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Energy management system using Mimosa Pudica optimization technique for microgrid applications

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  • V, Kavitha
  • V, Malathi
  • Guerrero, Josep M.
  • Bazmohammadi, Najmeh

Abstract

A microgrid is an powerful alternative promising solution, which fosters a reliable power supply to supplement conventional electrical framework. However, microgrid encounters various challenges subjected to energy flow, power quality, and net profit due to the high integration of distributed energy resources (DERs) in the network. Hence, the Mimosa pudica-based energy management scheme has been designed for optimal scheduling of microgrid to reduce the production overheads with underlying system constraints. Besides, the proposed approach is formulated to manage the power balancing among the distributed resources and utility through a standard communication protocol. Mainly, the optimization process is developed through the sensitive and intelligent behavior of mimosa plants that enable the adaptation of dynamic context by reusing past information through memory, and maintaining variation across the solution leads to good solution accuracy. Moreover, the proposed algorithm corroborates the potency of microgrid over isolated, grid-tied, and resynchronized conditions. Numerical results illustrate that the proposed technique has obtained more profit (8%) than the existing approaches with superior optimization ability and fast convergence.

Suggested Citation

  • V, Kavitha & V, Malathi & Guerrero, Josep M. & Bazmohammadi, Najmeh, 2022. "Energy management system using Mimosa Pudica optimization technique for microgrid applications," Energy, Elsevier, vol. 244(PA).
  • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028541
    DOI: 10.1016/j.energy.2021.122605
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    References listed on IDEAS

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    1. Pesaran H.A., Mahmoud & Nazari-Heris, Morteza & Mohammadi-Ivatloo, Behnam & Seyedi, Heresh, 2020. "A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks," Energy, Elsevier, vol. 209(C).
    2. McLarty, Dustin & Panossian, Nadia & Jabbari, Faryar & Traverso, Alberto, 2019. "Dynamic economic dispatch using complementary quadratic programming," Energy, Elsevier, vol. 166(C), pages 755-764.
    3. Min-fan He & Fu-xing Zhang & Yong Huang & Jian Chen & Jue Wang & Rui Wang, 2019. "A Distributed Demand Side Energy Management Algorithm for Smart Grid," Energies, MDPI, vol. 12(3), pages 1-19, January.
    4. Xuejie Wang & Yanchao Ji & Jianze Wang & Yuanjun Wang & Lei Qi, 2020. "Optimal energy management of microgrid based on multi-parameter dynamic programming," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    5. Hee-Jun Cha & Dong-Jun Won & Sang-Hyuk Kim & Il-Yop Chung & Byung-Moon Han, 2015. "Multi-Agent System-Based Microgrid Operation Strategy for Demand Response," Energies, MDPI, vol. 8(12), pages 1-15, December.
    6. Roy, Kallol & Mandal, Kamal Krishna & Mandal, Atis Chandra, 2019. "Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system," Energy, Elsevier, vol. 167(C), pages 402-416.
    7. Qian Liu & Rui Wang & Yan Zhang & Guohua Wu & Jianmai Shi, 2018. "An Optimal and Distributed Demand Response Strategy for Energy Internet Management," Energies, MDPI, vol. 11(1), pages 1-16, January.
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    1. Chakraborty, Amit & Ray, Saheli, 2023. "Operational cost minimization of a microgrid with optimum battery energy storage system and plug-in-hybrid electric vehicle charging impact using slime mould algorithm," Energy, Elsevier, vol. 278(PA).
    2. Md Shafiullah & Akib Mostabe Refat & Md Ershadul Haque & Dewan Mabrur Hasan Chowdhury & Md Sanower Hossain & Abdullah G. Alharbi & Md Shafiul Alam & Amjad Ali & Shorab Hossain, 2022. "Review of Recent Developments in Microgrid Energy Management Strategies," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    3. Wenshuai Bai & Dian Wang & Zhongquan Miao & Xiaorong Sun & Jiabin Yu & Jiping Xu & Yuqing Pan, 2023. "The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency," Sustainability, MDPI, vol. 15(8), pages 1-21, April.

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