My research is mainly motivated by the great challenges that the UK electricity system is facing. It is expected that about 40% of the UK electricity demand will be met by renewable generation by 2020. A low-carbon electricity future requires a massive reduction in the utilisation of conventional electricity generation, transmission and distribution assets. The large-scale deployment of energy storage could mitigate this reduction in utilisation, producing significant savings.
Expansion planning under uncertainty:
Challenges in expansion planning of electricity systems with storage include:
- Consideration of exogenous and endogenous uncertainty with respect to future demand and generation developments.
- Plethora of conventional and smart assets that complement and/or compete with distributed and large-scale storage solutions.
- Consideration of uncertainty at operational time-scales with respect to electrical demand and intermittent power injections due to renewables.
The above problem features necessitate the development of novel stochastic mixed integer-linear optimisation models. Suitable decomposition techniques are being investigated to take advantage of the problem structure and render it tractable for long-term cost-benefit studies that will inform the current debate regarding the deployment of storage in UK and European electricity grids.
Control Theory and Optimization:
My expertise is in the design of efficient optimization-based identification and control methods, such as model predictive control (MPC), to handle nonlinearities and uncertainties in a systematic way. The research is motivated by a variety of problems arising in energy chemical, mechanical and biological systems.
- Model Predictive Control
- Numerical methods for solving optimization, control and estimation problems
- System identification