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Energy management system for DC railway smart grid based on substation power forecast and energy storage system optimization

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  • Shmaysani, Mhamad
  • Almaksour, Khaled
  • Caron, Hervé
  • Robyns, Benoît
  • Saudemont, Christophe

Abstract

This paper presents a day-ahead energy management strategy for a DC smart railway grid integrating a photovoltaic (PV) power generator and energy storage systems (ESS) at the traction substation (TSS). The proposed energy management method relies on the forecasted power consumption profile of the TSS for the upcoming day and a non-linear optimization approach to manage the ESS in an economically efficient way while respecting physical constraints and maintaining power balance within the power system. An adequate forecast method is used to address the unique characteristics of TSS power consumption, particularly the occurrence of multiple short-duration peak loads during the day. The non-linear optimization approach accounts for both the system’s behavior and the inherent non-linearity of the problem. The forecasted TSS consumption profile is combined with a PV production profile as input to the optimization algorithm, which determines the optimal ESS schedule for the next 24 h. The impact of forecast accuracy on optimization outcomes is evaluated using key performance indicators. To demonstrate the superiority of the proposed optimization method in scheduling the ESS, its results are compared against those of a basic baseline case.

Suggested Citation

  • Shmaysani, Mhamad & Almaksour, Khaled & Caron, Hervé & Robyns, Benoît & Saudemont, Christophe, 2025. "Energy management system for DC railway smart grid based on substation power forecast and energy storage system optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 238(C), pages 497-515.
    • Handle: RePEc:eee:matcom:v:238:y:2025:i:c:p:497-515
    DOI: 10.1016/j.matcom.2025.06.028
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    References listed on IDEAS

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    1. Segarra-Tamarit, Jorge & Pérez, Emilio & Moya, Eric & Ayuso, Pablo & Beltran, Hector, 2021. "Deep learning-based forecasting of aggregated CSP production," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 306-318.
    2. Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
    3. Bourbon, R. & Ngueveu, S.U. & Roboam, X. & Sareni, B. & Turpin, C. & Hernandez-Torres, D., 2019. "Energy management optimization of a smart wind power plant comparing heuristic and linear programming methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 418-431.
    4. Almaksour, Khaled & Krim, Youssef & Kouassi, N’guessan & Navarro, Nicolas & François, Bruno & Letrouvé, Tony & Saudemont, Christophe & Taunay, Lionel & Robyns, Benoit, 2021. "Comparison of dynamic models for a DC railway electrical network including an AC/DC bi-directional power station," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 244-266.
    5. Aouad, Anthony & Almaksour, Khaled & Abbes, Dhaker, 2024. "Storage management optimization based on electrical consumption and production forecast in a photovoltaic system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 128-147.
    6. Pankovits, Petronela & Abbes, Dhaker & Saudemont, Christophe & Brisset, Stephane & Pouget, Julien & Robyns, Benoit, 2016. "Multi-criteria fuzzy-logic optimized supervision for hybrid railway power substations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 130(C), pages 236-250.
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