Laiq KHAN, Saghir AHMAD, Shafaat ULLAH, Muhammad AWAIS, Sidra MUMTAZ, Rabiah BADAR

Legendre-wavelet embedded NeuroFuzzy feedback linearization based control scheme for PHEVs charging station in a microgrid

The immense emergence of plug-in hybrid electric vehicles (PHEVs) is envisioned in the future. The rapid proliferation of PHEVs and their charging triggers intense surges in the load during load peak hours. A sophisticated controlled charging station is developed for PHEVs to alleviate grid load during peak demand hours. A novel feedback linearization embedded full recurrent adaptive NeuroFuzzy Legendre wavelet control (FBL-FRANF-Leg-WC) technique is employed to control the charging of PHEVs. The antecedent part of the NeuroFuzzy framework is based on recurrent Gaussian membership function while the consequent part comprises of recurrent Legendre wavelet. The charging station is integrated into a grid-connected microgrid hybrid power system. The charging station consists of five different PHEVs with seven different modes of operation. The performance of the control scheme is tested for various power quality and power system stability parameters. The effectiveness of the suggested control scheme is validated through simulation results by comparing with adaptive NeuroFuzzy, adaptive PID, and conventional PID control scheme.

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[1] Taghavipour A, Vajedi M, Azad NL. Intelligent control of connected plug-in hybrid electric vehicles. Cham, Switzerland AG: Springer, 2019. doi: 10.1007/978-3-030-00314-2.
[2] Ban D, Michailidis G, Devetsikiotis M. Demand response control for PHEV charging stations by dynamic price adjustments. In: IEEE 2012 PES Innovative Smart Grid Technologies Conference; Massachusetts Ave., NW Washington, DC, USA; 2012. pp. 1-8. doi: 10.1109/ISGT.2012.6175601.
[3] Mumtaz S, Ali S, Ahmad S, Khan L, Hassan SZ et al. Energy management and control of plug-In hybrid electric vehicle charging stations in a grid-connected hybrid power system. Energies 2017; 10 (11). doi: 10.3390/en10111923.
[4] Zhen Y, Bao Y, Zhong Z, Rinderknecht S, Zhou S. Development of a phev hybrid transmission for low-end mpvs based on amt. Vehicles 2020; 2 (2): 236-248. doi: 10.3390/vehicles2020013.
[5] Nafisi H, Agah SMM, Askarian Abyaneh H, Abedi M. Two-stage optimization method for energy loss minimization in microgrid based on smart power management scheme of phevs. IEEE Transactions on Smart Grid 2016; 7 (3): 1268-1276. doi: 10.1109/TSG.2015.2480999.
[6] Moeini-Aghtaie M, Abbaspour A, Fotuhi-Firuzabad M. Online multicriteria framework for charging management of phevs. IEEE Transactions on Vehicular Technology 2014; 63 (7): 3028-3037. doi: 10.1109/TVT.2014.2320963.
[7] Devaraj E, Joseph PK, Karuppa Raj Rajagopal T, Sundaram S. Renewable energy powered plugged-in hybrid vehicle charging system for sustainable transportation. Energies 2020; 13 (8). doi: 10.3390/en13081944.
[8] Malikopoulos AA. Supervisory power management control algorithms for hybrid electric vehicles: a survey. IEEE Transactions on Intelligent Transportation Systems 2014; 15 (5): 1869-1885. doi: 10.1109/ TITS.2014.2309674.
[9] Rahbari-Asr N, Chow M. Cooperative distributed demand management for community charging of phev/pevs based on kkt conditions and consensus networks. IEEE Transactions on Industrial Informatics 2014; 10 (3): 1907- 1916. doi: 10.1109/TII.2014.230441210.1109/TII.2014.2304412.
[10] Faraji J, Abazari A, Babaei M, Muyeen SM, Benbouzid M. Day-ahead optimization of prosumer considering battery depreciation and weather prediction for renewable energy sources. Applied Sciences 2020; 10 (8). doi: 10.3390/app10082774.
