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Affiliation

National Institute of Applied Sciences and Technology, Carthage University, Tunis, Tunisia, Computer Science Department, Science Faculty of Tunis, Tunis, Tunisia

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1 Papers

Smart Grid Reconfiguration based on Prediction Model for Technical Teams Intervention Integration and Recovery Enhancement

Topics: Software Change and Configuration Management; Software and System Complexity; Software and Service Maintenance and Evolution; Software Engineering for Emerging Domains

Authors: Leila Ziouche ¹ , Syrine Ben Meskina ² , Mohamed Khalgui ³ and Laid Kahoul ⁴

Affiliations: ¹ National Institute of Applied Sciences and Technology, Carthage University, Tunis, Tunisia, Computer Science Department, Science Faculty of Tunis, Tunis, Tunisia , ² Computer Science Department, ESPRIT - School of Business, Tunis, Tunisia , ³ National Institute of Applied Sciences and Technology, Carthage University, Tunis, Tunisia , ⁴ LINFI LAB, Computer Science Department, Biskra University, Algeria

Digital Object Identifier: 10.5220/0009406703820389

Proceedings of ENASE 2020, ISBN: 978-989-758-421-3

Quick Abstract

To overcome the problem of critical failures recovery and improve reliability, quality of service and recovery performance, it is essential to provide and apply a new oriented solution for smart grid reconfiguration. This solution allows for resolving the problem of the late intervention of technical teams and the insufficiency of energy for recovery, by implementing a prediction model that assists the integration of a number of technical teams. In addition, it estimates the newly added number of emergency lines coming from new integrated renewable sources. This heuristic is programmed based on the linear programming and the simplex algorithm. This approach is implemented in python as a tool called SGREP, then tested and validated at run-time on four real different smart grids. Thereby, the proposed solution improves the guaranteed gains in terms of power availability, waiting time and financial cost.