@inproceedings{07d8b41a66e24b10b5ab08c58b600b6a,
title = "A Model Predictive Approach for Voltage Support in Microgrids using Energy Storage Systems",
abstract = "Low voltage microgrid systems are characterized by high sensitivity to both active and reactive power for voltage support. Also, the operational conditions of microgrids connected to active distribution systems are time-varying. Thus, the ideal controller to provide voltage support must be flexible enough to handle technical and operational constraints. This paper proposes a model predictive control (MPC) approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system along with operational constraints to solve an online finite-horizon optimization problem. Control signals are then computed such that the defined cost function is minimized. By proper selection of MPC weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in Matlab/Simulink validates the proposed approach for a simplified version of a 100 kVA, 208 V microgrid using typical parameters. Results show that performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.",
keywords = "Voltage control, microgrids, model predictive control, optimal control, voltage support",
author = "Niranjan Bhujel and Astha Rai and Hansen, {Timothy M.} and Reinaldo Tonkoski and Ujjwol Tamrakar",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Power and Energy Society General Meeting, PESGM 2021 ; Conference date: 26-07-2021 Through 29-07-2021",
year = "2021",
doi = "10.1109/PESGM46819.2021.9638037",
language = "English",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2021 IEEE Power and Energy Society General Meeting, PESGM 2021",
}
