Abstract
This report elaborates on the possibilities for reducing computation time in the Fansi power market model by applying relaxation techniques and temporal decomposition.
Findings and conclusions are supported by computational experiments conducted on a realistic large-scale dataset.
Relaxation of reservoir balance constraints are investigated, and an effective heuristic method is presented.
Both serial and parallel implementations of Benders decomposition applied to a deterministic linear programming problem are investigated. Although the parallel
implementation shows potential for improving computational performance, further investigations are recommended.
Findings and conclusions are supported by computational experiments conducted on a realistic large-scale dataset.
Relaxation of reservoir balance constraints are investigated, and an effective heuristic method is presented.
Both serial and parallel implementations of Benders decomposition applied to a deterministic linear programming problem are investigated. Although the parallel
implementation shows potential for improving computational performance, further investigations are recommended.
