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Assessing the performance and benefits of customer distributed generation developers under uncertainties

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  • Zangiabadi, Mansoureh
  • Feuillet, Rene
  • Lesani, Hamid
  • Hadj-Said, Nouredine
  • Kvaløy, Jan T.

Abstract

In this paper, the performance of customer-owned distributed generation (DG) units is quantified from different perspectives through an uncertainty study. A Monte Carlo-based method is applied to assess the stochastic operation of the customer-owned DG units in the power distribution system. Several cases are studied to analyze the impact on system performance of using such generators, with the emphasis on benefits. The results of the studied cases show that proper operation of customer-owned DG units placed close to significant consumption centers offers several benefits which lead to significant energy savings and improvement in the performance indices while maintaining the cost-effectiveness. Furthermore, based on the energy demand, different electricity price scenarios considering a cost sensitivity analysis are performed to indicate how the variations in electricity price influence each scenario’s feasibility. It is concluded that implementation of a proper energy purchase policy, and allocating the benefits of DG units to the owners, improves the economic performance of their investments and encourages customer DG developers to connect DG to the distribution network.

Suggested Citation

  • Zangiabadi, Mansoureh & Feuillet, Rene & Lesani, Hamid & Hadj-Said, Nouredine & Kvaløy, Jan T., 2011. "Assessing the performance and benefits of customer distributed generation developers under uncertainties," Energy, Elsevier, vol. 36(3), pages 1703-1712.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:3:p:1703-1712
    DOI: 10.1016/j.energy.2010.12.058
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    1. Li, Xuping, 2012. "Understanding the Design and Performance of Distributed Tri-Generation Systems for Home and Neighborhood Refueling," Institute of Transportation Studies, Working Paper Series qt0h87d4sm, Institute of Transportation Studies, UC Davis.
    2. Aman, M.M. & Jasmon, G.B. & Bakar, A.H.A. & Mokhlis, H., 2014. "A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm," Energy, Elsevier, vol. 66(C), pages 202-215.
    3. Canizes, Bruno & Soares, João & Vale, Zita & Khodr, H.M., 2012. "Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems," Energy, Elsevier, vol. 45(1), pages 1007-1017.
    4. Leon, A.E. & Solsona, J.A. & Figueroa, J.L. & Valla, M.I., 2011. "Optimization with constraints for excitation control in synchronous generators," Energy, Elsevier, vol. 36(8), pages 5366-5373.
    5. Sultana, U. & Khairuddin, Azhar B. & Aman, M.M. & Mokhtar, A.S. & Zareen, N., 2016. "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 363-378.
    6. Simpson, Genevieve, 2017. "Network operators and the transition to decentralised electricity: An Australian socio-technical case study," Energy Policy, Elsevier, vol. 110(C), pages 422-433.
    7. He, Yongxiu & Wang, Bing & Wang, Jianhui & Xiong, Wei & Xia, Tian, 2012. "Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China," Energy, Elsevier, vol. 47(1), pages 230-236.
    8. Fridgen, Gilbert & Halbrügge, Stephanie & Olenberger, Christian & Weibelzahl, Martin, 2020. "The insurance effect of renewable distributed energy resources against uncertain electricity price developments," Energy Economics, Elsevier, vol. 91(C).
    9. José A. G. Cararo & João Caetano Neto & Wagner A. Vilela Júnior & Márcio R. C. Reis & Gabriel A. Wainer & Paulo V. dos Santos & Wesley P. Calixto, 2021. "Spatial Model of Optimization Applied in the Distributed Generation Photovoltaic to Adjust Voltage Levels," Energies, MDPI, vol. 14(22), pages 1-37, November.
    10. Anaya, Karim L. & Pollitt, Michael G., 2017. "Going smarter in the connection of distributed generation," Energy Policy, Elsevier, vol. 105(C), pages 608-617.
    11. Cosentino, Valentina & Favuzza, Salvatore & Graditi, Giorgio & Ippolito, Mariano Giuseppe & Massaro, Fabio & Riva Sanseverino, Eleonora & Zizzo, Gaetano, 2012. "Smart renewable generation for an islanded system. Technical and economic issues of future scenarios," Energy, Elsevier, vol. 39(1), pages 196-204.
    12. Ahmadigorji, Masoud & Amjady, Nima, 2016. "A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm," Energy, Elsevier, vol. 102(C), pages 199-215.

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