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MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations

Author

Listed:
  • Rae-Kyun Kim

    (Encored Technologies, Seoul 06109, Korea)

  • Mark B. Glick

    (Hawaii Natural Energy Institute, Honolulu, HI 96822, USA)

  • Keith R. Olson

    (Natural Energy Laboratory of Hawaii Authority, Kailua, HI 96740, USA)

  • Yun-Su Kim

    (School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea)

Abstract

This paper presents the optimal scheduling of a diesel generator and an energy storage system (ESS) while using a detailed battery ESS energy efficiency model. Optimal scheduling has been hampered to date by the nonlinearity and complexity of the battery ESS. This is due to the battery ESS efficiency being a multiplication of inverter and battery efficiency and the dependency of an inverter and any associated battery efficiencies on load and charging and discharging. We propose a combined mixed-integer linear programming and particle swarm optimization (MILP-PSO) algorithm as a novel means of addressing these considerations. In the algorithm, MILP is used to find some initial points of PSO, so that it can find better solution. Moreover, some additional algorithms are added into PSO to modify and, hence, improve its ability of dealing with constraint conditions and the local minimum problem. The simulation results show that the proposed algorithm performs better than MILP and PSO alone for the practical microgrid. The results also indicated that simplification or neglect of ESS efficiency when applying MILP to scheduling may cause a constraint violation.

Suggested Citation

  • Rae-Kyun Kim & Mark B. Glick & Keith R. Olson & Yun-Su Kim, 2020. "MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations," Energies, MDPI, vol. 13(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1898-:d:345022
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    References listed on IDEAS

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    1. Roberto S. Faranda & Hossein Hafezi & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "The Optimum PV Plant for a Given Solar DC/AC Converter," Energies, MDPI, vol. 8(6), pages 1-18, May.
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    Cited by:

    1. Vanderlei Aparecido Silva & Alexandre Rasi Aoki & Germano Lambert-Torres, 2020. "Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System," Energies, MDPI, vol. 13(19), pages 1-28, October.
    2. Yong-Rae Lee & Hyung-Joon Kim & Mun-Kyeom Kim, 2021. "Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids," Energies, MDPI, vol. 14(2), pages 1-21, January.
    3. Woan-Ho Park & Hamza Abunima & Mark B. Glick & Yun-Su Kim, 2021. "Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy," Energies, MDPI, vol. 14(19), pages 1-15, September.
    4. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    5. Joo Won Lee & Emily Craparo & Giovanna Oriti & Arthur Krener, 2022. "Optimizing Fuel Efficiency on an Islanded Microgrid under Varying Loads," Energies, MDPI, vol. 15(21), pages 1-21, October.
    6. Andrés Rengel & Alexander Aguila Téllez & Leony Ortiz & Milton Ruiz, 2023. "Optimal Insertion of Energy Storage Systems Considering the Economic Dispatch and the Minimization of Energy Not Supplied," Energies, MDPI, vol. 16(6), pages 1-26, March.
    7. Yujiang Ye & Ruifeng Shi & Yuqin Gao & Xiaolei Ma & Di Wang, 2023. "Two-Stage Optimal Scheduling of Highway Self-Consistent Energy System in Western China," Energies, MDPI, vol. 16(5), pages 1-18, March.
    8. Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
    9. David Domínguez-Barbero & Javier García-González & Miguel A. Sanz-Bobi & Eugenio F. Sánchez-Úbeda, 2020. "Optimising a Microgrid System by Deep Reinforcement Learning Techniques," Energies, MDPI, vol. 13(11), pages 1-18, June.
    10. Grzegorz Maślak & Przemysław Orłowski, 2022. "Microgrid Operation Optimization Using Hybrid System Modeling and Switched Model Predictive Control," Energies, MDPI, vol. 15(3), pages 1-21, January.

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