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

1
Department of Electrical Energy Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea
2
Department of Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Sangyeok-dong, Buk-gu, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Energies 2019, 12(8), 1436; https://doi.org/10.3390/en12081436
Received: 15 February 2019 / Revised: 31 March 2019 / Accepted: 7 April 2019 / Published: 15 April 2019
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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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. View Full-Text
Keywords: plugged-in electric vehicles (PEV); vehicle-to-grid (V2G); demand-side management; stochastic optimization; density forecast; dimension reduction; K-means; building energy-management systems (BEMS) plugged-in electric vehicles (PEV); vehicle-to-grid (V2G); demand-side management; stochastic optimization; density forecast; dimension reduction; K-means; building energy-management systems (BEMS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Acquah, M.A.; Han, S. Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties. Energies 2019, 12, 1436.

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