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Understanding and predicting the impact of location and load on microgrid design

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  • Zachar, Michael
  • Daoutidis, Prodromos

Abstract

This paper examines the impact of location and load shape selection on microgrid optimal design. 96 unique combinations of location and load shape are considered to provide a broader scope than any previous work in microgrid design sensitivity analysis. In addition, the level of autonomy from the macrogrid is considered as a tunable parameter in the optimization. A generic system is considered consisting of photovoltaics, wind turbine, microturbines, electric and natural gas boilers, thermal storage, and a battery bank. The microgrid is grid-connected and designed to supply both heat and power. A mixed integer linear program is used to minimize the expected cost of energy supply over a 20 year horizon. Trends in the design results are discussed and important input parameters that depend on the location and load shape are identified. Finally, a procedure to quantify these trends and predict optimal design results in new locations is proposed.

Suggested Citation

  • Zachar, Michael & Daoutidis, Prodromos, 2015. "Understanding and predicting the impact of location and load on microgrid design," Energy, Elsevier, vol. 90(P1), pages 1005-1023.
    • Handle: RePEc:eee:energy:v:90:y:2015:i:p1:p:1005-1023
    DOI: 10.1016/j.energy.2015.08.010
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    2. Athila Quaresma Santos & Zheng Ma & Casper Gellert Olsen & Bo Nørregaard Jørgensen, 2018. "Framework for Microgrid Design Using Social, Economic, and Technical Analysis," Energies, MDPI, vol. 11(10), pages 1-22, October.
    3. Thomas T. D. Tran & Amanda D. Smith, 2019. "Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use," Energies, MDPI, vol. 12(3), pages 1-26, February.
    4. Touretzky, Cara R. & McGuffin, Dana L. & Ziesmer, Jena C. & Baldea, Michael, 2016. "The effect of distributed electricity generation using natural gas on the electric and natural gas grids," Applied Energy, Elsevier, vol. 177(C), pages 500-514.
    5. Wu, Zhongqun & Yang, Chan & Zheng, Ruijin, 2022. "Developing a holistic fuzzy hierarchy-cloud assessment model for the connection risk of renewable energy microgrid," Energy, Elsevier, vol. 245(C).
    6. Allman, Andrew & Daoutidis, Prodromos, 2017. "Optimal design of synergistic distributed renewable fuel and power systems," Renewable Energy, Elsevier, vol. 100(C), pages 78-89.
    7. Goodall, G.H. & Hering, A.S. & Newman, A.M., 2017. "Characterizing solutions in optimal microgrid procurement and dispatch strategies," Applied Energy, Elsevier, vol. 201(C), pages 1-19.
    8. Lotfi, Hossein & Khodaei, Amin, 2017. "Hybrid AC/DC microgrid planning," Energy, Elsevier, vol. 118(C), pages 37-46.
    9. Husted, Mark A. & Suthar, Bharatkumar & Goodall, Gavin H. & Newman, Alexandra M. & Kohl, Paul A., 2018. "Coordinating microgrid procurement decisions with a dispatch strategy featuring a concentration gradient," Applied Energy, Elsevier, vol. 219(C), pages 394-407.
    10. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    11. Rodriguez, Mauricio & Arcos–Aviles, Diego & Martinez, Wilmar, 2023. "Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms," Applied Energy, Elsevier, vol. 335(C).

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