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Background Solar photovoltaic technology is one of the promising renewable energy solutions of the twenty-first century. It successfully provides electricity to industries, homes and even the transport sector. The decreasing prices of solar modules from 2010 have made Solar Home Systems Technology (SHST) increasingly attractive compared to other renewable energy technologies. Paradoxically, in rural communities of South Africa the usage of SHS remains low. Households continue to rely on unclean energy sources such as firewood for cooking and water heating. Previous efforts to electrify rural communities with SHS have failed considerably. Thus, a comprehensive study was conducted in the Vhembe District, encompassing three villages, to explore this issue and develop a contextualised solution using a behavioural change model. A 35-item questionnaire was randomly administered to 310 households to understand the factors that contribute to the low adoption rate of SHS technology. The data gathered were analysed using IBM SPSS Statistics and Amos version 28. Confirmatory factor analysis and hypothesis testing were employed as the principal statistical methods. Results A 12-item model with five distinct factors consolidated into a single measurement model was revealed. All standardised factor loadings exceeded 0.7. Composite reliability values (CR) were above 0.8 and higher than MaxR(H) values, indicating the model’s reliability. Among the five factors influencing SHS adoption (perceived behavioural control, attitude, intention, trust, and subjective norms), only trust and attitude significantly impacted the intention to adopt SHS in the district ( P  < 0.05). Based on these findings, the conceptualised structural model reflected SHS adoption as determined by the integration of the technology’s social, technical and policy factors. Because of this, this should be regarded as a true reflection of the practical and behavioural intentions of local communities. Moreover, in this paper the barriers hindering SHS adoption are explained, emphasising the significance of attitude and trust. Highlights of policy imperatives are included together with a proposal for a contextual framework, and the way of promoting sustainable solutions. Emphasis is placed on the importance of scaling up renewable energy access. Conclusions This research provides a compelling academic exploration of the barriers to the adoption of SHS, the influential role of attitudes and trust, policy considerations, a contextual framework, and the need for promoting sustainable solutions and expanding access to renewable energy. The South African government should lead a change in how solar PV is deployed, considering its social impact, associated technical skills and policy support.

One of the most challenging problems related to the operation of smart microgrids is the optimal home energy management scheme with multiple and conflicting objectives. Moreover, there is a noticeable increase in homes equipped with renewable energy sources (RESs), where the coordination of loads and generation can achieve extra savings and minimize peak loads. In this paper, a solar-powered smart home with optimal energy management is designed in an affordable and secure manner, allowing the owner to control the home from remote and local sites using their smartphones and PCs. The Raspberry Pi 4 B is used as the brain of the proposed smart home automation management system (HAMS). It is used to collect the data from the existing sensors and store them, and then take the decision. The home is monitored using a graphical interface that monitors room temperature, humidity, smoke, and lighting through a set of sensors, as well as PIR sensors to monitor the people movement. This action enables remote control of all home appliances in a safe and emission-free manner. This target is reached using Cayenne, which is an IoT platform, in addition to building some codes related to some appliances and sensors not supported in Cayenne from scratch. Convenience for people with disabilities is considered by using the Amazon Echo Dot (Alexa) to control home appliances and the charging point by voice, implementing the associated code for connecting the Raspberry pi with Alexa from scratch, and simulating the system on LabVIEW. To reach the optimal operation and reduce the operating costs, an optimization framework for the home energy management system (HEMS) is proposed. The operating costs for the day amounted to approximately 16.039 €. There is a decrease in the operating costs by about 23.13%. The consumption decreased after using the smart HAMS by 18.161 kWh. The results of the optimization also show that the least area that can be used to install solar panels to produce the desired energy with the lowest cost is about 118.1039 m2, which is about 23.62% of the total surface area of the home in which the study was conducted. The obtained results prove the effectiveness of the proposed system in terms of automation, security, safety, and low operating costs.

Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.

As an alternative source of off-grid electric power, solar home systems (SHS) stand out above all other options (e.g., wind, hydro, geo-thermal, tidal systems) because of their wide-scale potential at latitudes less than 45° north or south of the Equator where daily solar irradiance is more constant throughout the year and where the bulk of the Third World’s population live. A questionnaire-based survey study was carried out in a rural area of Bangladesh to ascertain the impacts of SHSs on the lives of the rural population. The installation of an SHS was found to improve the comfort and living standard of rural dwellers. Easier access to TV, radio, cellphone, and the Internet helped the rural population become part of a more global culture. More attractive down-payment and installment package options will allow poor target groups to adopt this system. The standard of SHS components and after-sales service should be improved to ensure sustainably and popularity among the mass population for at least 10 years at minimal cost to the consumer. Our findings can also help policymakers adopt more SHS-friendly policies to further the interests of inhabitants of rural areas that are not connected to the grid.

