Work

An Improved Non-dominated Sorting Genetic Algorithm for the Optimal Economic Emission Dispatch Problem with Wind Power Sources

Year: 2024

Type: article

Abstract: This study proposes a novel multi-objective technique for the Stochastic Economic Emission Dispatch Problem (SEEDP) integrating wind energy sources. To do this, the SEEDP is first formulated as a Chance Constrained Programming (CCP) problem where the randomness of the Wind Power (WP) output is obtained with the Weibull distribution function. Nevertheless, the chance constraint is employed to describe the fulfillment of the power balance constraint. In fact, after applying the probability theory, the proposed CCP issue is converted into a deterministic optimization problem. Moreover, the impact of WP penetration on the optimal solutions is investigated. To resolve the proposed multi-objective approach, the second version of the Non-dominated Sorting Genetic Algorithm (NSGAII) is applied. Moreover, to test the robustness of the proposed strategy, a ten-unit system is used and the acquired results are compared with those of other optimization techniques.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.7171

Authors: Imene Khenissi, Sultan M. Alotaibi, Muhammad Tajammal Chughtai, Tawfik Guesmi

Institutions: University of Sfax, University of Ha'il

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $431