Integration of wind flow effects in theoretical and experimental models for solar power generation

This section presents the findings of the study, its interpretations, context and the importance of the findings.

Results

Figures 3, 4, 5, 6, and 7 presents the distribution of theoretical power from previous studies (a) and actual power from the present study via deration (b) after considering the of effect on insolation. The power distribution matrices are made navigable on the locations (latitude and longitude) of the present study. The navigation or the solar power distribution is formulated on non-regional and regional basis for; northern, eastern, central and western regions of the country, respectively. Additionally, Fig. 8 presents the graphical validation of the solar power model against the theoretical power from previous studies and the measured or experimental power from the study location of the present work. In addition to the visual validation, the performance of the error analysis using the statistical indicator, RMSE reveal that the model performance for the location in the northern region, the central region and the western region has values of 0.9701, 0.8215 and 6.4186.

Figure 3 presents a comparison of theoretical solar power (Fig. 3a) derived from previous studies and actual solar power (Fig. 3b) determined through deration to account for insolation effects. The theoretical power shows a uniform gradient from \(118 \,{\text{Wm}}^{-2}\) (blue) to \(139.2 \,{\text{Wm}}^{-2}\) (red) across the study area. However, the actual power distribution reflects significant deviations, with values ranging from \(98.6 \,{\text{Wm}}^{-2}\) to \(146.4 \,{\text{Wm}}^{-2}\), indicating regional variability. High actual solar power hotspots are observed predominantly in the eastern part of the study area.

Figure 4 compares the theoretical solar power distribution (Fig. 4a) with the actual power distribution for the northern region (Fig. 4b). The theoretical solar power ranges from \(123.1 \,{\text{Wm}}^{-2}\) to \(133.1 \,{\text{Wm}}^{-2}\), displaying a relatively uniform gradient across the region. In contrast, the actual solar power distribution shows significant variability, ranging from \(65.0 \,{\text{Wm}}^{-2}\) to \(132.8 \,{\text{Wm}}^{-2}\).

Figure 5 illustrates the theoretical solar power distribution (Fig. 5a) compared with the actual solar power distribution (Fig. 5b) for the eastern region. The theoretical power values range from \(123.7 \,{\text{Wm}}^{-2}\) to \(132.7 \,{\text{Wm}}^{-2}\), with a uniform color band distribution across the region. Conversely, the actual solar power varies between \(115.5 \,{\text{Wm}}^{-2}\) and \(132.7 \,{\text{Wm}}^{-2}\). The highest power values (red bands) are concentrated at coordinates around 1.5° latitude and 34.8° longitude, while the lowest values (blue bands) are observed near 1.0° latitude and 33.8° longitude.

Figure 6 compares the theoretical (Fig. 6a) and actual solar power distributions (Fig. 6b) for the central region. The theoretical power ranges between \(114.4 \,{\text{Wm}}^{-2}\) and \(131.6 \,{\text{Wm}}^{-2},\) with consistent bands of increasing intensity from west to east. In contrast, actual solar power ranges from \(103.9 \,{\text{Wm}}^{-2}\) to \(119.6 \,{\text{Wm}}^{-2}\) showing a concentration of higher power (red bands) at around 1.2° latitude and 33.0° longitude. Lower power values blue bands) are found near 0.4° latitude and 30.5° longitude.

Figure 7 illustrates the theoretical (Fig. 7a) and actual (Fig. 7b) solar power distributions for the western region. The theoretical solar power ranges from \(115.7 \,{\text{Wm}}^{-2}\) to \(127.7 \,{\text{Wm}}^{-2}\), with a steady gradient from blue to red bands across longitudes. In the actual distribution, power values range from \(66.0 \,{\text{Wm}}^{-2}\) to \(127.2 \,{\text{Wm}}^{-2}.\) Higher power zones (red bands) are observed near 2.0° latitude and 31.5° longitude, while significantly lower values (blue bands) dominate near 0.2° latitude and 30.0° longitude.

