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Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks

Author

Listed:
  • Niyam Haque

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Anuradha Tomar

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Phuong Nguyen

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
    Sustainable Energy Systems Group, SUSTAIN—ERIN, Luxembourg Institute of Science and Technology, L-4422 Belvaux, Luxembourg)

  • Guus Pemen

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

Abstract

Capacity challenges are becoming more frequent phenomena in residential distribution networks with new forms of loads, distributed renewable energy resources (RES) and price-responsive applications. Different types of demand response programs have been introduced to tackle these challenges through iterative changes in price and/or contractual participations based on incentives. In this research, a dynamic network tariff-based demand response program is proposed to address congestion problems in low-voltage (LV) networks. The formulation takes advantage of the scalable architecture of the agent-based systems that allows local decision making with limited communication. Energy consumption schedules are developed on a day-ahead basis depending on the expected cost of overloading for a number of probable scenarios. The performance of the proposed approach has been tested through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 82% reduction in congestion on a monthly basis, while maintaining the quality of supply in the network.

Suggested Citation

  • Niyam Haque & Anuradha Tomar & Phuong Nguyen & Guus Pemen, 2020. "Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks," Energies, MDPI, vol. 13(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:318-:d:306745
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    References listed on IDEAS

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    1. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    2. Simone Minniti & Niyam Haque & Phuong Nguyen & Guus Pemen, 2018. "Local Markets for Flexibility Trading: Key Stages and Enablers," Energies, MDPI, vol. 11(11), pages 1-21, November.
    3. Lampropoulos, Ioannis & van den Broek, Machteld & van der Hoofd, Erik & Hommes, Klaas & van Sark, Wilfried, 2018. "A system perspective to the deployment of flexibility through aggregator companies in the Netherlands," Energy Policy, Elsevier, vol. 118(C), pages 534-551.
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    Cited by:

    1. Fco. Javier Zarco-Soto & Pedro J. Zarco-Periñán & Jose L. Martínez-Ramos, 2021. "Centralized Control of Distribution Networks with High Penetration of Renewable Energies," Energies, MDPI, vol. 14(14), pages 1-13, July.

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