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Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles

Author

Listed:
  • Se-Hyeok Choi

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea)

  • Akhtar Hussain

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea)

  • Hak-Man Kim

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea)

Abstract

The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs.

Suggested Citation

  • Se-Hyeok Choi & Akhtar Hussain & Hak-Man Kim, 2018. "Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2646-:d:173590
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    References listed on IDEAS

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    1. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2016. "Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties," Energies, MDPI, vol. 9(4), pages 1-21, April.
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    4. Mohammadi, Sirus & Mozafari, Babak & Solimani, Soodabeh & Niknam, Taher, 2013. "An Adaptive Modified Firefly Optimisation Algorithm based on Hong's Point Estimate Method to optimal operation management in a microgrid with consideration of uncertainties," Energy, Elsevier, vol. 51(C), pages 339-348.
    5. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
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    Cited by:

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    3. Akhtar Hussain & Hak-Man Kim, 2020. "Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids," Sustainability, MDPI, vol. 12(19), pages 1-18, October.
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    5. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).

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