Recently, a hybrid microgrid system is playing a vital role to supply sustainable power to remote areas using renewable energy sources. The main motto of this research is to design the optimal hybrid microgrid system by identifying the potentials of power production in selected location. The proposed design provides sustainable power to meet the different load demands of various communities in Doddipalli village, Chittoor, Andhra Pradesh, India. Here, a Hydrogen tank and hydrogen loads are also integrated with the grid-integrated hybrid microgrid system for hydrogen production with excess electricity for energy management strategies. Optimization process, techno-economic, and environmental analysis were carried out to construct the optimal system using HOMER software. The domestic average daily load demand is about 704.86 kWh/day. Homer software generates several possible designs, and the top eight are evaluated for use in the case studies. The optimal system (PV/WT/Grid) is decided by comparing the lowest levelized cost of energy ($0.0751/kWh/day), minimum net present cost ($6.92M) and the high renewable energy fraction (97.8%). The proposed design consists of solar photovoltaic (PV)-13.9 kW, wind turbine (WT)-800kW (4), hydrogen tank-10 kg, electrolyser-700 kW and power converter-94 kW. Based on the results, the proposed system has less carbon dioxide ( CO ₂ ) emission, making it better for the environment. In this research work, the optimal design would be more economical when solar PV, Wind Turbine became the primary energy sources. This analysis shows that proposed hybrid system can be an appropriate model to provide reliable power at a low generation cost for the considered area.

Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, wind turbine, battery storage, converter, and diesel generator. There is a steady increase in the utilization of hybrid renewable energy sources with hybrid AC/DC microgrids; consequently, it is necessary to solve optimization techniques. Therefore, the present study proposed utilizing multi-objective optimization methods using evolutionary algorithms. In this context, a few papers were reviewed regarding multi-objective optimization to determine the capacity and optimal design of a hybrid AC/DC microgrid with RESs. Here, the optimal system consisted of the minimum cost of energy, minimum net present cost, low operating cost, low carbon emissions and a high renewable fraction. These were determined by using multi-objective optimization (MOO) algorithms. The sizing optimization of the hybrid AC/DC microgrid was based on the multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO). Similarly, multi-objective optimization with different evolutionary algorithms (MOGA, MOGOA etc.) reduces energy cost and net present cost, and increases the reliability of islanded hybrid microgrid systems.

Due to the lack of grid power availability in rural areas, hybrid renewable energy sources are integrated with microgrids to distribute reliable power to remote locations. This optimal hybrid system is created using a solar photovoltaic system, wind turbine, diesel generator, battery storage system, converter, electrolyzer and hydrogen tank to provide uninterrupted power and meet different load demands of different communities in Doddipalli village, Chittoor, Andhra Pradesh, India. Optimization and techno-economic analysis are performed to design the proposed system using HOMER Software. Various configurations are obtained from the software among which the best four combinations are considered for case studies. This research article aims to design the optimal hybrid renewable energy system, wherein the design consists of PV/BS (1476 kW-solar PV, 417 batteries, electrolyser-200 kW, hydrogen tank-20 kg and 59.6 kW-converter) by comparing the minimum net present cost (NPC: $7.01 M), levelized cost of energy (LCOE: 0.244 $/kWh), and the high renewable fraction (RF: 84.1%). In this research, the proposed system would be more economical when solar energy becomes the primary source and is integrated with the battery. This research also presents a sensitivity analysis of the off-grid HRES system with various electrical load demands, project lifetime, and derating factors.

There has been expeditious development and significant advancements accomplished in the electrified transportation system recently. The primary core component meant for power backup is a lithium‐ion battery. One of the keys to assuring the vehicle’s safety and dependability is an accurate remaining useful life (RUL) forecast. Hence, the exact prediction of RUL plays a vital part in the management of battery conditions. However, because of its complex working characteristics and intricate deterioration mechanism inside the battery, predicting battery life by evaluating exterior factors is exceedingly difficult. As a result, developing improved battery health management technology successfully is a massive effort. Because of the complexity of ageing mechanisms, a single model is unable to describe the complex deterioration mechanisms. As a result, this paper review is organised into three sections. First is to study about the battery degradation mechanism, the second is about battery data collections using mercantile and openly accessible Li‐ion battery data sets and third is the estimation of battery RUL. The important performance parameters of distinct RUL forecast and estimation are categorised, analysed and reviewed. In the end, a brief explanation is given of the various performance error indices. This article classifies and summarises the RUL prediction by data‐dependent models using machine learning (ML), deep learning (DL) and ensemble learning (EL) algorithms suggested in a last few years. The goal of this work in this context is to present an overview of all recent advancements in RUL prediction utilising all three data‐driven models. This article is also followed by a categorisation of several types of ML, DL and EL algorithms for RUL prediction. Finally, this review‐based study includes the pros and cons of the models.