Arnab Bhattacharjee

About Arnab

Arnab completed a Bachelor of Technology (Honours) in Electrical and Electronics Engineering with minor in Computer Applications from the National Institute of Technology Tiruchirappalli in 2019 and was awarded a gold medal for achieving the highest score in his department. Arnab was conferred with the DAAD WISE scholarship and Cargill Global Scholarship for his academic and leadership qualities.

Arnab’s interests are around autonomous systems developed during a research internship that he carried out at the Max Planck Institute for Intelligent systems in Tuebingen, Germany, where he worked to develop robotic systems that learnt motion-governing equations on their own with the help of a neural network that goes by the name of the “Equation Learner”.

In his future research work undertakings, Arnab primarily wants to address the issue of climate change. More specifically, during the completion of his PhD, Arnab is interested in undertaking research on the development of smart grids (focusing broadly on their operation, analysis, modelling and protection) and autonomous electric vehicles.

Project details

IoT based EV infrastructure: Data Driven Approach for analysis and optimization of Distribution system Operation (DSO) under uncertainties

Traditionally, the distribution systems are designed for uni-directional power flow with power source elsewhere and are always connected to transmission system. Also, the changes in system configuration were not too frequent. Introduction of renewable energy based distributed generations (DGs), battery energy storages, and electric vehicles (EVs) is changing the overall structure and conventions of the traditional distribution systems. Various market mechanism and more pronounced role of distribution system operator (DSO) is emerging due to multiple ownership and need for demand response (DR) till individual house hold level. The biggest challenge in distribution systems is the availability of data and network information for accurate state estimation and modelling. An indirect approach using measurement/historical data to determine the network configuration/other states needs to be explored. Further for more data availability low cost measurement, accumulation and storage should be explored. Uncertainties in PV/EV can be complementary and should be seen as opportunity for balancing each other’s effect along with already existing natural storage in distribution system. The IoT based platform can provide data in the cloud base which can be further used to develop predictive models for distribution network under various uncertainties. Emphasis in this project will be to develop 1) low cost measuring infrastructure for LV distribution system with EV penetration 2) Data driven approach for determining the distribution system models 3) Optimal scheduling and voltage management in weak distribution systems with large EV penetration utilizing V2G and G2V operations

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UQ Supervisor

Professor Tapan Saha

School of Information Technology and Electrical Engineering
IITD Supervisor

Professor Sukumar Mishra

Department of Electrical Engineering