Sanchari Deb, University of Warwick

Sanchari Deb, PhD

• Education

Sanchari completed her Bachelor's of Engineering (BE) in Electrical Engineering from Assam Engineering College (AEC), Guwahati in the year 2014.
Master of Engineering from Birla Institute of Technology (BIT)bMesra, Ranchi with specialization in Power Systems in the year 2016.
PhD in Energy from the Centre for Energy, Indian Institute of Technology (IIT), Guwahati in the year 2020.

• Experience

Post PhD, she joined Alliance for an Energy-Efficient Economy, New Delhi as Research Associate and worked on a project related to Vehicle GridIntegration in the Indian aspect. Later, she was associated with the Centre for Advanced Research in Electrified Transportation (CARET) of Aligarh Muslim University (AMU) as a postdoctoral researcher for a short duration. In 2020, she received a European Research Council of Informatics and Mathematics (ERCIM) fellowship and joined VTT Technical Research Centre, Finland.

She has published nearly 30 research articles and co-edited a book on Smart Charging. Sanchari serves as the reviewer of several peer-reviewed journals of Elsevier, IEEE and Springer. She is the recipient of several prestigious fellowships and awards such as the ERCIM fellowship, EUTOPIA fellowship, and Anandaram Baruah award. She is also a member of the IEEE Power and Energy Society.

Sanchari Deb is currently associated with the Smart e-fleet Institute of Advanced Studies (IAS) and Power and Control Laboratory of School of Engineering, University of Warwick as EUTOPIA postdoctoral fellow. Earlier, she was associated with Smart e fleet group of VTT Technical Research Centre, Finland as a researcher. Her research interests broadly cover different aspects of power and energy such as e mobility, charging infrastructure planning, Artificial intelligence applications in power and energy, microgrid planning, distribution network planning, local energy systems with cogeneration, Vehicle Grid integration, optimization, metaheuristics algorithms, tool development for charging station placement, machine learning applications, power system reliability, intelligent transport, autonomous vehicle, quantum computing applications, solar-based charging infrastructure for Electric Vehicles (EVs).

In recent years, it is evident that energy systems across the world are having more power generation connected at the power distribution level aiming to generate locally and use locally which will reduce the pressure on power transmission infrastructures. The local energy system considers electricity, heating, and transportation in a smart collaborative way to improve energy efficiency and reduce cost. With the changes, conventional energy consumers are becoming prosumers as they consume energy and also supply energy at the same time. The changes bring great challenges to the traditional one-way energy system operation, management, and energy trading system.

Driven by the aforementioned challenges, this project aims to delve into a smart local energy system having cogeneration. Further, this research will also explore the capacity of Electric Vehicles (EVs) for being used as a medium of storage in local energy systems. The current initiatives to electrify the transport sector have made EVs a popular mode of transportation. Apart from being a medium of transportation, EV batteries can be utilized as a storage medium to tap the excess energy produced in local energy systems when the load demand is low.

Thus, this research will specifically focus on planning and operation of smart local energy systems having cogeneration and EVs as a storage medium. The planning and operation of a local energy system is a complex problem. Hence, this problem will rely on using an Artificial Intelligence (AI) inspired methodology such as metaheuristics, machine learning for its solution. The novelty of this project lies in proposing an AI-assisted planning and operation model for local energy systems having cogeneration and EVs as a storage medium. Also, the technical as well as economic feasibility of this model, regulations required, user acceptance of the model will be critically assessed in this research.