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Development of an optimization based design framework for microgrid energy systems

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  • Cao, Tao
  • Hwang, Yunho
  • Radermacher, Reinhard

Abstract

A comprehensive optimization study considering both system configurations and operation signals is needed for microgrid energy systems. For this, this study provides an advanced optimization based design framework that extends existing energy system optimization studies in following four aspects: complete system optimization from the beginning, comprehensive energy conversion equipment modeling, modeling of cascaded configurations, and consideration of transient loads and weather profiles. This framework aims to find optimum system configurations and operation signals for any given equipment options, and load- and weather-profiles. The framework is presented through a case study on an oceanic container transportation application. Optimized system configurations and operation signals are found under three different scenarios: minimizing life cycle cost as single objective, minimizing capital cost and annual fuel energy consumption as bi-objective, and minimizing life cycle cost considering uncertainties in load profiles. The optimized system could reduce system life cycle cost by 40% in single objective scenario and 45–52% at different levels of robustness. Exploring waste heat from the main ship propulsion engine is the key to reduce life cycle cost. The framework can be applied for a wide range of applications as an efficient tool to search for novel system designs and their evaluations.

Suggested Citation

  • Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2017. "Development of an optimization based design framework for microgrid energy systems," Energy, Elsevier, vol. 140(P1), pages 340-351.
    • Handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:340-351
    DOI: 10.1016/j.energy.2017.08.120
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    Cited by:

    1. 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.
    2. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Wang, Xuan & Jin, Ming & Feng, Wei & Shu, Gequn & Tian, Hua & Liang, Youcai, 2018. "Cascade energy optimization for waste heat recovery in distributed energy systems," Applied Energy, Elsevier, vol. 230(C), pages 679-695.
    4. Ghanaee, Reza & Akbari Foroud, Asghar, 2019. "Enhanced structure and optimal capacity sizing method for turbo-expander based microgrid with simultaneous recovery of cooling and electrical energy," Energy, Elsevier, vol. 170(C), pages 284-304.
    5. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    6. Ajagekar, Akshay & You, Fengqi, 2019. "Quantum computing for energy systems optimization: Challenges and opportunities," Energy, Elsevier, vol. 179(C), pages 76-89.
    7. Xiao Gong & Fan Li & Bo Sun & Dong Liu, 2020. "Collaborative Optimization of Multi-Energy Complementary Combined Cooling, Heating, and Power Systems Considering Schedulable Loads," Energies, MDPI, vol. 13(4), pages 1-17, February.
    8. Fioriti, Davide & Pintus, Salvatore & Lutzemberger, Giovanni & Poli, Davide, 2020. "Economic multi-objective approach to design off-grid microgrids: A support for business decision making," Renewable Energy, Elsevier, vol. 159(C), pages 693-704.
    9. Hak-Ju Lee & Ba Hau Vu & Rehman Zafar & Sung-Wook Hwang & Il-Yop Chung, 2021. "Design Framework of a Stand-Alone Microgrid Considering Power System Performance and Economic Efficiency," Energies, MDPI, vol. 14(2), pages 1-28, January.

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