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Mixed-Integer Programming Model for Transmission Network Expansion Planning with Battery Energy Storage Systems (BESS)

Author

Listed:
  • Camilo Andres Mora

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021, Colombia)

  • Oscar Danilo Montoya

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021, Colombia
    Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia)

  • Edwin Rivas Trujillo

    (Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021, Colombia)

Abstract

This article assesses the costs and benefits of incorporating battery energy storage systems (BESS) in transmission network expansion planning (TEP) over multiple time periods. We propose a mixed-integer programming model (MIP) for joint planning of the installation of battery energy storage systems (BESS) and construction of new transmission lines in multiple periods of time. The mathematical formulation of the presented model is based on the strategies of the agents of a transmission network to maximize their benefit, and on the operational restrictions of the power flows in transmission networks. This analysis is performed for the Garver 6 node test system takes into account the power losses in the lines and the restrictions for the energy stored in BESS. The power flows obtained with the MIP model are compared with AC power flows generated with specialized software for flows in power systems. This allows us to demonstrate the potential of models based on DC power flows to achieve approximate results applicable to the behavior and characteristics of real transmission networks. The results show that the BESS increase the net profit in the transmission networks and reduce their power losses.

Suggested Citation

  • Camilo Andres Mora & Oscar Danilo Montoya & Edwin Rivas Trujillo, 2020. "Mixed-Integer Programming Model for Transmission Network Expansion Planning with Battery Energy Storage Systems (BESS)," Energies, MDPI, vol. 13(17), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4386-:d:403967
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    References listed on IDEAS

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    1. Ioannis Mexis & Grazia Todeschini, 2020. "Battery Energy Storage Systems in the United Kingdom: A Review of Current State-of-the-Art and Future Applications," Energies, MDPI, vol. 13(14), pages 1-31, July.
    2. Tsianikas, Stamatis & Zhou, Jian & Birnie, Dunbar P. & Coit, David W., 2019. "Economic trends and comparisons for optimizing grid-outage resilient photovoltaic and battery systems," Applied Energy, Elsevier, vol. 256(C).
    3. Denholm, Paul & Sioshansi, Ramteen, 2009. "The value of compressed air energy storage with wind in transmission-constrained electric power systems," Energy Policy, Elsevier, vol. 37(8), pages 3149-3158, August.
    4. Kevin Marnell & Manasseh Obi & Robert Bass, 2019. "Transmission-Scale Battery Energy Storage Systems: A Systematic Literature Review," Energies, MDPI, vol. 12(23), pages 1-31, December.
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    Cited by:

    1. Hamdi Abdi & Mansour Moradi & Sara Lumbreras, 2021. "Metaheuristics and Transmission Expansion Planning: A Comparative Case Study," Energies, MDPI, vol. 14(12), pages 1-23, June.
    2. Luis Fernando Grisales-Noreña & Bonie Johana Restrepo-Cuestas & Brandon Cortés-Caicedo & Jhon Montano & Andrés Alfonso Rosales-Muñoz & Marco Rivera, 2022. "Optimal Location and Sizing of Distributed Generators and Energy Storage Systems in Microgrids: A Review," Energies, MDPI, vol. 16(1), pages 1-30, December.

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