Reserve Constrained Dynamic Economic Dispatch: A New Fast Self-Adaptive Modified Firefly Algorithm

This paper provides a more practical formulation of the reserve constrained dynamic economic dispatch that considers highly realistic constraints, such as transmission losses, ramp rate limits, and valve-point effects over a short-term time span. Unlike the previous approaches, three types of system spinning reserve requirements are explicitly modeled in the problem and a new self-adaptive modified firefly algorithm is suggested to solve it. Firefly algorithm has some similar features with particle swarm optimization that hunts for the global or near-global optimal solutions through moving individuals, i.e., fireflies in the problem search space. Although this algorithm is completely suitable for tuning off its parameters, it suffers from the difficulties of trapping in local optima as some of the algorithms. To overcome this problem, a self-adaptive parameter tuning and mutation strategy has been utilized. The proposed technique is suitable from a regard of economical aspects, and is also appropriate for high-speed real-time application due to fast and less execution time. In order to evaluate the efficiency and feasibility of the proposed framework in practical power systems, four small, medium, and large test systems are presented as case studies.