Resiliency-Driven Multi-Step Critical Load Restoration Strategy Integrating On-Call Electric Vehicle Fleet Management Services

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Erenoglu, AK (Author) Other; Sancar, S (Author) Other; Terzi, IS (Author) Other; Erdinc, O (Author) Other; Shafie-Khah, M (Author) Other; Catalao, JPS (Author) FEUP

Journal

Title: IEEE Transactions on Smart Grid

Vol. 13

Pages: 3118-3132

ISSN: 1949-3053

Publisher: IEEE

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 30 Citations

Other information

Authenticus ID: P-00W-RPA

DOI: 10.1109/tsg.2022.3155438

Abstract (EN): In order to enhance the restoration capability of the distribution system during emergency conditions, a resiliency-driven critical load restoration strategy is propounded in this paper. Electric vehicles (EVs) are considered for the grid-support services to deal with challenges on such occasions, in order to maintain the power supply continuity of critical loads by reducing the number of outage periods. The collaboration between fleet operator and distribution system operator is considered in the proposed scheme, making it possible to direct available EVs to the damaged areas. The random characteristic of the seismic event is captured by generating numerous hazard scenarios using a probabilistic approach with the Monte Carlo Simulation (MCS) technique. Afterwards, the unavailability of overhead distribution branches is determined within the fragility curve concept. Besides, the uncertainties caused by EV mobility are considered by performing learning-based analyses for forecasting the location and amount of EVs in the related zone. The obtained data is processed as input parameters in a mixed-integer linear programming (MILP) framework-based stochastic model. Besides, the conceptually developed interfaces for all stakeholders in the proposed scheme are described in detail for bridging the gap between the theoretical background of the concept and practical real-world implementation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15