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A fuzzy environmental-technical-economic model for distributed generation planning

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  • Zangeneh, Ali
  • Jadid, Shahram
  • Rahimi-Kian, Ashkan

Abstract

To determine the optimal size, location and also the proper technology of distributed generation (DG) units in distribution systems, a static fuzzy multiobjective model is proposed in this paper. The proposed model can concurrently optimize a number of conflicting and competing objective functions including economic, technical and environmental attributes. The economic function is the profit of a distribution company (DisCo) from selling the DG output power to its customers. The contribution of this model is the consideration of some DG marginal revenues in the economic function. Inclusion of marginal revenues would not only reduce the investment risks of DG technologies, but also would enable the optimal penetration of DG units. The proposed DG planning framework considers various DG technologies such as photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), gas turbine (GT) and diesel engine (DE). The system uncertainties (including those for the energy demand, energy price and DG technologies operating and investment costs) are modeled using fuzzy numbers. The numerical case studies have been carried out using the IEEE 37-node distribution test system to demonstrate the performance of the proposed DG planning model.

Suggested Citation

  • Zangeneh, Ali & Jadid, Shahram & Rahimi-Kian, Ashkan, 2011. "A fuzzy environmental-technical-economic model for distributed generation planning," Energy, Elsevier, vol. 36(5), pages 3437-3445.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:3437-3445
    DOI: 10.1016/j.energy.2011.03.048
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    References listed on IDEAS

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    1. Zangeneh, Ali & Jadid, Shahram & Rahimi-Kian, Ashkan, 2009. "Promotion strategy of clean technologies in distributed generation expansion planning," Renewable Energy, Elsevier, vol. 34(12), pages 2765-2773.
    2. Zangeneh, Ali & Jadid, Shahram & Rahimi-Kian, Ashkan, 2009. "A hierarchical decision making model for the prioritization of distributed generation technologies: A case study for Iran," Energy Policy, Elsevier, vol. 37(12), pages 5752-5763, December.
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    2. Viral, Rajkumar & Khatod, D.K., 2012. "Optimal planning of distributed generation systems in distribution system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5146-5165.
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    7. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
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    11. Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2017. "Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations," Renewable Energy, Elsevier, vol. 101(C), pages 1311-1324.
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    13. Ippolito, M.G. & Di Silvestre, M.L. & Riva Sanseverino, E. & Zizzo, G. & Graditi, G., 2014. "Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sources under different design scenarios," Energy, Elsevier, vol. 64(C), pages 648-662.
    14. Dong, Cong & Huang, Guohe & Cai, Yanpeng & Cheng, Guanhui & Tan, Qian, 2016. "Bayesian interval robust optimization for sustainable energy system planning in Qiqihar City, China," Energy Economics, Elsevier, vol. 60(C), pages 357-376.
    15. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    16. Wei, F. & Wu, Q.H. & Jing, Z.X. & Chen, J.J. & Zhou, X.X., 2016. "Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach," Energy, Elsevier, vol. 111(C), pages 933-946.
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