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Using synthetic traces for robust energy system sizing

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1145/3307772.3328306
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FormatText, Article
Conferencee-Energy '19: The Tenth ACM International Conference on Future Energy Systems, June 25-28, 2019, Phoenix, Arizona
Subjectmathematics of computing; solar; storage; generative models
Abstract
Due to the inherent randomness of both solar power generation and residential electrical load, jointly sizing solar panel and storage capacity to meet a given quality-of-service (QoS) constraint is challenging. The challenge is greater when there is limited representative historical data. We therefore propose generating synthetic solar and load traces, corresponding to different realizations of the underlying stochastic processes. Specifically, we compare the effectiveness of three generative models: autoregressive moving-average (ARMA) models, Gaussian mixture models (GMMs), and generative adversarial networks (GANs) -- as well as two direct sampling methods -- for synthetic trace generation. These traces are then used for robust joint sizing by a technique described in recent work. Extensive experiments based on real data show that our approach finds robust sizing with only one year's worth of hourly trace data. Moreover, assuming that solar data are available, given a database of load traces, we demonstrate how to perform robust sizing with access to only twelve data points of load, one for each month of one year.
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PublisherACM
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LanguageEnglish
Peer reviewedYes
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"Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.

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Record created2021-09-10
Record modified2021-09-14
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