Imperial College London

DrPaolaFalugi

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate
 
 
 
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Contact

 

p.falugi

 
 
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Location

 

1107Electrical EngineeringSouth Kensington Campus

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Summary

 

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