Data management and visualization for cluster-based grid operations

Leksawat, S., Schmelter, A., Ortjohann, E., Premgamone, T., Holtschulte, D., Kortenbruck, J. and Morton, Danny (2017) Data management and visualization for cluster-based grid operations. 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, pp. 223-228. ISBN 978-1-5090-4682-9

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Abstract

The increase of electricity demand has raised requirements of more reliable and efficient grid operations as well as higher security of supply. Meanwhile, the transition towards clean and sustainable energy supply systems in the present power systems is under the spotlight [1]–[3]. High penetration of renewable energy sources (RESs), which are usually in the form of distributed generation (DG), can be expected. The RESs-based DG units can reside in distribution level, whose original purpose is to distribute power from electricity utilities to end users. Presently, to cope with the power penetration in distribution level, conventional power grids are being evolved into the smarter ones, known as smart grids. A smart grid is proposed to overcome the arising environmental and technical challenges [4], [5]. To smarten the grid, information and communication technologies are incorporated into the conventional power grids. They allow the cooperation of heterogeneous grid components, e.g. control centers and DG units, or users, e.g. operators and customers. Decentralization of grid control architecture is possible [6], and many actors can actively participate in the operation of the grid.

Item Type: Book Section
Additional Information: Paper given at and published in proceedings of 6th International Conference on Clean Electrical Power (ICCEP 2017), held 27-29 June 2017, Santa Margherita Ligure, Italy.© Copyright IEEE
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: School of Engineering > Engineering
Depositing User: Prof Danny Morton
Date Deposited: 27 Mar 2018 07:59
Last Modified: 27 Mar 2018 13:15
Identification Number: 10.1109/ICCEP.2017.8004819
URI: http://ubir.bolton.ac.uk/id/eprint/1598

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