This thesis draws on techniques from Management Science and Artificial Intelligence to explore asset management in electricity transmission enterprises. In this research, factors that influence policies and practices of asset management within electricity transmission enterprises have been identified, in order to examine their interaction and how they impact the policies, practices and performance of transmission businesses. It has been found that, while there is extensive literature on the economics of transmission regulation and pricing, there is little published research linking the engineering and financial aspects of transmission asset management at a management policy level. To remedy this situation, this investigation has drawn on a wide range of literature, together with expert interviews and personal knowledge of the electricity industry, to construct a conceptual model of asset management with broad applicability across transmission enterprises in different parts of the world. A concise representation of the model has been formulated using a Causal Loop Diagram (CLD).
To investigate the interactions between factors of influence it is necessary to implement the model and validate it against known outcomes. However, because of the nature of the data (a mix of numeric and non-numeric data, imprecise, incomplete and often approximate) and complexity and imprecision in the definition of relationships between elements, this problem is intractable to modelling by traditional engineering methodologies. The solution has been to utilise techniques from other disciplines. Two implementations have been explored: a multi-level fuzzy rule-based model and a system dynamics model; they offer different but complementary insights into transmission asset management. Each model shows potential to be used by transmission businesses for strategic-level decision support.
The research demonstrates the key impact of routine maintenance effectiveness on the condition and performance of transmission system assets. However, performance of the transmission network, is not only related to equipment performance, but is a function of system design and operational aspects, such as loading and load factor. Type and supportiveness of regulation, together with the objectives and corporate culture of the transmission organisation also play roles in promoting various strategies for asset management. The cumulative effect of all these drivers is to produce differences in asset management policies and practices, discernable between individual companies and at a regional level, where similar conditions have applied historically and today
