@inproceedings{79e76c47b21c43a1a331a076f4477177,
title = "Representative Scenarios to Capture Renewable Generation Stochasticity and Cross-Correlations",
abstract = "Generating representative scenarios for power system planning in which the stochasticity of renewable generation and cross-correlations between renewables and load are fully captured, is a challenging problem. Traditional methods for scenario generation often fail to generate diverse scenarios that include both seasonal (frequently occurring) and atypical (extreme) days required for planning purposes. This paper presents a methodical approach to generate representative scenarios. It also proposes new metrics that are more relevant for evaluating the generated scenarios from an applications perspective. When applied to historical data from a power utility, the proposed approach resulted in scenarios that included a good mix of seasonal and atypical days. The results also demonstrated pertinence of the proposed cluster validation metrics. Finally, the paper presents a trade-off for determining optimal number of scenarios for a given application.",
keywords = "Cluster validation, Clustering, Dynamic time warping, Renewable planning, Scenario generation",
author = "Dhaval Dalal and Anamitra Pal and Philip Augustin",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Power and Energy Society General Meeting, PESGM 2022 ; Conference date: 17-07-2022 Through 21-07-2022",
year = "2022",
doi = "10.1109/PESGM48719.2022.9917243",
language = "English (US)",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2022 IEEE Power and Energy Society General Meeting, PESGM 2022",
}