DrPaola Falugi
Visiting Researcher
Department of Electrical and Electronic Engineering - Faculty of Engineering
Orcid identifier0000-0002-3619-6936 (opens in a new tab)
- Visiting ResearcherDepartment of Electrical and Electronic Engineering - Faculty of Engineering
- 1107, Electrical Engineering, South Kensington Campus, United Kingdom
RESEARCH
Research Interests
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
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