A grey wolf optimization approach for solving constrained economic dispatch in power systems

Abstract

In this study, the economic dispatch problems, which are indispensable in electrical engineering, are addressed utilizing Grey Wolf Optimization (GWO). Conventional mathematical methods struggle to provide quick, reliable solutions to nonlinear problems in power systems with many generation units. An economic dispatch solution operates by allocating generation sets with the lowest fuel costs to meet predetermined power balance constraints. GWO is a meta-heuristic set of rules that has garnered significant attention in the literature due to its suitable exploratory and exploitative properties, rapid and mature convergence rate, and straightforward architecture. When dealing with a nonlinear constraints problem, such as ED, it has gained significant recognition for its balance of exploration and exploitation, reliable convergence characteristics, and simple implementation framework. The proposed Grey Wolf Optimization algorithm is evaluated using real-world generation case benchmark comparisons for 3-unit, 6-unit, and 15-unit systems. Results demonstrate the impact of incorporating renewable energy source (RES) uncertainty; fuel costs increase significantly from USD 7598 to USD 21,240 for the 3-unit system, USD 13,397 to USD 46,216,658 for the 6-unit system, and USD 32,622.55 to USD 33,723.11 for the 15-unit system, highlighting that RES integration is more economically viable in larger systems. The paper’s significant contribution is its essential mechanism for power systems, which enables lower global energy costs, improved operational efficiency, and enhanced grid reliability through strategic resource allocation in a constrained economic dispatch energy management system.