Abstract
This paper presents a new approach for identifying and site contour optimization of wind farms in the context of transmission expansion planning for the Republic of Ghana to support its renewable energy development plan. The proposed approach uses spatial multi-criteria analysis, density-based clustering, and analytical hierarchy process to identify, optimize, and rank candidate sites. The proposed methodology provides an automated procedure for optimizing site boundary contours using density-based clustering. It provides decisional flexibility in identifying clusters of the minimum required size, unlike the traditional approach. The analysis identifies 14 geographical clusters with high wind energy availability with an average area of 19 km2 and a maximum area of up to 32 km2. All the clusters found are in relative proximity to both transportation and transmission networks. Results from the techno-economic and environmental assessment identified the least levelized cost of energy of 0.21 $/kW h at clusters C, E, M and N. The power plant modeled at cluster M recorded the least simple payback period of 4.30 years and highest internal rate of return of 22.8 %. The worst site was cluster L because it recorded the highest emissions of about 354,474 kg/year for carbon dioxide and 130 kg/year for carbon monoxide.
Published Version
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