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Robust economic optimization and environmental policy analysis for microgrid planning: An application to Taichung Industrial Park, Taiwan

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  • Yu, Nan
  • Kang, Jin-Su
  • Chang, Chung-Chuan
  • Lee, Tai-Yong
  • Lee, Dong-Yup

Abstract

This study aims to provide economical and environmentally friendly solutions for a microgrid system with distributed energy resources in the design stage, considering multiple uncertainties during operation and conflicting interests among diverse microgrid stakeholders. For the purpose, we develop a multi-objective optimization model for robust microgrid planning, on the basis of an economic robustness measure, i.e. the worst-case cost among possible scenarios, to reduce the variability among scenario costs caused by uncertainties. The efficacy of the model is successfully demonstrated by applying it to Taichung Industrial Park in Taiwan, an industrial complex, where significant amount of greenhouse gases are emitted. Our findings show that the most robust solution, but the highest cost, mainly includes 45% (26.8 MW) of gas engine and 47% (28 MW) of photovoltaic panel with the highest system capacity (59 MW). Further analyses reveal the environmental benefits from the significant reduction of the expected annual CO2 emission and carbon tax by about half of the current utility facilities in the region. In conclusion, the developed model provides an efficient decision-making tool for robust microgrid planning at the preliminary stage.

Suggested Citation

  • Yu, Nan & Kang, Jin-Su & Chang, Chung-Chuan & Lee, Tai-Yong & Lee, Dong-Yup, 2016. "Robust economic optimization and environmental policy analysis for microgrid planning: An application to Taichung Industrial Park, Taiwan," Energy, Elsevier, vol. 113(C), pages 671-682.
  • Handle: RePEc:eee:energy:v:113:y:2016:i:c:p:671-682
    DOI: 10.1016/j.energy.2016.07.066
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    11. Comodi, Gabriele & Bartolini, Andrea & Carducci, Francesco & Nagaranjan, Balamurugan & Romagnoli, Alessandro, 2019. "Achieving low carbon local energy communities in hot climates by exploiting networks synergies in multi energy systems," Applied Energy, Elsevier, vol. 256(C).
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