In recent years, renewable energy sources have been introduced rapidly, and the enhancement of power grid facilities, including tie lines, is under consideration. In addition, the Energy Supply Strengthening Act, which passed in 2020, suggests the possibility of introducing a scheme in which each area shares the cost of the grid enhancement. However, which and how much tie line should be augmented on the economical point of view are under discussion now. Also, how to determine the cost allocation of each area is not completely decided. In this study, a basic analysis of supply and demand operation in multi areas and cost allocation of grid augmentation was conducted by using cooperative game theory concepts. In this study, an annual power supply and demand model was constructed. Simulations were conducted in the current scenario and the 2030 scenario, respectively. Based on the simulation results, the amount of tie lines augmentation was decided. The concept of cooperative game theory, nucleolus, was used to allocate the construction cost.
Household CO2 statistics are a valuable statistical survey that can directly analyze the relationship between people's energy-saving behavior, household-use energy-saving technology, and household CO2 emissions. On the other hand, when formulating regional environmental policies, there is a need to capture household CO2 emissions related to specific local regions. However, since household CO2statistics are sample surveys, the local government may not always be able to obtain information about its own area. Therefore, we have developed a method for capturing household CO2 emissions in a specific area. According to this method, it is possible to estimate household CO2 emissions by residents living in a specific area using information such as the average age and household attributes of the residents in that area, which are often captured by the census and other government statistics. Currently, policies are underway in each region of Japan to build smart cities that optimize energy consumption and transportation. This study will contribute to advancing these policies in an evidence-based manner.
This study proposes a method that disaggregates whole-house energy consumption data into six end-uses: heating, cooling, cooking, hot water supply, snow melting, and others. About 70% of the energy consumed at home is highly correlated with the climate. The method to propose in this research uses this relationship. This method can correctly disassemble even if the data contains errors. As a result of analyzing the household CO2 statistics in the method manner, its characteristics could be rationally explained. This study revealed how the COVID-19 epidemic changed the energy consumption of the house. However, improvements are required to estimate the amount of solar heat and the amount of snow melting energy.