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

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  • 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
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    References listed on IDEAS

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    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.

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