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Integrated supply- and demand-side energy management for expeditionary environmental control

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  • Craparo, E.M.
  • Sprague, J.G.

Abstract

This paper examines efficiencies achieved by optimal scheduling of power generation equipment and electrical loads in a hybrid smart micro-grid under conditions where load activity may be delayed or advanced with negligible impact to system performance. We model military expeditionary energy systems to analyze the performance of existing unmanaged components and estimate potential savings obtained by coordinated management of battlefield heating and cooling systems. We employ rolling horizon optimization models to examine performance under varying degrees of uncertainty about future load demand and renewable production.

Suggested Citation

  • Craparo, E.M. & Sprague, J.G., 2019. "Integrated supply- and demand-side energy management for expeditionary environmental control," Applied Energy, Elsevier, vol. 233, pages 352-366.
    • Handle: RePEc:eee:appene:v:233-234:y:2019:i::p:352-366
    DOI: 10.1016/j.apenergy.2018.09.220
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    References listed on IDEAS

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    1. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
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    4. Wu, Zhou & Tazvinga, Henerica & Xia, Xiaohua, 2015. "Demand side management of photovoltaic-battery hybrid system," Applied Energy, Elsevier, vol. 148(C), pages 294-304.
    5. Jiang, Tao & Li, Zening & Jin, Xiaolong & Chen, Houhe & Li, Xue & Mu, Yunfei, 2018. "Flexible operation of active distribution network using integrated smart buildings with heating, ventilation and air-conditioning systems," Applied Energy, Elsevier, vol. 226(C), pages 181-196.
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    Citations

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    Cited by:

    1. Joo Won Lee & Emily Craparo & Giovanna Oriti & Arthur Krener, 2022. "Optimizing Fuel Efficiency on an Islanded Microgrid under Varying Loads," Energies, MDPI, vol. 15(21), pages 1-21, October.
    2. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    3. Balasubramanian, S. & Balachandra, P., 2021. "Effectiveness of demand response in achieving supply-demand matching in a renewables dominated electricity system: A modelling approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    4. Oveis Abedinia & Mehdi Bagheri, 2021. "Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning," Energies, MDPI, vol. 14(10), pages 1-18, May.
    5. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.
    6. Tianliang Wang & Xin Jiang & Yang Jin & Dawei Song & Meng Yang & Qingshan Zeng, 2019. "Peaking Compensation Mechanism for Thermal Units and Virtual Peaking Plants Union Promoting Curtailed Wind Power Integration," Energies, MDPI, vol. 12(17), pages 1-20, August.
    7. Talaat, M. & Hatata, A.Y. & Alsayyari, Abdulaziz S. & Alblawi, Adel, 2020. "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach," Energy, Elsevier, vol. 190(C).

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