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

IF 2 3区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Optimization and Engineering Pub Date : 2023-12-19 DOI:10.1007/s11081-023-09876-y
Milad Dehghani Filabadi, Chen Chen, Antonio Conejo
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Abstract

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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提高配电系统可靠性的混合整数指数圆锥优化法
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。
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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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