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A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

Author

Listed:
  • Upasana Lakhina

    (Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Irraivan Elamvazuthi

    (Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Nasreen Badruddin

    (Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Ajay Jangra

    (University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra 136119, India)

  • Bao-Huy Truong

    (Institute of Engineering and Technology, Thu Dau Mot University, Thu Dau Mot 7500, Vietnam)

  • Joseph M. Guerrero

    (Centre of Research on Microgrids, Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms.

Suggested Citation

  • Upasana Lakhina & Irraivan Elamvazuthi & Nasreen Badruddin & Ajay Jangra & Bao-Huy Truong & Joseph M. Guerrero, 2023. "A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid," Sustainability, MDPI, vol. 15(8), pages 1-25, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6358-:d:1118221
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    References listed on IDEAS

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    1. Maher G. M. Abdolrasol & Mahammad Abdul Hannan & S. M. Suhail Hussain & Taha Selim Ustun & Mahidur R. Sarker & Pin Jern Ker, 2021. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks," Energies, MDPI, vol. 14(20), pages 1-19, October.
    2. Bruno Domenech & Laia Ferrer-Martí & Facundo García & Georgina Hidalgo & Rafael Pastor & Antonin Ponsich, 2022. "Optimizing PV Microgrid Isolated Electrification Projects—A Case Study in Ecuador," Mathematics, MDPI, vol. 10(8), pages 1-24, April.
    3. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    4. Manuela Sechilariu & Fabrice Locment & Baochao Wang, 2015. "Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid," Energies, MDPI, vol. 8(8), pages 1-23, July.
    5. Sadeghi, Delnia & Hesami Naghshbandy, Ali & Bahramara, Salah, 2020. "Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization," Energy, Elsevier, vol. 209(C).
    6. Mohsin Shahzad & Waseem Akram & Muhammad Arif & Uzair Khan & Barkat Ullah, 2021. "Optimal Siting and Sizing of Distributed Generators by Strawberry Plant Propagation Algorithm," Energies, MDPI, vol. 14(6), pages 1-13, March.
    7. Alain Aoun & Hussein Ibrahim & Mazen Ghandour & Adrian Ilinca, 2019. "Supply Side Management vs. Demand Side Management of a Residential Microgrid Equipped with an Electric Vehicle in a Dual Tariff Scheme," Energies, MDPI, vol. 12(22), pages 1-21, November.
    8. Rovick Tarife & Yosuke Nakanishi & Yining Chen & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2022. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines," Energies, MDPI, vol. 15(6), pages 1-29, March.
    9. Makbul A.M. Ramli & H.R.E.H. Bouchekara & Abdulsalam S. Alghamdi, 2019. "Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources," Sustainability, MDPI, vol. 11(14), pages 1-28, July.
    10. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    11. Silvente, Javier & Papageorgiou, Lazaros G., 2017. "An MILP formulation for the optimal management of microgrids with task interruptions," Applied Energy, Elsevier, vol. 206(C), pages 1131-1146.
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