Transmission losses account for about 5% of total demand. While the introduction of variable renewable energy resources such as PV and wind power generation is expanding toward achieving carbon neutrality by 2050, curbing the national burden has become an issue. Since reduction of transmission losses leads to suppression of the national burden through effective utilization of fuel resources and transmission facilities, it is important to study the overall optimization of the power system that takes transmission losses into account. This paper proposes a supply-demand analysis model that is based on the DC optimal power flow method and is capable of calculating the unit commitment of generators considering transmission losses. The purpose of this study is to report the usefulness of the proposed model by quantitatively evaluating the effect of considering transmission loss costs in the merit order using the proposed model. The proposed model can be utilized in the future supply-demand analysis for offshore multi-terminal DC transmission networks by considering the transmission losses.

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Variable renewable energy sources, such as photovoltaics (PV) and wind turbine generation, are experiencing increasing penetration. Accurate time-series data of renewable generation at each substation is crucial for assessing transmission congestion in power demand and supply analysis. This paper proposes a methodology that utilizes ERA-5, reanalysis climate data published by the European Centre for Medium-Range Weather Forecasts (ECMWF), to obtain time-series data of renewable generation at different geological points. We developed a method to correct solar irradiance data from ERA-5, aiming to reduce the mean bias error of ERA-5 against the actual measurements. We also developed a method to determine the system coefficients for PV systems and adjust the wind speed data to align the simulated annual energy production (AEP) with the actual AEP. Through validation within the Tohoku Electric Power Company region, a positive correlation was found between the monthly energy production of the simulated data and that of the actual data. Although further studies for improving accuracy will be needed, it can be suggested that ERA-5 is a possible option for input data of power demand and supply analysis.

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The Japanese government has announced the GHG reduction target of 46% by 2030 and net zero by 2050. The target for the transportation sector by 2030 is 35%. It is necessary to implement global warming measurements as soon as possible to achieve greenhouse gas reduction targets. There are various measures to mitigate global warming caused by the automotive sector: vehicle-specific measures, traffic flow measures, and fuel diversification. The authors developed estimating method for long-term CO₂ emissions of the automotive sector in a previous study. Diffusion of next generation vehicles is one of the most effective measurements among vehicle-specific measures. However, next-generation vehicles are known to have greater energy consumption in fuel and vehicle production than conventional vehicles. In this study, we developed a life-cycle CO₂ emissions estimation method for the long-term future that adds CO₂ emissions of vehicle production, fuel production, and vehicle disposal. Using the developed methodology, three scenarios were analyzed. The results of the scenario analysis show that Tank to Wheel CO₂ emissions decrease the most in the CME Case, in which electrification has progressed. On the other hand, the life-cycle CO₂ emissions reduction of the CME case is lower than Tank to Wheel CO₂ emissions reduction because the CO₂ emissions of fuel and vehicle products of next-generation vehicle are higher.

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Various organizations are making efforts to reduce CO₂ emissions toward a decarbonized society. Among them, for railway operators, effective utilization of regenerative power is one of the measures to reduce CO₂ in the traction power supply system. However, because the equipment for effective utilization of regenerative power is expensive, there is a problem that it can be installed only in places where the effect is high. Therefore, in this research, we focused on highly versatile EV storage batteries, and examined ways to utilize these EV storage batteries, assuming that the company vehicles owned by railway operators were converted to EVs. After understanding the potential of effectively utilizing regenerative power, we conducted the annual simulation in which the absorbed regenerative power was used to supply energy for transportation by company vehicles and nearby station load. As a result, we were able to understand the charging and discharging schedule of EVs in detail, as well as the optimal number of EVs.

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Currently, the only way to know the power consumption of a residential refrigerator is to check the data listed in the product catalog. However, there are many reports that this value differs from the actual value. Therefore, in this study, I developed a statistical model to estimate the amount of electricity consumed by refrigerators from utility bill data. To create the statistical model, this study collected 72 sets of refrigerator data that were operating from 1995 to 2018 and conducted statistical analysis.　 As a result, it was found that the annual electricity consumption of refrigerators had a high correlation with the quarterly electricity consumption of four types. Therefore, we define the refrigerator power index (IR_x), which is an index of QE_x based on QE₁ By using this value as a variable in the RSVD (Regularized Singular Value Decomposition) method, we were able to obtain a more accurate value than the catalog value. Finally, we analyzed approximately 40,000 individual surveys using the RSVD method and created a statistical model formula for the annual power consumption of refrigerators.

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