TY - JOUR
T1 - A Tube-based Distributed MPC Based Method for Low-Carbon Energy Networks with Exogenous Disturbances
AU - Jia, Yubin
AU - Dong, Zhao Yang
AU - Sun, Changyin
AU - Meng, Ke
PY - 2025/1
Y1 - 2025/1
N2 - With the increasing integration of renewable energy into power systems, two key challenges emerge in low-carbon energy networks: the distributed topology resulting from distributed energy resources (DERs), and the fluctuations caused by the intermittency of renewable energy sources (RES). This paper proposes a distributed model predictive control (MPC) for the frequency regulation of low-carbon energy networks that encompass both conventional generators (including hydro and gas turbine power plants) and wind turbines. First, the cooperation based distributed model predictive controller of each subsystem accounts for the communication between the subsystems and global control objectives while the constraints are considered. Second, a tube-based controller containing two cascaded MPCs is proposed to deal with the system exogenous disturbance such as wind speed fluctuation. The simulation cases illustrate the efficiency and the advantages of the proposed method. © 2024 IEEE.
AB - With the increasing integration of renewable energy into power systems, two key challenges emerge in low-carbon energy networks: the distributed topology resulting from distributed energy resources (DERs), and the fluctuations caused by the intermittency of renewable energy sources (RES). This paper proposes a distributed model predictive control (MPC) for the frequency regulation of low-carbon energy networks that encompass both conventional generators (including hydro and gas turbine power plants) and wind turbines. First, the cooperation based distributed model predictive controller of each subsystem accounts for the communication between the subsystems and global control objectives while the constraints are considered. Second, a tube-based controller containing two cascaded MPCs is proposed to deal with the system exogenous disturbance such as wind speed fluctuation. The simulation cases illustrate the efficiency and the advantages of the proposed method. © 2024 IEEE.
KW - distributed model predictive control
KW - load frequency control
KW - low-carbon energy networks
KW - tube-based
KW - wind power system
UR - http://www.scopus.com/inward/record.url?scp=85209651682&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85209651682&origin=recordpage
U2 - 10.1109/TNSE.2024.3497577
DO - 10.1109/TNSE.2024.3497577
M3 - RGC 21 - Publication in refereed journal
SN - 2327-4697
VL - 12
SP - 381
EP - 391
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 1
ER -
