IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v128y2023ics0140988323006710.html

Impact of renewable energy generation on power reserve energy demand

Author

Listed:
  • Deman, Laureen
  • Boucher, Quentin

Abstract

With increasing shares of renewable energy generation, the evolution of reserve needs may represent an important challenge for the power system. The evolution of reserve capacity needs in this context has been extensively studied in the literature (Hirth and Ziegenhagen, 2015). On the other hand, reserve energy needs have been less studied. This work focuses on analysing the relationship between reserve energy demand and load, wind, and solar generation. To this end, autoregressive models with exogenous variables are estimated on 2019 data of the French power system. We find a positive impact of renewable energy generation on downward reserve energy demand and a negative impact on upward reserve energy demand. These results can be interpreted through the relationship between the level of residual load and the probability to find a counterpart on the intraday market to balance forecast errors, thus allowing to reduce the imbalance of the system in real-time and thus the need for reserve energy.

Suggested Citation

  • Deman, Laureen & Boucher, Quentin, 2023. "Impact of renewable energy generation on power reserve energy demand," Energy Economics, Elsevier, vol. 128(C).
    • Handle: RePEc:eee:eneeco:v:128:y:2023:i:c:s0140988323006710
    DOI: 10.1016/j.eneco.2023.107173
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988323006710
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2023.107173?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

    for a different version of it.

