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The surge in interest surrounding renewable energy stems from concerns regarding pollution and the finite supply of nonrenewable resources. Solar PV and wind hybrid renewable energy systems (HRES) are increasingly recognized as practical and cost-effective solutions, particularly in remote areas. However, the intermittent nature of solar and wind power presents a challenge. To address this, incorporating a hydrogen source into the system has been proposed. This study focuses on modelling and sizing a hybrid energy system tailored for remote areas, accommodating both home and electric vehicle loads. The simulation is conducted for Siliguri, West Bengal, India, with the goal of optimizing productivity, minimizing expenses, and considering economic factors using HOMER Pro software. The integration of green hydrogen-based power generation with photovoltaic and wind HRES emerges as an effective solution. Solar power, in particular, showcases promising opportunities for the electrolysis process and HRES systems. The presented work facilitates the modelling of a green hydrogen-based green energy system, taking into account capacity, cost, and emission constraints. Various case studies are conducted to enhance system efficiency and reduce the costs of energy (COE). In this paper, three cases of grid-connected and three cases of off-grid or grid-disconnected systems are considered for highlighting the benefits of hydrogen energy incorporation in both types of systems. This research contributes to sustainable energy solutions, advancing a greener and more efficient energy landscape, especially in addressing the recent development in load combinations of home and electric vehicle loads in both grid-connected as well as grid-disconnected system. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %. [ABSTRACT FROM AUTHOR]

Literature has highlighted the existence of a gap in clean fuel usage between the social groups in most emerging nations across the world, which is detrimental to global efforts to achieve Sustainable Development Goal (SDG) 7 (access to clean energy) and SDG 10 (reducing inequalities) and just energy transition with recognition justice. This paper, assesses the clean fuel gap across social groups in India using three rounds of representative data (2004, 2014, and 2018) by applying the logit model, exogenous switching treatment effect regression (ESTER), and the Blinder–Oaxaca Decomposition Model. Such an analysis is instrumental in identifying the clean energy usage gap and its causes, thereby providing insight to policymakers to design policies to achieve just energy transition and carbon neutrality by 2060. The results show a substantive clean fuel gap between social groups in India, which is a cause of concern for just energy transition. Clean energy utilization gaps among social groups are alarming across geographical locations and consumption quartiles, highlighting the importance of recognition justice for just energy transition. The results show that in rural areas, only 19.6 % of the General Caste households and 14.5 % of the disadvantaged social groups (Scheduled Tribes, Scheduled Castes, and Other Backward Class) in quartile one use clean energy, while in urban areas, the shares are 60.4 % and 46.3 %, respectively. To achieve a just distribution of the benefits of energy transition, energy policies should focus on disadvantaged social groups and simultaneously target General Caste households from a lower economic status and those residing in rural areas, indicating the need for targeting the neglected regions. [Display omitted] • Clean energy use has steadily increased among all social groups in India, indicating some improvement in energy justice. • Clean energy access gap among social groups has declined, but this gap still persists. • Scheduled Castes and Scheduled Tribes are the most disadvantaged social groups in terms of clean energy. • All social groups from low-income families have limited access to clean energy. • All social groups from households with low education have limited access to clean energy. [ABSTRACT FROM AUTHOR]