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Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs

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  • Poria Astero

    (Department of Computer Science, The State University of New York Korea, Incheon 21985, Korea
    Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
    Department of Electric Power and Energy Systems, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden)

  • Bong Jun Choi

    (Department of Computer Science, The State University of New York Korea, Incheon 21985, Korea
    Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA)

  • Hao Liang

    (Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada)

  • Lennart Söder

    (Department of Electric Power and Energy Systems, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden)

Abstract

Due to environmental concerns, economic issues, and emerging new loads, such as electrical vehicles (EVs), the importance of demand side management (DSM) programs has increased in recent years. DSM programs using a dynamic real-time pricing (RTP) method can help to adaptively control the electricity consumption. However, the existing RTP methods, particularly when they consider the EVs and the power system constraints, have many limitations, such as computational complexity and the need for centralized control. Therefore, a new transactive DSM program is proposed in this paper using an imperfect competition model with high EV penetration levels. In particular, a heuristic two-stage iterative method, considering the influence of decisions made independently by customers to minimize their own costs, is developed to find the market equilibrium quickly in a distributed manner. Simulations in the IEEE 37-bus system with 1141 customers and 670 EVs are performed to demonstrate the effectiveness of the proposed method. The results show that the proposed method can better manage the EVs and elastic appliances than the existing methods in terms of power constraints and cost. Also, the proposed method can solve the optimization problem quick enough to run in real-time.

Suggested Citation

  • Poria Astero & Bong Jun Choi & Hao Liang & Lennart Söder, 2017. "Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs," Energies, MDPI, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1640-:d:115503
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    References listed on IDEAS

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    Cited by:

    1. Zhanle Wang & Raman Paranjape & Zhikun Chen & Kai Zeng, 2019. "Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing," Energies, MDPI, vol. 12(15), pages 1-15, July.
    2. Schwidtal, J.M. & Piccini, P. & Troncia, M. & Chitchyan, R. & Montakhabi, M. & Francis, C. & Gorbatcheva, A. & Capper, T. & Mustafa, M.A. & Andoni, M. & Robu, V. & Bahloul, M. & Scott, I.J. & Mbavarir, 2023. "Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    3. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

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