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Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization

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  • Pawan Singh
  • Baseem Khan

Abstract

At present, renewable energy sources (RESs) integration using microgrid (MG) technology is of great importance for demand side management. Optimization of MG provides enhanced generation from RES at minimum operation cost. The microgrid optimization problem involves a large number of variables and constraints; therefore, it is complex in nature and various existing algorithms are unable to handle them efficiently. This paper proposed an artificial shark optimization (ASO) method to remove the limitation of existing algorithms for solving the economical operation problem of MG. The ASO algorithm is motivated by the sound sensing capability of sharks, which they use for hunting. Further, the intermittent nature of renewable energy sources is managed by utilizing battery energy storage (BES). BES has several benefits. However, all these benefits are limited to a certain fixed area due to the stationary nature of the BES system. The latest technologies, such as electric vehicle technologies (EVTs), provide all benefits of BES along with mobility to support the variable system demands. Therefore, in this work, EVTs incorporated grid connected smart microgrid (SMG) system is introduced. Additionally, a comparative study is provided, which shows that the ASO performs relatively better than the existing techniques.

Suggested Citation

  • Pawan Singh & Baseem Khan, 2017. "Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization," Complexity, Hindawi, vol. 2017, pages 1-22, October.
    • Handle: RePEc:hin:complx:2158926
    DOI: 10.1155/2017/2158926
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    References listed on IDEAS

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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Azizipanah-Abarghooee, Rasoul & Niknam, Taher & Roosta, Alireza & Malekpour, Ahmad Reza & Zare, Mohsen, 2012. "Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method," Energy, Elsevier, vol. 37(1), pages 322-335.
    3. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    4. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
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    Cited by:

    1. Chun Wei & Xiangzhi Xu & Youbing Zhang & Xiangshan Li, 2019. "A Survey on Optimal Control and Operation of Integrated Energy Systems," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    2. Lin Sun & Suisui Chen & Jiucheng Xu & Yun Tian, 2019. "Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation," Complexity, Hindawi, vol. 2019, pages 1-20, February.
    3. Muhammad Sulaiman & Ashfaq Ahmad & Asfandyar Khan & Shakoor Muhammad, 2018. "Hybridized Symbiotic Organism Search Algorithm for the Optimal Operation of Directional Overcurrent Relays," Complexity, Hindawi, vol. 2018, pages 1-11, January.
    4. Maciej Ławryńczuk, 2018. "Towards Reduced-Order Models of Solid Oxide Fuel Cell," Complexity, Hindawi, vol. 2018, pages 1-18, July.

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