My PhD project aims at investigating the electricity system transition to a low-carbon future. We are developing a technology evaluation metric which quantifies the value of a technology within the power system explicitly taking whole-system dynamics into account. The System Value of a technology is a function of the power system conditions and constraints, and varies between first-of-a-kind and nth-of-a-kind power plants.
The heart of the System Value concept is an electricity systems optimisation (ESO) framework based on mixed-integer linear programming taking detailed technical as well as system-level economic, environmental, and security constraints into account. The ESO model can perform short-term and long-term unit commitment and capacity expansion planning for a national-scale power system of up to 2000 units of generation, energy storage, and interconnector technologies.
Prepared for the International Energy Agency (IEA) Greenhouse Gas R&D Programme we are conducting the FLEX-EVAL project which aims at determining the value of flexible CCS power plants and their role in the future UK electricity system.
- Research visit at the Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh
- Master thesis at the Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh
- Master study of Energy Technology at RWTH Aachen University, abroad at MONASH University, Kuala Lumpur
- Bachelor of Science in Mechanical Engineering at RWTH Aachen University, Bachelor thesis at juwi technologies GmbH
et al., 2017, A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks, Computers & Chemical Engineering, Vol:107, ISSN:0098-1354, Pages:247-256
et al., 2017, Power capacity expansion planning considering endogenous technology cost learning, Applied Energy, Vol:204, ISSN:0306-2619, Pages:831-845
et al., Levelised Value of Electricity - A Systemic Approach to Technology Valuation, 26th European Symposium on Computer Aided Process Engineering - ESCAPE 26
et al., 2017, Power Generation Expansion Considering Endogenous Technology Cost Learning, 27th European Symposium on Computer Aided Process Engineering, Elsevier