Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/24592
Title: | A Stochastic-Robust Approach for Resilient Microgrid Investment Planning Under Static and Transient Islanding Security Constraints | Authors: | Nakiganda, Agnes Dehghan, Shahab Markovic, Uros Hug, Gabriela Aristidou, Petros |
Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Keywords: | unscheduled islanding;resilience;microgrids;Investment planning;low-inertia;frequency constraints | Issue Date: | 2-Jan-2022 | Source: | IEEE Transactions on Smart Grid, 2022 | Journal: | IEEE Transactions on Smart Grid | Abstract: | When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unscheduled islanding events may trigger cascading disconnections of Distributed Energy Resources (DERs) inside the MG due to the transient response, leading to a partial or full loss of load. In this paper, a min-max-min, hybrid, stochastic-robust investment planning model is proposed to obtain a resilient MG considering both High-Impact-Low-Frequency (HILF) and Low-Impact-HighFrequency (LIHF) uncertainties. The HILF uncertainty pertains to the unscheduled islanding of the MG after a disastrous event, and the LIHF uncertainty relates to correlated loads and DER generation, characterized by a set of scenarios. The MG resilience under both types of uncertainty is ensured by incorporating static and transient islanding constraints into the proposed investment model. The inclusion of transient response constraints leads to a min-maxmin problem with a non-linear dynamic frequency response model that cannot be solved directly by available optimization tools. Thus, in this paper, a three-stage solution approach is proposed to find the optimal investment plan. The performance of the proposed algorithm is tested on the CIGRE 18-node distribution network | URI: | https://hdl.handle.net/20.500.14279/24592 | ISSN: | 19493061 | DOI: | 10.1109/TSG.2022.3146193 | Rights: | © IEEE | Type: | Article | Affiliation : | Cyprus University of Technology Imperial College London Leeds University ETH Zurich |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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2022JNakiganda.pdf | 3.65 MB | Adobe PDF | View/Open |
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