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Reliability assessment of distribution system with the integration of renewable distributed generation


  • Adefarati, T.
  • Bansal, R.C.


Recent studies have shown that renewable energy resources will contribute substantially to future energy generation owing to the rapid depletion of fossil fuels. Wind and solar energy resources are major sources of renewable energy that have the ability to reduce the energy crisis and the greenhouse gases emitted by the conventional power plants. Reliability assessment is one of the key indicators to measure the impact of the renewable distributed generation (DG) units in the distribution networks and to minimize the cost that is associated with power outage. This paper presents a comprehensive reliability assessment of the distribution system that satisfies the consumer load requirements with the penetration of wind turbine generator (WTG), electric storage system (ESS) and photovoltaic (PV). A Markov model is proposed to access the stochastic characteristics of the major components of the renewable DG resources as well as their influence on the reliability of a conventional distribution system. The results obtained from the case studies have demonstrated the effectiveness of using WTG, ESS and PV to enhance the reliability of the conventional distribution system.

Suggested Citation

  • Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
  • Handle: RePEc:eee:appene:v:185:y:2017:i:p1:p:158-171
    DOI: 10.1016/j.apenergy.2016.10.087

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    References listed on IDEAS

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    Cited by:

    1. Luo, Lizi & Gu, Wei & Zhang, Xiao-Ping & Cao, Ge & Wang, Weijun & Zhu, Gang & You, Dingjun & Wu, Zhi, 2018. "Optimal siting and sizing of distributed generation in distribution systems with PV solar farm utilized as STATCOM (PV-STATCOM)," Applied Energy, Elsevier, vol. 210(C), pages 1092-1100.
    2. Das, Barun K. & Hoque, Najmul & Mandal, Soumya & Pal, Tapas Kumar & Raihan, Md Abu, 2017. "A techno-economic feasibility of a stand-alone hybrid power generation for remote area application in Bangladesh," Energy, Elsevier, vol. 134(C), pages 775-788.
    3. Escalera, Alberto & Hayes, Barry & Prodanović, Milan, 2018. "A survey of reliability assessment techniques for modern distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 344-357.
    4. Mi, Yang & Chen, Xin & Ji, Hongpeng & Ji, Liang & Fu, Yang & Wang, Chengshan & Wang, Jianhui, 2019. "The coordinated control strategy for isolated DC microgrid based on adaptive storage adjustment without communication," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Zhang, Xinjing & Chen, Haisheng & Xu, Yujie & Li, Wen & He, Fengjuan & Guo, Huan & Huang, Ye, 2017. "Distributed generation with energy storage systems: A case study," Applied Energy, Elsevier, vol. 204(C), pages 1251-1263.
    6. Das, Barun K. & Al-Abdeli, Yasir M. & Kothapalli, Ganesh, 2017. "Optimisation of stand-alone hybrid energy systems supplemented by combustion-based prime movers," Applied Energy, Elsevier, vol. 196(C), pages 18-33.
    7. Adefarati, T. & Bansal, R.C., 2017. "Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources," Applied Energy, Elsevier, vol. 206(C), pages 911-933.
    8. Das, Barun K. & Zaman, Forhad, 2019. "Performance analysis of a PV/Diesel hybrid system for a remote area in Bangladesh: Effects of dispatch strategies, batteries, and generator selection," Energy, Elsevier, vol. 169(C), pages 263-276.
    9. Alturki, Mansoor & Khodaei, Amin & Paaso, Aleksi & Bahramirad, Shay, 2018. "Optimization-based distribution grid hosting capacity calculations," Applied Energy, Elsevier, vol. 219(C), pages 350-360.
    10. Ghasemi, Mostafa & Dashti, Reza, 2018. "Designing a decision model to assess the reward and penalty scheme of electric distribution companies," Energy, Elsevier, vol. 147(C), pages 329-336.
    11. Postnikov, Ivan & Stennikov, Valery & Mednikova, Ekaterina & Penkovskii, Andrey, 2018. "Methodology for optimization of component reliability of heat supply systems," Applied Energy, Elsevier, vol. 227(C), pages 365-374.
    12. Mendoza-Vizcaino, Javier & Raza, Muhammad & Sumper, Andreas & Díaz-González, Francisco & Galceran-Arellano, Samuel, 2019. "Integral approach to energy planning and electric grid assessment in a renewable energy technology integration for a 50/50 target applied to a small island," Applied Energy, Elsevier, vol. 233, pages 524-543.
    13. Chen, J.J. & Zhuang, Y.B. & Li, Y.Z. & Wang, P. & Zhao, Y.L. & Zhang, C.S., 2017. "Risk-aware short term hydro-wind-thermal scheduling using a probability interval optimization model," Applied Energy, Elsevier, vol. 189(C), pages 534-554.
    14. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    15. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, Open Access Journal, vol. 10(8), pages 1-31, July.
    16. Fuentes-Cortés, Luis Fabián & Flores-Tlacuahuac, Antonio, 2018. "Integration of distributed generation technologies on sustainable buildings," Applied Energy, Elsevier, vol. 224(C), pages 582-601.


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