Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract
This paper presents a comprehensive robust distributed intelligent control for optimum self-healing activities in smart distribution systems considering the uncertainty in loads. The presented agent based framework obviates the requirements for a central control method and improves the reliability of the self-healing mechanism. Agents possess three characteristics including local views, decentralizations and autonomy. The message, exchanged among neighboring agents, is used to develop a global information discovery algorithm and updates the topology information of out-of-service areas, available supply capacity and routing information. Fuzzy description is employed to take into account the uncertainties of measurements in which are exchanged between agents. Moreover, to find the optimal restoration plan, incorporating the discovered data, a routing problem is developed as a fuzzy binary linear optimization problem. This problem is approached by a novel method using a specific ranking function. Finally, robustness and applicability of the proposed self-healing method is tested on two standard case studies. The obtained results emphasize that ignoring the uncertainties may lead to non-realistic solutions.
Zendehdel, N. (2015). Robust Agent Based Distribution System Restoration with Uncertainty in Loads in Smart Grids. Journal of Operation and Automation in Power Engineering, 3(1), 1-22.
MLA
N. Zendehdel. "Robust Agent Based Distribution System Restoration with Uncertainty in Loads in Smart Grids", Journal of Operation and Automation in Power Engineering, 3, 1, 2015, 1-22.
HARVARD
Zendehdel, N. (2015). 'Robust Agent Based Distribution System Restoration with Uncertainty in Loads in Smart Grids', Journal of Operation and Automation in Power Engineering, 3(1), pp. 1-22.
VANCOUVER
Zendehdel, N. Robust Agent Based Distribution System Restoration with Uncertainty in Loads in Smart Grids. Journal of Operation and Automation in Power Engineering, 2015; 3(1): 1-22.
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