4.7 Article

A Reliability-Constrained Expansion Planning Model for Mesh Distribution Networks

期刊

IEEE TRANSACTIONS ON POWER SYSTEMS
卷 36, 期 2, 页码 948-960

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.3015061

关键词

Planning; Load modeling; Power system reliability; Conductors; Indexes; Reliability engineering; Distribution network; multistage expansion planning; reliability assessment; network reconfiguration

资金

  1. National Science Foundation of China [51725703]

向作者/读者索取更多资源

The study introduces a novel multistage expansion planning model for mesh distribution networks, with explicit reliability assessment and customization of different investment/reliability preferences for buses. The model is formulated as a mixed-integer linear programming instance and effectively solved by off-the-shelf solvers, with simulation results demonstrating the effectiveness and flexibility of the approach.
To achieve high reliability, the urban distribution networks are mesh-constructed and radial-operated, in which the outage load can be restored to adjacent feeders via tie-lines after faults. Conventionally, iterative optimization-simulation methods and heuristics are adopted for distribution network planning, which cannot guarantee global optimality. Besides, existing reliability-constrained planning model cannot explicitly assess the reliability indices for mesh distribution networks, so the resulted plan scheme may be overly invested. In this paper, we propose a novel multistage expansion planning model for mesh distribution networks, in which reliability assessment is explicitly implemented as constraints. The different investment/reliability preferences for buses are also customized. Specifically, post-fault load restoration between feeders through tie-lines is modeled as a case of post-fault network reconfiguration. The planning model is then cast as an instance of mixed-integer linear programming and can be effectively solved by off-the-shelf solvers. We use a 54-node system to test the performance of proposed model. Simulation results show the effectiveness and flexibility of this methodology.

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