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Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm

Author

Listed:
  • Nagaraju Dharavat

    (School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, India)

  • Suresh Kumar Sudabattula

    (School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, India)

  • Suresh Velamuri

    (Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune 412115, India)

  • Sachin Mishra

    (School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, India)

  • Naveen Kumar Sharma

    (Department of Electrical Engineering, I. K. Gujral Punjab Technical University, Jalandhar 144603, India)

  • Mohit Bajaj

    (Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
    Department of Electrical and Electronics Engineering, National Institute of Technology, Delhi 110040, India)

  • Elmazeg Elgamli

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Mokhtar Shouran

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Salah Kamel

    (Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

This paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (P Loss ) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes P Loss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects.

Suggested Citation

  • Nagaraju Dharavat & Suresh Kumar Sudabattula & Suresh Velamuri & Sachin Mishra & Naveen Kumar Sharma & Mohit Bajaj & Elmazeg Elgamli & Mokhtar Shouran & Salah Kamel, 2022. "Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm," Energies, MDPI, vol. 15(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6698-:d:913928
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    References listed on IDEAS

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    1. Aijun Zhu & Zhanqi Gu & Cong Hu & Junhao Niu & Chuanpei Xu & Zhi Li, 2021. "Political optimizer with interpolation strategy for global optimization," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-40, May.
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    Cited by:

    1. Dominic Savio Abraham & Balaji Chandrasekar & Narayanamoorthi Rajamanickam & Pradeep Vishnuram & Venkatesan Ramakrishnan & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station," Energies, MDPI, vol. 16(6), pages 1-17, March.
    2. Sunday Adeleke Salimon & Gafari Abiola Adepoju & Isaiah Gbadegesin Adebayo & Harun Or Rashid Howlader & Samson Oladayo Ayanlade & Oludamilare Bode Adewuyi, 2023. "Impact of Distributed Generators Penetration Level on the Power Loss and Voltage Profile of Radial Distribution Networks," Energies, MDPI, vol. 16(4), pages 1-32, February.
    3. Ajit Kumar Mohanty & Perli Suresh Babu & Surender Reddy Salkuti, 2022. "Fuzzy-Based Simultaneous Optimal Placement of Electric Vehicle Charging Stations, Distributed Generators, and DSTATCOM in a Distribution System," Energies, MDPI, vol. 15(22), pages 1-22, November.
    4. Abdul Ghani Olabi & Enas Taha Sayed, 2023. "Developments in Hydrogen Fuel Cells," Energies, MDPI, vol. 16(5), pages 1-5, March.
    5. Soheil Younesi & Bahman Ahmadi & Oguzhan Ceylan & Aydogan Ozdemir, 2022. "Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems," Energies, MDPI, vol. 15(24), pages 1-18, December.
    6. Nikhil Pachauri & Vigneysh Thangavel & Velamuri Suresh & Mvv Prasad Kantipudi & Hossam Kotb & Ravi Nath Tripathi & Mohit Bajaj, 2023. "A Robust Fractional-Order Control Scheme for PV-Penetrated Grid-Connected Microgrid," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    7. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    8. Thangaraj Yuvaraj & Thirukoilur Dhandapani Suresh & Arokiasamy Ananthi Christy & Thanikanti Sudhakar Babu & Benedetto Nastasi, 2023. "Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems," Energies, MDPI, vol. 16(19), pages 1-31, September.
    9. Polisetty, S.P.R. Swamy & Jayanthi, R. & Sai Veerraju, M., 2023. "An intelligent optimal charging stations placement on the grid system for the electric vehicle application," Energy, Elsevier, vol. 285(C).
    10. Zbigniew Kłosowski & Łukasz Mazur, 2023. "Influence of the Type of Receiver on Electrical Energy Losses in Power Grids," Energies, MDPI, vol. 16(15), pages 1-22, July.

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