An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms

Abstract

In recent years, wind energy has been widely used as an alternative energy source as it is a clean energy with a low running cost. However, the high penetration of wind power (WP) in power networks has created major challenges due to their intermittency. In this study, an elitist multi-objective evolutionary algorithm called non-dominated sorting particle swarm optimization (NSPSO) is proposed to solve the dynamic economic emission dispatch (DEED) problem with WP. The proposed optimization technique referred to as NSPSO uses the non-dominated sorting principle to rank the non-dominated solutions. A crowding distance calculation is added at the end of all iterations of the algorithm. In this study, WP is represented by a chance-constraint which describes the probability that the power balance cannot be met. The uncertainty of WP is described by the Weibull distribution function. In this study, the chance constraint DEED problem is converted into a deterministic problem. Then, the NSPSO is applied to simultaneously minimize the total generation cost and emission of harmful gases. To proof the performance of the proposed method, the ten-unit and forty-unit systems—including wind farms—are used. Simulation results obtained by the NSPSO method are compared with other optimization techniques that were presented recently in the literature. Moreover, the impact of the penetration ratio of WP is investigated.

Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

“…To conduct the multi‐objective optimization described by Equations (14) and (21), the use of NSPSO) [37] is herein proposed. Each particle with $${n_d}$$ decision variables is assumed to be an energy portfolio with DERs, charging/discharging EVs, and the upstream grid.…”

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

“…The economic dispatch of micro-grids including renewable and non-renewable energy sources has been studied by Nazari-Heris et al [24] considering the impacts of heat buffer tank and battery energy storage. In [25], the dynamic CEED problem was investigated in the presence of wind power. In this reference, the problem was first turned into a deterministic problem using the chance constraint to deal with uncertainty, and then it was solved by PSO integrated with the concept of non-dominated sorting.…”

A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

“…The authors in [24,31,[35][36][37] implemented the particle elitism approach to speed up the convergence of PSO. Elitism is a process in which particles with poor fitness values are replaced with particles with the best fitness value (elite particles) after a certain number of iterations, resulting in the production of a new swarm with a better particle's average fitness.…”