Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/89481
Title: | Two-stage stochastic scheduling of transportable energy storage systems for resilient distribution systems | Authors: | Yao, Shuhan Zhao, Tianyang Zhang, Huajun Wang, Peng Goel, Lalit |
Keywords: | Engineering::Electrical and electronic engineering Transportable Energy Storage Microgrids Resilience |
Issue Date: | 2018 | Source: | Yao, S., Zhao, T., Zhang, H., Wang, P. & Goel, L. (2018). Two-stage stochastic scheduling of transportable energy storage systems for resilient distribution systems. 2018 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). doi:10.1109/PMAPS.2018.8440544 | Conference: | 2018 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) | Abstract: | Transportable energy storage systems (TESSs) provide critical flexibility to enhance the distribution system (DS) resilience. In this paper, a two-stage stochastic restoration scheme is proposed to minimize the total cost. In this model, uncertainties of load consumption and PV power are considered to generate scenarios by using Monte Carlo simulation. A modified temporal-spatial network (TSN)-based scheduling model is proposed to schedule TESSs among MGs. The restoration problem is formulated as a mixed-integer linear programming, satisfying self- adequacy, operation and topology constraints. Case studies are implemented on a modified 33-bus test system to demonstrate the effectiveness of the proposed method. | URI: | https://hdl.handle.net/10356/89481 http://hdl.handle.net/10220/49575 |
DOI: | 10.1109/PMAPS.2018.8440544 | Schools: | School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) |
Organisations: | Institute of Catastrophe Risk Management | Research Centres: | Energy Research Institute @ NTU (ERI@N) | Rights: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/PMAPS.2018.8440544 | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Conference Papers ERI@N Conference Papers IGS Conference Papers |
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