Germany and South Korea are the world’s sixth and seventh largest emitters of greenhouse gases, respectively; their main sources of pollution being fossil-fueled power plants. Since both countries ...signed the Paris Agreement in 2016, renewable energy transition is emerging as an effective means and method for avoiding air pollutant emissions and for replacing old fossil-fueled power plants. This paper attempts to evaluate-by using a grid emission factor dependent on a series of energy mix scenarios-the potential for South Korea and Germany to reduce their air pollutants (CO2, NOx, SOx, PM (particulate matter)) until 2030. South Korea plans to reduce greenhouse gas emissions by increasing nuclear power, while Germany aims to do so by shutting down its nuclear power plants and expanding the proportion of renewable energy in the energy mix to over 50%. Therefore, both countries are able to achieve their voluntary greenhouse gas reduction targets in the power sector. However, since the uncertainty of the CO2 emission factor of coal power plants in South Korea is as high as 10%, efforts to reduce that uncertainty are required in order to produce a reliable assessment of the avoided emissions.
Access to modern energy is essential for socioeconomic development, yet Africa faces significant challenges in this regard. For example, Sub-Saharan Africa (SSA) is marked by economic ...underdevelopment and poverty largely due to the non-environmentally friendly energy used (wood, charcoal) and limited access to modern energy resources. Indeed, this review provides an overview of the African energy landscape; it provides a comprehensive renewables-focused energy pathway for developing a cleaner and more sustainable African energy system. It explores end-use sector electrification in both rural and urban areas in Africa. It emphasizes the rapid expansion of renewable generation, the challenges facing and solutions for the implementation of renewable energy, and the role of emerging technologies. It also presents technological pathways and investment opportunities that will enrich the regional debate and help accelerate the energy transformation across Africa. The analysis demonstrated that the current trends of renewable energy used are hydropower, wind power, biomass, and geothermal energy. The electrification rate in West Africa is less than 58% in urban areas and less than 25% in rural areas. Results show that 65% of the SSA population does not have access to electricity and 81% rely on wood and charcoal. In West Africa, only Ghana (70% or so) and Cape Verde (95.9% or so) have equitable access to electricity between rural and urban areas. The potentiality of solar irradiance in Africa ranges between 3 and 7 KWh/m2/day. The wind speed ranges from 3 m/s to 10 m/s; the wave power can range from 7 to 25 kW/m per site in island regions. Egypt, Morocco, Ethiopia, Tunisia, and South Africa are, respectively, countries leading in wind power technology, and solar energy technology was more advanced in North Africa and South Africa. Finally, geothermal is only developed in Kenya and Tanzania and Kenya is the leader in that field. Although renewable energy in Africa is still growing year to year, it still faces power outages because most renewable energy potentialities are not yet exploited, the technologies used are weak, there is insufficient funding, there is ineffective infrastructure, and there are inadequate or no policies in that field.
The need for high‐resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make ...these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography‐derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi‐resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.
Satellite imagery-based solar irradiance mapping studies are essential for large-scale solar energy assessments but are limited in spatial resolution and accuracy. Despite efforts to increase map ...resolution by correcting inaccuracies caused by shadows on the terrain, the computational time of these models and the massive volume of generated data still pose challenges. Particularly, forecasting generates large amounts of time series data, and the data production rate is faster than the computational speed of traditional terrain correction. Moreover, while previous research has been conducted to expedite computations, a novel and innovative technology in terrain correction is still required. Therefore, we propose a new correction method that can bypass complex calculations and process enormous data within seconds. This model extends the lookup table concept, optimizes the results of many shadow operations, and stores them in memory for use. The model enabled 90 m scale computations across Korea within seconds on a local desktop computer. Optimization was performed based on domain knowledge to reduce the required memory to a realistic level. A quantitative analysis of computation time was also conducted, revealing a previously overlooked computational bottleneck. In conclusion, the developed model enables real-time terrain correction and subsequent processing of massive amounts of data.
This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) ...measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.
In solar resource assessment, the climatological environment of the target area is objectively quantified by the cloudiness or clear sky index, which is defined as the ratio of global horizontal ...irradiance to clear sky solar insolation. The clear sky model calculates incoming solar irradiance on the ground surface considering several atmospheric parameters such as water vapor and aerosol optical depth. This study investigated the importance of aerosol optical depth for deriving clear sky irradiance in radiative transfer models and examined its viability in a universal or community model for public use. The evaluation was conducted based on ground observations at the Korea Institute of Energy Research (KIER) station from January to December 2021. The original simulation was performed using the monthly mean of aerosol optical depth obtained from the Aerosol Robotic Network station; the mean absolute error was 29.9 W m−2. When the daily mean of in situ observations at KIER was incorporated into the clear sky model, the mean absolute error was reduced to 9.7 W m−2. Our results confirm that the clear sky model using gridded datasets of aerosol optical depth is suitable for use as a universal or community model.
