Reliability assessment of very large distribution networks using the sequential Monte Carlo metho

Under the current performance-based regulation regimes, challenges are posed to distribution network operators (DNOs) to develop programmes to increase quality of supply, cost effectively. The current Interruption incentive scheme rewards and penalises DNOs relatively to their annual average quality of electricity supply performance. This has led to an increasing interest in the development of techniques and methodologies that can support predictive and quantitative assessments of DNOs' investment strategies in terms of cost and quality of supply benefits achieved. Individual customer's quality of supply performance is also being closely considered by the UK's energy regulator (OFGEM) through Guaranteed Standards (OS) of performance. These set supply levels that must be met by DNOs to each individual customer. Assessment tools are also required capable of quantifying the likelihood that the quality of supply experienced by individual customers will be worse than specified thresholds set by the GS. The assessment of future system performance behaviour should also take into account the natural variability of the performance indices under evaluation. This research is focused on the investigation of techniques and methods for the development of a simulation framework based on the Sequential Monte Carlo Simulation approach. This enables the evaluation of a number of system performance indices including the probability distribution of these indices, which reflect the natural variability of reliability performance indices. The proposed framework can also support distribution system planners in developing cost-effective investment programmes to improve the quality of supply to their customers. The modelling of real distribution systems restoration process is proposed hence techniques are created capable of efficiently modelling the assumed post-fault actions. These techniques are independent of the size of the distribution network being modelled, which allows the successful practical application of the proposed framework to a real and very large UK distribution network. Within this research methodologies have also been developed which investigate multiple interruptions and the duration of restoration of supply under normal and severe weather conditions. The average number of worse performing customers for different standards of performance thresholds is investigated, which illustrates the ability of the developed SMCS framework to quantify the likelihood of customers' performance exceeding specified thresholds. A methodology is investigated which evaluates the impact of the number and availability of fault response teams (FRTs) during the restoration process under severe weather conditions. The key feature of the proposed FRTs deployment strategy is the ability to prioritise faults to be repaired in order to maximise the number of customers to be recovered. The proposed simulation framework can also quantify the economic losses customers experience as a consequence of interruptions to the electricity supply, measured as customer interruption costs.