Self-adaptive multi-population quadratic approximation guided jaya optimization applied to economic load dispatch problems with or without valve-point effects

Economic Load Dispatch (ELD) is a key component of power system operation and planning, aimed at allocating power generation to minimize costs while satisfying operational constraints. The Non-linear generator characteristics, such as valve point effects, make ELD challenging for traditional methods. This study applies the Self-Adaptive Multi-Population Quadratic Approximation Guided Jaya (SMP-JaQA) algorithm, a state-of-the-art parameter-free optimization technique, to solve the ELD problem effectively. SMP-JaQA, was originally proposed to address constrained optimization problems, adapts the positions of search agents based on the best solutions identified in each generation to converge efficiently to the optimal solution. To evaluate its performance, The algorithm is tested on six systems with 10, 38, 40, 110, 140, and 160 generating units. Key findings include: 1) SMP-JaQA achieved the lowest generation cost of 111,490 $/hr for the 10-unit system, reducing costs by up to $2030 compared to other methods; 2) For the 38-unit system, SMP-JaQA provided a $5–$8/hr cost reduction, achieving 9,417,230.62$/hr, the lowest operational cost; 3) A significant difference of 2952.93$/hr is observed between SMP-JaQA and GA-API for the 40-unit system; 4) SMP-JaQA reduces costs by $59.44/hr for the 110-unit system; 5) For the 140-unit system, cost reductions of $1234.66/hr, $1040.89/hr, and $836.24/hr are achieved compared to SDE, GWO, and HHO, respectively; 6) A maximum reduction of $104.31/hr is noted for 160 unit system. Experimental results highlight SMP-JaQA's efficiency, robustness, and adaptability in solving complex, large-scale ELD problems while maintaining low computational costs. This demonstrates its potential for broader applications in power system optimization.