When introducing a microgrid system (MGS) for consumers, installing equipment according to loads of consumers is essential. Small MGS requires algorithms that ensure computational scalability because they have consumer-specific circumstances and are difficult to consider versatility. Therefore, we optimize the capacity of every MGS facility from the viewpoint of life cycle cost (LCC), which is the sum of the initial cost and running cost. As an optimization method suitable for this, we propose a method to optimize the capacity of MGS facilities by particle swarm optimization (PSO) based on an operation plan simulating one year of MGS using pattern data and mixed-integer linear programming (MILP). The problem with this method is that it takes time to calculate annual operations. Expressing one year of customer data with four types of pattern data while keeping the characteristics of data for each season reduces the amount of computation required for operational planning. It provides the optimum facility capacity within a reasonable time. By running the proposed method under several conditions and conducting a case study of the installed capacity, we have found an LCC that has less computational complexity and less installed capacity than when searching for everything. This demonstrates the effectiveness of this approach, although there is room for improved PSO retrieval effectiveness.

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The Japanese government has set a goal of reducing greenhouse gases by 46% by 2030 compared to 2013 levels. In the household sector, the target is as high as 66%. In order to reach the target, all municipalities need to have an accurate understanding of their energy demand and CO₂ emissions from primary data. However, it is difficult for small municipalities to obtain primary data on their own. The traditional proration method cannot determine whether the estimation results are reliable. Therefore, in this research, we proposed a method that utilizes the recursiveness of statistical data. One is an empirical Bayesian method and the other is an energy selection model when using heat. Next, I estimated the environmental load intensity of 1,718 municipalities nationwide. However, the accuracy of the data is still insufficient to run the PDCA cycle. In order to further improve the estimation accuracy, it is necessary to comprehensively utilize various statistics and related information. In addition, it is necessary to quickly aggregate statistical data and publish the results promptly, and to develop advanced analysis tools and future prediction tools for collected data.

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In Japan, according to Plan for Global Warming Countermeasures, various countermeasures are implemented to reduce CO₂ emissions in the residential sector by 66% by 2030, compared to 2013. To achieve the target, carbon management through the PDCA (Plan-Do-Check-Action) cycle is useful. However, quantitative analysis of the plan’s progress【Check】and action based on the analysis【Action】still have not been carried out enough. In this paper, we evaluated the effects of the countermeasures and influence of weather condition via TREES model, which considers the heterogeneity of the energy use among households due to occupant behavior, appliance/equipment ownership, and weather condition. In addition, we proposed the next steps of the plan by analyzing the marginal abatement cost of each energy-saving measure. Simulation results suggest that 1) there has been not enough progress to achieve the 2030 target and 2) it is effective to promote measures with differentiating household categories, especially in households with lower marginal abatement cost to reduce CO₂ emissions and household expenses.

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Distributed energy system is expected helpful for the resilient energy supply against disasters. The objective of this study is to design a distributed energy system with co-generation and renewable energy technologies which can keep independent energy supply for 4 days under the emergencies of power or gas utilities. It is assumed that the system is installed in the building combining a convenience store and a restaurant because they are expected to be a center when emergencies happen. An optimization model was employed to investigate the configuration of the distributed energy system where 8,760 cases of energy supply shortage are considered as constraints to maintain energy supply. The objective function represents the sum of the cost of facilities and the running cost under regular situation. The results show that it is possible to keep independent energy supply for 4 days in the case with 1,000 kg of LPG tank, where a gas engine co-generation is operated. SOFC and battery will be adopted when the cost is reduced. The results also suggest that the distributed energy system contributes to the reduction of energy consumption for regular operation compared with a conventional system.

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Utilization of personal data is expected to provide consumers various benefits such as convenience, comfort, and safety/security. To accelerate the use of personal data in the society, understanding consumers’ perception for providing their personal data is important. This paper conducted a conjoint analysis to reveal consumers’ intention on provision of personal data including energy data. It focused on the difference of data types and utilization purposes. The conjoint analysis dealt with four attributes, one of which includes eight utilization purposes of personal data and corresponding data types. An online questionnaire survey was conducted for the analysis, which collected 2,132 samples of consumers. Discrete choice models were estimated for the analysis, which revealed that psychological resistance is low in the case of providing anonymized data to receive indirect benefits through solving local issues. The results also found that the resistance is low among those who actively choose new services.

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It is important to transfer of good policies from regions that have achieved decarbonization first to other region. This study aims to gain insight into the current status and efforts of municipalities to decarbonize their economy through the use of national open data in the context of inter-municipal comparisons. We analyzed the current status of emission reductions, their relationship to target progress, and efforts to reduce emissions. The analysis showed that 366 municipalities had already met their targets. This represents 37.2% of the total. Looking at the top emission reduction municipalities by prefecture, we see that western Japan prefectures such as Tokushima and Miyazaki prefectures have a high percentage of municipalities with high reduction rates, while eastern Japan prefectures such as Miyagi and Akita prefectures have a low percentage of municipalities. There is no statistically significant difference between the rate of CO₂ emission reductions and efforts such as global warming prevention ordinances and municipalities' master plans. There is also no correlation between the emission reduction rate and the rate of population decline, the total floor space of administrative facilities, or the financial capability index. It is expected that this research will develop as the scope of national open data disclosure is expanded in the future.

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