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Day-Ahead DSM-Integrated Hybrid-Power-Management-Incorporated CEED of Solar Thermal/Wind/Wave/BESS System Using HFPSO

Author

Listed:
  • Kothalanka Kameswara Pavan Kumar

    (Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, Assam, India)

  • Nirmala Soren

    (Department of Electrical Engineering, Birsa Institute of Technology Sindri, Dhanbad 828123, Jharkhand, India)

  • Abdul Latif

    (Advanced Power & Energy Center, EECS Department, Khalifa University, Abu Dhabi 127788, United Arab Emirates)

  • Dulal Chandra Das

    (Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, Assam, India)

  • S. M. Suhail Hussain

    (Fukushima Renewable Energy Institute (FREA), National Institute of Advanced Science and Technology (AIST), Koriyama 963-0298, Japan
    Current address: Department of Computer Science, National University of Singapore (NUS), Singapore 119077, Singapore.)

  • Ahmed Al-Durra

    (Advanced Power & Energy Center, EECS Department, Khalifa University, Abu Dhabi 127788, United Arab Emirates)

  • Taha Selim Ustun

    (Fukushima Renewable Energy Institute (FREA), National Institute of Advanced Science and Technology (AIST), Koriyama 963-0298, Japan)

Abstract

This paper presents a day-ahead demand-side management (DSM)-integrated hybrid power management algorithm (PMA) with an objective of combined economic and emission load dispatch (CEED) considering losses. The algorithm was tested on an IEEE 30-bus six-generator system consisting of solar thermal/wind/wave/battery energy storage systems (BESSs) considering real-time data of the Gujarat (19°07′ N, 72°51′ E) coastal region and diverse renewable energy (RES) and storage sources. A maiden attempt of utilizing hybrid firefly particle swarm optimization (HFPSO) to reduce thermal energy consumption and carbon emission was presented. Further, a novel attempt for a versatile renewable power management system was proposed based on a day-ahead pricing scheme to manage load demand and generation effectively. The PMA permits the users to bring down the general load demand and adjust the load curve during the peak time frame. The comparative performance of particle swarm optimization (PSO), firefly algorithm (FA), and HFPSO algorithms in solving the objective was presented. The HFPSO algorithm was found to be the best in terms of a fuel cost of 544.160 (USD/h), emission 20.301 (kg/h), and peak-load reduction of 31.292%, 24.210%, and 51.197% for residential, commercial, and industrial loads, respectively, when contrasted with the other two algorithms PSO and FA.

Suggested Citation

  • Kothalanka Kameswara Pavan Kumar & Nirmala Soren & Abdul Latif & Dulal Chandra Das & S. M. Suhail Hussain & Ahmed Al-Durra & Taha Selim Ustun, 2022. "Day-Ahead DSM-Integrated Hybrid-Power-Management-Incorporated CEED of Solar Thermal/Wind/Wave/BESS System Using HFPSO," Sustainability, MDPI, vol. 14(3), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1169-:d:729462
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    Citations

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    Cited by:

    1. Chakraborty, Amit & Ray, Saheli, 2024. "Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid," Renewable Energy, Elsevier, vol. 222(C).
    2. Huseyin Balta & Zehra Yumurtaci, 2024. "Investigation and Optimization of Integrated Electricity Generation from Wind, Wave, and Solar Energy Sources," Energies, MDPI, vol. 17(3), pages 1-34, January.

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