研究目的
To show the complementarity between solar and wind energy potentials in Benin Republic and find locations offering optimal complementarity.
研究成果
The study successfully identified optimal locations for solar and wind energy complementarity in Benin, specifically between the Littoral department for wind and Collines department for solar, with a Pearson correlation coefficient of -0.4291 indicating remarkable inverse correlation. This approach aids in stabilizing power generation from renewable sources and can be applied to other regions for renewable energy planning.
研究不足
The study is based on data from a single year (2013), which may not capture long-term variability. The optimization focuses only on minimizing correlation and does not consider other factors like economic or environmental constraints. The precision of solar data (0.05°) and interpolation methods might introduce errors.
1：Experimental Design and Method Selection:
The study uses an optimization approach with Particle Swarm Optimization (PSO) to minimize the Pearson correlation coefficient between solar and wind energy potentials. The dimensionless index approach models the potentials, and PSO is implemented in Matlab? with specific coefficients and parameters.
2：Sample Selection and Data Sources:
Daily wind speed data from Cadjehoun airport in Cotonou for 2013 (98.45% data availability) and daily solar radiation data (DNI) covering Benin with 0.05° precision from the Heliosat (SARAH) dataset for the same period.
3：45% data availability) and daily solar radiation data (DNI) covering Benin with 05° precision from the Heliosat (SARAH) dataset for the same period.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: No specific equipment or materials are mentioned; the study relies on computational tools like Matlab? for data analysis and optimization.
4：Experimental Procedures and Operational Workflow:
Data collection from databases, modeling of solar and wind potentials using equations (1) and (2), calculation of Pearson correlation coefficient using equation (5), and optimization using PSO algorithm with parameters from Table 3 to find optimal geographic coordinates.
5：Data Analysis Methods:
Statistical analysis using Pearson correlation coefficient, linear interpolation for solar data, and PSO for optimization; results are visualized using plots and maps in Matlab?.
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