Profile

Dr Chenghong Gu is a Professor in Smart Energy Systems with the University of Bath. His research designs novel models and tools for planning and operating smart energy systems, including electricity, natural gas, heating/cooling, and transportation toward net zero. Prof Gu is a co-Editor-in-Chief for IET Smart Grid, editor for IEEE Transactions on Power Systems.  He now serves as a member of IEEE Committee for power economics and market and IEEE Working Group on network charging. He co-chaires Supergen Energy Networks Hub - Markets and Regulation Working Group. 

Research

Prof Gu’s primary research is concerned with planning and operating resilient smart multi-vector energy systems of electricity, natural gas, heating/cooling and transportation towards zero emissions by 2050 under the dramatic climate change. His research is primarily focused on three areas:

1. Multi-vector energy systems: The centrepiece of his research is to develop novel data-driven and advanced optimisation models for multi-vector energy systems. His research is to develop new robust optimisation and machine learning facilitated models, algorithms and methods for multi-vector energy systems. They can help energy system operators to enhance security costly-efficiently, reduce energy bills for customers, and increase penetration of renewables and low-carbon technologies, promoting the move towards zero emissions.

2. Energy system resilience under climate change: He is developing data-driven methods to study UK transmission system resilience under extreme weather events. It will identify system strengths and weaknesses that can hardly be obtained using traditional model-based approaches, thus informing new system planning and operation practices for network operators, and standards and regulatory frameworks for Ofgem and BEIS.

3. Energy market design: This area is explored with Prof Li at CSPD. The research identifies the future roles of DSOs in future low carbon energy, and designs novel local energy/network markets to facilitate local balancing. This strand of research can inform the pathways of DSO transition, the architecture of future local markets, thus creating a vibrant level playing field for all parties who will reshape the UK energy landscape from now till 2050.

Willing to supervise doctoral students

• Enery system modelling, operation, and markets
• Energy, transportation, economics nexus
• Energy system resilience and impact of climate change
• Application of data analytics and AI to energy systems