Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles
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 microg...
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doaj-30880bff2ae44599b27a37b0bf6f52782020-11-24T21:28:03ZengMDPI AGEnergies1996-10732018-10-011110264610.3390/en11102646en11102646Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric VehiclesSe-Hyeok Choi0Akhtar Hussain1Hak-Man Kim2Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, KoreaDepartment of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, KoreaDepartment of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, KoreaThe 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.http://www.mdpi.com/1996-1073/11/10/2646adaptive robust optimizationelectrical vehicleenergy management systemmicrogrid operationoptimal operation considering uncertainty |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Se-Hyeok Choi Akhtar Hussain Hak-Man Kim |
spellingShingle |
Se-Hyeok Choi Akhtar Hussain Hak-Man Kim Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles Energies adaptive robust optimization electrical vehicle energy management system microgrid operation optimal operation considering uncertainty |
author_facet |
Se-Hyeok Choi Akhtar Hussain Hak-Man Kim |
author_sort |
Se-Hyeok Choi |
title |
Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles |
title_short |
Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles |
title_full |
Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles |
title_fullStr |
Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles |
title_full_unstemmed |
Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles |
title_sort |
adaptive robust optimization-based optimal operation of microgrids considering uncertainties in arrival and departure times of electric vehicles |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2018-10-01 |
description |
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. |
topic |
adaptive robust optimization electrical vehicle energy management system microgrid operation optimal operation considering uncertainty |
url |
http://www.mdpi.com/1996-1073/11/10/2646 |
work_keys_str_mv |
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