Minimizing Generation Cost and Emissions in Economic Dispatch Using the Gold Rush Optimizer

Abstract

Economic Dispatch (ED) in power systems requires minimizing generation cost and emissions while satisfying operational constraints, a task that has grown more difficult as non-linear, multi-objective formulations expose the limits of classical methods and even established metaheuristics such as Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), both of which remain prone to premature convergence in complex search spaces. Although the Gold Rush Optimizer (GRO), a metaheuristic based on the migration, collaboration, and mining behavior of historical gold prospectors has shown strong results on benchmark functions, it has not yet been applied to power system ED. This study addresses that gap by applying GRO to the ED problem and benchmarking it against PSO and GWO on four IEEE test systems (3-bus, 6-bus, 10-bus, and 40-bus) using MATLAB and MATPOWER. GRO produced the lowest total generation cost on every system tested—from ${\$}$21,071.99 on the 3-bus system to ${\$}$340,079.45 on the 40-bus system—and its emissions were consistently the lowest or close to it, cutting emissions by roughly 41% relative to PSO on the 40-bus system. Its only real drawback is a small power-loss disadvantage against GWO and PSO on the larger systems. Taken together, these results show GRO handling the cost-emission trade-off better than PSO or GWO, confirming its potential as a reliable, scalable tool for modern power system optimization.