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Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs

Author

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  • Mahmoud M. Gamil

    (Department of Electrical and Electronics Engineering, Faculty of engineering, University of The Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

  • Makoto Sugimura

    (Department of Electrical and Electronics Engineering, Faculty of engineering, University of The Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

  • Akito Nakadomari

    (Department of Electrical and Electronics Engineering, Faculty of engineering, University of The Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

  • Tomonobu Senjyu

    (Department of Electrical and Electronics Engineering, Faculty of engineering, University of The Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

  • Harun Or Rashid Howlader

    (Department of Electrical and Electronics Engineering, Faculty of engineering, University of The Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

  • Hiroshi Takahashi

    (Fuji Elctric Co., Ltd, Tokyo 141-0032, Japan)

  • Ashraf M. Hemeida

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

Abstract

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.

Suggested Citation

  • Mahmoud M. Gamil & Makoto Sugimura & Akito Nakadomari & Tomonobu Senjyu & Harun Or Rashid Howlader & Hiroshi Takahashi & Ashraf M. Hemeida, 2020. "Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs," Energies, MDPI, vol. 13(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3666-:d:385199
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    References listed on IDEAS

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    Cited by:

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    2. Yongyi Huang & Hasan Masrur & Ryuto Shigenobu & Ashraf Mohamed Hemeida & Alexey Mikhaylov & Tomonobu Senjyu, 2021. "A Comparative Design of a Campus Microgrid Considering a Multi-Scenario and Multi-Objective Approach," Energies, MDPI, vol. 14(11), pages 1-20, May.
    3. Evgeny Solomin & Shanmuga Priya Selvanathan & Sudhakar Kumarasamy & Anton Kovalyov & Ramyashree Maddappa Srinivasa, 2021. "The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations," Energies, MDPI, vol. 14(9), pages 1-18, May.
    4. Nirbheram, Joshi Sukhdev & Mahesh, Aeidapu & Bhimaraju, Ambati, 2023. "Techno-economic analysis of grid-connected hybrid renewable energy system adapting hybrid demand response program and novel energy management strategy," Renewable Energy, Elsevier, vol. 212(C), pages 1-16.
    5. Md. Rashedul Islam & Homeyra Akter & Harun Or Rashid Howlader & Tomonobu Senjyu, 2022. "Optimal Sizing and Techno-Economic Analysis of Grid-Independent Hybrid Energy System for Sustained Rural Electrification in Developing Countries: A Case Study in Bangladesh," Energies, MDPI, vol. 15(17), pages 1-21, September.

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