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Under the Sustainable Development Goals (SDGs), the world has pledged to “leaving no one behind”. Responding to goal No. 7 on the agenda, efforts to provide modern energy to all the world population must be pushed forward. This is important because electrification in the rural area can indirectly support opportunities for social and economic development resulting in an acceleration of the eradication of poverty. The research goal of this study is to contribute insights about the scale of energy demand in unelectrified villages in the Southeast Asian countries and to discuss some factors that might influence the energy demand growth. This is done by making projections based on surveys and interviews, including a time-use survey, in three off-grid villages located in Myanmar, Indonesia, and Laos. Our analysis presented the living condition, highlight the types of energy sources, how, and in what rhythms people use energy on a daily basis in those villages. The demands in each case study villages were then projected based on several constructed scenarios. It was found that the factors of household size, proximity to the city, climate, and topography may influence the present and future growth of energy demands in the villages. The estimated energy demand may be useful for project managers to design a pilot off-grid energy system project in a similar environment and pointed out important factors to consider when formulating off-grid energy policies in the region.

Investing in large-scale hydropower is on the rise in Ethiopia in accordance with the country’s climate-resilient green economy strategy. Rural electrification is a top priority on the development agenda of the country, with very limited off-grid interventions. Although small-scale hydropower can bring various social and economic benefits compared to other off-grid solutions, it is hardly localized in the country. The motivation for this research is to break this technological bottleneck by synergizing and strengthening the local capacity. Accordingly, this paper presents the full-scale crossflow turbine design and development process of a power plant constructed to give electricity access to about 450 households in a rural village called Amentila. Based on a site survey and the resource potential, the power plant was designed for a 125 kW peak at 0.3 m3/s of discharge with a 53 m head. The crossflow was selected based on the head, discharge, and simplicity of development with the available local capacities. The detailed design of the turbine and its auxiliary components was developed and simulated using SolidWorks and CFD ANSYS CFX. The power plant has a run-of-river design, targeting provision of power during peak hours. This study demonstrates an off-grid engineering solution with applied research on the water–energy–food–environment nexus.

This paper examines the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for voltage regulation in a small-scale wind turbine (SWT) system intended for off-grid rural electrification in Uzbekistan. The proposed architecture consists of a wind turbine, a permanent-magnet DC generator, and a buck converter supplying a regulated 48 V DC load. While ANFIS-based control has been reported previously for wind energy systems, the novelty of this work lies in its focused application to a DC-generator-based SWT topology using real wind data from the Bukhara region, together with a rigorous quantitative comparison against a conventional PI controller under both constant- and reconstructed variable-wind conditions. Dynamic performance was evaluated through MATLAB/Simulink simulations incorporating IEC-compliant wind turbulence modeling. Quantitative results show that the ANFIS controller achieves faster settling, reduced voltage ripple, and improved disturbance rejection compared to PI control. The findings demonstrate the technical feasibility of ANFIS-based voltage regulation for decentralized DC wind energy systems, while recognizing that economic viability and environmental benefits require further system-level and experimental assessment.

In the quest for sustainable energy solutions, this study analyses the hybrid solar photovoltaic (PV)-diesel generator system's feasibility for reliable electricity supply to a mini-mart in Ajase-Ipo, Nigeria. Utilizing HOMER software, the system was modelled and optimized with a focus on both technical and economic factors. The optimal configuration comprises a 10.2 kW solar PV system, 8 batteries, a 6-kW converter, and a 10-kW diesel generator, meeting the annual energy demand of 29,200 kWh. The system's technical performance is highlighted by a capacity utilization factor of 17.9% and a performance ratio of 82.16%, demonstrating the efficiency of the solar PV system. However, heavy reliance on diesel fuel, with an annual consumption of 5,632 litres, leads to a high levelized cost of energy (LCOE) of ₦306.16 per kWh. This underscores the need for strategies to reduce diesel dependency and improve long-term economic viability through improved solar capture and optimized battery storage. The system also demonstrates significant environmental benefits, with CO₂ emissions reduced by 8,614 kg annually, alongside reductions in other harmful pollutants such as CO, SO₂, and NOₓ. These findings reinforce the potential of renewable energy to provide a more sustainable, cost-effective, and environmentally friendly solution for electricity generation in similar contexts.

