时间：7月10日（星期四）下午4：00 - 5💁‍♀️：00

地点：舜德楼北510会议室

报告人：David W. Coit, Associate Professor in the Department of Industrial & Systems Engineering at Rutgers University,USA

报告题目🔫：System Reliability and Component Replacement Analysis for Electricity Transmission/Distribution Systems

报告摘要：

Electricity transmission and distribution networks are generally highly eliable, but they are complex networks with aging components, including reakers, 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.

报告人简历：

David W. Coit is an Associate Professor in the Department of Industrial &Systems Engineering at Rutgers University, Piscataway, NJ, USA. His research involves reliability prediction & optimization, risk analysis, and multi-criteria optimization considering uncertainty. In 1999, he was awarded a CAREER grant from the US National Science Foundation (NSF) to study reliability optimization. He also has over ten years of industrial experience working for IIT Research Institute (IITRI). He received a BS degree in Mechanical Engineering from Cornell University, an MBA from Rensselaer Polytechnic Institute, and MS & PhD in Industrial Engineering from the University of Pittsburgh.

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