Proactive Islanding of the Power Grid to Mitigate High-Impact Low-Frequency Events

Biswas, Shuchismita; Bernabeu, E.; Picarelli, David

doi:10.1109/isgt45199.2020.9087788

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The objective function in (ODNP-3) is the sample-based estimator of the objective in (ODNP-2) designed to maximize average load served across considered scenarios. Constraint (18) fixes bus consumptions at zero when constraint (14b) is not satisfied. This motivates the optimal solution for the ODNP to be one that also increases 1 T z, lower bounded by…”

Section: G Sample Average Approximationmentioning

confidence: 99%

“…However, since distribution networks are only served by a limited number of switching devices at present, implementing these suggestions may be difficult in practice. A method for determining self-sufficient islands in transmission networks is described in [18], but cannot be directly extended to ODNP without including distribution system specific constraints.…”

Section: Introductionmentioning

confidence: 99%

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Chance-Constrained Optimal Distribution Network Partitioning to Enhance Power Grid Resilience

2021

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This paper proposes a method for identifying potential self-adequate sub-networks in the existing power distribution grid. These sub-networks can be equipped with control and protection schemes to form microgrids capable of sustaining local loads during power systems contingencies, thereby mitigating disasters. Towards identifying the best microgrid candidates, this work formulates a chance-constrained optimal distribution network partitioning (ODNP) problem addressing uncertainties in load and distributed energy resources; and presents a solution methodology using the sample average approximation (SAA) technique. Practical constraints like ensuring network radiality and availability of grid-forming generators are considered. Quality of the obtained solution is evaluated by comparison with-a) an upper bound on the probability that the identified microgrids are supply-deficient, and b) a lower bound on the objective value for the true optimization problem. Performance of the ODNP formulation is illustrated through case-studies on a modified IEEE 37-bus feeder. It is shown that the network flexibility is well utilized; the partitioning changes with risk budget; and that the SAA method is able to yield good quality solutions with modest computation cost.

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Section: G Sample Average Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chance-Constrained Optimal Distribution Network Partitioning to Enhance Power Grid Resilience

2021

Self Cite

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“…If the lower limit is violated (as indicated by z e i,k = 0 in (7)), the limits remain unchanged; otherwise, minimum and maximum operating limits of the generators (P min, * i , P max, * i ) are updated using ( 8)- (9).…”

Section: B Power System Operation Modelmentioning

confidence: 99%

“…However, if the emergency warning and caution (EWAC) direction is received from various stakeholders, operators typically aim to predict outage risks to take necessary control measures to minimize loss of loads, and systems will move to resiliency mode. Techniques, such as Markovian model-based proactive sequential re-dispatch of generators [8], proactive splitting the transmission grid into islands [9], [10], transmission system reconfiguration [11], the coordinated control of multiple microgrids connected via transmission system [12], [13], defensive islanding formation [14], optimal power shut-offs [15], transmission line derating [2], [16], are common approaches for resilient transmission network operation and control. Abundant monitoring data from SCADA and PMUs can be leveraged in outage forecasting, which can be utilized for proactive resource allocation and deployment of necessary measures to serve consumers during emergency conditions.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

Kadir,

Majumder,

Chhokra

et al. 2021

Preprint

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Power grid operation subject to an extreme event requires decision-making by human operators under stressful condition with high cognitive load. Decision support under adverse dynamic events, specially if forecasted, can be supplemented by intelligent proactive control. Power system operation during wildfires require resiliency-driven proactive control for load shedding, line switching and resource allocation considering the dynamics of the wildfire and failure propagation. However, possible number of line-and load-switching in a large system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use greedy algorithms. We model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. We transform the enormous wildfire-propagation observation space and utilize it as part of a heuristic for proactive de-energization of transmission assets. We integrate this heuristic with a reinforcement-learning based proactive policy for controlling the generating assets. Our approach allows this controller to provide setpoints for a part of the generation fleet, while a myopic operator can determine the setpoints for the remaining set, which results in a symbiotic action. We evaluate our approach utilizing the IEEE 24-node system mapped on a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load loss during an extreme event, reduce power flow through lines that are to be de-energized, and reduce the likelihood of infeasible power-flow solutions, which would indicate violation of shortterm thermal limits of transmission lines.

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“…Such assessments are useful to system planners aiming to make decisions about infrastructure development and to operators while handling emergency system conditions. A common drawback of this simulation-based approach is that it requires detailed information regarding the power network and associated components, such as locations and capacities of generation, load demands, and line parameters ( 12 – 15 ). Furthermore, since the majority of grid infrastructure advancements are being done at the low-voltage (LV) distribution level, a high-resolution analysis of the power distribution systems is important.…”

mentioning

confidence: 99%

Ensembles of realistic power distribution networks

Meyur

Vullikanti

Swarup

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

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Proactive Islanding of the Power Grid to Mitigate High-Impact Low-Frequency Events

Cited by 6 publications

References 11 publications

Chance-Constrained Optimal Distribution Network Partitioning to Enhance Power Grid Resilience

Chance-Constrained Optimal Distribution Network Partitioning to Enhance Power Grid Resilience

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

Ensembles of realistic power distribution networks

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