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Optimal Partitioning of Smart Distribution Systems Into Supply-Sufficient Microgrids

handle: 11250/2594257
This paper presents a systematic procedure for partitioning smart distribution systems into supply-sufficient microgrids. First, renewable distributed generations (DGs) are optimally allocated in the distribution system. A multiobjective performance index including voltage profile and energy losses indices is utilized in this problem as the objective function. Two alternative control approaches of future smart grids, including on load tap changer control and adaptive power factor control are assessed to maximize potential benefits and increase the penetration level of DGs. Then, optimal allocation of protection devices and energy storage systems for constructing supply-sufficient microgrids is presented for a feeder equipped with capacity-constrained DGs. To this end, two different optimization problems are formulated and proper indices are developed for minimizing power exchange between microgrids and minimizing generation-load imbalance within microgrids. Finally, test results of the proposed models on 33-bus IEEE radial distribution system are presented and discussed.
- Norwegian University of Science and Technology Norway
- Shiraz University of Technology Iran (Islamic Republic of)
- Shiraz University of Technology Iran (Islamic Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).57 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
