Resilient Operation of Electric Power Distribution Grids Under Progressive Wildfires

Nazemi, Mostafa; Dehghanian, Payman; Alhazmi, Mohannad; Darestani, Yousef Mohammadi

doi:10.1109/tia.2022.3145780

Cited by 15 publications

(4 citation statements)

References 22 publications

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“…According to the principle of least square method, when the error between the real value and the estimated value in stage l+1 is the smallest, the risk transfer matrix Y obtained is the optimal solution under the operating conditions of the system at that time. The solution model for Y is ( 6) and (7).…”

Section: Risk Propagation Model Of the Intelligent Device Layermentioning

confidence: 99%

“…Strategies have been developed to enhance the resilience of distribution systems to extreme events. With respect to the impact of extreme weather on system resilience, the problem of optimizing the operation of distribution systems after summer wildfires has been analyzed [7]. In [8], the authors investigated extremely icy weather and proposed a method to enhance the resilience of the distribution network by optimizing the routing of mobile dicing equipment.…”

Section: Introductionmentioning

confidence: 99%

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Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

Wang

et al. 2023

IET Renewable Power Gen

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Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness.

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Section: Risk Propagation Model Of the Intelligent Device Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

Wang

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…The work in [25] expanded the previous heat transfer model to consider the non-steady state heat balance equation on the DLR modeling. Some recent works [26]- [28] used the same wildfire progression model to investigate select operation strategies, including network reconfiguration and the deployment of distributed generation and energy storage systems, to improve the distribution system resilience during such events. In [29], the authors integrated the DLR of transmission lines into the optimal power flow model to reduce the risk of wildfire ignitions.…”

Section: B Literature Reviewmentioning

confidence: 99%

Unbalanced Distribution System Expansion and Energy Storage Planning Under Wildfire Risk

Bertoletti,

Campos do Prado

2023

IEEE Access

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Reducing the risks of wildfire ignition has become a major concern for many electric utilities. In recent years, they have relied on Public Safety Power Shutoff (PSPS) programs to de-energize select power lines to prevent wildfire risks. A cost-effective solution for the power distribution system expansion planning in regions with increased wildfire risk could be achieved by jointly combining new distribution lines and energy storage systems (ESSs) while taking into account the overhead distribution lines availability given the increasing number of PSPS events. This paper proposes a two-stage stochastic optimization approach for the expansion planning of a power distribution system under wildfire risk with a compliance check on unbalanced power flow and system operation limits. The resulting model is a mixed-integer linear programming (MILP) optimization problem. The proposed model is validated on a modified version of the IEEE 13-node system and the the IEEE 123-node system. Simulation experiments and sensitivity analysis are performed to validate the effectiveness of the proposed formulation using different High

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“…To enhance the reliability and resilience of power and energy systems, various analytical and population-based heuristic approaches employing dynamic modeling and optimization techniques have been used in the literature. For instance, previous work [9] has utilized dynamic modeling and classical optimization to address threats like wildfires to power distribution networks, focusing on resilience with renewable energy resources and evaluated on a 33-node distribution system. Another study [10] analyzed microgrid operation using a multicarrier energy hub, considering various energy carriers and resources to reduce environmental impact and operational costs while enhancing resilience and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Gautam

2023

Electricity

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In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

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Resilient Operation of Electric Power Distribution Grids Under Progressive Wildfires

Cited by 15 publications

References 22 publications

Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

Unbalanced Distribution System Expansion and Energy Storage Planning Under Wildfire Risk

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

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