arXiv subjects

Hamidreza Nazaripouya

Publications and source records attributed to Hamidreza Nazaripouya.

7 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-on-the-loop Resilient Control of InverterBased Resources Under Actuator Degradation

This paper proposes a human-on-the-loop resilient control architecture for grid-supporting inverter-based resources (IBRs) operating under actuator degradation. Conventional fault-tolerant control and adaptive control strategies each face notable limitations in this setting: active FTC depends on fast, accurate fault detection and isolation, leaving it vulnerable to misdiagnosis of incipient or ambiguous degradation, while passive FTC tends toward overly conservative operation. Adaptive controllers face a related problem as they typically assume sufficient control authority, but when actuator degradation erodes that authority, the adaptive law may misinterpret tracking errors, leading to parameter drift, performance loss, or instability. To overcome these limitations, the proposed framework embeds human supervisory judgment directly into the control loop, detecting subtle off-nominal behavior, validating or overriding controller parameters, and adjusting operational objectives when conditions exceed the modeled fault space. Two new metrics underpin this resilient decision-making: Generation Reserve Capacity (GRC), which quantifies remaining inverter capacity available for future contingencies, and Controlled Performance Degradation (CPD), which allows temporary, deliberate performance relaxation to preserve overall system operability. A human-selected resilience tuning parameter, μ, governs the trade-off between immediate tracking accuracy and long-term operational readiness. A stability analysis of the proposed HOTL μ-mod adaptive controller is presented. The approach is also validated through simulations of a grid-connected inverter under sequential actuator degradation. Results show that the proposed architecture preserves control reserves, prevents actuator saturation, and achieves superior voltage regulation compared with conventional adaptive control and FTC methods.

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arXiv2026

A Human-AI Teaming Framework for Deep Reinforcement Learning-Based Voltage Regulation in Distribution Networks

The growing penetration of distributed energy resources (DERs) has increased the operational variability of distribution networks, making voltage regulation increasingly challenging. Conventional deep reinforcement learning (DRL) methods exhibit unsafe exploration behavior, slow convergence, and limited reliability, which restrict their applicability in safety-critical power system settings. This paper presents a human-interactive reinforcement learning framework that enhances the safety and robustness of autonomous voltage regulation. The proposed approach integrates a Soft Actor-Critic (SAC) agent with an adaptive Lagrange constraint mechanism to enforce voltage limits, while a human-guidance module provides sensitivity-based corrections through coordinated capacitor-bank and Battery Energy Storage System (BESS) dispatch. These corrections are incorporated into a human-regularized actor loss, enabling the policy to internalize safe and interpretable control behavior. The framework is implemented in the PowerGym-OpenDSS environment using the IEEE 13-node feeder. Simulation results show that the proposed Human-Interactive Lagrangian SAC (HI-LSAC) achieves significantly lower voltage-violation severity and reduced power losses compared with the other baseline methods.

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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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arXiv2026

Human-in-the-Loop Distributed Control of Grid-Interactive Buildings for Demand Response Participation

This paper proposes a human-in-the-loop distributed consensus control approach for demand-side management across multiple buildings. Specifically, a novel framework is introduced in which a human acts as the non-autonomous leader in consensus control of cooperative buildings participating in demand response programs. In this system, the facility manager in the facility building serves as the leader, determining the participation level of cooperative buildings in demand response events while simultaneously considering occupants' comfort. Cooperative buildings align their responses with the facility management building, despite lacking direct access to the facility manager's decisions, which presents a challenge for observer design. To address this, a nonlinear unknown input sliding-mode observer is proposed, tailored for leader-follower multi-agent systems (MASs). Furthermore, a human-in-the-loop leader-follower consensus protocol is introduced, enabling a framework to flexibly manage energy use and balance thermal comfort during demand response events. Simulation results validate the effectiveness of the proposed approach, demonstrating its ability to achieve consensus, maintain system performance, and enhance the adaptability of power grid operations under various demand response scenarios.

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arXiv2026

Power Flow Solvability with Volt-Var Controlled Inverter-Based Resources

This paper establishes a sufficient condition for guaranteeing power flow solvability in distribution grids with inverter-based resources (IBRs) operating under IEEE 1547 compliant Volt-Var control. While designed to improve voltage profiles, reactive power injection can drive the system toward its operational limits. Under these stressed conditions, any further incremental reactive power injection can trigger voltage collapse, the point at which a power flow solution ceases to exist. In this paper, by leveraging a phasor-based voltage representation, the power flow equations with Volt-Var control are developed in the complex fixed point form, enabling a compact formulation and the rigorous application of fixed-point theorems. Addressing the challenges posed by the non-holomorphicity of the complex power flow equations due to the Volt-Var function's dependence on voltage magnitude, the solvability conditions are then developed using the Brouwer fixed-point theorem. The proposed conditions are validated through simulations on distribution test feeders, with a primary focus on their application to real-time decision-making for voltage regulation services.

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