arXiv subjects

Farshad Amani

Publications and source records attributed to Farshad Amani.

6 recordsLinked to original sources

arXiv2026

Nested-Loop Trajectory-Informed Variational Quantum Solver for Interior-Point OPF

Optimal power flow (OPF) solved by an interior-point method (IPM) requires repeatedly solving Newton linear systems. When variational quantum linear solvers (VQLS) are used, each IPM iteration involves an additional nested inner variational optimization loop, which can significantly slow the overall quantum-assisted IPM convergence. To address this challenge, this paper proposes a dual-level trainable quantum IPM framework for OPF that leverages early solver-generated trajectories rather than relying on single-point prediction. The key observation is that early IPM iterates provide informative primal-dual, slack, and barrier-variable evolution about the path to optimality, while early VQLS parameter updates provide useful information about the later variational search. At the quantum-solver level, a trainable parameter model uses a short prefix of the VQLS parameter trajectory to project the remaining variational search toward a lower-cost region. At the OPF-solver level, a second trainable model uses early primal-dual IPM iterates to project a later central path state, which is restored to an admissible point before IPM refinement continues. Simulation studies show that the proposed approach reduces the number of variational updates by up to $95\%$ while maintaining OPF objective values close to the classical IPM reference. A 2-bus demonstration on real quantum hardware is also included to validate the implementation of the proposed workflow.

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arXiv2026

Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration

Natural hazards such as hurricanes and floods damage power grid equipment, forcing operators to replan restoration repeatedly as new information becomes available. This paper develops a deep reinforcement learning (DRL) dispatcher that serves as a real-time decision engine for crew-to-repair assignments. We model restoration as a sequential, information-revealing process and learn an actor-critic policy over compact features such as component status, travel/repair times, crew availability, and marginal restoration value. A feasibility mask blocks unsafe or inoperable actions, such as power flow limits, switching rules, and crew-time constraints, before they are applied. To provide realistic runtime inputs without relying on heavy solvers, we use lightweight surrogates for wind and flood intensities, fragility-based failure, spatial clustering of damage, access impairments, and progressive ticket arrivals. In simulated hurricane and flood events, the learned policy updates crew decisions in real time as new field reports arrive. Because the runtime logic is lightweight, it improves online performance (energy-not-supplied, critical-load restoration time, and travel distance) compared with mixed-integer programs and standard heuristics. The proposed approach is tested on the IEEE 13- and 123-bus feeders with mixed hurricane/flood scenarios.

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arXiv2025

Learning Optimal Crew Dispatch for Grid Restoration Following an Earthquake

Post-disaster crew dispatch is a critical but computationally intensive task. Traditional mixed-integer linear programming methods often require minutes to several hours to compute solutions, leading to delays that hinder timely decision-making in highly dynamic restoration environments. To address this challenge, we propose a novel learning-based framework that integrates transformer architectures with deep reinforcement learning (DRL) to deliver near real-time decision support without compromising solution quality. Crew dispatch is formulated as a sequential decision-making problem under uncertainty, where transformers capture high-dimensional system states and temporal dependencies, while DRL enables adaptive and scalable decision-making. Earthquake-induced distribution network damage is first characterized using established seismic standards, followed by a scenario generation and reduction pipeline that aggregates probable outcomes into a single geospatial impact map. Conditioned on this map, the proposed framework generates second-level dispatch strategies, trained offline on simulated and historical events and deployed online for rapid response. In addition to substantial runtime improvements, the proposed method enhances system resilience by enabling faster and more effective recovery and restoration. Case studies, particularly on the 2869-bus European gas and power network, demonstrate that the method substantially accelerates restoration while maintaining high-quality solutions, underscoring its potential for practical deployment in large-scale disaster response.

