IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v252y2022ics0360544222009574.html
   My bibliography  Save this article

Determinants of reserve margin volatility: A new approach toward managing energy supply and demand

Author

Listed:
  • Lee, Juyong
  • Cho, Youngsang

Abstract

This study introduces the concept of reserve margin volatility to analyze factors influencing the fluctuation of reserve margin. This study defines reserve margin volatility as the percentage difference between the expected reserve margin and actual reserve margin. Internal and external factors increase reserve margin volatility, which can lead to regional or national blackouts and cause an economic waste problem. In this regard, this study derives significant variables affecting the reserve margin volatility of South Korea using robust regressions by season through heteroskedasticity and autocorrelation consistent estimators. Meteorological factors including temperature, humidity, heating and cooling degree days, holidays, peak load forecasting error, and the proportion of renewable energy are used as variables to identify the significant determinants. This study found that meteorological variables affect reserve margin volatility in summer and winter to a greater degree than in spring and autumn. Holiday variables decrease reserve margin volatility regardless of season. Mean humidity increases reserve margin volatility only in summer. In addition, peak load forecasting error and the proportion of renewable energy significantly increases reserve margin volatility regardless of season.

Suggested Citation

  • Lee, Juyong & Cho, Youngsang, 2022. "Determinants of reserve margin volatility: A new approach toward managing energy supply and demand," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222009574
    DOI: 10.1016/j.energy.2022.124054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222009574
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124054?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. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).
    2. Ioannou, Anastasia & Fuzuli, Gulistiani & Brennan, Feargal & Yudha, Satya Widya & Angus, Andrew, 2019. "Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling," Energy Economics, Elsevier, vol. 80(C), pages 760-776.
    3. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    4. Imbs, Jean, 2007. "Growth and volatility," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 1848-1862, October.
    5. Huang, Shoujun & Abedinia, Oveis, 2021. "Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market," Energy, Elsevier, vol. 225(C).
    6. Zapata, Sebastian & Castaneda, Monica & Garces, Estefany & Franco, Carlos Jaime & Dyner, Isaac, 2018. "Assessing security of supply in a largely hydroelectricity-based system: The Colombian case," Energy, Elsevier, vol. 156(C), pages 444-457.
    7. Wooyoung Jeon & Sangmin Cho & Seungmoon Lee, 2020. "Estimating the Impact of Electric Vehicle Demand Response Programs in a Grid with Varying Levels of Renewable Energy Sources: Time-of-Use Tariff versus Smart Charging," Energies, MDPI, vol. 13(17), pages 1-22, August.
    8. Hyung-Chul Jo & Rakkyung Ko & Sung-Kwan Joo, 2019. "Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response," Energies, MDPI, vol. 12(9), pages 1-14, April.
    9. Kim, Kayoung & Nam, Heekoo & Cho, Youngsang, 2015. "Estimation of the inconvenience cost of a rolling blackout in the residential sector: The case of South Korea," Energy Policy, Elsevier, vol. 76(C), pages 76-86.
    10. Xiong, Linyun & Li, Penghan & Wang, Ziqiang & Wang, Jie, 2020. "Multi-agent based multi objective renewable energy management for diversified community power consumers," Applied Energy, Elsevier, vol. 259(C).
    11. Taimur Al Shidhani & Anastasia Ioannou & Gioia Falcone, 2020. "Multi-Objective Optimisation for Power System Planning Integrating Sustainability Indicators," Energies, MDPI, vol. 13(9), pages 1-32, May.
    12. Mohammed, Nooriya A., 2018. "Modelling of unsuppressed electrical demand forecasting in Iraq for long term," Energy, Elsevier, vol. 162(C), pages 354-363.
    13. Hekkenberg, M. & Benders, R.M.J. & Moll, H.C. & Schoot Uiterkamp, A.J.M., 2009. "Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands," Energy Policy, Elsevier, vol. 37(4), pages 1542-1551, April.
    14. Saiah, Saiah Bekkar Djelloul & Stambouli, Amine Boudghene, 2017. "Prospective analysis for a long-term optimal energy mix planning in Algeria: Towards high electricity generation security in 2062," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 26-43.
    15. V. Ramesh Kumar & Pradipkumar Dixit, 2018. "Artificial Neural Network Model for Hourly Peak Load Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 155-160.
    Full references (including those not matched with items on IDEAS)

