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Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties

Author

Listed:
  • Moses Amoasi Acquah

    (Department of Electrical Energy Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea)

  • Sekyung Han

    (Department of Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Sangyeok-dong, Buk-gu, Daegu 41566, Korea)

Abstract

Robust operation of load management control for a building is important to account for the uncertainty in demand as well as any distributed sources connected to the building. This paper discussed an online load management control solution using distributed energy storage (DES) while considering uncertainties in demand as well as DES to reduce peak demand for economic benefit. In recent years’ demand-side management (DSM) solutions using DES such as stationary energy management system (BESS) and plugged-in electric vehicles (PEV) have been popularised. Most of these solutions resort to deterministic load forecast for the day ahead energy scheduling and do not consider the uncertainties in demand and DES making these solutions vulnerable to uncertainties. This study presents an online density demand forecast, k-means clustering of PEV groups and stochastic optimisation for robust operation of BESS and PEV for a building. The proposed method accounts for uncertainties in demand and uncertainties due to mobile energy storage as presented by PEVs. For a case study, we used data obtained from an industrial site in South Korea. The verified results as compared to other methods with a deterministic approach prove the solution is efficient and robust.

Suggested Citation

  • Moses Amoasi Acquah & Sekyung Han, 2019. "Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties," Energies, MDPI, vol. 12(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1436-:d:222716
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    References listed on IDEAS

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    4. Moses Amoasi Acquah & Daisuke Kodaira & Sekyung Han, 2018. "Real-Time Demand Side Management Algorithm Using Stochastic Optimization," Energies, MDPI, vol. 11(5), pages 1-14, May.
    5. Peng, Chao & Zou, Jianxiao & Lian, Lian & Li, Liying, 2017. "An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits," Applied Energy, Elsevier, vol. 190(C), pages 591-599.
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    Cited by:

    1. Lidan Chen & Yao Zhang & Antonio Figueiredo, 2019. "Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network," Energies, MDPI, vol. 12(10), pages 1-20, May.

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