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Summary A photovoltaic (PV) module or a solar cell is electrically characterized by a circuit model with specific parameters. For a PV system simulation and operation, the solar cell parameters must be precisely calculated using experimental data. Unknown parameters extraction of the solar PV system is necessary to analyze the system performance using I‐V characteristics under various operating conditions such as variable solar radiation and temperatures. However, the solar PV model problem is highly nonlinear in nature. To solve this problem, an efficient algorithm is necessary. Hence, in this study, we proposed a novel metaheuristic optimization algorithm inspired by ancient times' war strategy. The proposed war strategy optimization (WSO) algorithm is developed based on the army troop's strategic movement during the war. War strategy is modeled as an optimization process wherein each soldier dynamically moves toward the global best optimum value. Every soldier is assigned with a unique weight and their current position is dynamically updated based on the success rate of the previous iteration. The objective function employed in prior research of solar PV model parameter extraction is erroneous. However, in this work, we integrated the Newton Raphson method with the WSO algorithm to improve the accuracy of the output solutions. The experimental results prove that the proposed algorithm has shown superior performance when compared with the state‐of‐the‐art algorithms.

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A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or cost-effective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB© environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiency-based scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system’s performance.

Concerns about pollution, climate change, limited fossil fuel supplies, and the desire to eliminate energy dependency have sparked a surge in interest in electric vehicles (EVs). EV requirements have resulted in a variety of difficulties and remedies in EV technology. One of them is the use of DC-DC converters to transfer the level of voltage from the battery in an EV to other needed voltage levels. An independent converter for each operating voltage might be used as a remedy. On the other hand, single input multiple output (SIMO) converters can be utilized to decrease costs, reduce switching loss, and thus enhance the system efficiency. In this paper, a nonisolated step-up converter with the integration of the Luo network is proposed for multiple outputs (24 V and 48 V). In electric vehicles, 48 V is utilized for battery backup, while 24 V is utilized for the horns, headlights, telematics, or the microcontroller. The experimental observations of a 36 V, 600 mA, 24 W prototype confirm the theoretic examination and demonstrate the advantages of the proposed converter over other multioutput converters. The STM microcontroller, based on an ARM cortex microprocessor, is linked into the Luo network for making pulses. The proposed converter achieves 94.2% efficiency at full power. The proposed converter’s performance is evaluated through MATLAB/SIMULINK software, and the results are validated experimentally.

The number of users of current mobile and cellular networks is constantly increasing. The allocation of spectrum to its users is constantly facing various hurdles. This causes users to leave that network and connect to another network. Thus, those telecom companies are constantly struggling to provide high-speed services to the users. As a result, the demand for 5G networks is currently increasing. Considering these, an algorithm has been proposed here to suit the needs of the users. Its main feature is that it easily identifies the primary and secondary users of the 5G network and creates a spectrum allocation system for them accordingly. Generally, all other methods are designed with the primary user in mind. Furthermore, the spectrum hole calculation that is currently being proposed is done accurately so that the spectrum switching processes required for the secondary user can take place here very quickly so that the secondary user can use the spectrum without any hindrance. The proposed model achieved 93.29% of spectrum blocking, 6.71% of spectrum band dropping, 94.03% bandwidth utilization, 1073 ms end-to-end delay, and 17273 bps of throughput. The proposed model effectively handles the spectrum and intelligent approach to resolve the spectrum hole problems. The existing models are practically faced with these problems. The proposed model spectrum utilization and efficiency were increased compared with the existing models.