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A Novel Oppositional Approach for Solving Different Economic Emission Dispatch Problems

Abstract An oppositional-based learning approach with a real coded chemical reaction algorithm (ORCCRO) has been considered in this manuscript. The ORCCRO algorithm has been used here to find the near-global optimum solution for multi-objective economic-emission load dispatch problem with having nonlinear constraints. Emission extract from thermal power plants like NO x , inequality constraints like power generation operating limit and equality constraints like power balance consideration are considered here. ORCCRO follows the process to reach a stable state in lower energy with different molecular chemical collision. Oppositional-based learning mechanism has also hybrid here with RCCRO to find out more effective solution. Three different test systems are considered for simulation studies revealed that the ORCCRO method is much superior in comparison with other effective algorithms. The results obtained in these three test systems prove the robustness and the efficiency of ORCCRO.