NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

Abstract

-Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

A new approach to economic-emission load dispatch using NSGA II. Case study

“…Nowadays, this algorithm is very used to solve the EELD and other problems related to power system optimization . In Deb et al, the NSGA II was compared with other algorithms when applied to 4 problems selected from literature.…”

UPFC damping controller design using multi-objective evolutionary algorithms

“…Khadanga and Satapathy (2015) applied hybrid GA-GSA algorithm for UPFC damping controller tuning by considering integral time absolute error of speed deviation as objective. Evolutionary multi-objective optimisation algorithms named non-dominated sorting genetic algorithm-II (NSGA-II) (Deb, 2001;Deb et al, 2002) (EMOAs) proposed by Deb et al is extensively used to solve various multi-objective engineering optimisation problems (Panda, 2010(Panda, , 2011Kalaivani et al, 2013;Rajkumar et al, 2014). Modified non-dominated sorting genetic algorithm-II (MNSGA-II) which incorporates the control elitism and dynamic crowding distance (DCD) features to ensure better convergence and diversity (Jeyadevi et al, 2011), is also widely used to optimise engineering problems (Narayanan et al, 2012;Rajkumar et al, 2013).…”

Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid

“…Therefore it is more in accordance with the actual microgrid system operation. Since multi-objective dispatch was first introduced, there have been many studies choosing objective functions, considering not only operating cost but also environmental benefit, and the dispatch algorithm is multiple [12][13][14][15][16][17]. There are the lowest fuel cost, minimum SO2 emissions, minimum NOx emissions in [14], but in terms of pollution, SO2 and NOx can be a unified consideration.…”

“…That is improper. NSGA-II is employed in [14] and [16], and the performance of the algorithm is good. But NSGA-II can only be employed to solve a single period of scheduling, while dynamic dispatch needs to coordinate adjacent periods, so it does not apply.…”