Distribution Loss Minimization With Guaranteed Error Bound
| Autor: | Yasuhiro Hayashi, Keiji Takano, Shin-ichi Minato, Takayuki Watanabe, Ryo Yoshinaka, Jun Kawahara, Akihiro Kishimoto, Koji Tsuda, Takeru Inoue |
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| Rok vydání: | 2014 |
| Předmět: |
Mathematical optimization
Optimization problem General Computer Science power supply quality hard discrete optimization problem Electric power system distributed power generation Approximation error binary decision diagrams distribution loss minimization Boolean functions switchgear Boolean function Mathematics search problems minimisation dispersed generator shortest path-finding problem Data structures Junctions Minimization Optimization Vectors Vegetation Distribution network loss minimization network reconfiguration zero-suppressed binary decision diagram Binary decision diagram large-scale distribution network Data structure binary decision diagram losses switch Maxima and minima large-scale model network power system distribution networks highly compressed search space Minification guaranteed error bound |
| Zdroj: | IEEE Transactions on Smart Grid. 5:102-111 |
| ISSN: | 1949-3061 1949-3053 |
| DOI: | 10.1109/tsg.2013.2288976 |
| Popis: | Determining loss minimum configuration in a distribution network is a hard discrete optimization problem involving many variables. Since more and more dispersed generators are installed on the demand side of power systems and they are reconfigured frequently, developing automatic approaches is indispensable for effectively managing a large-scale distribution network. Existing fast methods employ local updates that gradually improve the loss to solve such an optimization problem. However, they eventually get stuck at local minima, resulting in arbitrarily poor results. In contrast, this paper presents a novel optimization method that provides an error bound on the solution quality. Thus, the obtained solution quality can be evaluated in comparison to the global optimal solution. Instead of using local updates, we construct a highly compressed search space using a binary decision diagram and reduce the optimization problem to a shortest path-finding problem. Our method was shown to be not only accurate but also remarkably efficient; optimization of a large-scale model network with 468 switches was solved in three hours with 1.56% relative error bound. |
| Databáze: | OpenAIRE |
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