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Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

Author

Listed:
  • Mark Kipngetich Kiptoo

    (Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

  • Oludamilare Bode Adewuyi

    (Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

  • Mohammed Elsayed Lotfy

    (Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
    Department of Electrical Power and Machines, Zagazig University, Zagazig 44519, Egypt)

  • Theophilus Amara

    (Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
    Distribution Operation Section, Electricity Distribution and Supply Authority, Freetown 32023, Sierra Leone)

  • Keifa Vamba Konneh

    (Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

  • Tomonobu Senjyu

    (Graduate School of Science and Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

Abstract

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB ® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

Suggested Citation

  • Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Mohammed Elsayed Lotfy & Theophilus Amara & Keifa Vamba Konneh & Tomonobu Senjyu, 2019. "Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning," Future Internet, MDPI, vol. 11(10), pages 1-16, October.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:10:p:219-:d:279104
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    References listed on IDEAS

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    Cited by:

    1. Om P. Malik, 2020. "Global Trends and Advances Towards a Smarter Grid and Smart Cities," Future Internet, MDPI, vol. 12(2), pages 1-3, February.
    2. Juan Roberto López Gutiérrez & Pedro Ponce & Arturo Molina, 2021. "Real-Time Power Electronics Laboratory to Strengthen Distance Learning Engineering Education on Smart Grids and Microgrids," Future Internet, MDPI, vol. 13(9), pages 1-16, September.
    3. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
    4. Rusche, Simon & Weissflog., Jan & Wenninger, Simon & Häckel, Björn, 2023. "How flexible are energy flexibilities? Developing a flexibility score for revenue and risk analysis in industrial demand-side management," Applied Energy, Elsevier, vol. 345(C).
    5. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Harun Or Rashid Howlader & Akito Nakadomari & Tomonobu Senjyu, 2023. "Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies," Energies, MDPI, vol. 16(10), pages 1-25, May.

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