Economic Load Dispatch Problem Analysis Based on Modified Moth Flame Optimizer (MMFO) Considering Emission and Wind Power

Hani Albalawi, Abdul Wadood () and Herie Park ()
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Hani Albalawi: Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi Arabia
Abdul Wadood: Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi Arabia
Herie Park: Department of Electrical Engineering, Dong-A University, Busan 49315, Republic of Korea

Mathematics, 2024, vol. 12, issue 21, 1-24

Abstract: In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include valve-point loading effects (VPLEs), generator limits, emissions, and wind power integration. In this study, both emission constraints and wind power are incorporated into the ELD problem formulation, with the influence of wind power quantified using the incomplete gamma function (IGF). This study proposes a novel metaheuristic algorithm, the modified moth flame optimization (MMFO), which improves the traditional moth flame optimization (MFO) algorithm through an innovative flame selection process and adaptive adjustment of the spiral length. MMFO is a population-based technique that leverages the intelligent behavior of flames to effectively search for the global optimum, making it particularly suited for solving the ELD problem. To demonstrate the efficacy of MMFO in addressing the ELD problem, the algorithm is applied to four well-known test systems. Results show that MMFO outperforms other methods in terms of solution quality, speed, minimum fuel cost, and convergence rate. Furthermore, statistical analysis validates the reliability, robustness, and consistency of the proposed optimizer, as evidenced by the consistently low fitness values across iterations.

Keywords: economic load dispatch; wind power; emission; nature-inspired optimization; modified technique; moth flame optimizer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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