City Research Online

Abstract

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.

Publication Type:	Article
Additional Information:	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	Feeder Hosting Capacity; Solar Generation; Wind Generation; Stochastic Simulation Framework
Departments:	School of Science & Technology > Engineering

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