IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v283y2021ics0306261920316846.html
   My bibliography  Save this article

Residual electricity demand: An empirical investigation

Author

Listed:
  • Do, Linh Phuong Catherine
  • Lyócsa, Štefan
  • Molnár, Peter

Abstract

Residual electricity demand represents the load that cannot be met by renewable production and that therefore must be provided by conventional power plants, electricity imports or storage capacity. Residual demand is thus a key variable for power system operators and electricity market participants. However, the literature lacks a comprehensive study exploring the drivers of residual demand. Using linear and quantile regression models, we are able to identify previous demand, major and minor holidays, day of the week and temperature as having a significant influence on demand and residual demand. However, the influence of these factors differs not only for lower (left-) and upper (right-tail) levels of total and residual demand but also for total and residual demand during the day. We find that i) the influence of the outside temperature on electricity demand is weakened by the spatial variation in the temperature across a country, ii) the heating and cooling degree influences residual demand much more than they influence total demand, and iii) residual demand is much harder to predict than total demand. Our results imply, that electricity producers, risk managers, market participants and policy makers need comprehensive empirical models to predict residual demand.

Suggested Citation

  • Do, Linh Phuong Catherine & Lyócsa, Štefan & Molnár, Peter, 2021. "Residual electricity demand: An empirical investigation," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316846
    DOI: 10.1016/j.apenergy.2020.116298
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920316846
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.116298?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Haas, Reinhard & Lettner, Georg & Auer, Hans & Duic, Neven, 2013. "The looming revolution: How photovoltaics will change electricity markets in Europe fundamentally," Energy, Elsevier, vol. 57(C), pages 38-43.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Osborn, Denise R, et al, 1988. "Seasonality and the Order of Integration for Consumption," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(4), pages 361-377, November.
    4. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sousa, Ricardo M., 2014. "Energy prices and CO2 emission allowance prices: A quantile regression approach," Energy Policy, Elsevier, vol. 70(C), pages 201-206.
    5. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    6. Hui, Wallace & Gel, Yulia R. & Gastwirth, Joseph L., 2008. "lawstat: An R Package for Law, Public Policy and Biostatistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i03).
    7. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    8. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    9. Hagfors, Lars Ivar & Bunn, Derek & Kristoffersen, Eline & Staver, Tiril Toftdahl & Westgaard, Sjur, 2016. "Modeling the UK electricity price distributions using quantile regression," Energy, Elsevier, vol. 102(C), pages 231-243.
    10. Mark J. Kamstra & Lisa A. Kramer & Maurice D. Levi, 2003. "Winter Blues: A SAD Stock Market Cycle," American Economic Review, American Economic Association, vol. 93(1), pages 324-343, March.
    11. Do, Linh Phuong Catherine & Lin, Kuan-Heng & Molnár, Peter, 2016. "Electricity consumption modelling: A case of Germany," Economic Modelling, Elsevier, vol. 55(C), pages 92-101.
    12. Lee, Chien-Chiang & Zeng, Jhih-Hong, 2011. "The impact of oil price shocks on stock market activities: Asymmetric effect with quantile regression," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1910-1920.
    13. F. Cribari-Neto & S. G. Zarkos, 1999. "Bootstrap methods for heteroskedastic regression models: evidence on estimation and testing," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 211-228.
    14. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    15. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    16. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    17. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    18. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238.
    19. Hirth, Lion, 2013. "The market value of variable renewables," Energy Economics, Elsevier, vol. 38(C), pages 218-236.
    20. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    21. Schill, Wolf-Peter, 2014. "Residual Load, Renewable Surplus Generation and Storage Requirements in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 73, pages 65-79.
    22. Tarroja, Brian & Mueller, Fabian & Eichman, Joshua D. & Samuelsen, Scott, 2012. "Metrics for evaluating the impacts of intermittent renewable generation on utility load-balancing," Energy, Elsevier, vol. 42(1), pages 546-562.
    23. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
    24. Bašta, Milan & Helman, Karel, 2013. "Scale-specific importance of weather variables for explanation of variations of electricity consumption: The case of Prague, Czech Republic," Energy Economics, Elsevier, vol. 40(C), pages 503-514.
    25. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    26. David Wozabal & Christoph Graf & David Hirschmann, 2016. "The effect of intermittent renewables on the electricity price variance," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(3), pages 687-709, July.
