IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6896-d1251273.html
   My bibliography  Save this article

A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids

Author

Listed:
  • Ssadik Charadi

    (Electronics Power and Control Team, Mohammadia School of Engineers (EMI), Mohammed V University, Rabat 10000, Morocco
    SMARTiLab, Moroccan School of Engineering Sciences (EMSI), Rabat 10000, Morocco)

  • Houssam Eddine Chakir

    (EEIS-Lab, ENSET Mohammedia, Hassan II University, Casablanca 20000, Morocco)

  • Abdelbari Redouane

    (Electromechanical Department, Ecole des Mines de Rabat (ENSMR), Rabat 10000, Morocco)

  • Abdennebi El Hasnaoui

    (Electromechanical Department, Ecole des Mines de Rabat (ENSMR), Rabat 10000, Morocco)

  • Brahim El Bhiri

    (SMARTiLab, Moroccan School of Engineering Sciences (EMSI), Rabat 10000, Morocco)

Abstract

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

Suggested Citation

  • Ssadik Charadi & Houssam Eddine Chakir & Abdelbari Redouane & Abdennebi El Hasnaoui & Brahim El Bhiri, 2023. "A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids," Energies, MDPI, vol. 16(19), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6896-:d:1251273
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6896/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6896/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammadi-ivatloo, Behnam & Rabiee, Abbas & Soroudi, Alireza & Ehsan, Mehdi, 2012. "Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch," Energy, Elsevier, vol. 44(1), pages 228-240.
    2. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    3. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    4. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ji-Won Lee & Mun-Kyeom Kim & Hyung-Joon Kim, 2021. "A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy," Energies, MDPI, vol. 14(3), pages 1-21, January.
    2. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    3. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    4. Li, Shenglin & Zhu, Jizhong & Chen, Ziyu & Luo, Tengyan, 2021. "Double-layer energy management system based on energy sharing cloud for virtual residential microgrid," Applied Energy, Elsevier, vol. 282(PA).
    5. Álex Omar Topa Gavilema & José Domingo Álvarez & José Luis Torres Moreno & Manuel Pérez García, 2021. "Towards Optimal Management in Microgrids: An Overview," Energies, MDPI, vol. 14(16), pages 1-25, August.
    6. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(C).
    7. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    8. Ahmad Alzahrani & Senthil Kumar Ramu & Gunapriya Devarajan & Indragandhi Vairavasundaram & Subramaniyaswamy Vairavasundaram, 2022. "A Review on Hydrogen-Based Hybrid Microgrid System: Topologies for Hydrogen Energy Storage, Integration, and Energy Management with Solar and Wind Energy," Energies, MDPI, vol. 15(21), pages 1-32, October.
    9. Muhammad Umair Safder & Mohammad J. Sanjari & Ameer Hamza & Rasoul Garmabdari & Md. Alamgir Hossain & Junwei Lu, 2023. "Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions," Energies, MDPI, vol. 16(18), pages 1-28, September.
    10. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    11. Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    12. Qunpeng Fan, 2022. "Management and Policy Modeling of the Market Using Artificial Intelligence," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    13. Ramin Nourollahi & Pouya Salyani & Kazem Zare & Behnam Mohammadi-Ivatloo & Zulkurnain Abdul-Malek, 2022. "Peak-Load Management of Distribution Network Using Conservation Voltage Reduction and Dynamic Thermal Rating," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    14. Jicheng Liu & Fangqiu Xu & Shuaishuai Lin & Hua Cai & Suli Yan, 2018. "A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization," Energies, MDPI, vol. 11(12), pages 1-22, November.
    15. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
    16. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    17. Alkmini Michaloglou & Nikolaos L. Tsitsas, 2021. "Feasible Optimal Solutions of Electromagnetic Cloaking Problems by Chaotic Accelerated Particle Swarm Optimization," Mathematics, MDPI, vol. 9(21), pages 1-23, October.
    18. Zhang, Li & Gao, Yan & Zhu, Hongbo & Tao, Li, 2022. "Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach," Energy, Elsevier, vol. 239(PA).
    19. e Silva, Danilo P. & Félix Salles, José L. & Fardin, Jussara F. & Rocha Pereira, Maxsuel M., 2020. "Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data," Applied Energy, Elsevier, vol. 278(C).
    20. Wang, Yajun & Wang, Jidong & Cao, Man & Kong, Xiangyu & Abderrahim, Bouchedjira & Yuan, Long & Vartosh, Aris, 2023. "Dynamic emission dispatch considering the probabilistic model with multiple smart energy system players based on a developed fuzzy theory-based harmony search algorithm," Energy, Elsevier, vol. 269(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6896-:d:1251273. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.