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Effects of solar and wind generation integration on feeder hosting capacity

With the increased penetration of distributed generation (DG) utilities are beginning to see impacts on their system, especially on the ability of a feeder to accommodate DG. In this paper we introduce a stochastic simulation framework to assess the effects on hosting capacity from solar and wind generation for various loading scenarios. The general approach includes the use of a k-means clustering algorithm for segmenting and grouping the raw wind, solar, and load data to define patterns and assign probabilities to each pattern. Monte Carlo simulations are adopted for calculating probabilistic outcomes for a variety of wind, solar, and load scenarios, with the use of a distribution planning software. The outcomes of the simulations, i.e., statistics of minimum and maximum feeder hosting capacity, are used to derive their probability distribution functions (pdfs). The pdfs of the minimum and maximum hosting capacity provide insights into the effects on loading from various wind and solar DG scenarios. The proposed framework is illustrated for a representative utility feeder.