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Decentralised Optimisation and Control in Electrical Power Systems

LOUKARAKIS, EMMANOUIL (2016) Decentralised Optimisation and Control in Electrical Power Systems. Doctoral thesis, Durham University.

PDF (PhD Thesis) - Accepted Version


Emerging smart-grid-enabling technologies will allow an unprecedented degree of observability and control at all levels in a power system. Combined with flexible demand devices (e.g. electric vehicles or various household appliances), increased distributed generation, and the potential development of small scale distributed storage, they could allow procuring energy at minimum cost and environmental impact. That however presupposes real-time coordination of demand of individual households and industries down at the distribution level, with generation and renewables at the transmission level. In turn this implies the need to solve energy management problems of a much larger scale compared to the one we currently solve today. This of course raises significant computational and communications challenges.
The need for an answer to these problems is reflected in today’s power systems literature where a significant number of papers cover subjects such as generation and/or demand management at both transmission and/or distribution, electric vehicle charging, voltage control devices setting, etc. The methods used are centralized or decentralized, handling continuous and/or discrete controls, approximate or exact, and incorporate a wide range of problem formulations. All these papers tackle aspects of the same problem, i.e. the close to real-time determination of operating set-points for all controllable devices available in a power system. Yet, a consensus regarding the associated formulation and time-scale of application has not been reached. Of course, given the large scale of the problem, decentralization is unavoidably part of the solution. In this work we explore the existing and developing trends in energy management and place them into perspective through a complete framework that allows optimizing energy usage at all levels in a power system.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Faculty and Department:Faculty of Science > Engineering and Computing Science, School of (2008-2017)
Thesis Date:2016
Copyright:Copyright of this thesis is held by the author
Deposited On:01 Jun 2016 10:01

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