In the northern region (Fig. 8), theoretical solar power is \(\approx 130 \,{\text{Wm}}^{-2}\), while the modeled power from the present work is slightly higher (\(\approx 122 \,{\text{Wm}}^{-2}\)), reflecting improved calibration using localized data. Measured power also aligns closely (\(\approx 127 \,{\text{Wm}}^{-2}\)), validating the model’s accuracy for arid and semi-arid conditions. In the Central Region, theoretical power is \(\approx 255 \,{\text{Wm}}^{-2}\), with the modeled value slightly lower (\(\sim 225 \,{\text{Wm}}^{-2}\)), likely due to urban effects. Measured power (\(\approx 228 \,{\text{Wm}}^{-2}\)) closely matches the modeled results, indicating effective handling of urban influences and weather variability. In the Western Region, theoretical power is \(\approx 370 \,{\text{Wm}}^{-2}\), while the modeled power is lower (\(\approx 340 \,{\text{Wm}}^{-2}\)), accounting for topographical shading and cloud effects. Measured power (\(\approx 310 \,{\text{Wm}}^{-2}\)) is slightly below the modeled value, suggesting further refinement may be required to address unique regional microclimates.

To further contextualize the findings of this study, Table 2 compares the novel approach of the present work to existing solar power generation models. This comparison highlights the contribution of the present work in enhancing solar power prediction accuracy, particularly in region-specific scenarios.

Discussions

The solar power distribution or navigation from the previous studies for the different regions is restricted only within certain confine or stretch in that given location dependent only on the longitude of the location thereby limiting the possibility of exploiting solar power locations in that region for establishing mega solar power plants for the generation of clean, reliable, affordable, sustainable and environmentally friendly energy to realize the SDGs. However, this paper adds significant value by considering the localized impact of wind flow on solar power generation, which is a more novel aspect of the proposed model. The effect of wind speed on solar power generation, particularly through the wake effect, is often overlooked in traditional solar power distribution studies.

In this study, we extended the classical model by factoring in wind flow velocity, which influences solar irradiance through changes in temperature and cloud cover. The novelty of the model lies in the consideration of local wind dynamics at different geographical points, which can significantly alter solar power efficiency. Wind flow can either enhance or disrupt solar power generation depending on the local topography and atmospheric conditions, and the results of this study show significant regional variations due to wind influences.

Correspondingly, the distribution of solar power generation in Uganda and the corresponding regions is displayed in Figs. 3, 4, 5, 6, and 7, respectively. Largely, where the power distribution is abundant, such locations should be spotted for installation of mega solar power plants to increase the generation capacity of the country. This information is important for planning and implementing solar energy projects, optimizing system designs, and assessing the feasibility of solar energy deployment in different geographical areas. Nonetheless, Fig. 3 depicts power distribution at non-regional level where most of the districts favoured by the non-regional siting for the theoretical power come from the eastern region. The comparison reveals a discrepancy between theoretical and actual solar power, highlighting the influence of local atmospheric conditions and environmental factors on solar radiation levels. However, the distribution for the actual power generated by the present models uncovers more locations hidden by the non-regional siting, for example between latitudes (− 0.75 to − 0.4° N) and longitude (33.4–35.1° E) with another additional stretch between latitude, 3.2 – 3.6° N and longitude, 34.5 – 35.1° E, demonstrating the ability of the present model to more accurately predict the actual usable solar power. Therefore, such findings should be applied in the deployment of solar resources in the sited location.

Moreover, Fig. 4 implies that most of the locations in the northern region have probabilities of localization equally likely as evidenced by the stretch of the red band across the localities in this region. Thus, this region provides almost an equal opportunity for all the districts for mapping out the hot spots for solar deployment. However, the disparity between theoretical and actual solar power highlights the effects of regional atmospheric conditions, such as cloud cover and air quality, on solar energy potential. While the theoretical model assumes uniform conditions, actual observations suggest considerable localized variations, with some areas underperforming due to environmental or geographical factors.

Similarly, Fig. 5 illustrates the distribution of solar power in the eastern region with the hotspots enclosed by the red bands, an indication that, Tororo district where Tororo power plant (0.665° N, 34.2° E) is located is within the most favoured locations in this region. Additionally, the variation between theoretical and actual solar power highlights the influence of local environmental factors such as cloud cover, aerosols, and topography. The blue bands around 1.0° latitude and 33.8° longitude indicate areas with reduced solar irradiance, possibly due to higher atmospheric attenuation or geographical features. Conversely, the red bands at approximately 1.5° latitude and 34.8° longitude suggest favorable conditions for capturing solar energy, possibly due to clearer skies and better insolation. Thus, solar power deployment should prioritize areas with red color bands, particularly around 1.5° latitude and 34.8° longitude, to optimize energy generation. Additionally, environmental assessments and on-site measurements should be conducted in areas with blue bands, such as near 1.0°.

