Although power system reliability analysis is a mature research area, there is a renewed interest in updating available network models and formulating improved reliability assessment procedures. The main driver of this interest is the current transition to a new flexible and actively controlled power supply system with a high penetration of distributed generation (DG) and energy storage (ES) technologies, wider implementation of demand-side management (DSM) and application of automated control, monitoring, protection and communication infrastructures. One of the aims of this new electricity supply network (’the smart grid’) is an improved reliability and power quality performance, realised through the delivery of an uninterrupted and high-quality supply of electrical energy. However, there is currently no integrated methodology to measure the effects of these changes on the overall system reliability performance. This PhD research aims to update the standard power system simulation engine with improved numerical software models offering new capabilities for the correct assessment of quality of supply in future electricity networks. The standard reliability analysis is extended to integrate some relevant power quality aspects, enabling the classification of short and long supply interruptions by the correct modelling of network protection and reconfiguration schemes. In addition, the work investigates the formulation and analysis of updated reliability indicators for a more accurate validation and benchmarking of both system and end-user performance. A detailed database with typical configurations and parameters of UK/European power systems is established, providing a set of generic models that can correctly represent actual distribution networks supplying a mix of residential, commercial and industrial demand for different load sectors. A general methodology for reducing system complexity by calculating both electrical and reliability equivalent models of LV and MV distribution networks is also presented. These equivalent models, based on the aggregation of individual component models, help to reduce calculation times while preserving the accuracy assessment of network’s reliability performance at bulk supply points. In addition, the aggregated counterparts (same and mixed-type) of different ’smart’ component models (DG, ES and DSM) are also included in the analysis, showing how their co-ordinated implementation and control could improve quality of supply. Conventional reliability assessment procedures are also extended in this thesis to include accurate reliability equivalent models, network contingency statistics, actual load profiles and empirical fault probability distributions, which are employed to assess the frequency and duration of interruptions in the supply system for different scenarios. Both analytical and probabilistic simulation techniques (Monte Carlo method) are developed to include up-to-date security of supply legislation, introducing a new methodology for calculating the standard set of indices reported annually to energy regulators.