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Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

The pollution caused by gas flaring is hazardous to the health of flora, fauna, and humans settled around the flaring site. Gas flaring also incurs economic loss as natural gas, an energy source, is wasted in flares. Furthermore, the unreliable electrical infrastructure is a roadblock for oil and gas companies attempting to achieve their production targets. This paper presents a framework to design hybrid energy systems (HES) which utilize the gas flare waste along with the locally available renewable energy sources to generate electricity. A novel dispatch strategy to suit the requirements of the oil and gas fields has been used for real-time simulations and optimization of the HES. As a test case, six different hybrid energy configurations, modelled for two gas flaring sites, Lakwa and Geleky in Assam—India, were analyzed and compared on the basis of economic and environmental factors. The best suitable configuration comprised 2000 kW solar photovoltaic (PV) panel sets, one 200 kW gas microturbine, two 30 kW gas microturbines, and grid connection. The proposed system economically outperformed the existing power system in the area by 35.52% in terms of the net present cost. Moreover, it could save 850 tons of carbon dioxide emissions annually, and it has a renewable fraction of 93.7% in the total energy generation. Owing to these merits, the presented technique would be a promising option for generation of electricity from flare gas waste and to mitigate pollution.

The goal of this study was to examine the interlinkage of renewable energy, technology innovation, human capital, and governance on environment quality by using a panel quantile regression in Asian emerging economies over the period of 1990–2019. The results indicated that higher economic growth, population density, technological innovation in renewable energy, and exploitation of natural resources have significantly raised CO2 emissions in emerging Asia. Furthermore, larger capital, more use of renewable energy, green technology, and human capital development can improve environmental sustainability in Asia. As for governances, proxied by corruption rates, no evidence indicated that it has resulted in more damage, unlike earlier studies have suggested. The findings indicated that the three channels exposed in the Kuznets hypothesis can serve as a reference for proposals for environmental policies (scale of consumption, energy composition, and choice of technologies). There are opportunities to reduce CO2 emissions through investments in human development, investing in new technologies to increase efficiency in energy (generation and consumption), increasing working capital (GCF), and migrating to more environmentally friendly energy. The negative link between carbon dioxide emissions and economic growth, increases in population density, and exploitation of natural resources can compromise the achievement of sustainable environmental goals.

Rapid climate change and forecasted damage from fossil fuel combustion, forced researchers to investigate renewable and clean energy sources for the sustainable development of societies throughout the world. Biomass-based energy is one of the most important renewable energy sources for meeting daily energy needs, which are gaining in popularity daily. Gasification-based bioenergy production is an effective way to replace fossil fuels and reduce CO2 emissions. Even though biomass gasification has been studied extensively, there is still much opportunity for improvement in terms of high-quality syngas generation (high H2/CO ratio) and reduced tar formation. Furthermore, the presence of tar has a considerable impact on syngas quality. Downdraft gasifiers have recently shown a significant potential for producing high-quality syngas with lower tar concentrations. This article presents a comprehensive review on the advancement in biomass downdraft gasification technologies for high-quality synthesis gas. In addition, factors affecting syngas production and composition e.g., equivalency ratio, temperature, particle size, and gasification medium on synthesis gas generation are also comprehensively studied. The up-gradation and various applications of synthesis gas are also discussed in brief in this review article.

As the time spent by people indoors continues to significantly increase, much attention has been paid to indoor air quality. While many IAQ studies have been conducted through field measurements, the use of data-driven techniques such as machine learning has been increasingly used for the prediction of indoor air pollutants. For the present study, the concentrations of indoor air pollutants such as CO2, PM2.5, and VOCs in child daycare centers were predicted by using an artificial neural network model with three different training algorithms including Levenberg–Marquardt, Bayesian regularization, and Broyden–Fletcher–Goldfarb–Shanno quasi-Newton methods. For training and validation, data of indoor pollutants measured in child daycare facilities over a 1-month period were used. The results showed all the models produced a good performance for the prediction of indoor pollutants compared with the measured data. Among the models, the prediction by the LM model met the acceptable criteria of ASHRAE guideline 14 under all conditions. It was observed that the prediction performance decreased as the number of hidden layers increased. Moreover, the prediction performance was differed by the type of indoor pollutant. This was caused by patterns observed in the measured data. Considering the outcomes of the study, better prediction results can be obtained through the selection of suitable prediction models for time series data as well as the adjustment of training algorithms.