    References listed on IDEAS

    as
    1. repec:aen:journl:ej35-1-06 is not listed on IDEAS
    2. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    3. Chaves-Ávila, J.P. & Fernandes, C., 2015. "The Spanish intraday market design: A successful solution to balance renewable generation?," Renewable Energy, Elsevier, vol. 74(C), pages 422-432.
    4. Keele, Luke & Kelly, Nathan J., 2006. "Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables," Political Analysis, Cambridge University Press, vol. 14(2), pages 186-205, April.
    5. Hickey, Emily & Loomis, David G. & Mohammadi, Hassan, 2012. "Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs," Energy Economics, Elsevier, vol. 34(1), pages 307-315.
    6. Kiesel, Rüdiger & Paraschiv, Florentina, 2017. "Econometric analysis of 15-minute intraday electricity prices," Energy Economics, Elsevier, vol. 64(C), pages 77-90.
    7. Brijs, Tom & De Jonghe, Cedric & Hobbs, Benjamin F. & Belmans, Ronnie, 2017. "Interactions between the design of short-term electricity markets in the CWE region and power system flexibility," Applied Energy, Elsevier, vol. 195(C), pages 36-51.
    8. Schwert, G William, 2002. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 5-17, January.
    9. Kristof De Vos & Nicolas Stevens & Olivier Devolder & Anthony Papavasiliou & Bob Hebb & James Matthys-Donnadieu, 2019. "Dynamic dimensioning approach for operations reserves: proof of concept in Belgium," LIDAM Reprints CORE 2993, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
    11. Vandezande, Leen & Meeus, Leonardo & Belmans, Ronnie & Saguan, Marcelo & Glachant, Jean-Michel, 2010. "Well-functioning balancing markets: A prerequisite for wind power integration," Energy Policy, Elsevier, vol. 38(7), pages 3146-3154, July.
    12. Artur Silva Lopes, 2006. "Deterministic seasonality in Dickey–Fuller tests: should we care?," Empirical Economics, Springer, vol. 31(1), pages 165-182, March.
    13. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    14. repec:aen:journl:ej37-si2-praktiknjo is not listed on IDEAS
    15. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    16. Hirth, Lion & Ziegenhagen, Inka, 2015. "Balancing power and variable renewables: Three links," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1035-1051.
    17. Kyritsis, Evangelos & Andersson, Jonas & Serletis, Apostolos, 2017. "Electricity prices, large-scale renewable integration, and policy implications," Energy Policy, Elsevier, vol. 101(C), pages 550-560.
    18. repec:aen:journl:ej34-1-05 is not listed on IDEAS
    19. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Yang, Shanju & Zhang, Yao & Gao, Zening & Liu, Zhan, 2024. "Isobaric compressed air energy storage system: Water compensating cycle or CO2 compensating cycle?," Energy, Elsevier, vol. 312(C).
    2. Jai-Oan Cho & Jeong Ik Lee & Staffan Qvist, 2024. "Global Residual Demand Analysis in a Deep Variable Renewable Energy Penetration Scenario for Replacing Coal: A Study of 42 Countries," Energies, MDPI, vol. 17(6), pages 1-15, March.
    3. Zheng, J.H. & Su, Y.Y. & Guo, Y.W. & Li, Yuanzheng & Wu, Q.H., 2025. "Preference-based interactive multi-attribute decision-making support for stochastic scheduling of virtual power plants," Energy Economics, Elsevier, vol. 152(C).
    4. Richard, M. & Solier, B., 2025. "Dealing with renewables integration: A comparative study of Belgian and French balancing systems," Energy Economics, Elsevier, vol. 151(C).
    5. Deng, Xu & Lv, Tao & Meng, Xiangyun & Li, Cong & Hou, Xiaoran & Xu, Jie & Wang, Yinhao & Liu, Feng, 2024. "Assessing the carbon emission reduction effect of flexibility option for integrating variable renewable energy," Energy Economics, Elsevier, vol. 132(C).
    6. Zhou, Wei & Chen, Yan & Zhang, Cesheng, 2025. "How does FinTech promote the low-carbon transformation of energy consumption in China?," Energy Economics, Elsevier, vol. 149(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. Richard, M. & Solier, B., 2025. "Dealing with renewables integration: A comparative study of Belgian and French balancing systems," Energy Economics, Elsevier, vol. 151(C).
    2. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    3. Gürtler, Marc & Paulsen, Thomas, 2018. "The effect of wind and solar power forecasts on day-ahead and intraday electricity prices in Germany," Energy Economics, Elsevier, vol. 75(C), pages 150-162.
    4. Hu, Jing & Harmsen, Robert & Crijns-Graus, Wina & Worrell, Ernst & van den Broek, Machteld, 2018. "Identifying barriers to large-scale integration of variable renewable electricity into the electricity market: A literature review of market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2181-2195.
    5. Christopher Koch & Philipp Maskos, 2020. "Passive Balancing Through Intraday Trading: Whether Interactions Between Short-term Trading and Balancing Stabilize Germany s Electricity System," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 101-112.
    6. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Rai, Alan & Konstandatos, Otto, 2022. "Large-scale and rooftop solar generation in the NEM: A tale of two renewables strategies," Energy Economics, Elsevier, vol. 115(C).
    7. Spodniak, Petr & Ollikka, Kimmo & Honkapuro, Samuli, 2019. "The Relevance of Wholesale Electricity Market Places: The Nordic Case," Working Papers 126, VATT Institute for Economic Research.
    8. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    9. Sirin, Selahattin Murat & Yilmaz, Berna N., 2021. "The impact of variable renewable energy technologies on electricity markets: An analysis of the Turkish balancing market," Energy Policy, Elsevier, vol. 151(C).
    10. Bierens, H.J. & Broersma, L., 1991. "The relation between unemployment and interest rate : some international evidence," Serie Research Memoranda 0112, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    11. Marchetti, Isabella & Rego, Erik Eduardo, 2022. "The impact of hourly pricing for renewable generation projects in Brazil," Renewable Energy, Elsevier, vol. 189(C), pages 601-617.
    12. Angelica, Gianfreda & Lucia, Parisio & Matteo, Pelagatti, 2017. "The RES-induced Switching Effect Across Fossil Fuels: An Analysis of the Italian Day-Ahead and Balancing Prices and Their Connected Costs," Working Papers 360, University of Milano-Bicocca, Department of Economics, revised 03 Feb 2017.
    13. Koch, Christopher & Hirth, Lion, 2019. "Short-term electricity trading for system balancing: An empirical analysis of the role of intraday trading in balancing Germany's electricity system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    14. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2020. "The impact of the integration of renewable energy sources in the electricity price formation: is the Merit-Order Effect occurring in Portugal?," Utilities Policy, Elsevier, vol. 66(C).
    15. Brijs, Tom & De Jonghe, Cedric & Hobbs, Benjamin F. & Belmans, Ronnie, 2017. "Interactions between the design of short-term electricity markets in the CWE region and power system flexibility," Applied Energy, Elsevier, vol. 195(C), pages 36-51.
    16. Singh, Tarlok, 2008. "Testing the Saving-Investment correlations in India: An evidence from single-equation and system estimators," Economic Modelling, Elsevier, vol. 25(5), pages 1064-1079, September.
    17. Charles G. Renfro, 2009. "The Practice of Econometric Theory," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75571-5.
    18. Martina Assereto & Julie Byrne, 2020. "The Implications of Policy Uncertainty on Solar Photovoltaic Investment," Energies, MDPI, vol. 13(23), pages 1-20, November.
    19. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [euro;]36.18, Hardback, ISBN 0-521-770416-0, $90, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    20. Fathi Abid & Bilel Kaffel, 2018. "The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 561-590, February.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

    Statistics

    Access and download statistics

    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:eneeco:v:128:y:2023:i:c:s0140988323006710. 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/locate/eneco .

    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.