Journal of Renewable Energy

Many photovoltaic solar projects do not achieve optimum energy and power outputs due to poor technical sizing and system design approaches. Concerns on low-conversion rates, high intermittencies, and high-capital costs still haunt PV projects. The establishment of design methodologies that would result in increased outputs from solar arrays is crucial in addressing the aforementioned issues. The tilt angles of installed PV modules are critical factors that influence the power output of solar modules. Several resources are available that provide generic linear fits and estimation of tilt angles for various global regions. However, very few are capable of determining precise, location-specific tilt angles that would allow for optimal power output and energy generation. This paper presents a methodology developed to establish the optimum tilt angles for solar panels installed at specific locations, thus ensuring maximum energy generation. The modeling is based on the maximization of the solar irradiation incident on the surface of a PV panel by considering multiple site-specific variables. Different sets of transcendent equations have been derived which were used to calculate optimum tilt angles and the subsequent energy generation from specific configurations of photovoltaic arrays. The resulting algorithms were used to determine optimum tilt angles and energy generation for solar PV installations in Athi River, Kenya. Dynamic and static optimal tilt angles were compared with the region’s baseline industry practice of using a fixed tilt angle of 15^◦. It was observed that the dynamic tilt angles improved the daily solar energy output by up to 6.15%, while the computed optimal static tilt angle provided a 2.87% output increment. This improvement presents a significant impact on the technical specification of the PV system with a consequent reduction in the investment and operational cost of such installations. It further demonstrated that the use of the optimum static tilt angle results in cost and space savings of up to 2.8% as compared to the standard industry practice. Additionally, 5.8% cost and space savings were attained by the utilization of dynamic tilt angles.

Solar resource data derived from satellite imagery are widely available nowadays, either as an open-source or paid database. This article is intended to assess open-source databases, which cover the region of Indonesia. Here, four known solar resource databases, which spatially cover the Indonesian archipelago, have been used, namely, Prediction of Worldwide Energy Resource (POWER), Surface Solar Radiation–Heliosat-East (SARAH-E), CM SAF Cloud, Albedo, Radiation edition 2 (CLARA-A2), and SolarGIS. In addition, a minor portion of the Meteonorm database by Meteotest, around five sample points across Indonesia, has been assessed in terms of coherency to the four mentioned databases. Correlation coefficient and relative bias of the multiyear monthly mean annual cycle global horizontal irradiation (GHI) between pairs of databases are inspected. Three out of four databases are then validated through the available irradiation ground measurement data provided by the World Radiation Data Centre (WRDC). The correlation between each pair varies mostly between 0.7 and 1, which shows that the four databases to a certain extent agree on how the intermonthly variation would behave throughout the year. On the other hand, the validation result reveals that the three databases, i.e., POWER, CLARA-A2, and SARAH-E, are suffering from positive bias error ranging from 3% to 7%. Despite that fact, the correlation between measured and estimated values is still acceptable with SARAH-E showing the best performance among the three. Careful selections and adjustment enable the possibility of these databases to be utilized as a tool for depicting interannual and intermonthly variations of solar irradiation throughout the Indonesian archipelago.

A rapid decarbonisation of power systems is underway in order to limit greenhouse gas emissions and meet carbon-reduction targets. Renewable energy is a key ingredient to meet these targets; however, it is important that national power systems still maintain energy security with increasing levels of renewable penetration. The operating potential of renewable generation at times of peak demand (a critical time for power system stress) is not well understood. This study therefore uses a multidecadal dataset of national demand, wind power, and solar power generation to identify the meteorological conditions when peak demand occurs and the contribution of renewables during these events. Wintertime European peak power demand events are associated with high atmospheric pressure over Russia and Scandinavia and are accompanied by lower than average air temperatures and average wind speeds across Europe. When considering power demand extremes net of renewable power production, the associated meteorological conditions are shown to change. There is considerable spatial variability in the dates of national peak demand events and the amount of renewable generation present. Growth in renewable generation has the potential to reduce peak demands. However, these impacts are also not uniform with much larger reductions in peak demand seen in Spain than in central Europe. The reanalysis-derived energy models have allowed recent peak demand events to be put into a long-term context.

