Solution of Combined Economic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO

Abstract

This chapter formulates a multi-objective optimization problem to simultaneously minimize the objectives of fuel cost and emissions from the power plants to meet the power demand subject to linear and nonlinear system constraints. These conflicting objectives are formulated as a combined economic emission dispatch (CEED) problem. Various metaheuristic optimization algorithms have been developed and successfully implemented to solve this complex, highly nonlinear, non-convex problem. To overcome the shortcomings of the evolutionary multi-objective algorithms like slow convergence to Pareto-optimal front, premature convergence, local trapping, it is very natural to think of integrating various algorithms to overcome the shortcomings. This chapter proposes a hybrid evolutionary multi-objective optimization framework using Non-Dominated Sorting Genetic Algorithm II and Multi-Objective Particle Swarm Optimization to solve the CEED problem. The hybrid method along with the proposed constraint handling mechanism is able to balance the exploration and exploitation tasks. This hybrid method is tested on IEEE 30 bus system with quadratic cost function considering transmission loss and valve point effect. The Pareto front obtained using hybrid approach demonstrates that the approach converges to the true Pareto front, finds the diverse set of solutions along the Pareto front and confirms its potential to solve the CEED problem.

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

“…Traditionally, solving DEED problems involved incorporating losses computed using intricate nonlinear load flow equations or by using approximate loss formulas, both of which could be computationally expensive and the loss formula might not precisely capture actual transmission losses. The authors have employed powerful algorithms to solve the various economic dispatch formulations including the DEED problem in [31][32][33][34][35][36][37][38]. The integration of the machine learning model into the DEED problem is not widely explored in the literature.…”

Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

“…In [67], hybrid multiobjective GA-PSO is applied to solve the associate rule mining problem. In [68] and [69] the CEED problem is solved using a hybrid NSGAII-MOPSO algorithm and hybrid MOPSO-Differential Evolution (DE) algorithm, respectively. The results obtained by these hybrid models which are available in literature imply that the hybrid multiobjective frameworks are potent, can interchange information inside the model, can do parallel processing, can enhance the searching capabilities and can also produce more favourable performance than any single computational multiobjective model.…”

Constrained Multiobjective Equilibrium Optimizer Algorithm for Solving Combined Economic Emission Dispatch Problem

“…Furthermore, a new paradigm of the CEEDP-based MOP is proposed in [31], where CEEDP has been solved by relatively recent multiobjective algorithms. In [32], Sundaram and Erdogmus proposed a hybrid evolutionary multiobjective optimization system using nondominated sorting genetic algorithm II (NSGA II) and multiobjective PSO to solve the CEEDP. e hybrid approach with a constraint management system is capable of balancing the tasks of exploration and exploitation.…”