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Title: Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Abstract

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster themore » integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9]
  1. INESC Technology and Science, Porto (Portugal)
  2. Weather & Energy PROGnoses (WEPROG), Assens (Denmark)
  3. German Weather Service, Offenbach (Germany)
  4. Fraunhofer Inst. for Wind Energy and Energy System Technology (IWES), Kasse (Germany)
  5. Univ. of Strathclyde, Glasgow (United Kingdom). Dept. of Electronic and Electrical Engineering
  6. KTH Royal Inst. of Technology, Stockholm (Sweden). Dept. of Mechanics
  7. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  8. Univ. of North Carolina, Charlotte, NC (United States). Dept. of Engineering Technology and Construction Management
  9. Mines ParisTech and PSL Research Univ., Cedex (France). . Centre for Processes, Renewable Energies and Energy Systems (PERSEE)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1399352
Report Number(s):
NREL/JA-5D00-70107
Journal ID: ISSN 1996-1073; ENERGA
Grant/Contract Number:
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energies (Basel)
Additional Journal Information:
Journal Name: Energies (Basel); Journal Volume: 10; Journal Issue: 9; Journal ID: ISSN 1996-1073
Publisher:
MDPI AG
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; wind energy; uncertainty; decision-making; quantiles; ensembles; forecast; statistics; weather

Citation Formats

Bessa, Ricardo, Möhrlen, Corinna, Fundel, Vanessa, Siefert, Malte, Browell, Jethro, Haglund El Gaidi, Sebastian, Hodge, Bri-Mathias, Cali, Umit, and Kariniotakis, George. Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry. United States: N. p., 2017. Web. doi:10.3390/en10091402.
Bessa, Ricardo, Möhrlen, Corinna, Fundel, Vanessa, Siefert, Malte, Browell, Jethro, Haglund El Gaidi, Sebastian, Hodge, Bri-Mathias, Cali, Umit, & Kariniotakis, George. Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry. United States. doi:10.3390/en10091402.
Bessa, Ricardo, Möhrlen, Corinna, Fundel, Vanessa, Siefert, Malte, Browell, Jethro, Haglund El Gaidi, Sebastian, Hodge, Bri-Mathias, Cali, Umit, and Kariniotakis, George. Thu . "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry". United States. doi:10.3390/en10091402. https://www.osti.gov/servlets/purl/1399352.
@article{osti_1399352,
title = {Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry},
author = {Bessa, Ricardo and Möhrlen, Corinna and Fundel, Vanessa and Siefert, Malte and Browell, Jethro and Haglund El Gaidi, Sebastian and Hodge, Bri-Mathias and Cali, Umit and Kariniotakis, George},
abstractNote = {Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.},
doi = {10.3390/en10091402},
journal = {Energies (Basel)},
number = 9,
volume = 10,
place = {United States},
year = {Thu Sep 14 00:00:00 EDT 2017},
month = {Thu Sep 14 00:00:00 EDT 2017}
}

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