In Ethiopia, the majority of rural household uses firewood with three-stone fire for cooking. Due to poor performance of the stove, there are major health issues created by indoor air pollution. To alleviate this problem, various efforts are undergoing such as the use of plant oil as an alternative fuel for cooking. This plant’s oils are available in the rural areas with minimal effort and water. In this study, Jatropha oil was blended with kerosene to present it as an alternative fuel for the rural poor in Ethiopia. The blends of varying proportions of Jatropha oil and kerosene were prepared, analyzed, and compared with the fuel properties of kerosene. The viscosity of Jatropha oil was reduced in ranges 86.3% to 4.5% by heating the oil from 30°C to 100°C. In order to understand the value of the blended fuel, the blended fuel was used for the evaluation of the performance of a stove for its thermal efficiency and indoor air pollution. Thermal efficiency of the newly designed bio-oil stove (Jatrok stove) was 52–66% with its specific fuel consumption ranging from 30 to 37 g/L and the fire power of the stove ranging from 1398 to 1433 watt using 10% to 40% Jatropha oil in the blend. In the case of emission, the Jatrok stove showed 11.5 to 9.5 grams of carbon monoxide (CO) and 352 to 289 grams of carbon dioxide (CO₂) to boil 2.5 liters of water.The performance of the Jatrok stove using blended fuels was evaluated and compared with other domestic cooking stoves available in Ethiopia, making the stove comparable. A wider dissemination of such kind of plant oil blended with a kerosene-operated stove could reduce the environmental load in addition to lessoning the indoor air pollution in the kitchen.

We have developed a computationally efficient simulation model for the optimization of redirecting electrical front contacts for multijunction solar cells under concentration, and we present its validation by comparison with experimental literature results. The model allows for fast determination of the maximum achievable efficiency under a wide range of operating conditions and design parameters such as the contact finger redirecting capability, period and width of the fingers, the light concentration, and the metal and emitter sheet resistivity. At the example of a state-of-the-art four-junction concentrator solar cell, we apply our model to determine ideal operating conditions for front contacts with different light redirection capabilities. We find a 7% relative efficiency increase when enhancing the redirecting capabilities from 0% to 100%.

In the four decades since China’s reform and opening up, China has been playing an active role in global value chain (GVC) due to its abundant resources. China has gained enormous benefits from opening up, but has also suffered huge energy costs in the process. In this study, we incorporated global value chains and energy consumption into a unified analysis framework and calculated the energy total-factor productivity (ETFP) of China’s industry and the degree of participation in GVC. In addition, in order to discover the contradictions and problems between China's participation in global value chains and the improvement of total energy factor productivity, the panel smooth transformation model (PSTR) was used to empirically test the nonlinear relationship between the ETFP and the degree of participation in GVC in China. From the analysis results, GVC participation, as well as the subdivided shallow GVC participation and deep GVC participation, first promoted the effect on ETFP and then suppressed it, showing an inverted U-shaped single threshold characteristic. The results indicated that in the progress of starting to participate in the GVC, the effect of technological progress of the GVC overweighed the scale effect of energy consumption, resulting in the growth of ETFP. However, due to the gradual reduction of technology dividends and the “low-end lock-in” situation, China was placed in the value chain by the developed countries, and the technological effect was gradually smaller than the scale effect of energy consumption. As a result, the increase in the total-factor productivity of energy was inhibited. At the same time, in the further examination of industry heterogeneity, the inverted U-shaped influence trend was more significant in high energy-consuming industries. The conclusions of this study can provide a new perspective and policy focus for China's participation in GVC to achieve the goal of increasing ETFP.