東京科学大学 環境・社会理工学院 融合理工学系. クロス研究室
Institute of Science Tokyo, School of Environment and Society
Transdisciplinary Science and Engineering
エネルギー政策研究グループ
気候変動は私たちの時代の最大の課題です。エネルギー問題 は特に複雑で複数の分野に
またがっています。
エネルギー政策研究グループでは、高い志と多様なバックグラウンドを持った学生が、
現代社会が直面している課題をさまざまな方法で解決するために貢献しています。
Avinash Boodoo
IGP-C (MEXT Scholarship), GEDES, D3+ Student
The Dual Use of Wave Energy Converters (WECs) and Wave Farms for Coastal Protection and Renewable Energy Generation
Wave energy represents one of the most promising, albeit less developed, renewable energy solutions. With an estimated global wave resource ranging between 1-10 TW, wave energy presents a viable solution to meet increasing energy needs while significantly contributing to carbon emission reduction through carbon free energy generation. Despite the numerous advantages of harnessing wave energy, including its high predictability, high energy density, and minimal environmental impact, the development and widespread commercialization of Wave Energy Converters (WECs) and wave farms face numerous challenges. Key among these are technical and non-technical obstacles, with the high Levelized Cost of Electricity (LCoE) being a principal barrier. This high LCoE cost renders wave energy less competitive compared to other renewable energy sources and as such, there is a critical need for innovative solutions to reduce costs and enhance sustainability.
Integrating WECs with secondary functions, such as coastal protection, offers a novel approach to overcoming economic barriers, providing dual benefits of renewable energy generation and coastal protection. This research focuses on optimizing the design of WECs and wave farms to balance these dual purposes, including an in-depth assessment of their short-term and long-term impacts on coastal protection and energy output; modifying existing techno-economic models to integrate coastal protection benefits; examining WEC effectiveness in various environments, especially remote islands, and evaluating the environmental and social impacts. I am also working on optimizing the design of a novel onshore overtopping-type wave energy converter (WEC), which serves dual functions as a coastal protection structure and a renewable energy generation device. This will be evaluated through physical modeling in a wave flume and numerical modeling using Smooth Particle Hydrodynamics (SPH).
“It made me positively savage to think of all that power going to waste” ~ Thomas Edison, 1889, during a voyage across the Atlantic while studying waves.
Jinesh Mohan
Working-adult program, Energy Course, D2 student
Photovoltaic system power modeling utilizing transfer Learning
With the rapid adoption of photovoltaic (PV) systems and their integration into the electricity grid, it has become a necessity to accurately forecast the photovoltaic output at their intended site of use for effective energy management to mitigate the instability of the grid caused by the intermittency of solar power. However, the scarcity of data in new installations is a big impediment to effective energy management. In the master's research, a solar power forecast system utilizing inductive transfer learning from a pre-trained machine learning model that can predict solar irradiance was proposed and analyzed. The doctoral research aims to investigate the effect of cloud cover on forecast accuracy and address seasonal variations by clustering through case studies of Hyderabad Railway Station and Cochin International Airport Limited. See figure below for overall concept of the research in general.
