No abstract

This article proposes a hybrid optimization technique for optimal location and sizing of electric vehicle fast charging stations (EVFCSs) and renewable energy sources (RESs). The proposed hybrid optimization technique is the consolidation of recalling-enhanced recurrent neural network (RERNN) and Marine Predators Algorithm (MPA), hence it is called RERNN-m2MPA technique.Here, an enhanced MPA (m2MPA) is proposed. The major objective of this article is to energy loss reduction, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum installation cost. The lessening of voltage Abbreviations: F l , load force; F r , rolling resistance force; F acc , acceleration force; S, area; m ev , m ess , mass of EV and ESS; ψ, solar irradiance random variable; μ, σ, mean and SD of solar irradiance; F f , fill factor; i z , output of solar module temperature current; i MPP , V module current at the maximum power point; i SC , short circuit current; t, solar module temperature; t a , ambient temperature; i SC , short circuit current of the module; S I , scale index; T, temperature of electrolyte; T f , electrolyte freezing temperature; i b , battery current; i dis , average current of the discharge battery module; D D , Deviation distance; i B , branch current; n B , total branch present in the network; V Bus T ð Þ, bus voltage at time period T; ABC, phases; W g , weightage of the location of EVCS; L c , total cost of all the EVCS; EV De Ve , required energy of EV at Vth vehicle; C Bat Ve , battery capacity of EV at Vth vehicle; SOC Arrival Ve , SOC Depart Ve , arrival SOC and departure SOC of Vth vehicle; E Ra Ve , electric range of Vth vehicle; D ET , each trip distance; D Rate , discharging rate in G2V mode; l EV Bus , load of EVCS; EV Arrival Ve

No abstract

No abstract

No abstract

No abstract

No abstract

Spin coating was used to produce thin coatings on moderately flat substrates. The coating material solution was dumped onto the substrate and turned off at a high velocity in the range of 1000–8000 rpm, leaving a uniform layer. The ultimate thickness of the deposited film was determined by the angular speed, solution viscosity, and spinning time. The advantages of spin-coating include the ability to achieve very fine, fine, and uniform coverage; however, the problem with large samples is a drawback. In this paper, we propose a fine-tuning technique for a modified spin-coating device to coat a zinc oxide (ZnO) solution on a solar cell for increasing the efficiency of the solar cell. We examined nine samples prepared using different parametric conditions, such as the spinning speed, spinning time, and quantity of solution. The spinning speed was set as 1000, 1500, and 2000 rpm. Increasing the evaporation rate instantly in the spin coater resulted in a smooth and uniform thin film. The surface of the layer and the availability of the solution were examined using scanning electron microscopy. To determine the optimal process parameters of the spin coater, the Taguchi method was used. By studying the main effects plot of the signal-to-noise ratio, the optimal settings of the parameters were obtained: a spinning time of 3 s, spinning speed of 2000 rpm, and ZnO solution quantity of 5 µL yielded the desired thickness of 2.4 µm and good uniformity. The experimental results, process parameters, and response parameters were investigated via an analysis of variance. After the solar cell was coated with the modified spin-coating device, its efficiency was 5.4%.