題 目：System Reliability and Component Replacement Analysis for Electricity Transmission/Distribution Systems
主講人：Dave Coit教授 (Department of Industrial & Systems Engineering, Rutgers University)
時 間：2012年5月29 日 下午16點
地 點：主樓418會議室
主講人簡介：
David W. Coit is a Professor in the Department of Industrial & Systems Engineering at Rutgers University. His current research involves system reliability modeling and optimization, risk analysis, and multi-criteria optimization considering uncertainty. He received a BS degree in mechanical engineering from Cornell University, an MBA from Rensselaer Polytechnic Institute, and MS and PhD in industrial engineering from the University of Pittsburgh. He also has over ten years of experience working for IIT Research Institute (IITRI), Rome NY, where he was a reliability analyst, project manager, and engineering group manager. In 1999, he was awarded a CAREER grant from NSF to develop new reliability optimization algorithms. In 2010, he was awarded a NSF grant to study the integration of quality and reliability models for evolving technologies. In 2008, he was an instructor at the System Reliability Workshop sponsored by the Chinese Academy of Sciences (CAS) and held at Beihang University, Beijing, China. He also has been funded by U.S. Navy, industry, and power utilities. He is a member of IIE and INFORMS.
內容簡介：
Electricity transmission and distribution networks are generally highly reliable, but they are complex networks with aging components, including breakers, transformers, lines, etc. To maintain high reliability for these systems, it is necessary to upgrade and improve the systems using newer and more reliable components, but there are also severe budgetary constraints which make intelligent decision-making imperative. A component replacement methodology was developed to solve equipment replacement problems for systems composed of sets of heterogeneous assets subject to annual budgetary constraints over a finite planning horizon. The objective is to obtain the minimum cost policy such that the Net Present Value (NPV) of maintenance, purchase and opportunity costs (cost of lost electricity) is minimized for a finite planning horizon. The proposed methodology is based on an integrated dynamic and integer programming approach. First, a dynamic programming algorithm is solved for each individual component in the system based on deterministic cost information. Then, two different linear integer programming models are applied using the results of the dynamic programming model to determine a recommended system schedule. The method developed can potentially be applied to any replacement problem composed of sets of heterogeneous assets subject to constraints imposed on the system. In this work, the method is demonstrated on the replacement analysis of a common electricity distribution system configuration.
