A multiobjective chaotic bat algorithm for economic and emission dispatch

• Published in: 2017 Chinese Automation Congress (CAC) pp. 4684 - 4689
• Main Authors: Huijun Liang, Yungang Liu, Yanjun Shen, Fengzhong Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2017
• Subjects: bat algorithm; chaotic map; Cost function; economic and emission dispatch; Economics; Fuels; Mathematical model; Multiobjective optimization; Pareto optimization; Simulation
• DOI: 10.1109/CAC.2017.8243606

Environment problem is becoming a greatly concerned focus with more and more fossil fuels consumed now. How to balance economic dispatch and pollution gas emission in power systems is really a multiobjective optimization problem, which has been studied by many researchers. In this paper, we propose a novel multiobjective optimization algorithm to solve this problem by integrating bat algorithm and chaotic map together. The mathematical model of this optimization problem is analyzed and some related constraints are also given. Because the cost functions used in this paper are both convex functions, classical weighted sum method is blended with the proposed algorithm. Some works only get a single optimal solution for the multiobjective optimization problem with some rules, but it is usually not enough in practice. In order to describe the two conflicting optimization objectives, i.e., minimizing the fuel cost and NO x emission simultaneously, the Pareto optimal front is used. A price penalty factor is applied in the mathematical model to overcome the drawback by the usage of weighted sum method. Simulation results indicate the good tradeoff characteristic of the two optimization objectives through the generated Pareto optimal front.