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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.


“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.

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Jinesh Mohan

Working-adult program, Energy Course, D1 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.


Muneaki Kamioosako

Energy course, M2 student

Solar energy and heat storage on the moon

Due to NASA's Artemis program, manned exploration on the moon will resume, accelerating moon development and making it increasingly important in the field of space exploration. While solar power is an effective power source in space, there is a problem with interrupted power supply during the long moon nights, which can last about two weeks in equatorial regions. Therefore, a stable power supply system on the moon is necessary. The purpose of this study is to construct a stable power supply system on the moon and to contribute to Earth's power problem by applying the technology developed on the moon. The research method involves conducting an environmental survey on the moon, constructing a power generation, storage, and transmission system, predicting the electricity demand in the moon base, calculating the required power supply, and performing model analysis of the constructed power system.

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