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A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission

Author

Listed:
  • Mohammad Zohrul Islam

    (Department of Electrical and Electronic Engineering, Advanced Lightning Power and Energy Research (ALPER), Universiti Putra Malaysia (UPM), Serdang, Selangor 43400, Malaysia)

  • Noor Izzri Abdul Wahab

    (Department of Electrical and Electronic Engineering, Advanced Lightning Power and Energy Research (ALPER), Universiti Putra Malaysia (UPM), Serdang, Selangor 43400, Malaysia)

  • Veerapandiyan Veerasamy

    (Department of Electrical and Electronic Engineering, Advanced Lightning Power and Energy Research (ALPER), Universiti Putra Malaysia (UPM), Serdang, Selangor 43400, Malaysia)

  • Hashim Hizam

    (Department of Electrical and Electronic Engineering, Advanced Lightning Power and Energy Research (ALPER), Universiti Putra Malaysia (UPM), Serdang, Selangor 43400, Malaysia)

  • Nashiren Farzilah Mailah

    (Department of Electrical and Electronic Engineering, Advanced Lightning Power and Energy Research (ALPER), Universiti Putra Malaysia (UPM), Serdang, Selangor 43400, Malaysia)

  • Josep M. Guerrero

    (Center for Research on Microgrids (CROM), Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark)

  • Mohamad Nasrun Mohd Nasir

    (Department of Electrical and Electronic Engineering, Advanced Lightning Power and Energy Research (ALPER), Universiti Putra Malaysia (UPM), Serdang, Selangor 43400, Malaysia)

Abstract

The electric sector is majorly concerned about the greenhouse and non-greenhouse gas emissions generated from both conventional and renewable energy sources, as this is becoming a major issue globally. Thus, the utilities must adhere to certain environmental guidelines for sustainable power generation. Therefore, this paper presents a novel nature-inspired and population-based Harris Hawks Optimization (HHO) methodology for controlling the emissions from thermal generating sources by solving single and multi-objective Optimal Power Flow (OPF) problems. The OPF is a non-linear, non-convex, constrained optimization problem that primarily aims to minimize the fitness function by satisfying the equality and inequality constraints of the system. The cooperative behavior and dynamic chasing patterns of hawks to pounce on escaping prey is modeled mathematically to minimize the objective function. In this paper, fuel cost, real power loss and environment emissions are regarded as single and multi-objective functions for optimal adjustments of power system control variables. The different conflicting framed multi-objective functions have been solved using weighted sums using a no-preference method. The presented method is coded using MATLAB software and an IEEE (Institute of Electrical and Electronics Engineers) 30-bus. The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). Additionally, the study on placement of Distributed Generation (DG) reveals that the system losses and emissions are reduced by an amount of 9.8355% and 26.2%, respectively.

Suggested Citation

  • Mohammad Zohrul Islam & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy & Hashim Hizam & Nashiren Farzilah Mailah & Josep M. Guerrero & Mohamad Nasrun Mohd Nasir, 2020. "A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission," Sustainability, MDPI, vol. 12(13), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5248-:d:377481
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    References listed on IDEAS

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    1. Yuan, Xiaohui & Zhang, Binqiao & Wang, Pengtao & Liang, Ji & Yuan, Yanbin & Huang, Yuehua & Lei, Xiaohui, 2017. "Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm," Energy, Elsevier, vol. 122(C), pages 70-82.
    2. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
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    3. José A. G. Cararo & João Caetano Neto & Wagner A. Vilela Júnior & Márcio R. C. Reis & Gabriel A. Wainer & Paulo V. dos Santos & Wesley P. Calixto, 2021. "Spatial Model of Optimization Applied in the Distributed Generation Photovoltaic to Adjust Voltage Levels," Energies, MDPI, vol. 14(22), pages 1-37, November.
    4. Suprava Chakraborty & Sumit Verma & Aprajita Salgotra & Rajvikram Madurai Elavarasan & Devaraj Elangovan & Lucian Mihet-Popa, 2021. "Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects," Energies, MDPI, vol. 14(16), pages 1-26, August.
    5. Jamal, Raheela & Zhang, Junzhe & Men, Baohui & Khan, Noor Habib & Ebeed, Mohamed & Jamal, Tanzeela & Mohamed, Emad A., 2024. "Chaotic-quasi-oppositional-phasor based multi populations gorilla troop optimizer for optimal power flow solution," Energy, Elsevier, vol. 301(C).
    6. Sirote Khunkitti & Apirat Siritaratiwat & Suttichai Premrudeepreechacharn, 2021. "Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm," Sustainability, MDPI, vol. 13(13), pages 1-21, July.
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    8. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.

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