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Measuring the Risk of Supply and Demand Imbalance at the Monthly to Seasonal Scale in France

Author

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  • Bastien Alonzo

    (LMD-IPSL, Ecole Polytechnique, Institut Polytechnique de Paris, ENS, PSL Research University, Sorbonne Université, CNRS, 91120 Palaiseau, France
    Laboratoire de Probabilités et Modèles Aléatoires, Université Paris Diderot-Paris 7, 75013 Paris, France
    CREST, ENSAE Paris, Ecole Polytechnique, Institut Polytechnique de Paris, CNRS, 91120 Palaiseau, France)

  • Philippe Drobinski

    (LMD-IPSL, Ecole Polytechnique, Institut Polytechnique de Paris, ENS, PSL Research University, Sorbonne Université, CNRS, 91120 Palaiseau, France)

  • Riwal Plougonven

    (LMD-IPSL, Ecole Polytechnique, Institut Polytechnique de Paris, ENS, PSL Research University, Sorbonne Université, CNRS, 91120 Palaiseau, France)

  • Peter Tankov

    (CREST, ENSAE Paris, Ecole Polytechnique, Institut Polytechnique de Paris, CNRS, 91120 Palaiseau, France)

Abstract

Transmission system operator (TSOs) need to project the system state at the seasonal scale to evaluate the risk of supply-demand imbalance for the season to come. Seasonal planning of the electricity system is currently mainly adressed using climatological approach to handle variability of consumption and production. Our study addresses the need for quantitative measures of the risk of supply-demand imbalance, exploring the use of sub-seasonal to seasonal forecasts which have hitherto not been exploited for this purpose. In this study, the risk of supply-demand imbalance is defined using exclusively the wind energy production and the consumption peak at 7 pm. To forecast the risks of supply-demand imbalance at monthly to seasonal time horizons, a statistical model is developed to reconstruct the joint probability of consumption and production. It is based on a the conditional probability of production and consumption with respect to indexes obtained from a linear regression of principal components of large-scale atmospheric predictors. By integrating the joint probability of consumption and production over different areas, we define two kind of risk measures: one quantifies the probablity of deviating from the climatological means, while the other, which is the value at risk at 95% confidence level ( V a R 95 ) of the difference between consumption and production, quantifies extreme risks of imbalance. In the first case, the reconstructed risk accurately reproduces the actual risk with over 0.80 correlation in time, and a hit rate around 70–80%. In the second case, we find a mean absolute error (MAE) between the reconstructed and real extreme risk of 2.5 to 2.8 GW, a coefficient of variation of the root mean square error (CV-RMSE) of 3.8% to 4.2% of the mean actual V a R 95 and a correlation of 0.69 and 0.66 for winter and fall, respectively. By applying our model to ensemble forecasts performed with a numerical weather prediction model, we show that forecasted risk measures up to 1 month horizon can outperform the climatology often used as the reference forecast (time correlation with actual risk ranging between 0.54 and 0.82). At seasonal time horizon (3 months), our forecasts seem to tend to the climatology.

Suggested Citation

  • Bastien Alonzo & Philippe Drobinski & Riwal Plougonven & Peter Tankov, 2020. "Measuring the Risk of Supply and Demand Imbalance at the Monthly to Seasonal Scale in France," Energies, MDPI, vol. 13(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4888-:d:415383
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    References listed on IDEAS

    as
    1. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    2. Bett, Philip E. & Thornton, Hazel E., 2016. "The climatological relationships between wind and solar energy supply in Britain," Renewable Energy, Elsevier, vol. 87(P1), pages 96-110.
    3. Leahy, P.G. & Foley, A.M., 2012. "Wind generation output during cold weather-driven electricity demand peaks in Ireland," Energy, Elsevier, vol. 39(1), pages 48-53.
    4. Bett, Philip E & Thornton, Hazel E. & Troccoli, Alberto & De Felice, Matteo & Suckling, Emma & Dubus, Laurent & Saint-Drenan, Yves-Marie & Brayshaw, David J., 2019. "A simplified seasonal forecasting strategy, applied to wind and solar power in Europe," Earth Arxiv kzwqx, Center for Open Science.
    5. de Queiroz, A.R. & Mulcahy, D. & Sankarasubramanian, A. & Deane, J.P. & Mahinthakumar, G. & Lu, N. & DeCarolis, J.F., 2019. "Repurposing an energy system optimization model for seasonal power generation planning," Energy, Elsevier, vol. 181(C), pages 1321-1330.
    6. Alonzo, Bastien & Tankov, Peter & Drobinski, Philippe & Plougonven, Riwal, 2020. "Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height," International Journal of Forecasting, Elsevier, vol. 36(2), pages 515-530.
    7. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
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