Title
Sensitivity analysis of renewable energy integration on stochastic energy management of automated reconfigurable hybrid AC-DC microgrid considering DLR security constraint
Document Type
Article
Publication Date
1-1-2020
Abstract
This paper aims to investigate the optimal scheduling of stochastic reconfigurable hybrid ac-dc microgrid (MG) in the presence of renewable energies and also considering dynamic line rating (DLR) constraint. DLR is a practical limitation that can potentially affect the ampacity of lines, particularly in the islanded mode when the lines reach their maximum capacity in lack of main generation source at the point of interconnection with the utility. In order to prevent overloading of the lines, the reconfiguration technique is developed to change the topology of the network by some prelocated switches. A linearization technique is adapted to address the nonlinearity of both nodal ac power flow and the DLR constraints. The unscented transform technique is utilized to model uncertainties including renewable energy generations, hourly load demands, and hourly market prices along with the DLR uncertainties such as solar radiation, wind speed, and ambient temperature. Finally, a sensitivity analysis is performed to see the effect of wind speed and solar radiation on the energy management of hybrid ac-dc MG. The performance of the proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac-dc MG operation cost while all of the constraints of the network are satisfied.
Publication Source (Journal or Book title)
IEEE Transactions on Industrial Informatics
First Page
120
Last Page
131
Recommended Citation
Dabbaghjamanesh, M., Kavousi-Fard, A., Mehraeen, S., Zhang, J., & Dong, Z. (2020). Sensitivity analysis of renewable energy integration on stochastic energy management of automated reconfigurable hybrid AC-DC microgrid considering DLR security constraint. IEEE Transactions on Industrial Informatics, 16 (1), 120-131. https://doi.org/10.1109/TII.2019.2915089