ANN-Based Solutions for Dynamic Economic Load Dispatch: A Review

Abstract:-Dynamic Economic Load Dispatch (DELD) is a critical optimization problem in modern power systems, aiming to schedule generator outputs over multiple time periods at minimum cost while obeying operational constraints. This review examines recent developments in applying Artificial Neural Networks (ANN) to solve the DELD problem. We first outline the DELD formulation and its dynamic constraints (like generator ramp limits). We then discuss ANN-based solution techniques, highlighting their strengths (such as handling nonlinearity and fast computation) and limitations (such as training data requirements and constraint enforcement). A comparative analysis is presented between ANN approaches and other AI-based methods – notably Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and hybrid techniques – to underscore performance differences in convergence speed, accuracy, and practicality. Also explore applications of ANN-driven DELD in smart grids, including integration of renewable energy sources and electric vehicles, where ANN models facilitate real-time dispatch under uncertainty. Recent simulation studies (primarily in MATLAB) are reviewed to demonstrate the effectiveness of ANN models, and key challenges (convergence reliability, solution accuracy, real-time performance) are identified alongside trends from current literature. The paper is organized into sections covering introduction, literature survey of ANN in DELD, comparative analysis with other methods, simulation tools and case studies, and conclusions.

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