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Characterizing Meteorological Forecast Impact on Microgrid Optimization Performance and Design

Author

Listed:
  • Robert Jane

    (US Army Research Laboratory, US Army Combat Capabilities Development Command, US Army Futures Command, Adelphi, MD 20783, USA
    Current address: United State Military Academy (USMA), West Point, NY 10996, USA)

  • Gordon Parker

    (Department of Mechanical Engineering - Engineering Mechanics, College of Engineering, Michigan Technological University, Houghton, MI 49931, USA)

  • Gail Vaucher

    (US Army Research Laboratory, US Army Combat Capabilities Development Command, US Army Futures Command, White Sands Missile Range, NM 88002, USA)

  • Morris Berman

    (US Army Research Laboratory, US Army Combat Capabilities Development Command, US Army Futures Command, Adelphi, MD 20783, USA)

Abstract

A microgrid consists of electrical generation sources, energy storage assets, loads, and the ability to function independently, or connect and share power with other electrical grids. Thefocus of this work is on the behavior of a microgrid, with both diesel generator and photovoltaic resources, whose heating or cooling loads are influenced by local meteorological conditions. Themicrogrid's fuel consumption and energy storage requirement were then examined as a function of the atmospheric conditions used by its energy management strategy (EMS). A fuel-optimal EMS, able to exploit meteorological forecasts, was developed and evaluated using a hybrid microgrid simulation. Weather forecast update periods ranged from 15 min to 24 h. Four representative meteorological sky classifications (clear, partly cloudy, overcast, or monsoon) were considered. Forall four sky classifications, fuel consumption and energy storage requirements increased linearly with the increasing weather forecast interval. Larger forecast intervals lead to degraded weather forecasts, requiring more frequent charging/discharging of the energy storage, increasing both the fuel consumption and energy storage design requirements. The significant contributions of this work include the optimal EMS and an approach for quantifying the meteorological forecast effects on fuel consumption and energy storage requirements on microgrid performance. The findings of this study indicate that the forecast interval used by the EMS affected both fuel consumption and energy storage requirements, and that the sensitivity of these effects depended on the 24-hour sky conditions.

Suggested Citation

  • Robert Jane & Gordon Parker & Gail Vaucher & Morris Berman, 2020. "Characterizing Meteorological Forecast Impact on Microgrid Optimization Performance and Design," Energies, MDPI, vol. 13(3), pages 1-23, January.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:577-:d:313107
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    References listed on IDEAS

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    1. Simone Sala & Alfonso Amendola & Sonia Leva & Marco Mussetta & Alessandro Niccolai & Emanuele Ogliari, 2019. "Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant," Energies, MDPI, vol. 12(23), pages 1-19, November.
    2. Munir Husein & Il-Yop Chung, 2019. "Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach," Energies, MDPI, vol. 12(10), pages 1-21, May.
    3. Altan Dombaycı, Ömer & Gölcü, Mustafa, 2009. "Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey," Renewable Energy, Elsevier, vol. 34(4), pages 1158-1161.
    4. Silvano Vergura, 2016. "A Complete and Simplified Datasheet-Based Model of PV Cells in Variable Environmental Conditions for Circuit Simulation," Energies, MDPI, vol. 9(5), pages 1-12, April.
    5. Moretti, L. & Polimeni, S. & Meraldi, L. & Raboni, P. & Leva, S. & Manzolini, G., 2019. "Assessing the impact of a two-layer predictive dispatch algorithm on design and operation of off-grid hybrid microgrids," Renewable Energy, Elsevier, vol. 143(C), pages 1439-1453.
    6. Tao Hong & Jason Wilson & Jingrui Xie, 2013. "Long term probabilistic load forecasting and normalization with hourly information," HSC Research Reports HSC/13/13, Hugo Steinhaus Center, Wroclaw University of Technology.
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    Cited by:

    1. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    2. Miguel Ángel Pardo & Héctor Fernández & Antonio Jodar-Abellan, 2020. "Converting a Water Pressurized Network in a Small Town into a Solar Power Water System," Energies, MDPI, vol. 13(15), pages 1-26, August.
    3. Héctor Fernández Rodríguez & Miguel Ángel Pardo, 2023. "A Study of the Relevant Parameters for Converting Water Supply to Small Towns in the Province of Alicante to Systems Powered by Photovoltaic Solar Panels," Sustainability, MDPI, vol. 15(12), pages 1-24, June.

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