IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p879-d103056.html
   My bibliography  Save this article

Demand Response Resource Allocation Method Using Mean-Variance Portfolio Theory for Load Aggregators in the Korean Demand Response Market

Author

Listed:
  • Jaeyong Chae

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Sung-Kwan Joo

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

Abstract

Since the demand response (DR) market was introduced in Korea, load aggregators have also been allowed to participate in the electricity market. However, a risk-management-based method for the efficient operation of demand response resources (DRRs) has not been studied from the load aggregators’ perspective. In this paper, a systematic DRR allocation method is proposed for load aggregators to operate DRRs using mean-variance portfolio theory. The proposed method is designed to determine the lowest-risk DRR portfolio for a given level of expected return using mean-variance portfolio theory from the perspective of load aggregators. The numerical results show that the proposed method can be used to reduce the risk compared to that obtained by the baseline method, in which all individual DRRs are allocated in a DRR group by maximum curtailment capability.

Suggested Citation

  • Jaeyong Chae & Sung-Kwan Joo, 2017. "Demand Response Resource Allocation Method Using Mean-Variance Portfolio Theory for Load Aggregators in the Korean Demand Response Market," Energies, MDPI, vol. 10(7), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:879-:d:103056
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/879/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/7/879/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bar-Lev, Dan & Katz, Steven, 1976. "A Portfolio Approach to Fossil Fuel Procurement in the Electric Utility Industry," Journal of Finance, American Finance Association, vol. 31(3), pages 933-947, June.
    2. Delarue, Erik & De Jonghe, Cedric & Belmans, Ronnie & D'haeseleer, William, 2011. "Applying portfolio theory to the electricity sector: Energy versus power," Energy Economics, Elsevier, vol. 33(1), pages 12-23, January.
    3. Borovkova, Svetlana & Schmeck, Maren Diane, 2017. "Electricity price modeling with stochastic time change," Energy Economics, Elsevier, vol. 63(C), pages 51-65.
    4. Deng, Shi-Jie & Xu, Li, 2009. "Mean-risk efficient portfolio analysis of demand response and supply resources," Energy, Elsevier, vol. 34(10), pages 1523-1529.
    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. Paulino Martinez-Fernandez & Fernando deLlano-Paz & Anxo Calvo-Silvosa & Isabel Soares, 2019. "Assessing Renewable Energy Sources for Electricity (RES-E) Potential Using a CAPM-Analogous Multi-Stage Model," Energies, MDPI, vol. 12(19), pages 1-20, September.
    2. Manish Mohanpurkar & Yusheng Luo & Danny Terlip & Fernando Dias & Kevin Harrison & Joshua Eichman & Rob Hovsapian & Jennifer Kurtz, 2017. "Electrolyzers Enhancing Flexibility in Electric Grids," Energies, MDPI, vol. 10(11), pages 1-17, November.
    3. Xiao Han & Ming Zhou & Gengyin Li & Kwang Y. Lee, 2017. "Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium," Energies, MDPI, vol. 10(12), pages 1-17, December.
    4. Rakkyung Ko & Sung-Kwan Joo, 2019. "Stochastic Mixed-Integer Programming (SMIP)-Based Distributed Energy Resource Allocation Method for Virtual Power Plants," Energies, MDPI, vol. 13(1), pages 1-10, December.

