A large population of India is living in villages, some of them are living in remote areas isolated from the grid. It is not feasible or economical to extend the grid connection to provide electricity for those villages, but an autonomous integrated hybrid renewable energy system can be a viable option. Hence, this study proposed to provide electricity and freshwater availability for the un-electrified off-grid villages of Odisha state in India with available renewable energy resources. In order to provide a continuous power supply, the study focused on two energy storage technologies, such as hydrogen and battery storage systems along with a diesel generator. In the context of battery technology, Lead Acid (LA), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered. To obtain an optimally configured Integrated Hybrid Renewable Energy System (IHRES), a total of four IHRES configurations are modeled such as Photo Voltaic Panels (PV)/Wind Turbines (WT)/Biomass Generator (BMG)/Diesel Generator (DG)/Ni-Fe, PV/WT/BMG/DG/LA, PV/WT/BMG/DG/Li-Ion, and PV/WT/BMG/DG/Hydrogen Storage System (HSS) and conducted a study with two dispatch strategies, such as load following (LF) and cycle charging (CC), using four metaheuristic algorithms in the MATLAB® environment. From the results, it is found that the proposed Salp Swarm Algorithm has proven its robustness and convergence efficiency when compared to other algorithms in finding the global best optimal values. The Ni-Fe battery-based IHRES with CC strategy provided a minimum life cycle cost (LCC) and cost of energies (COE) as $6,33,789 and 0.23793 $/kWh, respectively, and is considered as a base case. The other energy storage technology-based IHRESs with LA, Li-Ion, and HSSs are obtained an LCC and COEs respectively 74%, 161%, and 231% higher than the base case LCC and COEs. In view of the total annual fuel consumption and carbon emissions, it has shown its best performance with CC strategy compared to LF strategy. Finally, the sensitivity analysis has been conducted to the optimal configuration with variable input parameters, such as interest rate and diesel prices.

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.

Rural electrification is necessary for both the country’s development and the well-being of the villagers. The current study investigates the feasibility of providing electricity to off-grid villages in the Indian state of Odisha by utilizing renewable energy resources that are currently available in the study area. However, due to the intermittent nature of renewable energy sources, it is highly improbable to ensure a continuous electricity supply to the off-grid areas. To ensure a reliable electricity supply to the off-grid areas, three battery technologies have been incorporated to find the most suitable battery system for the study area. In addition, we evaluated various demand side management (DSM) techniques and assessed which would be the most suitable for our study area. To assess the efficiency of the off-grid system, we applied different metaheuristic algorithms, and the results showed great promise. Based on our findings, it is clear that energy-conservation-based DSM is the ideal option for the study area. From all the algorithms tested, the salp swarm algorithm demonstrated the best performance for the current study.

Several families in India live in remote places with no access to grid-connected power supply due to their remoteness. The study area chosen from the Indian state of Odisha does not have an electrical power supply due to its distant location. As a result, this study analyzed the electrification process using Renewable Energy (RE) resources available in the locality. However, these RE resources are limited by their dependency on weather conditions and time. So, a robust battery storage system is needed for a continuous power supply. Hence, the Nickel Iron (Ni-Fe), Lithium-Ion (Li-Ion) and Lead Acid (LA) battery technologies have been analyzed to identify a battery technology that is both technologically and economically viable. Using the available RE resources in the study area, such as photovoltaic and biomass energy resources, as well as the various battery technologies, three configurations have been modelled, such as Photovoltaic Panels (PVP)/Biomass Generator(BIOMG)/BATTERY_(Ni-Fe), PV/BIOMG/BATTERY_(Li-Ion) and PVP/BMG/BATTERY_(LA). These three configurations have been examined using nine prominent metaheuristic algorithms, in which the PVP/BIOMG/BATTERY_(Ni-Fe) configuration provided the optimal Life Cycle Cost value of 367,586 USD. Among the all metaheuristic algorithms, the dynamic differential annealed optimization algorithm was given the best Life Cycle Cost values for all of the three configurations.

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.