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Title: Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems

Hybrid energy systems consisting of multiple energy inputs and multiple energy outputs have been proposed to be an effective element to enable ever increasing penetration of clean energy. In order to better understand the dynamic and probabilistic behavior of hybrid energy systems, this paper proposes a model combining Fourier series and autoregressive moving average (ARMA) to characterize historical weather measurements and to generate synthetic weather (e.g., wind speed) data. In particular, Fourier series is used to characterize the seasonal trend in historical data, while ARMA is applied to capture the autocorrelation in residue time series (e.g., measurements minus seasonal trends). The generated synthetic wind speed data is then utilized to perform probabilistic analysis of a particular hybrid energy system con guration, which consists of nuclear power plant, wind farm, battery storage, natural gas boiler, and chemical plant. As a result, requirements on component ramping rate, economic and environmental impacts of hybrid energy systems, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated.
Authors:
ORCiD logo [1] ;  [1]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
Publication Date:
Report Number(s):
INL/JOU-16-39654
Journal ID: ISSN 0360-5442; PII: S036054421631742X
Grant/Contract Number:
AC07-05ID14517
Type:
Accepted Manuscript
Journal Name:
Energy (Oxford)
Additional Journal Information:
Journal Name: Energy (Oxford); Journal Volume: 120; Journal Issue: C; Journal ID: ISSN 0360-5442
Publisher:
Elsevier
Research Org:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org:
USDOE Office of Nuclear Energy (NE)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS; 17 WIND ENERGY; autoregressive moving average; hybrid energy systems; renewable energy integration; synthetic data generation
OSTI Identifier:
1361550
Alternate Identifier(s):
OSTI ID: 1397060