[11] Kaur K, Dua A, Jindal A, Kumar N, Singh M, Vinel A. A novel resource reservation scheme for mobile phevs in v2g environment using game theoretical approach. IEEE Transactions on Vehicular Technology 2015; 64 (12): 5653-5666. doi: 10.1109/TVT.2015.2482462.
[12] Kreeumporn W, Ngamroo I. Optimal superconducting coil integrated into pv generators for smoothing power and regulating voltage in distribution system with phevs. IEEE Transactions on Applied Superconductivity 2016; 26 (7): 1-5. doi: 10.1109/TASC.2016.2591981.
[13] Hassan SZ, Kamal T, Riaz MH, Shah SAH, Ali HG et al. Intelligent control of wind-assisted phevs smart charging station. Energies 2019; 12 (5). doi: 10.3390/en12050909.
[14] Shafee S, Fotuhi-Firuzabad M, Rastegar M. Investigating the impacts of plug-in hybrid electric vehicles on power distribution systems. IEEE Transactions on Smart Grid 2013; 4 (3): 1351-1360. doi: 10.1109/TSG.2013.2251483.
[15] Pahasa J, Ngamroo I. Coordinated phev, pv, and ess for microgrid frequency regulation using centralized model predictive control considering variation of phev number. IEEE Access 2018; 6: 69151-69161. doi: 10.1109/ACCESS.2018.2879982.
[16] Pahasa J, Ngamroo I. PHEVs bidirectional charging/discharging and soc control for microgrid frequency stabilization using multiple mpc. IEEE Transactions on Smart Grid 2015; 6 (2): 526-533. doi: 10.1109/TSG.2014.2372038.
[17] Aragon-Aviles S, Trivedi A, Williamson SS. Smart power electronics-based solutions to interface solar-photovoltaics (pv), smart grid, and electrified transportation: state-of-the-art and future prospects. Applied Sciences 2020; 10 (14). doi: 10.3390/app10144988.
[18] Li S, Bao K, Fu X, Zheng H. Energy management and control of electric vehicle charging stations. Electric Power Components and Systems 2014; 42 (3-4): 339-347. doi: 10.1080/15325008.2013.837120.
[19] Clement-Nyns K, Haesen E, Driesen J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems 2010; 25 (1): 371-380. doi: 10.1109/TPWRS.2009.2036481.
[20] Nguyen HNT, Zhang C, Mahmud MA. Optimal coordination of g2v and v2g to support power grids with high penetration of renewable energy. IEEE Transactions on Transportation Electrification 2015; 1 (2): 188-195. doi: 10.1109/TTE.2015.2430288.
[21] Haddadian G, Khalili N, Khodayar M, Shahidehpour M. Optimal scheduling of distributed battery storage for enhancing the security and the economics of electric power systems with emission constraints. Electric Power Systems Research 2015; 124: 152-159. doi: 10.1016/j.epsr.2015.03.002.
[22] Awais M, Khan L, Ahmad S, Mumtaz S, Badar R. Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control schemes for photovoltaic system in microgrid. PLOS ONE 2020; 15 (6): 1-36. doi: 10.1371/journal.pone.0234992.
[23] Lin X, Zhou K, Li H. AER adaptive control strategy via energy prediction for PHEV. IET Intelligent Transport Systems 2019; 13 (12): 1822-1831. doi: 10.1049/iet-its.2018.5582.
[24] Kamal T, Karabacak M, Hassan SZ, Fernandez-Ramirez LM, Riaz MH et al. Energy management and switching control of phev charging stations in a hybrid smart micro-grid system. Electronics 2018; 7 (9). doi: 10.3390/electronics7090156.
[25] Badar R, Khan L. Online adaptive legendre wavelet embedded neurofuzzy damping control algorithm. In: Proceedings of the 16th International Multi-Topic Conference; UET, Lahore, Pakistan. pp. 7-12. doi: 10.1109/INMIC.2013.6731316.
[26] IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces. IEEE Std 1547-2018 (Revision of IEEE Std 1547-2003) 2018 April; 1-138. doi: 10.1109/IEEESTD.2018.8332112.