Robust Resiliency-Oriented Operation of Active Distribution Networks Considering Windstorms

Esfahani, M. Mokhlespour; Amjady, Nima; Bagheri, Bahareh; Hatziargyriou, Nikos D.

doi:10.1109/tpwrs.2020.2977405

Cited by 51 publications

(20 citation statements)

References 27 publications

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“…Additionally, α i is the parameter representing the critical load fraction (positive real number) served. Availability of a large number of switchable loads within the distribution network connected at the transmission substation enables treating the sheddable loads as a continuous variable [30]. Consequently, critical and non-critical sheddable loads (∆P c_l i,k+1 , ∆P nc_l i,k+1 ) is bounded as follows:…”

Section: B Power System Operation Modelmentioning

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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Section: B Power System Operation Modelmentioning

confidence: 99%

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

Kadir,

Majumder,

Chhokra

et al. 2021

Preprint

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“…Equation (8) gives the original state of charge of the energy storage. Equation (9) defines the SOC at time t. Equation (10) limits the maximum and minimum SOC to prevent over charging and discharging.…”

Section: mentioning

confidence: 99%

“…In [8] the network restoration problem is extended to the secondary network, where the transient frequency stability is considered. In [9] a resilience-oriented operation scheduling model is presented to against windstorm, where the micro-turbines, wind generation and ESS are committed by the distribution system operators (DSOs). To reduce computational cost of the ever-increasing mathematical programming models, the author in [10] and [11] introduces decentralized multiagent system to the network restoration problem.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty

Wang

Lei

et al. 2021

IEEE Access

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The active distribution network management (ADNM) equipped by active distribution networks (ADNs) can enhance the resilience of the network after failure. This paper proposes a novel comprehensive post-event network restoration model and its chance-constrained variant for ADN that considering the dispatch of distributed generation (DG), energy storage system (ESS), demand response (DR), static var compensator (SVC), and network reconfiguration to fully investigate the potential of the ADNM in network restoration. In this paper, the line failure, bus failure, and the uncertainty from load and DG output forecast error are considered. Specifically, the line and bus failures are modeled by an enhanced fictitious network technique, while the uncertainty of load and DG output forecast is modeled by the chance-constrained optimization. The power flow is described by a linearized DistFlow model, and thus the deterministic and chance-constrained network restoration models are programmed by the mixed-integer linear programming (MILP). The proposed deterministic and chance-constrained restoration models are tested on the IEEE 33 bus system. Results demonstrate the effectiveness of the proposed deterministic and chance-constrained network restoration model.

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“…In Ref. 156, a robust resilient operation‐scheduling model for active distribution networks is proposed. This model considers the unavailability uncertainties of contingencies caused by windstorms and the forecast uncertainties of load demand, wind power, and solar power.…”

Section: Taxonomy Of Uncertainty Characterization In Resiliencymentioning

confidence: 99%

A critical reviewon definitions, indices, and uncertainty characterizationin resiliency‐orientedoperation ofpower systems

Izadi

Hosseinian

Dehghan

et al. 2020

Int Trans Electr Energ Syst

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Robust Resiliency-Oriented Operation of Active Distribution Networks Considering Windstorms

Cited by 51 publications

References 27 publications

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty

A critical reviewon definitions, indices, and uncertainty characterizationin resiliency‐orientedoperation ofpower systems

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