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

Author

Listed:
  • Ricardo J. Bessa

    (INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

  • Corinna Möhrlen

    (WEPROG, 5610 Assens, Denmark)

  • Vanessa Fundel

    (Deutscher Wetterdienst, 63067 Offenbach, Germany)

  • Malte Siefert

    (Fraunhofer Institute for Wind Energy and Energy System Technology (IWES), 34119 Kassel, Germany)

  • Jethro Browell

    (University of Strathclyde, Department of Electronic and Electrical Engineering, Glasgow G1 1XQ, UK)

  • Sebastian Haglund El Gaidi

    (Royal Institute of Technology, Department of Mechanics, SE-100 44 Stockholm, Sweden)

  • Bri-Mathias Hodge

    (National Renewable Energy Laboratory, Golden, CO 80401, USA)

  • Umit Cali

    (University of North Carolina Charlotte, Dept. of Engineering Technology and Construction Management, Charlotte, NC 28223, USA)

  • George Kariniotakis

    (MINES ParisTech, PSL Research University, Centre for Processes, Renewable Energies and Energy Systems (PERSEE), 06904 Sophia Antipolis Cedex, France)

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. This 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. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

Suggested Citation

  • Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1402-:d:111989
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    as
    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    3. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    4. Roger Koenker & Samantha Leorato & Franco Peracchi, 2013. "Distributional vs. Quantile Regression," CEIS Research Paper 300, Tor Vergata University, CEIS, revised 17 Dec 2013.
    5. Sioshansi, Ramteen & Hurlbut, David, 2010. "Market protocols in ERCOT and their effect on wind generation," Energy Policy, Elsevier, vol. 38(7), pages 3192-3197, July.
    6. Òscar Jordà & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.
    7. Landry, Mark & Erlinger, Thomas P. & Patschke, David & Varrichio, Craig, 2016. "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1061-1066.
    8. Ricardo Bessa & Carlos Moreira & Bernardo Silva & Manuel Matos, 2014. "Handling renewable energy variability and uncertainty in power systems operation," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(2), pages 156-178, March.
    9. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
    10. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
    11. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
    12. Jan Kloppenborg Møller & Marco Zugno & Henrik Madsen, 2016. "Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 189-205, April.
    13. Tilmann Gneiting & Larissa Stanberry & Eric Grimit & Leonhard Held & Nicholas Johnson, 2008. "Rejoinder on: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 256-264, August.
    14. repec:zbw:inwedp:562014 is not listed on IDEAS
    15. Tilmann Gneiting & Larissa Stanberry & Eric Grimit & Leonhard Held & Nicholas Johnson, 2008. "Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 211-235, August.
    16. Ralph L. Keeney, 2002. "Common Mistakes in Making Value Trade-Offs," Operations Research, INFORMS, vol. 50(6), pages 935-945, December.
    17. P. Pinson, 2012. "Very-short-term probabilistic forecasting of wind power with generalized logit–normal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 555-576, August.
    18. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    19. Schmidt, Johannes & Cancella, Rafael & Junior, Amaro Olímpio Pereira, 2014. "Combing windpower and hydropower to decrease seasonal and inter-annual availability of renewable energy sources in Brazil," Discussion Papers DP-56-2014, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    20. Victor Richmond R. Jose & Robert L. Winkler, 2009. "Evaluating Quantile Assessments," Operations Research, INFORMS, vol. 57(5), pages 1287-1297, October.
    21. Gallego-Castillo, Cristobal & Cuerva-Tejero, Alvaro & Lopez-Garcia, Oscar, 2015. "A review on the recent history of wind power ramp forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1148-1157.
    22. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
    23. Pinson, P. & Nielsen, H.Aa. & Madsen, H. & Kariniotakis, G., 2009. "Skill forecasting from ensemble predictions of wind power," Applied Energy, Elsevier, vol. 86(7-8), pages 1326-1334, July.
    24. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, vol. 6(2), pages 1-34, February.
    25. Jakob W. Messner & Achim Zeileis & Jochen Broecker & Georg J. Mayr, 2013. "Improved Probabilistic Wind Power Forecasts with an Inverse Power Curve Transformation and Censored Regression," Working Papers 2013-01, Faculty of Economics and Statistics, Universität Innsbruck.
    Full references (including those not matched with items on IDEAS)

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