Diversification of the energy mix is one of the main challenges in the energy agenda of governments worldwide. Technology advances together with environmental concerns have paved the way for the increasing integration of Distributed Generation (DG) seen over recent years. Combined heat and power and renewable technologies are being encouraged and their penetration in distribution networks is increasing. This scenario presents Distribution Network Operators (DNOs) with several technical challenges in order to properly accommodate DG developments. However, depending on various factors, such as location, size, technology and robustness of the network, DG might also be beneficial to DNOs. In this thesis, the impact of DG on network planning is analysed and the implications for DNOs in incorporating DG within the network planning process are identified. In the first part, various impacts of DG to the network, such as network thermal capacity release, security of supply and on voltage, are quantified through network planning by using a modified successive elimination method and voltage sensitivity analysis. The results would potentially assist DNOs in assessing the possibilities and effort required to utilise privately-owned DG to improve network efficiency and save investment. The quantified values would also act as a fundamental element in deriving effective distribution network charging schemes. In the second part, a novel balanced genetic algorithm is introduced as an efficient means of tackling the problem of optimum network planning considering future uncertainties. The approach is used to analyse the possibilities, potential benefits and challenges to strategic network planning by considering the presence of DG in the future when the characteristics of DG are uncertain.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:562980 |
| Date | January 2010 |
| Creators | Wang, David Tse-Chi |
| Contributors | Harrison, Gareth P. : Ochoa, Luis F. |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/4621 |
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