TY - JOUR
T1 - Robust model predictive control based voltage regulation method for a distribution system with renewable energy sources and energy storage systems
AU - Xie, Yunyun
AU - Liu, Lin
AU - Wu, Qiuwei
AU - Zhou, Qian
PY - 2020/6/1
Y1 - 2020/6/1
N2 - With the integration of high penetration renewable energy sources (RESs) in distribution networks, the uncertainty of RES outputs brings a great challenge for the voltage regulation of distribution systems. This paper proposes a method based on robust model predictive control (RMPC) for voltage regulation by optimally coordinating the reactive power outputs of the RESs, energy storage systems and on-load tap changers (OLTCs). By considering the prediction error of the RES active outputs, the voltage regulation problem is formulated as a multitime period robust optimization model to obtain the optimal control actions in the prediction horizon. The control actions for the first time period are applied to the distribution network. Since the RMPC-based optimization model is nonlinear, it is linearized and transformed into a quadratic programming model that can be solved effectively by commercial software. The effectiveness of the proposed method is demonstrated in a real Finnish distribution network model.
AB - With the integration of high penetration renewable energy sources (RESs) in distribution networks, the uncertainty of RES outputs brings a great challenge for the voltage regulation of distribution systems. This paper proposes a method based on robust model predictive control (RMPC) for voltage regulation by optimally coordinating the reactive power outputs of the RESs, energy storage systems and on-load tap changers (OLTCs). By considering the prediction error of the RES active outputs, the voltage regulation problem is formulated as a multitime period robust optimization model to obtain the optimal control actions in the prediction horizon. The control actions for the first time period are applied to the distribution network. Since the RMPC-based optimization model is nonlinear, it is linearized and transformed into a quadratic programming model that can be solved effectively by commercial software. The effectiveness of the proposed method is demonstrated in a real Finnish distribution network model.
KW - Model predictive control
KW - Robust optimization
KW - Strong duality theory
KW - Voltage control
U2 - 10.1016/j.ijepes.2019.105749
DO - 10.1016/j.ijepes.2019.105749
M3 - Journal article
AN - SCOPUS:85075895334
SN - 0142-0615
VL - 118
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 105749
ER -