Short-term scheduling of hybrid thermal, pumped-storage, and wind plants using firefly optimization algorithm

This paper presents a novel method based on an enhanced firefly algorithm (EFA) to solve scheduling hybrid thermal, pumped-storage, and wind plants. Since the scheduling problem is inherently discrete, basic EFA and binary encoding/decoding techniques are used in the proposed EFA approach. Optimal power values of thermal and pumped-storage units are determined separately in the presence of uncertainty caused by wind speed. The proposed method is applied to a real plant, including four pumped-storage units, 34 thermal units with different characteristics, and one wind turbine plant. In addition, dynamic constraints of upstream and downstream sources and constraints regarding thermal and wind units are also considered for finding the optimal solution. In addition, the proposed EFA is successfully applied to a real plant, and the results are compared with those of the three available methods. The results show that the proposed method has converted to a more optimal cost than the other methods