Solar home systems (SHSs) are successfully addressing energy access deficits across the globe, particularly when combined with pay-as-you-go (PAYG) payment models, allowing households to pay for energy services in small instalments. To increase energy access, it is vital to understand the PAYG SHS customer journey in depth. To aid this, the paper presents unique data from active customers, consisting of structured interviews (n = 100) and two focus groups (n = 24) across two districts in Rwanda. These results are presented under a novel customer journey framework, which describes all the individual stages a customer might experience, including awareness and understanding, purchase, usage, upgrade, recommendation and retaining or switching energy source. The paper reveals that the customer journey is non-linear and cyclical in nature, acknowledging that a household operates in a social network within which they could influence or be influenced by others. It also highlights the growing importance of SHS recommendations in raising awareness of SHSs, pointing to the shifts in the off-grid energy market environment where customer awareness no longer appears to be a main adoption barrier.

Global warming has led to rising electricity demands due to soaring cooling load, resulting in different technologies being implemented with renewable energy options. Renewable energy has been used to partially or fully operate these cooing systems through different technology routes in both conventional and hybrid modes. The feasibility of a particular cooling process is influenced by several technological, economic, environmental and other related factors. Selection of the appropriate route also requires consideration of external factors such as local weather, cooling load requirements and the potential of possible renewable energy. Multi-criteria decision analysis is a useful tool to systematically arrive at the right option from several possible options. This tool is used to assess the feasibility of eight technology routes for three different climatic conditions. Other than the direct cooling processes, two routes of renewable energy utilization, namely, the solar photovoltaic system and solar thermal system, are considered. The normalized decision matrix is established and weighted decision matrix is estimated, and the best solution and the worst solution values are obtained by using equations. This study is performed for three climatic zones under the Koppen classification, namely, the tropical maritime arid condition with average midday temperature from 40 to 45 °C, with two different relative humidity ranges, namely, dry area and maritime area. Additionally, the temperate continental climatic zone is analyzed for comparison. The results of this study will help decision makers to judiciously implement air conditioning systems in the above climatic zones. The distance of each waste treatment strategy from the overall best alternative treatment strategy and the overall worst alternative treatment strategy is obtained. Finally, the cooling strategies are ranked for the best option for the cooling mechanism to be adopted for the three climatic conditions.

The transition to a more environmentally friendly energy matrix by reducing fossil fuel usage has become one of the most important goals to control climate change. Variable renewable energy sources (VRES) are a central low-carbon alternative. Nevertheless, their variability and low predictability can negatively affect the operation of power systems. On this issue, energy-system-modeling tools have played a fundamental role. When exploring the behavior of the power system against different levels of VRES penetration through them, it is possible to determine certain operational and planning strategies to balance the variations, reduce the operational uncertainty, and increase the supply reliability. In many developing countries, the lack of such proper tools accounting for these effects hinders the deployment potential of VRES. This paper presents a particular energy system model focused on the case of Bolivia. The model manages a database gathered with the relevant parameters of the Bolivian power system currently in operation and those in a portfolio scheduled until 2025. From this database, what-if scenarios are constructed allowing us to expose the Bolivian power system to a set of alternatives regarding VRES penetration and Hydro storage for that same year. The scope is to quantify the VRES integration potential and therefore the capacity of the country to leapfrog to a cleaner and more cost-effective energy system. To that aim, the unit-commitment and dispatch optimization problem are tackled through a Mixed Integer Linear Program (MILP) that solves the cost objective function within its constraints through the branch-and-cut method for each scenario. The results are evaluated and compared in terms of energy balancing, transmission grid capability, curtailment, thermal generation displacement, hydro storage contribution, and energy generation cost. In the results, it was found that the proposed system can reduce the average electricity cost down to 0.22 EUR/MWh and also reduce up to 2.22 × 106 t (96%) of the CO2 emissions by 2025 with very high penetration of VRES but at the expense of significant amount of curtailment. This is achieved by increasing the VRES installed capacity to 10,142 MW. As a consequence, up to 7.07 TWh (97%) of thermal generation is displaced with up to 8.84 TWh (75%) of load covered by VRES.