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Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

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  • Woan-Ho Park

    (Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)

  • Hamza Abunima

    (Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)

  • Mark B. Glick

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

  • Yun-Su Kim

    (Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)

Abstract

The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost minimization algorithm preferentially selects intervals with low curtailment costs to prevent the curtailment from being concentrated at a specific time. By inducing even distribution of curtailment, this novel optimization methodology has the potential to improve the cost-effectiveness of the PV-powered microgrid

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6038-:d:640871
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    References listed on IDEAS

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    1. Sung-Min Cho & Jin-Su Kim & Jae-Chul Kim, 2019. "Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation," Energies, MDPI, vol. 12(9), pages 1-21, May.
    2. Ha-Lim Lee & Yeong-Han Chun, 2018. "Using Piecewise Linearization Method to PCS Input/Output-Efficiency Curve for a Stand-Alone Microgrid Unit Commitment," Energies, MDPI, vol. 11(9), pages 1-13, September.
    3. Wouters, Carmen & Fraga, Eric S. & James, Adrian M., 2015. "An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study," Energy, Elsevier, vol. 85(C), pages 30-44.
    4. 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.
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    Cited by:

    1. 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).
    2. Rovick Tarife & Yosuke Nakanishi & Yining Chen & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2022. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines," Energies, MDPI, vol. 15(6), pages 1-29, March.

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