A Hesitant Fuzzy Programming Approach for Multi-Objective Economic Emission Load Dispatch under Uncertainty

Abstract

The current paper introduces a fuzzy programming-based optimization model of the multi-objective Economic Emission Load Dispatch (EELD) problem in contemporary power plants in a hesitant manner. The given strategy both reduces the cost of fuel and the production of pollutants and takes into consideration the uncertainty and reluctance to make expert decisions that is inherent in the conventional approaches of fuzzy or intuitionistic fuzzy (which may not be effectively modeled with the help of these methods). The model of EELD problem is developed as a nonlinear multi-objective optimization model with the consideration of the generator operating limits, power balance constraints, and transmission losses. As a result, an optimal compromise solution is obtained by constructing a systematic hesitant fuzzy nonlinear programming algorithm with the use of payoff matrices and auxiliary parameters. The proposed methodology was tested on a three-unit thermal power system and its effectiveness is proved by a numerical study. The simulation findings indicate that the hesitant fuzzy strategy strikes a balanced compromise between economic and environmental goals and at the same time is able to sustain a viable generator dispatch when the load changes. The graphical analysis also ensures that there is a stable system behavior, better emission control and cost effective way of allocating power. The findings reveal that the presented framework is a powerful and computationally efficient alternative to conventional dispatch methods and thus it can be used to optimize a power system under uncertainty with multiple objectives. The approach can be easily applied to larger systems and combined with the future smart grid applications by using sophisticated intelligent optimization methods.