Weight pattern evaluation for multiobjective hydrothermal generation scheduling using hybrid search technique

• Best weight pattern evaluation is applied for multi-objective hydrothermal scheduling.
• Weights are computed by statistical measures.
• Statistical measures characterize the correlation coefficients matrix evolution.
• The solution methodology hybridizes global and local search techniques.
• Predator-prey optimization as global and Powell’s pattern as local technique is taken.

This paper presents best weight pattern evaluation approach to solve short-term multi-objective hydrothermal generation scheduling (HTGS) which determines the allocation of power demand among the committed generating units, to minimize operating cost and minimal impacts on environment subjected to physical and technological constraints. A multi-chain interconnected hydro system having non-linear relationship between water discharge rate and power generation is undertaken with due consideration of water transport delay between connected reservoirs. The best weights are computed by conventional statistical measures, which characterize the correlation coefficients matrix evolution. The solution methodology hybridizes global and local search techniques. Predator-prey optimization (PPO) is undertaken as a global search technique and Powell’s pattern search (PPS) is exploited as a local search technique. The results among the competing objectives obtained by the proposed method are compared with various results reported in the literature. The sensitivity and robustness of the proposed technique are evaluated by performing statistical analysis of results obtained based on independent runs. The integration of PPO and PPS improves the quality of solution and convergence characteristics.