arXiv subjects

Arastoo H Salimi

Publications and source records attributed to Arastoo H Salimi.

3 recordsLinked to original sources

arXiv2026

Sequential Operational Decision-Making for Power System Resilience Under Evolving Wildfires

This paper proposes a novel automated decision-support framework aimed at enhancing the resilience of power systems and operational resilience against wildfires by formulating the decision-making process as a stochastic multi-stage programming during a progressive wildfire. The paper develops a framework that takes into account both preventive and corrective actions, enabling automated and adaptive decisions based on potential scenarios over the course of a wildfire's progression. This approach considers the evolving nature of the wildfire threat and seeks to optimize the response strategies accordingly throughout its duration. The objective is to minimize wildfire risk and operational costs while reducing load curtailment. The framework accounts for potential contingencies caused by progressive wildfires. A novel algorithm is proposed to construct a decision tree based on wildfire progression and system geographical information. Additionally, a novel stochastic dual dynamic programming approach is deployed to solve the proposed optimization problem, achieving a global optimum for the framework. The effectiveness of the proposed method is demonstrated on the IEEE 30-bus system under various wildfire impact scenarios and is then applied to the IEEE 300-bus system to illustrate the scalability of the proposed approach. The results highlight the advantages of the proposed automated framework over a single-stage operational optimization strategy in enhancing power grid resilience under wildfire conditions.

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arXiv2026

Enhancing Operational Grid Resilience Against Wildfires Under Decision-Dependent Uncertainties

This paper proposes a new automated decision-making framework to enhance the resilience of electrical systems against wildfires by applying operational strategies that account for decision-dependent uncertainty (DDU). The proposed framework incorporates both preventive and corrective measures, enabling adaptive and automated decision-making throughout the course of evolving wildfire scenarios. First, a baseline multistage optimization model is presented as a foundation to support wildfire-driven operational decision-making. The model then incorporates DDU, wherein Public Safety Power Shutoff (PSPS) decisions made in earlier stages influence the probabilities and parameters of future wildfire scenarios. To efficiently solve the resulting complex optimization problem, a mathematical decomposition algorithm is employed. The effectiveness of the proposed approach is demonstrated through case studies on the IEEE 30-bus system. Simulation results confirm that incorporating the impact of DDU into the optimization process provides more realistic, and operationally resilient solutions in the face of evolving wildfire threats.

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arXiv2026

Human-Aware Power Restoration for Fair Outage Experience in Distribution Systems

This paper proposes a novel methodology for human-aware and fair service restoration in power distribution networks, explicitly accounting for the customer experience of outage duration. The complexity of this problem stems from the inherently unpredictable and stochastic nature of power outage events. Traditional approaches often oversimplify the problem by treating failures as deterministic, overlooking the lived experiences of customers and the true uncertainty of outage patterns. In contrast, the proposed method incorporates the probability of potential failures to guide a customer-aware and fairness-driven resource allocation, ensuring that restoration is not only fast but also perceived as fair from the customer's perspective. To achieve this, a spatially distributed, adaptive, and scalable partitioning policy is designed to balance restoration time across all failure locations, promoting consistency and equity in the outage experience. Next, an adaptive and distributed repair crew dispatch algorithm is proposed to accelerate service restoration while ensuring that no customer segment is disproportionately affected. The framework leverages a Receding Horizon (RH) optimization algorithm to dynamically minimize total restoration time amid randomly occurring outages in both space and time. Simulation results on modified 69-bus distribution networks under stochastic outage conditions demonstrate the model's effectiveness in delivering socially fair and customer-sensitive restoration outcomes

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