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Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm

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  • Malekpour, Ahmad Reza
  • Tabatabaei, Sajad
  • Niknam, Taher

Abstract

Deregulation and restructuring in power systems, the ever-increasing demand for electricity, and concerns about the environment are the major driving forces for using Renewable Energy Sources (RES). Recently, Wind Farms (WFs) and Fuel Cell Power Plants (FCPPs) have gained great interest by Distribution Companies (DisCos) as the most common RES. In fact, the connection of enormous RES to existing distribution networks has changed the operation of distribution systems. It also affects the Volt/Var control problem, which is one of the most important schemes in distribution networks. Due to the intermittent characteristics of WFs, distribution systems should be analyzed using probabilistic approaches rather than deterministic ones. Therefore, this paper presents a new algorithm for the multi-objective probabilistic Volt/Var control problem in distribution systems including RES. In this regard, a probabilistic load flow based on Point Estimate Method (PEM) is used to consider the effect of uncertainty in electrical power production of WFs as well as load demands. The objective functions, which are investigated here, are the total cost of power generated by WFs, FCPPs and the grid; the total electrical energy losses and the total emission produced by WFs, FCPPs and DisCos. Moreover, a new optimization algorithm based on Improved Shuffled Frog Leaping Algorithm (ISFLA) is proposed to determine the best operating point for the active and reactive power generated by WFs and FCPPs, reactive power values of capacitors, and transformers’ tap positions for the next day. Using the fuzzy optimization method and max-min operator, DisCos can find solutions for different objective functions, which are optimal from economical, operational and environmental perspectives. Finally, a practical 85-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method.

Suggested Citation

  • Malekpour, Ahmad Reza & Tabatabaei, Sajad & Niknam, Taher, 2012. "Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm," Renewable Energy, Elsevier, vol. 39(1), pages 228-240.
  • Handle: RePEc:eee:renene:v:39:y:2012:i:1:p:228-240
    DOI: 10.1016/j.renene.2011.08.004
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    References listed on IDEAS

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    1. Snyder, Brian & Kaiser, Mark J., 2009. "Ecological and economic cost-benefit analysis of offshore wind energy," Renewable Energy, Elsevier, vol. 34(6), pages 1567-1578.
    2. Niknam, Taher & Meymand, Hamed Zeinoddini & Nayeripour, Majid, 2010. "A practical algorithm for optimal operation management of distribution network including fuel cell power plants," Renewable Energy, Elsevier, vol. 35(8), pages 1696-1714.
    3. Niknam, Taher & Firouzi, Bahman Bahmani & Ostadi, Amir, 2010. "A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators," Applied Energy, Elsevier, vol. 87(6), pages 1919-1928, June.
    4. Gunhui Chung & Kevin Lansey, 2009. "Application of the Shuffled Frog Leaping Algorithm for the Optimization of a General Large-Scale Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 797-823, March.
    5. Bourouni, K. & Ben M’Barek, T. & Al Taee, A., 2011. "Design and optimization of desalination reverse osmosis plants driven by renewable energies using genetic algorithms," Renewable Energy, Elsevier, vol. 36(3), pages 936-950.
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    Cited by:

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    6. Mortazi, Mohammad & Moradi, Ahmad & Khosravi, Mohsen, 2020. "Simultaneous optimization of transformer tap changer and network capacitors to improve the distribution system’s static security considering distributed generation sources," MPRA Paper 109052, University Library of Munich, Germany, revised 01 Jul 2020.
    7. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
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