Title :
Ant Colony Search algorithm for the optimal power flow problem
Author :
Soares, J. ; Sousa, T. ; Vale, Z.A. ; Morais, H. ; Faria, P.
Author_Institution :
GECAD - Knowledge Eng. & Decision-Support Res. Group, Electr. Eng. Inst. of Porto - Polytech. Inst. of Porto (ISEP/IPP), Porto, Portugal
Abstract :
To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours anticipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.
Keywords :
load dispatching; load flow; optimisation; power system economics; power system planning; search problems; ACS optimization algorithm; IEEE 30 bus network; OPF; ant colony search algorithm; combinatorial optimization problems; economic dispatch context; nonlinear combinatorial problem; optimal power flow problem; power system; time 48 hour to 24 hour; Economics; Generators; Load flow; Maintenance engineering; Optimization; Power generation; Reactive power; Ant colony search; Artificial intelligence; Economic dispatch; Optimal power flow; Optimization method;
Conference_Titel :
Power and Energy Society General Meeting, 2011 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4577-1000-1
Electronic_ISBN :
1944-9925
DOI :
10.1109/PES.2011.6039840