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Optimal placement and sizing of the storage supporting transmission and distribution networks

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  • Motalleb, Mahdi
  • Reihani, Ehsan
  • Ghorbani, Reza

Abstract

Developments of renewable energy resources imposes many uncertainties and variabilities in power grids. One of the best approaches to mitigate these stochastic disturbances is thought the use of Battery Energy Storage System (BESS). Besides application of the BESS such as decreasing the disturbances in distribution system, the grid frequency can be controlled in contingencies using the appropriate storage in transmission network to compensate the power shortage. Thus, the optimal siting and sizing of the BESS is important to have the minimum costs and losses. This paper describes a heuristic method to find the optimal location(s) and capacity of a multi-purpose BESS including transmission and distribution parts. In the transmission storage part, a sensitive analysis is performed using Complex-Valued Neural Networks (CVNN) and Time Domain Power Flow (TDPF) in order to detect the optimal BESS location(s). Additionally, running TDPF and Economic Dispatch (ED) leads to the optimal BESS size. In the distribution storage part, the optimal BESS size is calculated to perform distribution grid services such as peak load shaving and load curve smoothing. The proposed method has been applied to a real model (Maui island in Hawai'i -United States) to calculate the optimal results for both transmission and distribution sides.

Suggested Citation

  • Motalleb, Mahdi & Reihani, Ehsan & Ghorbani, Reza, 2016. "Optimal placement and sizing of the storage supporting transmission and distribution networks," Renewable Energy, Elsevier, vol. 94(C), pages 651-659.
    • Handle: RePEc:eee:renene:v:94:y:2016:i:c:p:651-659
    DOI: 10.1016/j.renene.2016.03.101
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    1. Denholm, Paul & Sioshansi, Ramteen, 2009. "The value of compressed air energy storage with wind in transmission-constrained electric power systems," Energy Policy, Elsevier, vol. 37(8), pages 3149-3158, August.
    2. Reihani, Ehsan & Motalleb, Mahdi & Ghorbani, Reza & Saad Saoud, Lyes, 2016. "Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1372-1379.
    3. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    4. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    5. Papaefthymiou, Stefanos V. & Papathanassiou, Stavros A., 2014. "Optimum sizing of wind-pumped-storage hybrid power stations in island systems," Renewable Energy, Elsevier, vol. 64(C), pages 187-196.
    6. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    7. Jae Ho Kim & Warren B. Powell, 2011. "Optimal Energy Commitments with Storage and Intermittent Supply," Operations Research, INFORMS, vol. 59(6), pages 1347-1360, December.
    8. Fossati, Juan P. & Galarza, Ainhoa & Martín-Villate, Ander & Fontán, Luis, 2015. "A method for optimal sizing energy storage systems for microgrids," Renewable Energy, Elsevier, vol. 77(C), pages 539-549.
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