Power distribution network partitioning in big data environment using k-means and fuzzy logic
Power distribution network partitioning in big data environment using k-means and fuzzy logic
- Author(s): A. Nasiakou ; M. Alamaniotis ; L.H. Tsoukala
- DOI: 10.1049/cp.2016.1078
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- Author(s): A. Nasiakou ; M. Alamaniotis ; L.H. Tsoukala Source: Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016), 2016 page ()
- Conference: Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016)
- DOI: 10.1049/cp.2016.1078
- ISBN: 978-1-78561-406-4
- Location: Belgrade, Serbia
- Conference date: 6-9 Nov. 2016
- Format: PDF
Transition from conventional power grid to smart grid provides a plethora of opportunities to the end-users, both to producers and consumers. The aim of this paper is to present our efforts on partitioning power distribution system using a synergistic framework of intelligent tools. In particular, we propose a partitioning method of the distribution grid by integrating an extended version of the k-means algorithm and a fuzzy logic inference engine. The fuzzy logic engine is utilized to select the group of consumers that will be privileged to buy energy supplied by renewable energy sources (RES) at a lower price; RES are utilized when the available energy offered in the market is not enough to satisfy the overall demand and are in the state of prodiding excess energy. Our simulation scenarios implementing a combination of the IEEE-123 and IEEE-37 test feeder exhibit the effect of the partition of the distribution network on the operation of the smart grid. In particular, a case study of a distribution network comprised of approximately 2700 houses of different energy demand, and of conventional as well as distributed energy producers is presented and analysed.
Inspec keywords: smart power grids; distribution networks; power engineering computing; fuzzy logic; fuzzy reasoning; Big Data
Subjects: Power engineering computing; Knowledge engineering techniques; Data handling techniques; Distribution networks
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