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arXiv2025

Learning Interior Point Method Central Path Projection for Optimal Power Flow

This paper proposes a learning-based approach to accelerate the interior-point method (IPM) for solving optimal power flow (OPF) problems by learning the structure of the IPM central path from its early stable iterations. Unlike traditional learning models that attempt to predict the OPF solution directly, our approach learns the structure of the IPM trajectory itself, since even accurate predictions may not reliably reduce IPM iterations. The IPM follows a central path that iteratively progresses toward the optimal solution. While this trajectory encodes critical information about the optimization landscape, the later iterations become increasingly expensive due to ill-conditioned linear systems. Our analysis of the IPM central path reveals that its initial segments contain the most informative features for guiding the trajectory toward optimality. Leveraging this insight, we model the central path as a time series and use a Long Short-Term Memory (LSTM) network to project the path using only the first few stable iterations. To ensure that the learned trajectory remains within the feasible region--especially near the optimal point--we introduce a grid-informed mechanism into the LSTM that enforces key operational constraints on generation, voltage magnitudes, and line flows. This framework, referred to as Learning-IPM (L-IPM), significantly reduces both the number of IPM iterations and overall solution time. To improve generalization, we use a sampling-based strategy to generate a diverse set of load conditions that effectively span the operational space. Simulation results across a range of test systems--including a 2869-bus European transmission network--demonstrate that L-IPM achieves up to a 94% reduction in solution time and an 85.5% reduction in iterations, without compromising feasibility or accuracy.

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arXiv2024

Quantum Optimization for Optimal Power Flow: CVQLS-Augmented Interior Point Method

This paper presents a quantum-enhanced optimization approach for solving optimal power flow (OPF) by integrating the interior point method (IPM) with a coherent variational quantum linear solver (CVQLS). The objective is to explore the applicability of quantum computing to power systems optimization and address the associated challenges. A comparative analysis of state-of-the-art quantum linear solvers - Harrow-Hassidim-Lloyd (HHL), variational quantum linear solver (VQLS), and CVQLS - revealed that CVQLS is most suitable for OPF due to its stability with ill-conditioned matrices, such as the Hessian in IPM. To ensure high-quality solutions, prevent suboptimal convergence, and avoid the barren plateau problem, we propose a quantum circuit parameter initialization technique along with a method to guide the IPM along the central path. Moreover, we design an ansatz tailored for OPF, optimizing the expressibility and trainability of the quantum circuit to ensure efficient convergence and robustness in solving quantum OPF. Various optimizers are also tested for quantum circuit parameter optimization to select the best one. We evaluate our approaches on multiple systems to show their effectiveness in providing reliable OPF solutions. Simulations for the 2-bus system are conducted on a commercial IBMQ quantum device, while simulations for the other larger cases are performed using the IBM quantum simulator. While promising, CVQLS is limited by current quantum hardware, especially for larger systems. We use a quantum noise simulator to test scalability.

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arXiv2023

Optimal Power Flow Solutions via Noise-Resilient Quantum-Inspired Interior-Point Methods

This paper presents three quantum interior-point methods (QIPMs) tailored to tackle the DC optimal power flow (DCOPF) problem using noisy intermediate-scale quantum devices. The optimization model is redefined as a linearly constrained quadratic optimization. By incorporating the Harrow-Hassidim-Lloyd (HHL) quantum algorithm into the IPM framework, Newton's direction is determined through the resolution of linear equation systems. To mitigate the impact of HHL error and quantum noise on Newton's direction calculation, we present a noise-tolerant quantum IPM (NT-QIPM) approach. This approach provides high-quality OPF solutions even in scenarios where inexact solutions to the linear equation systems result in approximated Newton's directions. Moreover, to enhance performance in cases of slow convergence and uphold the feasibility of OPF outcomes upon convergence, we propose a hybrid strategy, classically augmented NT-QIPM. This technique is designed to expedite convergence relative to classical IPM while maintaining the solution accuracy. The efficacy of the proposed quantum IPM variants is studied through comprehensive simulations and error analyses on 3-bus, 5-bus, 118-bus, and 300-bus systems, highlighting their potential and promise in addressing challenging OPF scenarios. By modeling the errors and incorporating quantum computer noise, we simulate the proposed algorithms on both Qiskit and classical computers to gain a deeper understanding of the effectiveness and feasibility of our methods under realistic conditions.

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