    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. Pérez Odeh, Rodrigo & Watts, David & Flores, Yarela, 2018. "Planning in a changing environment: Applications of portfolio optimisation to deal with risk in the electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3808-3823.
    2. Frank A. Wolak, 2016. "Level versus Variability Trade-offs in Wind and Solar Generation Investments: The Case of California," NBER Working Papers 22494, National Bureau of Economic Research, Inc.
    3. Westner, Günther & Madlener, Reinhard, 2011. "Development of cogeneration in Germany: A mean-variance portfolio analysis of individual technology’s prospects in view of the new regulatory framework," Energy, Elsevier, vol. 36(8), pages 5301-5313.
    4. Min, Daiki & Chung, Jaewoo, 2013. "Evaluation of the long-term power generation mix: The case study of South Korea's energy policy," Energy Policy, Elsevier, vol. 62(C), pages 1544-1552.
    5. de-Llano Paz, Fernando & Antelo, Susana Iglesias & Calvo Silvosa, Anxo & Soares, Isabel, 2014. "The technological and environmental efficiency of the EU-27 power mix: An evaluation based on MPT," Energy, Elsevier, vol. 69(C), pages 67-81.
    6. deLlano-Paz, Fernando & Calvo-Silvosa, Anxo & Iglesias Antelo, Susana & Soares, Isabel, 2015. "The European low-carbon mix for 2030: The role of renewable energy sources in an environmentally and socially efficient approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 49-61.
    7. Pérez Odeh, Rodrigo & Watts, David & Negrete-Pincetic, Matías, 2018. "Portfolio applications in electricity markets review: Private investor and manager perspective trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 192-204.
    8. Zhang, Shuang & Zhao, Tao & Xie, Bai-Chen, 2018. "What is the optimal power generation mix of China? An empirical analysis using portfolio theory," Applied Energy, Elsevier, vol. 229(C), pages 522-536.
    9. Carina Fagefors & Björn Lantz, 2021. "Application of Portfolio Theory to Healthcare Capacity Management," IJERPH, MDPI, vol. 18(2), pages 1-9, January.
    10. Inzunza, Andrés & Moreno, Rodrigo & Bernales, Alejandro & Rudnick, Hugh, 2016. "CVaR constrained planning of renewable generation with consideration of system inertial response, reserve services and demand participation," Energy Economics, Elsevier, vol. 59(C), pages 104-117.
    11. Lynch, Muireann Á. & Shortt, Aonghus & Tol, Richard S.J. & O'Malley, Mark J., 2013. "Risk–return incentives in liberalised electricity markets," Energy Economics, Elsevier, vol. 40(C), pages 598-608.
    12. Leroux, Anke D. & Martin, Vance L. & Zheng, Hao, 2018. "Addressing water shortages by force of habit," Resource and Energy Economics, Elsevier, vol. 53(C), pages 42-61.
    13. Yang, Yingkui & Solgaard, Hans Stubbe & Haider, Wolfgang, 2016. "Wind, hydro or mixed renewable energy source: Preference for electricity products when the share of renewable energy increases," Energy Policy, Elsevier, vol. 97(C), pages 521-531.
    14. Suzuki, Kengo & Uchiyama, Yohji, 2010. "Quantifying the risk of an increase in the prices of non-energy products by combining the portfolio and input-output approaches," Energy Policy, Elsevier, vol. 38(10), pages 5867-5877, October.
    15. Frank A. Wolak, 2016. "Level versus Variability Trade-offs in Wind and Solar Generation Investments: The Case of California," The Energy Journal, International Association for Energy Economics, vol. 0(Bollino-M).
    16. Turkson, Charles & Liu, Wenbin & Acquaye, Adolf, 2024. "A data envelopment analysis based evaluation of sustainable energy generation portfolio scenarios," Applied Energy, Elsevier, vol. 363(C).
    17. deLlano-Paz, Fernando & Calvo-Silvosa, Anxo & Antelo, Susana Iglesias & Soares, Isabel, 2017. "Energy planning and modern portfolio theory: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 636-651.
    18. Rombauts, Yannick & Delarue, Erik & D’haeseleer, William, 2011. "Optimal portfolio-theory-based allocation of wind power: Taking into account cross-border transmission-capacity constraints," Renewable Energy, Elsevier, vol. 36(9), pages 2374-2387.
    19. Chi Kong Chyong & Carmen Li & David Reiner & Fabien Roques, 2020. "A Portfolio approach to wind and solar deployment in Australia," Working Papers EPRG2022, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    20. Zeng, Ximei & Zhou, Zhongbao & Gong, Yeming & Liu, Wenbin, 2022. "A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).

    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:gam:jeners:v:10:y:2017:i:7:p:879-:d:103056. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.