A multi-objective transmission reinforcement planning approach for analysing future energy scenarios in the GB network

  • Malcolm Barnacle

Student thesis: Doctoral Thesis


Due to increasing worldwide environmental concern, the United Kingdom (UK) government, under the Climate Change Act (2008), has set a target of at least an 80% reduction in the net UK carbon account, from baseline 1990 levels, by 2050. Recently there has been a rise in the number of low-carbon policy related studies, creating a growing number of national energy scenarios, some of which achieve the emission targets for 2050. A key aspect of evaluating the technical and economic impact of these energy scenarios is in assessing the associated effect on the electrical transmission network. As a result of a new scenario-related generation background, network limitations are likely to occur on the system. By creating a transmission reinforcement plan to alleviate these network issues, a conclusion can be made as to the economic impact of a future scenario to the electrical transmission network; thereby aiding the overall assessment of the scenario. However, by its nature the transmission planning problem is multi-objective with multiple economic conflicts. For a reinforcement designed for the main interconnected transmission system to gain economic approval from the network regulator, the reinforcement needs to alleviate annual network congestion such that the cost savings associated are greater than the capital expenditure and maintenance costs of the project. Further, this reinforcement will need to be established with minimal outages to existing network assets. This thesis proposes a flexible framework to evaluate the thermal and economic effect of applying a future energy scenario to the GB network. This is achieved through locating an optimal set of transmission reinforcement plans for the multi-criteria problem outlined above. The framework utilises a novel systematic algorithm to generate individual reinforcements and overall reinforcement plans for a large-scale multi-voltage network. The systematic algorithm can alter the associated reinforcements should they exacerbate thermal constraints. Specific reinforcements are therefore created for the scenario, and the framework can therefore be used to evaluate a wide range of future scenarios.;The framework is designed to cater for three variations in reinforcement characteristic; location, configuration (line upgrading, single-circuit and double-circuit addition) and thermal capacity. The new framework carries out a thorough exploration of each characteristic and uses a proven multi-objective meta-heuristic technique to perform the optimisation, which can handle complex multi-criteria problems such as transmission network planning effectively. The reinforcement plans generated are assessed against a stochastic, seasonal evaluation of annual network congestion, which reflects the uncertainty of annual generation output and the impact of planned network outages on annual system constraints. Although meta-heuristic techniques have been successfully applied to solve a variant of the multi-objective transmission planning problem proposed in this thesis, these approaches often simplified the reinforcement characteristics considered and the impact of these reinforcements on the objectives involved, and were often tested against small-scale simplified network backgrounds. From the frameworks output, a verdict on the economic impact of a future scenario to the electrical transmission network can be made which considers the different perspectives and complexities of the transmission planning problem. By comparing verdicts, a scenario can be located that is the best route forward, from the perspective of the electrical transmission network, to economically meet governmental emission targets. Hence the approach proposed can be used to improve current understanding on the economic impact of a wide range of penetrations in renewable and conventional generation to the network, to guide governmental energy policy and transmission network owner investment. Results from several scenario studies show that the framework is valuable for use in the evaluation of a UK energy scenario which envisions the continuation of a centralised power system.
Date of Award25 Apr 2017
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SupervisorStuart Galloway (Supervisor) & Graham Ault (Supervisor)

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