Towards Multi-Objective ARQIEA for Solving Economic Load Dispatch and Emission Problem

Economic Dispatch problem is just not related to minimizing the cost of power generation but also correlates with environmental pollution issues. The major part of generating units is fossil fuel-based and releases different kinds of pollutant gases. Every day a huge amount of fossil fuel is used in thermal power plants and a bulk amount of harmful gases are released into the environment. The thermal units based on fossil fuels are one of the main causes of air pollution and global warming. To overcome this issue many researchers proposed different solution methods. The present work is also focused on the solution of environmental/economic load dispatch. It is also called Economic Emission Load Dispatch (EELD). Evolutionary Algorithms (EAs) are the most popular solution technique adopted by researchers to solve mix-integer, non-linear and complex problems. EAs suffer from premature convergence and lack of diversity issues. The present work proposed an Adaptive Real Coded Quantum Inspired Evolutionary algorithm (ARQiEA) to solve the multi-objective EED problem. The constraints are satisfied using a repair-based constraint handling method. The proposed algorithm provided efficient and improved results during the test on IEEE 30 Bus system including six generators and the results are compared with well-known techniques and found competitive than others.