A Comprehensive Review of AI-Based Techniques for Economic Load Dispatch in modern Power Systems

Aliyu Sabo, Mathew Asih, Juliana Emamuyovwi Omatseye, Titilayo Oyedele, Ibrahim Danlami Akabe

Abstract


This paper reviews artificial intelligence (AI)–based methods for the Economic Load Dispatch (ELD) problem from 2015 to 2025, focusing on metaheuristic algorithms (Particle Swarm Optimization PSO, Genetic Algorithm GA, Differential Evolution DE, Grey Wolf Optimizer GWO, Ant Lion Optimizer ALO), hybrid AI approaches, and renewable-integrated dispatch. We searched peer-reviewed publications and major conferences for rigorous experimental work and describe trends, sample algorithmic breakthroughs, benchmarking practice (with a concentration on IEEE 30-bus and 118-bus studies), and operational adoption gaps. Advanced metaheuristics and their hybrids dominate ELD research for nonconvex constrained problems; hybridization with local search or ML surrogates improves convergence and feasibility; renewable integration drives stochastic, scenario-based, and multi-objective formulations; and DRL and surrogate metaheuristic pipelines show promise for real-time operation but require safety/constraint certification and reproducible benchmarking. We finish with targeted research to accelerate reliable, scalable AI-assisted dispatch. 


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References


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