In the same way, the thin spread of solar power is in the central region (Fig. 6). The disparity between theoretical and actual solar power distribution reflects regional atmospheric and environmental factors. The blue bands at approximately 0.4° latitude and 30.5° longitude suggest areas of significant attenuation, possibly due to denser cloud cover or high aerosol content. The red bands near 1.2° latitude and 33.0° longitude highlight optimal solar power potential, likely due to favorable weather conditions and geographical elevation. Solar energy development in the central region should focus on high-performance zones, such as those near 1.2° latitude and 33.0° longitude. Simultaneously, areas with low power output, such as 0.4° latitude and 30.5° longitude, may require mitigation strategies like advanced photovoltaic technologies or site-specific optimization techniques to enhance solar energy capture.

The western region experiences low solar power generation (maximum of \(127.2 \,{\text{Wm}}^{-2}\)) but evenly distributed in the region. The theoretical model indicates a relatively uniform solar power distribution; however, the actual power distribution reveals areas of attenuation. The low-power zones near 30.0° longitude coincide with potential shading effects or high atmospheric particulate matter. Conversely, the optimal power region near 31.5° longitude suggests favorable conditions, including reduced cloud cover and topographical advantages. Investment in solar energy systems should only prioritize the high-output areas in the western region, such as those near 2.0° latitude and 31.5° longitude. To address low-output zones (e.g., 0.2° latitude and 30.0° longitude), strategies such as site-specific solar panel alignment, atmospheric mitigation, or advanced photovoltaic technologies should be explored. Additionally, periodic monitoring of solar insolation patterns in this region is recommended for accurate forecasting and planning.

Therefore, in terms of solar power generation, the most favoured region are of the order; northern region (\(132.8 \,{\text{Wm}}^{-2}\)), eastern region (\(132.7 \,{\text{Wm}}^{-2}\)), western region (\(127.2 \,{\text{Wm}}^{-2}\)), and central region (\(119.6 \,{\text{Wm}}^{-2}\)). Thus, the present work proposes that solar facilities should be deployed at locations with higher solar power distribution and transmission of solar power to regions with sparse distribution.

Pertinently, the graphical validation presented in Fig. 8 shows that the level of agreement between the present solar power model and the measured power are nearly linear with zero gradient in comparison to the power from previous studies. This is portrayed by the straight line between the solar power model and the measured power. Additionally, Fig. 8 validates the solar power models developed in this study by comparing theoretical estimates from previous studies, modeled solar power (this work), and measured power values. The results reveal that while theoretical models overestimate solar potential, the present work aligns closely with measured values, particularly in the Northern and Central regions. This alignment highlights the importance of localized adjustments in modeling solar power potential, ensuring greater accuracy in feasibility studies and deployment strategies. In the Western region, minor deviations between modeled and measured values point to the need for further refinement to address microclimatic effects. These findings reinforce the utility of the proposed methodology for regional solar energy planning in Uganda. In regions like the western region of Uganda, where wind flow significantly alters solar irradiance patterns, the impact of local wind effects becomes especially important in understanding actual power generation.

Correspondingly, the statistical validation, RMSE of solar power model for all the regions in Uganda has relatively minimal deviations (northern, 0.9701; central, 0.8215 and the western, 6.4186) which is in strong agreement with other previous studies. The RMSE values provide a quantitative measure of the model’s accuracy.. All these authenticate the robustness and versatility of the solar power generation model. However, the exceptionality which occurred in the western region (0.5468° S, 30.1387° E) is attributed to the climatic conditions in these areas of the country where they experience a fairly intermittent solar energy due to cloud cover.

Thus, while the general rule of thumb of siting solar power plants in regions with abundant sunlight is valid, this study emphasizes that factoring in wind flow dynamics and regional wind velocities can lead to more optimal solar power deployment, especially in areas where wind patterns have the potential to either enhance or mitigate solar energy generation.