TY - JOUR
T1 - Intelligent Coordination of Traditional Power Plants and Inverters Air Conditioners Controlled With Feedback-Corrected MPC in LFC
AU - Oshnoei, Arman
AU - Khezri, Rahmat
AU - Oshnoei, Soroush
AU - Mahmoudi, Amin
AU - Azzouz, Maher A.
AU - Awad, Ahmed S.A.
N1 - Publisher Copyright:
IEEE
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Demand response programs have been receiving more serious attention as alternatives for participating in load frequency control. Inverter air conditioners (IAC) are acknowledged as suitable devices for demand response due to their increasing contribution to network consumption. Despite their potential, their use presents challenges, including delayed responses, variable interference, and the absence of coordination with traditional generation units, which may affect control performance. Also, existing control strategies fail to consider operational and physical constraints, resulting in possible model mismatches. In this paper, a model predictive control with feedback correction (MPCFC) is proposed to dispatch control signals to the IACs so they can effectively participate in the frequency control of an interconnected power system. The feedback correction method is presented to enhance prediction accuracy in the MPC and weaken the influence of model parameter mismatches and external disturbances. Furthermore, to minimize the impacts of communication delays on frequency overshoot/undershoot, this study introduces an intelligent supervisory coordinator based on an artificial neural network to coordinate the reaction of traditional generation units and IACs to correct significant frequency variations brought on by the time delays. The effectiveness of the developed control scheme is verified through numerical studies by comparing it with the IAC with PI and MPC controllers (without coordinator) and the system without IACs. Case studies are investigated on a two-area power system in MATLAB/Simulink environment, and the OPAL-RT real-time simulator is used to validate the results.
AB - Demand response programs have been receiving more serious attention as alternatives for participating in load frequency control. Inverter air conditioners (IAC) are acknowledged as suitable devices for demand response due to their increasing contribution to network consumption. Despite their potential, their use presents challenges, including delayed responses, variable interference, and the absence of coordination with traditional generation units, which may affect control performance. Also, existing control strategies fail to consider operational and physical constraints, resulting in possible model mismatches. In this paper, a model predictive control with feedback correction (MPCFC) is proposed to dispatch control signals to the IACs so they can effectively participate in the frequency control of an interconnected power system. The feedback correction method is presented to enhance prediction accuracy in the MPC and weaken the influence of model parameter mismatches and external disturbances. Furthermore, to minimize the impacts of communication delays on frequency overshoot/undershoot, this study introduces an intelligent supervisory coordinator based on an artificial neural network to coordinate the reaction of traditional generation units and IACs to correct significant frequency variations brought on by the time delays. The effectiveness of the developed control scheme is verified through numerical studies by comparing it with the IAC with PI and MPC controllers (without coordinator) and the system without IACs. Case studies are investigated on a two-area power system in MATLAB/Simulink environment, and the OPAL-RT real-time simulator is used to validate the results.
KW - artificial neural networks
KW - Artificial neural networks
KW - Atmospheric modeling
KW - Control systems
KW - Feedback correction
KW - Frequency control
KW - inverter air conditioner
KW - Inverters
KW - load frequency control
KW - Mathematical models
KW - model predictive control
KW - multi-area power systems
KW - Power systems
UR - http://www.scopus.com/inward/record.url?scp=85177076488&partnerID=8YFLogxK
U2 - 10.1109/TCSI.2023.3330323
DO - 10.1109/TCSI.2023.3330323
M3 - Journal article
AN - SCOPUS:85177076488
SN - 1549-8328
VL - 71
SP - 473
EP - 484
JO - IEEE Transactions on Circuits and Systems I: Regular Papers
JF - IEEE Transactions on Circuits and Systems I: Regular Papers
IS - 1
ER -