Satellite-derived solar irradiance is advantageous in solar resource assessment due to its high spatiotemporal availability, but its discrepancies to ground-observed values remain an issue for ...reliability. Site adaptation can be employed to correct these errors by using short-term high-quality ground-observed values. Recent studies have highlighted the benefits of the sequential procedure of a regressive and a distribution-mapping technique in comparison to their individual counterparts. In this paper, we attempted to improve the sequential procedure by using various distribution mapping techniques in addition to the previously proposed quantile mapping. We applied these site-adaptation techniques on the global horizontal irradiance (GHI) and direct normal irradiance (DNI) obtained from the UASIBS-KIER model in Daejeon, South Korea. The best technique, determined by a ranking methodology, can reduce the mean bias from −5.04% and 13.51% to −0.45% and −2.02% for GHI and DNI, respectively, and improve distribution similarity by 2.5 times and 4 times for GHI and DNI, respectively. Partial regression and residual plot analysis were attempted to examine our finding that the sequential procedure is better than individual techniques for GHI, whereas the opposite is true for DNI. This is an initial study to achieve generalized site-adaptation techniques for the UASIBS-KIER model output.
A precise estimate of solar energy output is essential for its efficient integration into the power grid as solar energy becomes a more significant renewable energy source. Contrarily, the creation ...of solar energy involves fluctuation and uncertainty. The integration and operation of energy systems are complicated by the uncertainty in solar energy projection. As a post-processing technique to lower systematic and random errors in the operational meteorological forecast model, the analog ensemble algorithm will be introduced in this study. When determining the appropriate historical and predictive data required to use the approach, an optimization is conducted for the historical period in order to further maximize the capabilities of the analog ensemble. To determine statistical consistency and spread skill, the model is evaluated against both the raw forecast model and observations. The outcome lowers the uncertainty in the predicted data by demonstrating that statistical findings improve significantly even with 1-month historical data. Nevertheless, the optimization with a year’s worth of historical data demonstrates a notable decrease in the outcomes, limiting overestimation and lowering uncertainty. Specifically, analog ensemble algorithms calibrate analog forecasts that are equivalent to the latest target forecasts within a set of previous deterministic forecasts. Overall, we conclude that analog ensembles assuming a 1-year historical period offer a comprehensive method to minimizing uncertainty and that they should be carefully assessed given the specific forecasting aims and limits.
The spectral mismatch factor for solar cells quantifies their relative performance in converting solar irradiance between the incident and reference solar spectra into electricity. This study ...attempted to evaluate the spectral mismatch factor for eight types of solar cells and investigate their sensitivity to changes in the solar spectral irradiance, which is dependent on the aerosol optical properties in a clear sky. Copper indium gallium diselenide cells have the highest mean value of the spectral mismatch factor, implying that they are less sensitive to changes in the solar spectral irradiance. In contrast, perovskite and amorphous silicon cells are more sensitive to atmospheric conditions, with broader distributions of the spectral mismatch factor values. Additionally, our study found that heterojunction with intrinsic thin-layer cells has the highest substantial efficiency, considering the nameplate efficiency. The spectral mismatch factor decreased with increasing aerosol optical depth at 500 nm and was proportional to the humidity. The effects of aerosol optical properties on the spectral mismatch factor for different solar cells were clarified using clustering analysis and back-trajectory modeling results. In the present study, the aerosol optical depth spectra were found to be more important in determining the spectral mismatch factor than the aerosol optical depth at 500 nm. This study recommends further research on the relationship between the aerosol optical properties and solar spectral irradiance to better predict or estimate the spectral mismatch factor in solar power forecasting.
The numerical weather prediction (NWP) method is one of the popular wind resource forecasting methods, but it has the limitation that it does not consider the influence of local topography. The ...NWP-CFD downscaling considers topographic features and surface roughness by performing computational fluid dynamics (CFD) with the meteorological data obtained by the NWP method as a boundary condition. The NWP-CFD downscaling is expected to be suitable for wind resource forecasting in Korea, but it lacks a quantitative evaluation of its reliability. In this study, we compare the actual measured data, the NWP-based data, and the NWP-CFD-based data quantitatively and analyze the three main input parameters used for the calculation of NWP-CFD (minimum vertical grid size Δzmin, the difference angle Δdir, and the forest model activation reference length l0). Compared to the actual measurement data, the NWP-based data overestimate wind resources by more than 35%, while the NWP-CFD-based data show an error of about 8.5%. The Δzmin and Δdir have little effect on the results, but the l0 has a large effect on the simulation results, and it is necessary to adjust the values appropriately corresponding to the characteristics of an area.
PDF
