The objective of this study is to clarify the optimum configuration of energy technologies to allow apartment houses to independently maintain its electricity and heat supply in emergencies where there is no external electricity and heat supply. An apartment house of 100 households is assumed to be equipped with a distributed energy system comprised of solar energy technologies, energy storage technologies and cogeneration. The energy system is optimized to minimize the annual operating cost as the objective function subject to a given energy-sustained days. As a result, four energy-sustained days can be achieved through a 10% increase in terms of cost compared with a conventional case that does not use distributed energy technologies. Ensuring more than five energy-sustained days requires the large-scale deployment of rechargeable batteries to save fuel for floor heating in winter. It is found that enhancing the thermal insulation of the building can reduce the necessary cost to hold seven energy sustained days by up to 15%.

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This paper provided the evaluation on energy saving effectiveness of residential cogeneration systems like micro gas engine and fuel cells based on household energy demand. In addition, indicators to determine the optimum energy saving system were analyzed by our residential energy end-use model. The energy demand and energy saving effect of micro gas engine and fuel cells in 200 households were estimated. The impact of household profile and demand characteristics on the energy saving was evaluated using the multiple regression analysis. The energy saving effect was characterized by the existence of household member in the house during the daytime of weekday. Energy saving effect was most influenced by the frequency of bathing in households. These household characteristics would be effective to determine the best system in households.

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The purpose of this study is to examine the long term impact of energy policies- translated into scenario settings- on the power sector and to evaluate the policies in terms of economic efficiency, environmental impact, and energy security simultaneously. At the core of this study is an optimization model to calculate the power generation mix in China through 2050 under certain scenario settings. While the projection of electricity demand over the same period was carried out by using the least square regression approach. Based on the simulation results, a quantified evaluation of impacts on economics, environment, and energy security (‘3E’) was taken out. The results suggested that there was no policy scenario that could solve the ‘3E’trillima. However, in order to select a scenario that could answer the expectations best, a decision matrix approach was deployed. In this approach, the scenarios were scored on economic efficiency, environmental impact, and energy security, which form an evaluation matrix. By multiplying the evaluation matrix with another matrix indicating the priority order of the 3 ‘E’s a ranking matrix could be yield. The scenario with the highest score in the ranking matrix is the one that should be selected.

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This study estimated CO₂ emission reduction effect of eco-driving cars using a microscopic traffic flow simulator with a case study at a section of Higashi-odori street in the city of Tsukuba. The effects of two eco-driving methods (the one was max speed suppression and another was slow starting acceleration), traffic volume and mixing of heavy duty tracks were examined. The results indicated that the eco-driving method with max speed suppression was a main factor of CO₂ reduction. The spillover effect caused by max speed suppression was found in the daytime with standard traffic volume. The spillover effect in the nighttime with low traffic volume was almost none because of easy overtaking. CO₂ emissions at the intersections were large. However, CO₂ reduction by eco-driving was observed at the single road between intersections. 10%-mixing of heavy duty trucks to traffic stream in the daytime had little influence to the traffic flow and CO₂ reduction effects, regardless of a truck’s characteristics with slow starting acceleration.

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After the experience of electric power shortage due to the Great East Japan Earthquake, we have to use the electric power more efficiently. To use the electricity efficiently, we have to manage our electricity usage under the forecasting model of electricity consumption. In the home energy management system, a forecasting model of electricity consumption with the inference algorithm based on electricity consumption data by a similar warm environmental condition are popular, but the forecasting model with the time series analysis are not. It is because that a processing of the static state to the time series data of electricity consumption isn't enough that modeling of these data with a time series analysis isn't done successfully. In this paper, we propose a forecasting model for electricity consumption based on a time series analysis. Firstly, to make the time series data of electricity consumption static state, we confirmed the seasonal and weekly trends which were included in these data with autocorrelation. Secondly, we removed these trend as the deterministic elements from the time series data of electricity consumption. Thirdly, after removing these deterministic elements from these data, a second order auto regressive moving average model was proposed. Finally, we confirmed this forecasting model with a second order auto regressive moving average was done in several houses.

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This paper describes the performance of an adsorption heat pump prototype modeled for use in data centers. Electric power is consumed to cool down central processing units (CPU) in the date centers, because the CPUs dissipate heat at approximately 50℃. An adsorption chiller driven by the waste heat dissipated from CPUs was proposed to utilize the cooled energy in the date centers. A double-stage adsorption heat pump comprising of zeolite adsorbent FAM-Z05 and active carbon was driven using the low temperature heat.
The effect of the cooled heat output, efficiency of the adsorbent utilization and coefficient of performance in cyclic performance of the adsorption chiller is experimentally verified. The results demonstrated that the double-stage adsorption chiller with FAM-Z05 and active carbon can be driven by heat below 50℃. A cooled heat output of 540 W and a coefficient of performance of 0.26 were observed. The efficiency of adsorbent utilization was higher than 80%. During the repetitive operations of 14 cycles, a maximum output in cooling power and adsorption fraction was maintained.

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The Japanese government decided the greenhouse gas emission reduction target for 2030 as an Intended Nationally Determined Contributions (INDCs). This study evaluated the target in terms of ambitions, international comparability, and long-term goals for climate change mitigation. Comparing with the historical data analyses on electricity elasticity of GDP not only in Japan but also in major EU countries, the assumed electricity demands in the INDCs are small, and consequently the emission reduction costs are estimated large. The marginal abatement cost for achieving the target is about 260-380 $/tCO₂. The emission reduction target of Japan is very ambitious in terms of several indicators measuring emission reduction efforts, such as CO₂ emission per GDP, marginal abatement cost, emission reduction cost per GDP, among those of major countries who have already submitted their INDCs. In addition, in order to evaluate a consistency of emission reduction efforts in 2030 with a long-term emission reduction, the emission reduction cost per GDP for 2030 was compared with that for 2050 for halving global emissions in 2050 with equal marginal abatement costs among all countries. The cost of INDCs in 2030 was evaluated to be almost the same as that in 2050.

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