Modeling and optimal operation of hybrid wave energy and PV system feeding supercharging stations based on golden jackal optimal control strategy. (15th January 2023)

• Record Type: Journal Article
• Authors: Mahdy, Ahmed; Hasanien, Hany M.; Turky, Rania A.; Abdel Aleem, Shady H.E.
• Is Part Of: Energy. Volume 263:Part D(2023)
• Publication Date: 2023-01-15
• Subjects: Archimedes wave swing -- Electric vehicles -- Hybrid renewable energy systems -- Optimization algorithms -- Photovoltaic system -- Wave power application; AGWO-CS Augmented Grey Wolf Optimizer and Cuckoo Search -- AWS Archimedes wave swing -- EV Electric vehicle -- EVCS Electric vehicle charging system -- GJOA Golden Jackal Optimization algorithm -- ISE Integral squared error -- LPMSG Linear permanent magnet synchronous generator -- MPPT Maximum power point tracking -- PSO Particle swarm optimization -- TS Tesla Supercharger -- TPRG Three-phase rectifier of the generator -- SC Supercapacitor -- SCESS Supercapacitor energy storage system -- WECS Wave energy conversion system; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125932
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper provides a novel application for the Archimedes wave swing (AWS) device that converts the sea waves into electrical energy using a linear permanent magnet synchronous generator (LPMSG), which is connected to a rectifier that extracts the most significant energy from sea waves and reduces the stator losses. The generator's power combined with a photovoltaic (PV) system provides a total of 250 kW, sufficient for feeding a V3 Tesla Supercharging system, which is currently the latest and most advanced electric vehicle (EV) charging technology. The hybrid system is connected to a DC link, its voltage is stabilized using a supercapacitor energy storage system, and the output is provided to a buck converter that reduces the voltage for the Tesla Supercharger. The control system comprises seven proportional-integral (PI) controllers with four anti-wind back-calculation coefficients to improve transient stability. The PI gains are optimized using the Golden Jackal Optimization Algorithm (GJOA), and the system stability is evaluated by applying different disturbances like a sudden variation in the DC load, temporary DC short circuit, and connection of the EV in various modes such as a grid to vehicle and vehicle to grid modes. The results obtained by the GJOA are compared with the Particle Swarm Optimization and the hybrid Augmented Grey Wolf Optimizer and Cuckoo Search algorithms. Finally, hybrid renewable energy systems can efficiently be employed in EV supercharging stations as validated with the strategy proposed in this work. Highlights: A hybrid wave and PV system is proposed. A nonlinear model of the AWS wave energy system is presented. A novel optimal control based on the GJOA is described. The proposed model is compared with other recent models. The effectiveness of the proposed model is verified under different disturbances.