IGP-C (MEXT Scholarship), GEDES, D2 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.
IGP-C (MEXT Scholarship), Energy Course, M2 student
Modeling of the solar power output forecast system for Hyderabad Railway Station (India) using Transfer Learning and Hidden Markov Model
Indian Railways has set an ambitious target of becoming a net carbon-zero transporter by 2030. With the rapid adoption of photovoltaic (PV) systems on the railways 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. The literature shows that transfer learning and Hidden Markov Models (HMM) have shown promising results in various applications. However, the scarcity of data in new installations is a big impediment to effective energy management. The research aims to model a solar power forecast system using transfer learning from a pretrained HMM model capable of predicting solar irradiance using the weather parameters as inputs. The real data of the solar power output of Hyderabad Railway Station in India is used to test the results. The input weather parameters (Global Horizontal Irradiance, Temperature maximum, Skin temperature, humidity, and wind speed) from the Indian Meteorological Department and the solar power output from the datalogger of the plant will be used to train and test the model. See figure below for overall concept of the research in general.
Energy course, M1 student
The stable power supply system on the moon