Energy policy research group

Climate change is the greatest challenge of our time. Energy issues are particularly complex, and cross multiple fields of knowledge. Motivated students with various backgrounds in "Energy Policy Research Group” contributes at its scale and in different ways to provide energy solutions to the challenges currently faced by society. Members research projects are summarized below.

1.  Energy policy research

There are a number of policy misalignments for decarbonization, such as finance, taxation, trade policies, innovation and adaptation, remained in three sectors: electricity, urban mobility and spatial planning. We propose aligning the regulation for new technology to accelerate the decarbonization by conducting international/domestic policy review and various of model analysis.


Proposal for grid management reform for Japan.

References; Tatsuya Wakeyama, Assessment on Interregional Grid Management for Renewable Energy Integration in Japan, 15th Wind Integration Workshop, Austria, November 2016


2.  Electricity system model analysis

For the purpose to delineate a picture of the 100% renewable energy power system, we conduct model analysis of energy scenario with the case of high integration of renewables for 2030 to 2050 by using GIS, capacity expansion model, economic dispatch model, unit commitment model, market model and grid model.


International collaboration: Study on integrating renewables into the Japanese power grid by 2030.

References; Rena Kuwahata, Peter Merk, Tatsuya Wakeyama, Dimitri Pescia, Steffen Rabe, Shota Ichimura, Renewables integration grid study for the 2030 Japanese power system, IET Renewable Power Generation


3.  Future energy technology and grid

In future energy system dominated by zero-marginal cost renewable energy, there are many prosumers on the grid and new trade, balancing, settlement and consumption of energy are expected. We propose the future energy technology and grid by transdisciplinary approaches by using engineering, economics, computer science, artificial intelligence and mathematical sociology.


Smart Grid Model for Arakkonam Railway Station (India) with Neural Networks based Energy Forecasting System

Jinesh Mohan, Energy Course M1 student, IGP-C (MEXT Scholarship)

The energy sector holds the key to the decarbonization of the globe, and hence there is a thrust for integration of renewable energy sources into the electrical grid mix. Unlike the islanded microgrids, the integration of Distributed Energy Resources (DERs) into the railway grid poses numerous challenges, especially considering the traction and non-traction requirements of the transportation system.
The research aims to address these challenges and propose a Smart Grid Model for a Railway Station in India wherein a feasible mix of DERs is determined as shown in the image below. However, the output of the DERs can vary based on the prevalent weather conditions. Therefore, an accurate forecast of the grid output can enable efficient energy management, duly maintaining the grid stability. Neural networks have demonstrated significant capabilities in forecasting. However, the feature selection is very critical for an accurate prediction. Hence, the research also proposes finding novel ways to enhance the prediction accuracy.

A multi-energy complementary microgrid superstructure model by improving the integration of renewable energy:Case study of Northern China


Hu Dongzi, M1 student, IGP(C) Energy course

Nowadays Northern China still mainly depends on fossil fuels to generate electricity while the use of renewable energy is relatively low portion of the energy mix. Therefore, with an increasing population their is greater need for electricity puts huge pressure on the local environment. The Chinese government is making a great effort to solve environmental pollution and energy shortage. In Northern China, the potential for increasing renewable energy is huge so that Smart microgird can be regarded as the alternative solution to use renewable energy as much as possible to alleviate serious environmental problems.
Through this research, the optimal smart microgrid configuration and size for the case would be worked out by using python. More renewable energy would be integrated to supply reliable, economical and environmental-friendly electricity. Peer-to-peer energy trading system(see fig. Below) is the power control and operation way that would be used in the research. Distributed energy resources are traded among local consumers and prosumers. It means user can decide whom they purchase electricity and whom they sold electricity out. P2P energy trading would be backed up by blockchain that enables consumers to trade energy safely,directly and economically.
figure Ref: Usman Gurmani, Muhammad & Sultana, Tanzeela & Ghaffar, Abdul & Azeem, Muhammad & Abubaker, Zain & Farooq, Hassan & Javaid, Nadeem. (2019). Energy Trading between Prosumer and Consumer in P2P Network Using Blockchain.