    27. Nicolosi, Marco, 2010. "Wind power integration and power system flexibility-An empirical analysis of extreme events in Germany under the new negative price regime," Energy Policy, Elsevier, vol. 38(11), pages 7257-7268, November.
    28. Andreas Wagner, 2014. "Residual Demand Modeling and Application to Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    29. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
    30. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    31. Donggyu Sul & Peter C. B. Phillips & Chi‐Young Choi, 2005. "Prewhitening Bias in HAC Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(4), pages 517-546, August.
    32. Lion Hirth, 2013. "The Market Value of Variable Renewables. The Effect of Solar and Wind Power Variability on their Relative Price," RSCAS Working Papers 2013/36, European University Institute.
    33. Linh Phuong Catherine Do & Štefan Lyócsa & Peter Molnár, 2019. "Impact of wind and solar production on electricity prices: Quantile regression approach," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1752-1768, October.
    34. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    35. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maria Juliana Suarrez Foréro & Frédéric Lantz & Pierre Nicolas & Patrice Geoffron, 2022. "The Impact of Electric Vehicle Fleets on the European Electricity Markets: Evidences from the German Passenger Car Fleet and Power Generation Sector," Working Papers hal-03898558, HAL.
    2. Khawaja Haider Ali & Mohammad Abusara & Asif Ali Tahir & Saptarshi Das, 2023. "Dual-Layer Q-Learning Strategy for Energy Management of Battery Storage in Grid-Connected Microgrids," Energies, MDPI, vol. 16(3), pages 1-17, January.
    3. Gallego, Camilo A., 2022. "Intertemporal effects of imperfect competition through forward contracts in wholesale electricity markets," Energy Economics, Elsevier, vol. 107(C).
    4. Maria Juliana Suarrez Foréro & Frédéric Lantz & Pierre Nicolas & Pierre Geoffron, 2022. "The impact of Electric Vehicle fleets on the European Electricity Markets : Evidences from the German Passenger Car Fleet and Power Generation Sector," Working Papers hal-03609361, HAL.
    5. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2022. "Energy storage to solve the diurnal, weekly, and seasonal mismatch and achieve zero-carbon electricity consumption in buildings," Applied Energy, Elsevier, vol. 312(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    2. Lyócsa, Štefan, 2014. "Growth-returns nexus: Evidence from three Central and Eastern European countries," Economic Modelling, Elsevier, vol. 42(C), pages 343-355.
    3. Sirin, Selahattin Murat & Yilmaz, Berna N., 2020. "Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect," Energy Policy, Elsevier, vol. 144(C).
    4. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
    5. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    6. Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Similarity of emerging market returns under changing market conditions: Markets in the ASEAN-4, Latin America, Middle East, and BRICs," Economic Systems, Elsevier, vol. 39(2), pages 253-268.
    7. Rice, William L. & Park, So Young & Pan, Bing & Newman, Peter, 2019. "Forecasting campground demand in US national parks," Annals of Tourism Research, Elsevier, vol. 75(C), pages 424-438.
    8. Diaz-Emparanza, Ignacio, 2014. "Numerical distribution functions for seasonal unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 237-247.
    9. Zipp, Alexander, 2017. "The marketability of variable renewable energy in liberalized electricity markets – An empirical analysis," Renewable Energy, Elsevier, vol. 113(C), pages 1111-1121.
    10. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    11. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    12. Gil-Alana, L.A., 2008. "Testing of seasonal integration and cointegration with fractionally integrated techniques: An application to the Danish labour demand," Economic Modelling, Elsevier, vol. 25(2), pages 326-339, March.
    13. Zhang, Shu & Wang, Yi & Zhang, Yutian & Wang, Dan & Zhang, Ning, 2020. "Load probability density forecasting by transforming and combining quantile forecasts," Applied Energy, Elsevier, vol. 277(C).
    14. Eduard Baumöhl & Štefan Lyócsa, 2014. "Risk-Return Convergence in CEE Stock Markets: Structural Breaks and Market Volatility," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(5), pages 352-373, November.
    15. Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš, 2022. "YOLO trading: Riding with the herd during the GameStop episode," Finance Research Letters, Elsevier, vol. 46(PA).
    16. Burridge, Peter & Taylor, A. M. Robert, 2001. "On regression-based tests for seasonal unit roots in the presence of periodic heteroscedasticity," Journal of Econometrics, Elsevier, vol. 104(1), pages 91-117, August.
    17. Ikerne Valle & Kepa Astorkiza & Ignacio Díaz-Emparanza, 2017. "Measuring species concentration, diversification and dependency in a macro-fishery," Empirical Economics, Springer, vol. 52(4), pages 1689-1713, June.
    18. Thomas Mobius & Mira Watermeyer & Oliver Grothe & Felix Musgens, 2023. "Enhancing Energy System Models Using Better Load Forecasts," Papers 2302.11017, arXiv.org.
    19. Pulapre Balakrishnan & M Parameswaran, 2019. "Modeling the Dynamics of Inflation in India," Working Papers 16, Ashoka University, Department of Economics.
    20. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316846. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.