    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. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    2. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    3. M. Jenabi & S. M. T. Fatemi Ghomi & S. A. Torabi & Moeen Sammak Jalali, 2022. "An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1304-1336, December.
    4. Joël Cariolle & Michaël Goujon, 2015. "Measuring Macroeconomic Instability: A Critical Survey Illustrated With Exports Series," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 1-26, February.
    5. Joya, Omar, 2015. "Growth and volatility in resource-rich countries: Does diversification help?," Structural Change and Economic Dynamics, Elsevier, vol. 35(C), pages 38-55.
    6. Larrain, Borja, 2011. "World betas, consumption growth, and financial integration," Journal of International Money and Finance, Elsevier, vol. 30(6), pages 999-1018, October.
    7. Frederick van der Ploeg & Steven Poelhekke, 2007. "Volatility, Financial Development and the Natural Resource Curse," Economics Working Papers ECO2007/36, European University Institute.
    8. Guo, Tianyu & Guo, Qi & Huang, Libin & Guo, Haiping & Lu, Yuanhong & Tu, Liang, 2023. "Microgrid source-network-load-storage master-slave game optimization method considering the energy storage overcharge/overdischarge risk," Energy, Elsevier, vol. 282(C).
    9. Zhou, Dengji & Yan, Siyun & Huang, Dawen & Shao, Tiemin & Xiao, Wang & Hao, Jiarui & Wang, Chen & Yu, Tianqi, 2022. "Modeling and simulation of the hydrogen blended gas-electricity integrated energy system and influence analysis of hydrogen blending modes," Energy, Elsevier, vol. 239(PA).
    10. Luca Agnello & Ricardo M. Sousa, 2009. "The Determinants of Public Deficit Volatility," NIPE Working Papers 11/2009, NIPE - Universidade do Minho.
    11. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    12. Irene Brunetti & Davide fiaschi & Lisa Gianmoena, 2013. "An Index of Growth Rate Volatility: Methodology and an Application to European Regions," Discussion Papers 2013/169, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    13. Brown, David P. & Muehlenbachs, Lucija, 2023. "The Value of Electricity Reliability: Evidence from Battery Adoption," Working Papers 2023-5, University of Alberta, Department of Economics, revised 26 Jul 2024.
    14. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    15. Lee, Juyong & Cho, Youngsang, 2020. "Estimation of the usage fee for peer-to-peer electricity trading platform: The case of South Korea," Energy Policy, Elsevier, vol. 136(C).
    16. Santágata, Daniela M. & Castesana, Paula & Rössler, Cristina E. & Gómez, Darío R., 2017. "Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971–2013)," Energy Policy, Elsevier, vol. 106(C), pages 404-414.
    17. Davide Furceri & Ernesto Crivelli & Mr. Joël Toujas-Bernate, 2012. "Can Policies Affect Employment Intensity of Growth? A Cross-Country Analysis," IMF Working Papers 2012/218, International Monetary Fund.
    18. Yang, Weijia & Sparrow, Sarah N. & Wallom, David C.H., 2024. "A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods," Applied Energy, Elsevier, vol. 368(C).
    19. Buch, Claudia M. & Döpke, Jörg & Stahn, Kerstin, 2008. "Great moderation at the firm level? Unconditional versus conditional output volatility," Discussion Paper Series 1: Economic Studies 2008,13, Deutsche Bundesbank.
    20. Ahmed M. Hussien & Jonghoon Kim & Abdulaziz Alkuhayli & Mohammed Alharbi & Hany M. Hasanien & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado, 2022. "Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation," Sustainability, MDPI, vol. 14(22), pages 1-22, November.

    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:energy:v:252:y:2022:i:c:s0360544222009574. 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.journals.elsevier.com/energy .

    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.