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Modelling of an integrated gas and electricity network with significant wind capacity

Qadrdan, Meysam 2012. Modelling of an integrated gas and electricity network with significant wind capacity. PhD Thesis, Cardiff University.
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Abstract

The large scale integration of wind generation capacity into an electricity network poses technical as well as economic challenges. In this research, three major challenges introduced by wind including non-correlated power output from geographically dispersed wind farms, wind variability and wind uncertainty were studied. In order to address each of the aforementioned challenges an appropriate modelling approach and case studies were used. The impacts of power output from dispersed wind farms on the Great Britain transmission reinforcement were studied using an optimal DC load flow combined with a power generation model. It was shown that Western and Eastern HVDC links play a crucial role to bypass the Scotland to England transmission bottleneck. The impacts of wind variability on the GB gas and electricity network were investigated through application of the Combined gas and Electricity Network (CGEN) Model. Additional gas storage capacity was shown to be an efficient option to compensate for wind variability. Two-stage and multi-stage stochastic programming models were developed to examine the impact of wind forecast uncertainty on the GB electricity and gas networks. Stochastic modelling approaches were shown to be efficient methods for scheduling and operating the system under wind uncertainty. The key contributions of this thesis are the investigation of the impacts of wind generation variability on the gas network, and development of twostage and multi-stage stochastic programming models of integrated gas and electricity network.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Engineering
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Uncontrolled Keywords: gas and electricity network; wind generation variability; Stochastic programming; Probabilistic wind forecast; Transmission reinforcement.
Date of First Compliant Deposit: 30 March 2016
Last Modified: 04 Jun 2017 03:38
URI: http://orca.cf.ac.uk/id/eprint/24178

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