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The annual yield of bifacial photovoltaic systems is highly dependent on the albedo of the underlying soil. There are currently no published data about the albedo of red soil in western Africa. In this study, the impact of the albedo of red soil in Ghana on the energy yield of bifacial photovoltaic systems is analysed. A bifacial photovoltaic simulation model is created by combining the optical view factor matrix with an electrical output simulation. For an exact simulation, the albedo of red soil at three different locations in Ghana is measured for the first time. The average albedo of every red soil is clearly determined, as well as the measurement span including instrumentation uncertainty; values between 0.175 and 0.335 were measured. Considering these data, a state-of-the-art bifacial photovoltaic system with an average of 19.8% efficient modules in northern Ghana can achieve an annual energy yield of 508.8 kWh/m2 and a bifacial gain of up to 18.3% in comparison with monofacial photovoltaic panels. To summarise, red soil in two out of three locations in Ghana shows higher albedo values than most natural ground surfaces and therefore positively impacts the annual yield of bifacial photovoltaic systems.
Potential analyses identify possible locations for renewable energy installations, such as wind turbines and photovoltaic arrays. The results of previous potential studies for Germany, however, are not consistent due to different assumptions, methods, and datasets being used. For example, different land-use datasets are applied in the literature to identify suitable areas for technologies requiring open land. For the first time, commonly used datasets are compared regarding the area and position of identified features to analyze their impact on potential analyses. It is shown that the use of Corine Land Cover is not recommended as it leads to potential area overestimation in a typical wind potential analyses by a factor of 4.7 and 5.2 in comparison to Basis-DLM and Open Street Map, respectively. Furthermore, we develop scenarios for onshore wind, offshore wind, and open-field photovoltaic potential estimations based on land-eligibility analyses using the land-use datasets that were proven to be best by our pre-analysis. Moreover, we calculate the rooftop photovoltaic potential using 3D building data nationwide for the first time. The potentials have a high sensitivity towards exclusion conditions, which are also currently discussed in public. For example, if restrictive exclusions are chosen for the onshore wind analysis the necessary potential for climate neutrality cannot be met. The potential capacities and possible locations are published for all administrative levels in Germany in the freely accessible database (Tool for Renewable Energy Potentials—Database), for example, to be incorporated into energy system models.
In the literature, many studies outline the advantages of agrivoltaic (APV) systems from different viewpoints: optimized land use, productivity gain in both the energy and water sector, economic benefits, etc. A holistic analysis of an APV system is needed to understand its full advantages. For this purpose, a case study farm size of 0.15 ha has been chosen as a reference farm at a village in Niger, West Africa. Altogether four farming cases are considered. They are traditional rain-fed, irrigated with diesel-powered pumps, irrigated with solar pumps, and the APV system. The APV system is further analyzed under two scenarios: benefits to investors and combined benefits to investors and farmers. An economic feasibility analysis model is developed. Different economic indicators are used to present the results: gross margin, farm profit, benefit-cost ratio, and net present value (NPV). All the economic indicators obtained for the solar-powered irrigation system were positive, whereas all those for the diesel-powered system were negative. Additionally, the diesel system will emit annually about 4005 kg CO2 to irrigate the chosen reference farm. The land equivalent ratio (LER) was obtained at 1.33 and 1.13 for two cases of shading-induced yield loss excluded and included, respectively.