Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604882
Title: Optimal generation expansion planning for a low carbon future
Author: Yuan, Chenchen
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2013
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Abstract:
Due to energy scarcity coupled with environment issues, it is likely to see the biggest shift in generation portfolio in the UK and world wide, stimulated by various governmental incentives policies for promoting renewable generation and reducing emission. The generation expansion in the future will be driven by not only peak demand growth but also emission reduction target. Thus, the traditional generation expansion planning (GEP) model has to be improved to reflect this change against the new environment. The policy makers need a better assessment tool to facilitate the new environment, so they can make appropriate policies for promoting renewable generation and emission reduction, and guide the generation mix to evolve appropriately over time. Since the expansion of new generation capacities is highly capital intensive, it makes the improvement of GEP quite urgent and important. The thesis proposes the GEP modelling improvement works from the following aspects: • Integrating short-term emission cost, unit commitment constraints in an emission target constrained GEP model. • Including the network transmission constraints and generation location optimization in an emission constrained GEP. • Investigating the impacts of multi-stage emission targets setting on an emission constrained GEP problem and its overall expansion cost. • Incorporating the uncertain renewable generation expansion and short-term DSR into the GEP problem and find out its potential contributions to the GEP problem. A real case study is made to determine the optimal generation mix of the Great Britain in 2020 in order to meet the 2020 emission reduction target. Different optimal generation mixes of the UK in 2020 are identified under a series of scenarios. The scenarios are constructed according to different GB network transmission capacity hypotheses and demand side response (DSR) level scenarios.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.604882  DOI: Not available
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