduce EVs toward the realization of a decarbonized society is accelerating worldwide. 　In re-sponse to this trend, the movement to convert vehicles to EVs is becoming active in Japan as well. Regarding the introduction of EVs, various researches have been conducted both in Japan and overseas, however, especially in Japan, there are few studies on the conversion of commercial vehicles to EVs, thus we have evaluated the power demand for charging when converted to EVs. In this study, in order to examine the future conversion of commercial vehicles to EVs, we evaluated the power demand for charg-ing when commercial vehicles are converted to EVs in the future, using probe data of internal combustion engine vehicles. As the result, it was found that the charging power demand curve changes greatly depending on how to set the charging timing and the output of the installed charger. This suggests that controlling the charging timing can alleviate or contribute to the impact on pow-er supply and demand. Future charging infrastructure design will need to take these into consideration. In addition, it is considered important to optimize the spread of EVs, including the characteristic of logistics itself.

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