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Keen interest in the development and utilization of wind-based distributed generations (DGs) has been currently observed worldwide for several reasons. Among those is controlling the emission of environmentally harmful substances, limiting the growth in energy costs associated with the use of conventional energy sources and encouraging the independent power producers for participation in the electricity market system. One of the most important issues is to quantitatively assess the impact of such type of DGs on the distribution system reliability. This paper presents a probabilistic technique to evaluate the distribution system reliability utilizing segmentation concept and a novel constrained Grey predictor technique for wind speed profile estimation. Index Terms--Distributed power generation, distribution system reliability, uncertainty, wind power.

This paper proposes a novel model to estimate the electric energy consumption of light duty fleet of plug-in electric vehicles (PEVs). This model can be used to evaluate the impacts of plugging such loads in distribution networks. Both vehicles users' habits and diversity of usage are considered in the presented model, as well as different electric ranges and ambient temperature effect. Moreover, the paper proposes a method to optimally allocate distributed generation (DG) units in the distribution network to mitigate the impacts of high penetration of PEVs. The proposed model shall help the local distribution companies (LDC) to better assess the expected effects of PEVs on their networks and evaluate the required upgrades. Furthermore, the proposed DG allocation methodology helps to identify the optimal buses on which to connect these DG units in the presence of high PEVs penetration. A genetic based approach is utilized for the planning problem of determining the optimal locations and sizes of DG units, which is defined as a multi-objective mixed integer programming. [ABSTRACT FROM PUBLISHER]