Optimization of the cost of power generation of an evolving load profile in a solar photovoltaic

This study employed dynamic economic dispatch with the particle swarm optimization method to optimize the generation cost of a solar photovoltaic-integrated power system. In the study, a load profile initially aligned with peak solar power production that evolved to be relatively flat was considered. Load profile flatness was measured by the load factor, whereas alignment between solar power production and peak loads was determined by the solar-load correlation coefficient. The results revealed that the load profile with the highest load factor exhibited the best generation cost in most cases. However, as solar power penetration increased, load profile with higher positive solar-load correlation coefficient was the most cost-effective, whereas that with the highest correlation offered the worst costs. This result implies that these flattening and coincidence techniques potentially improve generation costs at low and high solar penetrations, respectively. However, load profiles with high positive solar-load correlation coefficients may not effectively reduce generation costs at high solar penetration