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Title: Barriers to the implementation of Flexible Demand services within the GB electricity generation and supply system
Author: Hodgson, Graeme
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2013
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The implementation of a low carbon electricity system within the GB requires a significant change to the generation mix with an increasing role for renewable generation. Much of this generation will be intermittent. To date system balancing has largely relied on predicting demand and ensuring provision. With substantial intermittency, continuation of this paradigm necessitates significant investment in peaking plant and/or storage. However, some of this investment can be avoided by harnessing the flexibility inherent in many electrical loads. Despite the attractiveness of such services, we do not see their large-scale implementation. The aim of this thesis is to consider why. A historical analysis reveals that both nationalisation and subsequent privatisation provide precedents for significant structural change as the integration of large-scale flexible demand might require. The need for political will is identified as a crucial enabling factor. Without an ideological driver, however, a perception of economic and/or technological risk can preclude the implementation of supportive policy. This perception is addressed through demonstration. An effective demonstration must show the ability to aggregate many small loads in a coordinated manner. A genetic algorithm that provides this core dispatch and optimisation capability is presented. This algorithm is shown to be effective in aggregating many small loads to provide a net effect that can be used as a balancing service and to do so in an optimal way considering both cost and reliability. Having demonstrated feasibility appropriate incentives must be created. An initial outline for a framework based on SysML is presented that can be used to identify where structural barriers to implementation are present to aid the design of appropriate policy incentives.
Supervisor: Not available Sponsor: Engineering and Physical Sciences Research Council ; E.ON New Build & Technology Ltd
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Demand response ; Flexible demand ; Low carbon electricity ; Scheduling ; Combinatorial optimisation ; Genetic algorithms ; Enterprise relationship modelling ; SysML