TY - GEN
T1 - Decentralized fractional order control scheme for LFC of deregulated nonlinear power systems in presence of EVs and RER
AU - Alhelou, Hassan Haes
AU - Hamedani-Golshan, Mohamad Esmai
AU - Heydarian-Forushani, Ehsan
AU - Al-Sumaiti, Ameena Saad
AU - Siano, Pierluigi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/17
Y1 - 2018/10/17
N2 - Load frequency control scheme is one of the main control procedures in large electric grids incorporating electric vehicles. Load frequency control controllers play an important role in maintaining both the frequency in each area and the exchanged power between the different areas in permissible range. With moving conventional power systems toward the smart grid concept, the penetration level of electric vehicles and renewable energy resources has been rapidly increased. With such a growth in renewable energy sources integration into the grid, controlling the load frequency is a major operational challenge encountered in electric grids necessitating to be carefully investigated. To this end, a new fractional order control scheme is designed for the interconnected power systems considering the deregulation environment. The fractional order controller is characterized with a higher freedom degree compared to the conventional that make it possible to have a much better control performance. Also, the participation of electric vehicles in providing a secondary power reserve for a future smart grid is studied in this paper. The controllers' parameters are tuned via several evolutionary algorithms such as imperialist competitive algorithm, differential algorithm and others. Several numerical analyses are performed to evaluate the effectiveness of the control scheme that is proposed in this paper. Likewise, the effectiveness of electric vehicles and renewable generation's participation in load frequency control is examined.
AB - Load frequency control scheme is one of the main control procedures in large electric grids incorporating electric vehicles. Load frequency control controllers play an important role in maintaining both the frequency in each area and the exchanged power between the different areas in permissible range. With moving conventional power systems toward the smart grid concept, the penetration level of electric vehicles and renewable energy resources has been rapidly increased. With such a growth in renewable energy sources integration into the grid, controlling the load frequency is a major operational challenge encountered in electric grids necessitating to be carefully investigated. To this end, a new fractional order control scheme is designed for the interconnected power systems considering the deregulation environment. The fractional order controller is characterized with a higher freedom degree compared to the conventional that make it possible to have a much better control performance. Also, the participation of electric vehicles in providing a secondary power reserve for a future smart grid is studied in this paper. The controllers' parameters are tuned via several evolutionary algorithms such as imperialist competitive algorithm, differential algorithm and others. Several numerical analyses are performed to evaluate the effectiveness of the control scheme that is proposed in this paper. Likewise, the effectiveness of electric vehicles and renewable generation's participation in load frequency control is examined.
KW - electric vehicles
KW - fractional systems
KW - load frequency control
KW - renewable energy resource
UR - http://www.scopus.com/inward/record.url?scp=85056525692&partnerID=8YFLogxK
U2 - 10.1109/SEST.2018.8495858
DO - 10.1109/SEST.2018.8495858
M3 - Conference contribution
AN - SCOPUS:85056525692
T3 - 2018 International Conference on Smart Energy Systems and Technologies, SEST 2018 - Proceedings
BT - 2018 International Conference on Smart Energy Systems and Technologies, SEST 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Smart Energy Systems and Technologies, SEST 2018
Y2 - 10 September 2018 through 12 September 2018
ER -