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Distributed generation investment by a microgrid under uncertainty

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  • Siddiqui, Afzal S.
  • Marnay, Chris

Abstract

This paper examines a California-based microgrid's decision to invest in a distributed generation (DG) unit fuelled by natural gas. While the long-term natural gas generation cost is stochastic, we initially assume that the microgrid may purchase electricity at a fixed retail rate from its utility. Using the real options approach, we find a natural gas generation cost threshold that triggers DG investment. Furthermore, the consideration of operational flexibility by the microgrid increases DG investment, while the option to disconnect from the utility is not attractive. By allowing the electricity price to be stochastic, we next determine an investment threshold boundary and find that high electricity price volatility relative to that of natural gas generation cost delays investment while simultaneously increasing the value of the investment. We conclude by using this result to find the implicit option value of the DG unit when two sources of uncertainty exist.

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  • Siddiqui, Afzal S. & Marnay, Chris, 2008. "Distributed generation investment by a microgrid under uncertainty," Energy, Elsevier, vol. 33(12), pages 1729-1737.
    • Handle: RePEc:eee:energy:v:33:y:2008:i:12:p:1729-1737
    DOI: 10.1016/j.energy.2008.08.011
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    6. Karatepe, Engin & Ugranlı, Faruk & Hiyama, Takashi, 2015. "Comparison of single- and multiple-distributed generation concepts in terms of power loss, voltage profile, and line flows under uncertain scenarios," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 317-327.
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    8. Farnaz Farzan & Khashayar Mahani & Kaveh Gharieh & Mohsen Jafari, 2015. "Microgrid investment under uncertainty: a real option approach using closed form contingent analysis," Annals of Operations Research, Springer, vol. 235(1), pages 259-276, December.
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    10. Hu, Ming-Che & Lu, Su-Ying & Chen, Yen-Haw, 2016. "Stochastic programming and market equilibrium analysis of microgrids energy management systems," Energy, Elsevier, vol. 113(C), pages 662-670.
    11. Pless, Jacquelyn & Arent, Douglas J. & Logan, Jeffrey & Cochran, Jaquelin & Zinaman, Owen, 2016. "Quantifying the value of investing in distributed natural gas and renewable electricity systems as complements: Applications of discounted cash flow and real options analysis with stochastic inputs," Energy Policy, Elsevier, vol. 97(C), pages 378-390.
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    13. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    14. Ravindra, Kumudhini & Iyer, Parameshwar P., 2014. "Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis," Energy, Elsevier, vol. 76(C), pages 32-41.
    15. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    16. Safaei, Amir & Freire, Fausto & Antunes, Carlos Henggeler, 2013. "A model for optimal energy planning of a commercial building integrating solar and cogeneration systems," Energy, Elsevier, vol. 61(C), pages 211-223.
    17. Obara, Shin’ya & Watanabe, Seizi & Rengarajan, Balaji, 2011. "Operation method study based on the energy balance of an independent microgrid using solar-powered water electrolyzer and an electric heat pump," Energy, Elsevier, vol. 36(8), pages 5200-5213.
    18. Rezvan, A. Taghipour & Gharneh, N. Shams & Gharehpetian, G.B., 2012. "Robust optimization of distributed generation investment in buildings," Energy, Elsevier, vol. 48(1), pages 455-463.
    19. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2014. "A mathematical model for the optimal operation of the University of Genoa Smart Polygeneration Microgrid: Evaluation of technical, economic and environmental performance indicators," Energy, Elsevier, vol. 64(C), pages 912-922.
    20. Zhu, Lei & Fan, Ying, 2011. "A real options–based CCS investment evaluation model: Case study of China’s power generation sector," Applied Energy, Elsevier, vol. 88(12), pages 4320-4333.
    21. Jorge Luis Angarita-Márquez & Geev Mokryani & Jorge Martínez-Crespo, 2021. "Two-Stage Stochastic Model to Invest in Distributed Generation Considering the Long-Term Uncertainties," Energies, MDPI, vol. 14(18), pages 1-12, September.
    22. Mahdi Karami Darabi & Hamed Ganjeh Ganjehlou & Amirreza Jafari & Morteza Nazari-Heris & Gevork B. Gharehpetian & Mehrdad Abedi, 2021. "Evaluating the Effect of Demand Response Programs (DRPs) on Robust Optimal Sizing of Islanded Microgrids," Energies, MDPI, vol. 14(18), pages 1-20, September.

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