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Demand-Side Management with a State Space Consideration

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  • HyungSeon Oh

    (Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, MD 21402, USA)

Abstract

Power networks are gateways to transfer power from generators to end-users. Often, it is assumed that the transfer occurs freely without any limiting factors. However, power flows over a network can be limited by predetermined limits that may come from physical reasons, such as line capacity or Kirchhoff’s laws. When flow is constrained by these limits, this is called congestion, which reduces the energy efficiency and splits the price for electricity across the congested lines. One promising, cost-effective way to relieve the impact of the congestion is demand-side management (DSM). However, it is unclear how much DSM can impact congestion and where it can control the demand. This paper proposes a new DSM mechanism based on locational willingness-to-pay (WTP) centered around income statistics; utilizes a state-space tool to determine the possibility to alter prices by DSM; and formulates a convex optimization problem to decide the DSM. The proposed methodology is tested on IEEE (Institute of Electrical and Electronics Engineers) systems with two commonly used objectives: cost minimization and social welfare maximization.

Suggested Citation

  • HyungSeon Oh, 2018. "Demand-Side Management with a State Space Consideration," Energies, MDPI, vol. 11(9), pages 1-12, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2444-:d:169861
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    References listed on IDEAS

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    1. Stamatios Ntanos & Grigorios Kyriakopoulos & Miltiadis Chalikias & Garyfallos Arabatzis & Michalis Skordoulis, 2018. "Public Perceptions and Willingness to Pay for Renewable Energy: A Case Study from Greece," Sustainability, MDPI, vol. 10(3), pages 1-16, March.
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    Cited by:

    1. Neda Hajibandeh & Miadreza Shafie-khah & Sobhan Badakhshan & Jamshid Aghaei & Sílvio J. P. S. Mariano & João P. S. Catalão, 2019. "Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme," Energies, MDPI, vol. 12(7), pages 1-16, April.

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