Abstract
Technology enhancement of Distributed Generations, as well as deregulation and privatization in power system industry, shows a new perspective for power systems and subsystems. When a substantial portion of generation is in the form of dispersed and small units, a new connection pattern emerges whereby the dispersed units are embedded in reticulation infrastructure. Now, the flow of power is no longer the same as in the conventional systems, since the dispersed generating plants contribute with generation also at the distribution grids level. Connection of generation to distribution grids cannot effectively be made, unless the some especial control and monitoring tools are available and utilizable. State estimation in these kinds of networks, often called mixtribution, is the preliminary and essential tool to fulfill this requirement and also is the subject of this article. Actually, state estimation is an optimization problem including discrete and continuous variables, whose objective function is to minimize the difference between calculated and measured values of variables, i.e. voltage of nodes, and active/reactive powers in the branches. In this paper, a new approach based on Ant Colony Optimization (ACO) is proposed to solve this optimization problem. The feasibility of the proposed approach is demonstrated and compared with methods based on neural networks and genetic algorithms for two test systems.