提高配电系统可靠性的混合整数指数圆锥优化法

IF 2 3区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY
Milad Dehghani Filabadi, Chen Chen, Antonio Conejo
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引用次数: 0

摘要

本文建立了一个优化模型,用于确定开关、连接线和地下电缆的位置,以提高配电系统的可靠性。该模型的一个核心创新点是加入了节点可靠性约束条件,这些约束条件考虑了网络拓扑结构,在实际应用中非常重要。该模型既可以重新表述为混合整数指数圆锥优化问题,也可以表述为混合整数线性程序。我们从理论和经验两方面证明,明智地应用部分线性化是实现实际可操作性的关键。计算研究表明,在标准硬件上确实可以在合理的时间内解决现实的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mixed-integer exponential conic optimization for reliability enhancement of power distribution systems

Mixed-integer exponential conic optimization for reliability enhancement of power distribution systems

This paper develops an optimization model for determining the placement of switches, tie lines, and underground cables in order to enhance the reliability of an electric power distribution system. A central novelty in the model is the inclusion of nodal reliability constraints, which consider network topology and are important in practice. The model can be reformulated either as a mixed-integer exponential conic optimization problem or as a mixed-integer linear program. We demonstrate both theoretically and empirically that the judicious application of partial linearization is key to rendering a practically tractable formulation. Computational studies indicate that realistic instances can indeed be solved in a reasonable amount of time on standard hardware.

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来源期刊
Optimization and Engineering
Optimization and Engineering 工程技术-工程:综合
CiteScore
4.80
自引率
14.30%
发文量
73
审稿时长
>12 weeks
期刊介绍: Optimization and Engineering is a multidisciplinary journal; its primary goal is to promote the application of optimization methods in the general area of engineering sciences. We expect submissions to OPTE not only to make a significant optimization contribution but also to impact a specific engineering application. Topics of Interest: -Optimization: All methods and algorithms of mathematical optimization, including blackbox and derivative-free optimization, continuous optimization, discrete optimization, global optimization, linear and conic optimization, multiobjective optimization, PDE-constrained optimization & control, and stochastic optimization. Numerical and implementation issues, optimization software, benchmarking, and case studies. -Engineering Sciences: Aerospace engineering, biomedical engineering, chemical & process engineering, civil, environmental, & architectural engineering, electrical engineering, financial engineering, geosciences, healthcare engineering, industrial & systems engineering, mechanical engineering & MDO, and robotics.
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