Some 630 million people representing two-thirds of all Africans have no access to electricity, which is identified as a key barrier towards further development. Three main electrification options are considered within our work: grid extensions, mini-grids and solar home systems (SHS). A methodology is applied to all sub-Saharan African countries to identify in high geospatial resolution which electrification option is appropriate taking into account datasets for night light imagery, population distribution and grid infrastructure. Four different scenarios are considered reflecting grid development and electrification constraints due to low population density. The results clearly indicate a dominating role of SHS for achieving a fast electrification of the not supplied people. The share of supplied people by mini-grids is found to be rather low while grid extension serves a large share of the population. The decisive factors for these distinctions are population density and distance to grid. We applied several scenarios and sensitivities to understand the influence of these key parameters. The highest trade-off happens between SHS and grid extension depending on the selected thresholds. Mini-grid deployments remain in the range of 8 to 21%.

Off-grid solar home systems (SHSs) currently constitute a major source of providing basic electricity needs in un(der)-electrified regions of the world, with around 73 million households having benefited from off-grid solar solutions by 2017. However, in and of itself, state-of-the-art SHSs can only provide electricity access with adequate power supply availability up to tier 2, and to some extent, tier 3 levels of the Multi-tier Framework (MTF) for measuring household electricity access. When considering system metrics of loss of load probability (LLP) and battery size, meeting the electricity needs of tiers 4 and 5 is untenable through SHSs alone. Alternatively, a bottom-up microgrid composed of interconnected SHSs is proposed. Such an approach can enable the so-called climb up the rural electrification ladder. The impact of the microgrid size on the system metrics like LLP and energy deficit is evaluated. Finally, it is found that the interconnected SHS-based microgrid can provide more than 40% and 30% gains in battery sizing for the same LLP level as compared to the standalone SHSs sizes for tiers 4 and 5 of the MTF, respectively, thus quantifying the definite gains of an SHS-based microgrid over standalone SHSs. This study paves the way for visualizing SHS-based rural DC microgrids that can not only enable electricity access to the higher tiers of the MTF with lower battery storage needs but also make use of existing SHS infrastructure, thus enabling a technologically easy climb up the rural electrification ladder.

Power is one of the key requirements for the development of economies and upgrading of standards of living of developing countries. Countries such as Bangladesh depend largely on fossil fuels such as diesel fuel and natural gas to produce the main proportion of their electricity. However, this country’s combination of limited natural gas reserves high fuel prices and escalating costs of transmission and distribution lines has greatly increased the unit cost of electricity generation and it is becoming difficult for customers to pay for electricity. On the other hand, burning fuel causes environmental pollution that leads to global warming which is ultimately responsible for climate change and its devastating consequences. In this study, we have recommended a stand-alone system for the traditional consumption of domestic electric use at residential units in Bangladesh. We have shown a comparison of using the stand-alone photovoltaic (PV) system with the traditional grid connection. Although the initial set-up cost is high, it becomes profitable as people are supplied with electricity, which is being generated from PV as a result minimizing the energy cost from the grid, and in addition, they can later make savings from this system. This paper, therefore, aims at determining the optimum size of the rooftop solar home system that will fulfil all the criteria for powering up electrical appliances at an affordable price. Comparative analysis of both energy systems based on the cost calculation has been performed by means of the Hybrid Optimization of Multiple Energy Renewables (HOMER) software. The validity of this proposal and its usefulness is also analysed.

The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead–acid (VRLA) battery and the LiFePO 4 (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge ( S O C ) and open circuit voltage ( V OC ) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design.

Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, as well as tracking the progress of universal electrification goals, is shared access to high-quality data on individual SHS installations including information such as location and power capacity. Though recent studies utilizing satellite imagery and machine learning to detect solar panels have emerged, they struggle to accurately locate many SHS due to limited image resolution (some small solar panels only occupy several pixels in satellite imagery). In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery. More specifically, we explore three questions: (i) what is the detection performance of SHS using drone imagery; (ii) how expensive is the drone data collection, compared to satellite imagery; and (iii) how well does drone-based SHS detection perform in real-world scenarios? To examine these questions, we collect and publicly-release a dataset of high-resolution drone imagery encompassing SHS imaged under a variety of real-world conditions and use this dataset and a dataset of imagery from Rwanda to evaluate the capabilities of deep learning models to recognize SHS, including those that are too small to be reliably recognized in satellite imagery. The results suggest that UAV imagery may be a viable alternative to identify very small SHS from perspectives of both detection accuracy and financial costs of data collection. UAV-based data collection may be a practical option for supporting electricity access planning strategies for achieving sustainable development goals and for monitoring the progress towards those goals.