A novel chaotic differential evolution hybridized with quadratic programming for short-term hydrothermal coordination

In this paper, a viable global optimizer based on chaotic differential evolution is hybridized with sequential quadratic programming, an efficient local search technique to exploit short-term hydrothermal coordination (STHTC) involved for power generation and its efficient management. A multi-objective optimization framework is established for minimizing the total cost of thermal generators with valve point loading effects satisfying power balance constraint as well as generator operating and hydrodischarge limits, respectively. The proposed model is implemented on various systems comprising hydrogenerating units as well as different thermal units. The results are compared with state-of-the-art heuristic techniques recently employed on STHTC problems, while the reliability, stability and effectiveness of the proposed framework are validated through the comprehensive analysis of Monte Carlo simulations.