TY - JOUR
T1 - An MPC Based ESS Control Method for PV Power Smoothing Applications
AU - Lei, Mingyu
AU - Yang, Zilong
AU - Wang, Yibo
AU - Xu, Honghua
AU - Meng, Lexuan
AU - Quintero, Juan Carlos Vasquez
AU - Guerrero, Josep M.
PY - 2018/3
Y1 - 2018/3
N2 - Random fluctuation in photovoltaic (PV) power plants is becoming a serious problem affecting the power quality and stability of the grid along with the increasing penetration of PVs. In order to solve this problem, by the adding of energy storage systems (ESS), a grid-connected microgrid system can be performed. To make this system feasible, this paper proposes a model predictive control (MPC) based on power/voltage smoothing strategy. With the receding horizon optimization performed by MPC, the system parameters can be estimated with high accuracy, and at the same time the optimal ESS power reference is obtained. The critical parameters, such as state of charge, are also taken into account in order to ensure the health and stability of the ESSs. In this proposed control strategy, communication between PVs and ESS is not needed, since control command can be calculated with local measured data. At the same time, MPC can make a great contribution to the accuracy and timeliness of the control. Finally, experimental results from a grid-connected lab-scale microgrid system are presented to prove effectiveness and robustness of the proposed approach.
AB - Random fluctuation in photovoltaic (PV) power plants is becoming a serious problem affecting the power quality and stability of the grid along with the increasing penetration of PVs. In order to solve this problem, by the adding of energy storage systems (ESS), a grid-connected microgrid system can be performed. To make this system feasible, this paper proposes a model predictive control (MPC) based on power/voltage smoothing strategy. With the receding horizon optimization performed by MPC, the system parameters can be estimated with high accuracy, and at the same time the optimal ESS power reference is obtained. The critical parameters, such as state of charge, are also taken into account in order to ensure the health and stability of the ESSs. In this proposed control strategy, communication between PVs and ESS is not needed, since control command can be calculated with local measured data. At the same time, MPC can make a great contribution to the accuracy and timeliness of the control. Finally, experimental results from a grid-connected lab-scale microgrid system are presented to prove effectiveness and robustness of the proposed approach.
KW - Energy storage
KW - Model predictive control (MPC)
KW - Photovoltaic (PV)
KW - Power quality
KW - Power smoothing
KW - power smoothing
KW - photovoltaic (PV)
KW - power quality
KW - model predictive control (MPC)
UR - http://www.scopus.com/inward/record.url?scp=85038962400&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2017.2694448
DO - 10.1109/TPEL.2017.2694448
M3 - Journal article
SN - 0885-8993
VL - 33
SP - 2136
EP - 2144
JO - I E E E Transactions on Power Electronics
JF - I E E E Transactions on Power Electronics
IS - 3
M1 - 7900331
ER -