Recently, off-grid renewable power generation systems have become good alternatives for providing reliable electricity at a low cost in remote areas. According to the International Renewable Energy Agency, more than half the population of Nigerian rural communities are outside the electricity coverage area. This research examines the potential application of hybrid solar photovoltaic (PV)/hydro/diesel/battery systems to provide off-grid electrification to a typical Nigerian rural village. The performance of four different hybrid systems was evaluated via techno-economic and environmental analysis, and the optimized solution was selected using the HOMER analysis tool. The simulation results revealed that a hybrid PV solar/hydro/diesel with battery storage was the optimized solution and most suitable with the least net present cost (NPC) of$963,431 and a cost of energy (COE) of $ 0.112/kWh. The results also revealed that the optimal system prevented about 77.1% of CO2 gas emission from being released to the surrounding air as compared with the PV/diesel system (worst case). In addition, the results also showed better performance in technical aspects, making the system appropriate and ideal for rural electrification and clean energy development. Furthermore, the effects of varying some variables such as interest rate, solar radiation, water discharge, capacity shortage, and battery minimum state of charge on the system cost and operational performance were discussed via the sensitivity analysis because these parameters influence the economy and technical aspect of the power system.

There are 17 million people without access to electricity services in Latin America. This population lives in small isolated and scattered communities with low incomes where it is difficult to achieve 100% access to electricity by the grid extension. Therefore, it is necessary to create market mechanisms and promote off-grid electrification in which photovoltaic (PV) technology plays a fundamental role. This research assesses successful projects developed in Peru, Mexico, and Bolivia, where 3rd Generation Solar Home Systems (3G-SHSs) are being introduced to support off-grid initiatives. To do so, we applied a mixed-methods approach including a comparative case study analysis, an extensive literature review, focus group discussions, and field research. Thereby, the lessons learned reveal that confidence, commitment, and flexibility are the main pillars of rural electrification. Additionally, it is demonstrated that the combination of various business models—an energy service company, fee-for-service, pay-as-you-go, and a microfranchising—with 3G-SHSs is powerfully effective in terms of sustainability. Our findings are useful to policy makers, researchers, promoters, and other stakeholders to rethink intervention strategies in rural areas. Access to electricity must be a state policy to facilitate the participation of new actors, especially of the private sector and communities, and the introduction of innovative business models and high-quality technology.

In this progressing technological advancement world, hybrid systems for power generation is one of the most promising fields for any researcher. In this context, photovoltaic-biomass hybrid systems with off-grid applications have become extremely popular with both Governments and individual users in rural areas of any part of the world. This system has gained popularity because of low cost, sustainability and very effective outcome with the use of natural resources at the rural areas. In this paper a proposed hybrid system which contains photovoltaics (PV) and biomass along with an additional storage has been considered to find the different aspects from an end user point of view. It also discusses the feasibility of the proposed model for an off-grid power system located in the remote areas of Ashuganj, Bangladesh. In order to analyse the pollutant emissions and calculate the cost parameters of the proposed system, RETScreen simulation software was deployed. This research also carries out a brief financial analysis considering the annual income of the end user and the payback periods for the installed system. It endeavours to provide complete information about different parameters which also includes the environmental impacts involved in establishing the proposed system. The conventional system in the pilot area is a kerosene-based system, hence in this research, a comparison between the proposed and the conventional system has been analysed using simulated results. The simple payback of the project was estimated to be 6.9 years and this model will be able to reduce the CO2 emissions by approximately 3.81 tonnes per year. The results have significantly supported the proposed system to be more reliable, environmentally-friendly and less costly than the conventional kerosene-based system.

