Nested Formation Approach for Networked Microgrid Self-Healing in Islanded Mode

Ambia, Mir Nahidul; Meng, Ke; Xiao, Weidong; Zhang, Dong

doi:10.1109/tpwrd.2020.2977769

Cited by 40 publications

(24 citation statements)

References 32 publications

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“…Several resilience-based MG load restoration and self-healing techniques have been developed in the literature during the last decade [73][74][75][76][77][78][79][80][81][82][83][84][85] .…”

Section: Resilience-oriented Operational Measures For Restorationmentioning

confidence: 99%

“…The frameworks of refs. [75,[77][78][79][80][81][82]84,85] refer to distribution systems and refs. [73,74,76,83] are related to resilient MGs restoration.…”

Section: Resilience-based Microgrids Restoration Strategiesmentioning

confidence: 99%

“…[80] also utilizes duality theory to find the worstcase scenario of DER output. Other objective functions optimally decide whether feeder root nodes or mobile emergency generators should be used to restore critical loads [75] . Other frameworks aim at maximizing the unused capacity of networked MGs and at optimizing power exchanges among them to achieve faster and more efficient overall load restoration [76] .…”

Section: Resilience-based Microgrids Restoration Strategiesmentioning

confidence: 99%

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Microgrids for power system resilience enhancement

Stasinos

Trakas

Hatziargyriou

2022

iEnergy

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Power system resilience is defined as the ability of power grids to anticipate, withstand, adapt and recover from high-impact lowprobability (HILP) events. There are both long-term and short-term measures that system operators can employ for resilience reinforcement. Longer-term measures include infrastructure hardening and resilient planning, while short-term operational measures are applied in the pre-event, during-event and post-event phases. Microgrids (MGs) can effectively enhance resilience for both transmission and distribution systems, due to their ability to operate in a controlled, coordinated way, when connected to the main power grid and in islanded mode. In this paper, MG-based strategies for resilience enhancement are presented, including MGbased resilient planning and MG-based operational measures, consisting of preventive MG scheduling and emergency measures and MG-based system restoration. Classification of literature is made by considering whether the transmission system, distribution system or individual MG resilience is targeted. The way uncertainties are handled by various methods is also outlined. Finally, challenges and future research requirements for improving MG-based power system resilience are highlighted.

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Section: Resilience-oriented Operational Measures For Restorationmentioning

confidence: 99%

“…The frameworks of refs. [75,[77][78][79][80][81][82]84,85] refer to distribution systems and refs. [73,74,76,83] are related to resilient MGs restoration.…”

Section: Resilience-based Microgrids Restoration Strategiesmentioning

confidence: 99%

Section: Resilience-based Microgrids Restoration Strategiesmentioning

confidence: 99%

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Microgrids for power system resilience enhancement

Stasinos

Trakas

Hatziargyriou

2022

iEnergy

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“…During the post-disruption stage, several real-time operational frameworks have been proposed to guide how the system should behave after disruptive events. For instance, researchers try to attain a resilient operational framework by scheduling optimal repair tasks under uncertainties [5][6][7] as well as repair resources [8], forming self-sustainable microgrids [9,10], and guided recovery through control strategies [11][12][13]. All aforementioned studies focus on solving the optimal decisions of how to utilize the existing resources or back-ups to recover the ICI, on the promise that set up a contingency plan such as network reconfiguration beforehand.…”

Section: Parametersmentioning

confidence: 99%

“…Since external disruptions are usually confined within a specific region. For example, as shown in Figure 8, a storm with predefined 𝑃 𝑒 𝑖 is simulated to happen at cell (3,5), which leads to the highest probability of failure for node 10 and edge (10,11). Also, the components within the close proximity of cell (3,5), including node 10, 14 and edges around, incur smaller failure probability.…”

Section: Post-processing Simulationmentioning

confidence: 99%

Graph Learning based Generative Design for Resilience of Interdependent Network Systems

Wu¹,

Wang²

2022

Preprint

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Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria. While the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on power systems from the IEEE dataset have illustrated the applicability of the proposed method for designing resilient interconnected systems.

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