Operational optimisation of smart grid

Project description

Smart grid operational optimisation problems are characterised by abundant data, far-reaching control and persuasively
large potential benefits. However, due to time constraints, these problems tend to exceed the tractability limits of exact solution methods. Following an investigation of the trade-off between solution timeliness and accuracy in the context of a receding horizon controller with a heuristic subroutine, this work develops a smart grid controller which employs decision trees and thereby resides at the timely end of the spectrum. The following major contributions have been made:

  • Formulation of a theoretical argument for the role of control period selection in the operational performance of smart grids under the simulation-optimisation control scheme.


  • Presentation of experimental evidence supporting the identification of control period selection as an important factor in the operational optimisation of smart grids. Development of a new smart grid controller based on a learnt decision tree ensemble.


  • Experimental evidence supporting the suitability of the new controller for supply-temperature management in large district heating networks.


  • Development and comparison of control strategies for charge scheduling of plug-in hybrid electric vehicles.

KCB Steer, A Wirth, SK Halgamuge, "Decision tree ensembles for online operation of large smart grids", Energy Conversion and Management, 59, 9-18.

Project team

Leader: Saman Halgamuge

Staff: Saman Halgamuge,Andrew Wirth

Collaborators: Dr Kent Steer (IBM), Rudolf Kruse (University of Magdeburg)

Sponsors: DAAD/Go8

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