Meeting the growing global electricity demand in remote and off-grid regions requires cost-effective and reliable power solutions that overcome the intermittency of renewable energy sources. This paper presents a comprehensive techno-economic optimization framework for the design and operation of off-grid hybrid renewable energy systems (HRES) integrating photovoltaic (PV), wind turbine, biomass generator, diesel backup, and a dual-chemistry hybrid battery energy storage system (HBESS) combining lithium-ion and nickel-iron batteries. A detailed mathematical modeling approach is employed to capture the nonlinear dynamics, stochastic renewable behavior, battery degradation, and temperature-adjusted component efficiencies. The system is formulated as a multi-objective mixed-integer nonlinear programming problem targeting the minimization of life cycle cost (LCC), levelized cost of energy (LCOE), and CO2 emissions while satisfying reliability constraints such as loss of power supply probability (LPSP < 0.01). To solve the optimization problem, advanced metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimizer (GWO), and Differential Evolution (DE), and Salp Swarm Algorithm (SSA)—and a Deep Q-Network (DQN)-based reinforcement learning energy management strategy are implemented and benchmarked. The proposed DQN-based controller demonstrates superior performance over conventional rule-based and static dispatch methods by maintaining more stable battery state-of-charge (SOC) profiles, reducing degradation, and enabling intelligent real-time decision-making. Simulation results based on realistic meteorological and demand profiles reveal that the integrated DQN and HBESS strategy reduces total LCC by over 20%, CO2 emissions by up to 30%, and battery degradation costs by over 10% compared to baseline systems. The Salp Swarm Algorithm (SSA) achieves the fastest convergence and the highest-quality Pareto-optimal solutions among all metaheuristics evaluated. Sensitivity analysis identifies diesel price and interest rate as the most influential parameters on LCOE, while load shifting through aggressive demand-side management further minimizes battery usage, operating costs, and emissions. The proposed framework not only addresses key challenges in off-grid microgrid design but also provides a scalable and robust pathway for sustainable rural electrification using hybrid storage and intelligent control.

Rural electrification in developing regions remains a major challenge due to the absence of grid infrastructure and high costs of energy distribution. This study presents the design, simulation, and optimization of a hybrid photovoltaic (PV)–diesel–battery system to supply reliable electricity to an off-grid village in the Jawi region of Ethiopia. With an annual load demand of 153,396.36 kWh and average solar radiation of 1211.8 kWh/m 2 /year, a hybrid configuration comprising an 81 kW PV array, 25 kW diesel generator, 45 kW converter, and 200 batteries was modeled using PVsyst and HOMER software. Various system configurations were evaluated based on net present cost (NPC), cost of energy (COE), fuel consumption, and CO₂ emissions. The optimized hybrid system achieved a net present cost of 7,284,233 Birr, a cost of energy of 4.468 Birr/kWh, annual diesel consumption of 9,377 L, and CO₂ emissions of 24,693 kg, with a renewable contribution of 84%. Compared to the diesel-only configuration, the hybrid system significantly reduced both fuel usage and emissions while improving reliability and long-term sustainability. Sensitivity analyses confirmed system robustness against variations in fuel price and PV cost. The results demonstrate that a PV–diesel–battery hybrid system offers a technically feasible, economically viable, and environmentally sustainable solution for off-grid electrification in rural Ethiopia.

This study presents a novel multi-objective optimization (MOO) model for the design of an off-grid hybrid renewable energy system (HRES) to support sustainable agriculture and rural development in Sub-Saharan Africa (SSA). Based upon a case study selected in Linia (Chad), three system architectures are compared under different levels of the reliability requirements (LPSP = 1%, 5%, and 10%). A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to optimize the Levelized Cost of Energy (LCOE), CO2 emissions mitigation, and social impact, referring to the Human Development Index (HDI) enhancement and the job creation (JC) opportunity, using the MATLAB R2024b environment. The calculation results show that among the three configuration schemes, the PV–Wind–Battery configuration obtains the optimal techno–economic–environmental coordination, with the lowest LCOE (0.0948 $/kWh) and the largest CO2 emission reduction (9.58 × 108 kg), and the Wind–Battery system gets the most social benefit. The method developed provides users with a decision-support method for renewable energy systems (RES) integration into rural agricultural settings, taking into consideration financial cost, environmental sustainability, and community development. This information is important for policymakers and practitioners advocating for decentralized, socially inclusive clean energy access initiatives in underserved regions.