4.7 Article

A Stochastic-Robust Approach for Resilient Microgrid Investment Planning Under Static and Transient Islanding Security Constraints

Journal

IEEE TRANSACTIONS ON SMART GRID
Volume 13, Issue 3, Pages 1774-1788

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2022.3146193

Keywords

Investment; Planning; Transient analysis; Uncertainty; Generators; Islanding; Costs; Investment planning; microgrids; low-inertia; frequency constraints; unscheduled islanding; resilience

Funding

  1. Engineering and Physical Sciences Research Council (EPSRC), U.K. [EP/R030243/1, TSG-01038-2020]

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A hybrid, stochastic-robust investment planning model is proposed in this paper to consider both high-impact-low-frequency and low-impact-high-frequency uncertainties in Microgrids (MGs). The model incorporates static and transient islanding constraints to ensure the resilience of the MG.
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-High-Frequency (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-max-min 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.

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