Study of Multi-objective Optimization and Multi-attribute Decision-making for Dynamic Economic Emission Dispatch

Abstract

Dynamic economic dispatch plays an important role in power system operation, which is a complicated non-linear constrained optimization problem. It has non-smooth and non-convex characteristics when generation unit valve-point effects are taken into account. Environmental awareness and recent environmental policies have forced electric utilities to restructure their practices to account for their emission impacts. The dynamic economic/environmental dispatch problem is studied in the present analysis; this article proposes a hybrid approach. In the first stage, a non-dominated sorting genetic algorithm-II is employed to approximate the set of Pareto solutions through an evolutionary optimization process. In the subsequent stage, a multi-attribute decision-making approach is adopted to rank these solutions from best to worst and to determinate the best solution in a deterministic environment with a single decision-maker. This hybrid approach is tested on a ten-unit system to illustrate the analysis process. Pareto frontiers are obtained, and the ranking of Pareto solutions is based on entropy weight and the technique for order preference by similarity to ideal solution. A single objective optimization example is also provided for comparison. Results obtained show that the hybrid approach has a great potential in handling the multi-objective optimization problem.

Keywords:

• dynamic economic/environmental load dispatch
• multi-objective optimization
• multi-attribute decision-making
• entropy weight
• technique for order preference by similarity to ideal solution