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Electric vehicles, EVs, provide temporary distributed energy storage capacity for the evolving distribution grid. An aggregated storage capacity of multiple EVs is more meaningful for a distribution grid. Commercial car parks at metropolitan cities and parking lots (PLs) of hospitals, shopping centres, universities etc. are the potential areas that can be organised for these purposes. This study presents a distributed storage capacity formulation of an EV PL that can be used in distribution system reliability improvement. The storage capacity was determined by sequential Monte-Carlo simulations. Real car arrival-departure data provided by the PL authority of Istanbul, technical parameters (SoC of the batteries, battery capacity of EVs, charging and discharging rates), and some side-specific probabilistic parameters (driver behaviour, mean transportation distance etc.) were used for realistic model development. Special attention was given to some critical time periods when the storage support was more crucial for the utility grid. Moreover, future expectations for city urban planning and for travel behaviour of the community were also included in the model. Finally, impacts of several important parameters on the storage capacity of the PL were determined through sensitivity analysis. [ABSTRACT FROM AUTHOR]

In a competitive electric market, high maintenance costs brought the necessity of process recruitment to increase the maintenance process efficiency and to extend the asset life time. Reliability centred maintenance has been continually improved together with proper asset management techniques to meet this necessity. This study presents an improved power transformer maintenance plan for reliability centred asset management of Turkish National Power Transmission System. At first, Markov model of existing maintenance procedure is constructed. Model solutions are used to compute the reliability indices and the total costs of the transformer. Then, sensitivity analysis was performed to determine effective transition rates of the model. Finally, optimal maintenance rates and corresponding reliability indices, total cost and life time were determined using actual service data and estimated transition rates. The results are compared with the existing maintenance plan. [ABSTRACT FROM AUTHOR]