Quantitative techniques for analysis of large data sets in renewable distributed generation

Distributed generation (DG) reduces losses and eliminates some of the transmission and distribution costs. It may also reduce fossil fuel emissions, defer capital costs, and improve the distribution feeder voltage conditions. The calculation of the effects of small residential photovoltaic and wind DG systems on various feeder operating variables is complicated by both the probabilistic nature of their output and the variety of their possible spatial allocations. A method based on a combination of clustering techniques and a convex hull algorithm is proposed that may reduce the computational burden by an order of magnitude, while still allowing accurate estimation of DG-enhanced feeder operation.