As the worldwide largest electricity producer and national highest CO2 emitting sector, the Chinese power sector is facing considerable challenges to meet the current tremendous and future growing electricity need, while addressing the emission issues. As the primary CO2 release source in the power system, coal-fired power dominates the capacity mix with 59% of the total in 2019. Therefore, it is urgent for the coal power industry to speed up reducing carbon emission. Carbon capture and storage (CCS) is a promising technology for coal power plants to mitigate CO2 release. However, considerable uncertainty exists in its capital cost because of the technical immaturity. This work models China’s optimal power expansion plan by 2050 under the 2℃ emission target with a dynamic high spatial-temporal model and investigates the capital-cost impacts on power planning. The result shows that: (1) CCS need to be applied, and its capital cost must be lowered to maintain coal-fired power capacity, otherwise, its capacity ratio will have to be reduced to 9.48% of the national total by 2050 (1700 $/kW scenario) to meet the 2℃ emission target; (2) renewables would dominate the power generation by 52.6%~62.8%, while their variability problem must be handled with increasing power transmission, flexible operation of ramp generators, pumped-hydro storage and power curtailment.
The purpose of this research was improving the accuracy of the bottom-up residential energy end-use simulation model and estimating actual occupants’ behavior by optimizing the model using smart meter data. Using the simplex method, we optimized the input parameters of occupants’ behavior and the electricity consumption of appliances, and improved the accuracy of electricity consumption estimation for each household type divided by number of people and housing type. By optimizing, the error in daily electric consumption between the estimated value of the bottom-up model and the smart meter data could be improved to 9% or less. From the modified occupants’ behavior parameters, it was estimated that the difference between the bedtime data of the National time use survey, which was input data of the model, and the bedtime in reality is about 1 hour in the 5 person in apartment houses, whereas it was the 3hour difference in the 1-person families. It was presumed that there was a difference between the input data and the actual bedtime due to the attributes of the household members. From the analysis using the TV audience rating, it was revealed that the decrease in the activity rate at midnight can be accurately reproduced.
As installed capacity of Photovoltaic (PV) power generation increases, energy storage resources should be increased more in order to reduce PV power curtailment and to reduce CO2 emission. The increasing penetration of Electric Vehicles (EVs) may provide opportunities to reduce the PV curtailment by using EV for charging the surplus PV power output daytime and discharging it nighttime. Especially, commuting EVs of factories are suitable for PV charging at the factories, but one of the challenging points is that the available time as energy storage resource is limited compared to a stationary BESS. In order to increase the EV charging during daytime, we focused on the EV discharging to the factories in the evening. This study evaluates the usefulness of commuting EVs as energy storage resource based on their potential to reduce operating cost and CO2 emission by simulating a year-round operation of factory-scale power system featuring high PV power capacity.
Stochastic person-based activity models play an important role in the prediction of the realistic time-series energy demand for residential buildings. These models generally use input parameters developed based on time-use data. This paper models the parameters for a stochastic person-based activity models that can consider the variability in simulated activities among households. Modelling parameters were modeled by two steps: 1) classifying time-use data for six segments using the basic demographic conditions, 2) developing regression models for each segment considering detailed demographic conditions as explanatory variables. The developed regression models were validated by the Hosmer-Lemeshow goodness-of-fit test. Then, we compared the model performance with the conventional model only using the sample distributions of each six segment. Results showed that the proposed model improves the producibility of the variability among simulated occupants and households. The use of the developed model would contribute to improving the accuracy of residential energy demand models.
Conventional power distribution systems have issues such as rising maintenance costs and vulnerability to natural disasters. Inspired by the concept of battery sharing arising globally in the mobility sector, this paper proposes a new system that eliminates distribution lines in areas with low demand density and instead supplies electricity to consumers by battery delivery. The surplus power generated by the output suppression of solar power generation will be charged to the batteries, effectively reducing the cost of charging to zero. The levelized cost of electricity (LCOE) of the battery delivery system is estimated to be 37 JPY/kWh if it is implemented in part of the Kyushu area. It is suggested that, if 2% of the consumers in the area accept removing distribution lines and switching to battery delivery service, the length of distribution lines could be reduced by 11% and the maintenance cost of the distribution sector could be reduced by about 5 billion JPY per year.
This paper explores public attitudes towards support for climate change policies and perceptions of the use of low-carbon power sources in the UK and Japan based on web based online survey conducted in 2020. A larger proportion of UK-Japan respondents have similar concerns about climate change, but the UK has a higher level of awareness of the contribution of low-carbon power sources, including climate change measures, than Japan. Furthermore, the paper also explores the background factors, based on the public attitudes of the two countries toward the perception of the changing their behaviour with respect to climate change and priority to protecting the environment over the economic growth.
In this research, to verify the thermal performance of high-reflectance coating, three types of high reflectance coatings and
general paint were applied to the Folded-Plate roof on the existing factory in Gunma Prefecture, and horizontal global solar
radiation, heat flux vectors, surface temperature, and indoor temperature etc., were measured for a long time, through a year.
The reflectivity and emissivity of the roof surface were improved by the coating, and the heat flex was reduced, and the surface
temperature was lowered in all seasons compared with the case without coating. There is a negative correlation between the
amount of global solar radiation and the heat flux on the roof surface, and the slope of the approximate straight line becomes
much smaller by coating. In comparison with the ceramic high reflectivity coating, the ceramic balloon coating had higher
performance in winter and autumn, but the difference could not be confirmed in summer and spring.
Peer-to-peer (P2P) electricity transactions among prosumers are attracted attention because of the utilization of surplus power caused by distributed generators such as photovoltaic generation (PV). The geographically and electrically close transaction has a higher potential for not only economic but also supply reliability. Therefore, the author had proposed an ideal transaction method in P2P considering both economic and supply reliability based on the assumption, which whole prosumers in P2P can be coordinated transactions. The optimization in the proposed method aims to maximize profit and index regards stored energy in prosumers when a power outage happened. This paper evaluates the potential performance of economic and supply reliability by P2P transactions among prosumers or consumers from long-term perspectives through yearly simulation with actual household demand and weather data. Moreover, the paper reveals the impact of potential performance characteristics of applicable periods related to supply reliability.
This study was conducted to assess the energy conservation awareness of high school students in Jilin Province, China. A questionnaire survey with 16 multiple-choice and 2 freedom descriptions was administered to 469 students. Survey subjects include environmental protection, energy awareness, energy-saving behavior, water heater choice, and future use of energy-efficient household appliances. Questionnaire survey results indicate that students acknowledge the importance of environmental protection through severe difficulties caused by COVID-19. Recognition of resource issue importance was lower than that of climate change issues. Results of statistical analyses show that 89.6% of respondents choose to use solar water heaters, citing reasons that they " save energy and protect the environment." Next, we presented a video course on energy education to 86 students. We developed a movie, including sound, of about 20 minutes about low-carbon lifestyles. Comparison of questionnaire survey responses obtained before and after the class revealed that the weighted average score of personal norms related to resource waste improved the most. Furthermore, the weighted average score for recognition of the importance of fossil fuel resource issues increased. Of respondents, 93.2% gave positive comments about the energy education video course. This video course enabled students to cultivate interest in energy conservation.