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Multi-Objective Optimization for Peak Shaving with Demand Response under Renewable Generation Uncertainty

Author

Listed:
  • Sane Lei Lei Wynn

    (School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Watcharakorn Pinthurat

    (School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia)

  • Boonruang Marungsri

    (School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

Abstract

With high penetration of renewable energy sources (RESs), advanced microgrid distribution networks are considered to be promising for covering uncertainties from the generation side with demand response (DR). This paper analyzes the effectiveness of multi-objective optimization in the optimal resource scheduling with consumer fairness under renewable generation uncertainty. The concept of consumer fairness is considered to provide optimal conditions for power gaps and time gaps. At the same time, it is used to mitigate system peak conditions and prevent creating new peaks with the optimal solution. Multi-objective gray wolf optimization (MOGWO) is applied to solve the complexity of three objective functions. Moreover, the best compromise solution (BCS) approach is used to determine the best solution from the Pareto-optimal front. The simulation results show the effectiveness of renewable power uncertainty on the aggregate load profile and operation cost minimization. The results also provide the performance of the proposed optimal scheduling with a DR program in reducing the uncertainty effect of renewable generation and preventing new peaks due to over-demand response. The proposed DR is meant to adjust the peak-to-average ratio (PAR) and generation costs without compromising the end-user’s comfort.

Suggested Citation

  • Sane Lei Lei Wynn & Watcharakorn Pinthurat & Boonruang Marungsri, 2022. "Multi-Objective Optimization for Peak Shaving with Demand Response under Renewable Generation Uncertainty," Energies, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8989-:d:986410
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    References listed on IDEAS

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    1. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    2. Azizipanah-Abarghooee, Rasoul & Niknam, Taher & Roosta, Alireza & Malekpour, Ahmad Reza & Zare, Mohsen, 2012. "Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method," Energy, Elsevier, vol. 37(1), pages 322-335.
    3. Abdullah Khan & Hashim Hizam & Noor Izzri Abdul-Wahab & Mohammad Lutfi Othman, 2020. "Solution of Optimal Power Flow Using Non-Dominated Sorting Multi Objective Based Hybrid Firefly and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(16), pages 1-24, August.
    4. Hong, Ying-Yi & Lin, Jie-Kai, 2013. "Interactive multi-objective active power scheduling considering uncertain renewable energies using adaptive chaos clonal evolutionary programming," Energy, Elsevier, vol. 53(C), pages 212-220.
    5. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2019. "Energy management in hybrid microgrid with considering multiple power market and real time demand response," Energy, Elsevier, vol. 174(C), pages 10-23.
    6. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    7. Xu, Weiwei & Zhou, Dan & Huang, Xiaoming & Lou, Boliang & Liu, Dong, 2020. "Optimal allocation of power supply systems in industrial parks considering multi-energy complementarity and demand response," Applied Energy, Elsevier, vol. 275(C).
    8. Watcharakorn Pinthurat & Branislav Hredzak, 2021. "Distributed Control Strategy of Single-Phase Battery Systems for Compensation of Unbalanced Active Powers in a Three-Phase Four-Wire Microgrid," Energies, MDPI, vol. 14(24), pages 1-17, December.
    9. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
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