PV-Assisted grid connected multi output electric vehicle charger with PV2V, G2V and PV2G functions.

The demand for renewable energy-based Electric Vehicle (EV) charging infrastructure is increasing in recent years. Solar PV based EV charging method is preferred as it has simple energy harvesting technique. The PV system is an uncertain power source, where the power generation is varied with respect to the availability of sunlight. So, that the charging station requires a backup power supply for the uninterrupted charging. For the integrated power sources, the charging station requires a simple and efficient conversion unit for the DC/AC/DC conversion. In this work, a modified Z-source inverter (MZSI) is developed for the multiport EV charger using PV and grid. The proposed MZSI is connected between the input and output sides to boost the voltage as per the demand at the battery side. In order to connect many battery units with the charger, the capacitors used in the MZSI are split as per the required number of charging ports. This developed converter topology operates the systems in four different modes like PV-Grid, PV-battery, grid-battery, and battery-grid. The performance of this proposed work has been validated in MATLAB/Simulink® and in the experimental setup. The experimental setup has been developed with two charging ports for obtaining 250W at each charger end which cumulatively produces 500W output across both chargers with an efficiency of 90.18%.

Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids.

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.

Performance studies of a solar thermal-electric hybrid desalination system: 4E (energy-exergy-economics-enviroeconomics) analysis.

In this work, a hybrid desalination system uses solar thermal-electric clean energy to maximize production and consistency through optimum temperature management to deliver clean water for good health. It is an effort in the direction of aligning with few of UN's sustainable developmental goals. BIPV system-powered thermoelectric modules boost evaporation and condensation rates in a unique bio-inspired butterfly roof design-based twin wedge solar still (TWSS). A microcontroller-based temperature control unit (TCU) regulates and maintains the hybrid system to provide practically constant higher yields. To understand system performance, 3 days of testing has been carried out. Average yield, energy efficiency, exergy efficiency, cost per liter of freshwater, and payback period of hybrid TWSS (hTWSS) and passive TWSS over 15 years are 8.64 l/m2/day, 61.93, 9.05, and 0.116 $/l in 44 months, and 1.3 l/m2/day, 23.06, 1.26, 0.068 $/l in 20 months. The hTWSS mitigated 5.1 tons and TWSS 59.6 tons of CO2. This hybrid technology utilizes clean energy to deliver clean water and electricity in green energy buildings with a small footprint. As a futuristic work, AI and machine learning are suggested to be used to enhance and commercialize this solar still desalination method.

Luminescent MoS2 Quantum Dots with Tunable Operating Potential for Energy-Enhanced Aqueous Supercapacitors.

Wide band gap luminescent MoS2 quantum dots (QDs) and MoS2 nanocrystals (NCs) have been synthesized by using laser-assisted chemical vapour deposition and used as an electrode material in supercapacitors. Size-dependent properties of the MoS2 QDs and NCs were examined by UV-vis absorption, photoluminescence, and Raman spectroscopy. The morphological evolution of the NCs and QDs were characterized by using field emission scanning electron microscopy, high-resolution transmission electron microscopy, and atomic force microscopy. The as-synthesized uniform QDs with a size of ∼2 nm exhibited an extended electrochemical potential window of 0.9 V with a specific capacitance value of 255 F/g, while the NCs values were 205 F/g and 0.8 V and the pristine MoS2 with values of 105 F/g and 0.6 V at a scan rate of 1 mV s-1. A shorter conductive pathway and 3D quantum confinement of MoS2 QDs that exhibited a higher number of active sites ensure that the efficient charge storage kinetics along with the intercalation processes at the available edge sites enable significant widening of operating potential window and enhance the capacitance. The symmetric device constructed with the QDs showed a remarkable device capacitance of 50 F/g at a scan rate of 1 mV s-1 with an energy density of ∼5.7 W h kg-1 and achieved an excellent cycle stability of 10,000 consecutive cycles with ∼95% capacitance retention.