arXiv subjects

Mohannad Alkhraijah

Publications and source records attributed to Mohannad Alkhraijah.

7 recordsLinked to original sources

arXiv2026

A Control Architecture for Fast Frequency Regulation with Increasing Penetration of Inverter Based Resources (Extended Version)

This paper addresses frequency regulation under operational constraints in interconnected power systems with high penetration of inverter-based renewable generation. A two-layer control architecture is proposed that combines optimized droop and Virtual Synchronous Machine (VSM) primary control with a Model Predictive Control (MPC) secondary layer operating at realistic control-room update rates. Unlike recently proposed approaches, the proposed framework integrates MPC within existing grid control structures, enabling constraint-aware coordination. A reduced-order frequency response model is systematically derived from a detailed grid model using Hankel singular values, and a reduced-order Kalman-Bucy observer enables state and disturbance estimation using only measurable outputs. Evaluation using representative data from the Kingdom of Saudi Arabia demonstrates effective frequency regulation under realistic operating conditions.

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arXiv2025

A Decomposition Method for Solving Sensitivity-Based Distributed Optimal Power Flow

Efficiently solving large-scale optimal power flow (OPF) problems is challenging due to the high dimensionality and interconnectivity of modern power systems. Decomposition methods offer a promising solution via partitioning large problems into smaller subproblems that can be solved in parallel, often with local information. These approaches reduce computational burden and improve flexibility by allowing agents to manage their local models. This article introduces a decomposition method that enables a distributed solution to OPF problems. The proposed method solves OPF problems with a sensitivity-based formulation using the alternating direction method of multipliers (ADMM) algorithm. We also propose a distributed method to compute system-wide sensitivities without sharing local parameters. This approach facilitates scalable optimization while satisfying global constraints and limiting data sharing. We demonstrate the effectiveness of the proposed approach using a large set of test systems and compare its performance against existing decomposition methods. The results show that the proposed method significantly outperforms the typical phase-angle formulation with a 14-times faster computation speed on average.

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arXiv2025

On the Detection of Shared Data Manipulation in Distributed Optimization

This paper investigates the vulnerability of the Alternating Direction Method of Multipliers (ADMM) algorithm to shared data manipulation, with a focus on solving optimal power flow (OPF) problems. Deliberate data manipulation may cause the ADMM algorithm to converge to suboptimal solutions. We derive a sufficient condition for detecting data manipulation based on the theoretical convergence trajectory of the ADMM algorithm. We evaluate the performance of the detection condition on three data manipulation strategies with various levels of complexity and stealth. The simplest attack sends the target values and each iteration, the second attack uses a feedback loop to find the next target values, and the last attack uses a bilevel optimization to find the target values. We then extend the three data manipulation strategies to avoid detection by the detection conditions and a neural network (NN) detection model. We also propose an adversarial NN training framework to detect shared data manipulation. We illustrate the performance of our data manipulation strategy and detection framework on OPF problems. The results show that the proposed detection condition successfully detects most of the data manipulation attacks. However, the bilevel optimization attack strategy that incorporates the detection methods may avoid being detected. Countering this, our proposed adversarial training framework detects all the instances of the bilevel optimization attack.

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arXiv2024

A Fault-Tolerant Distributed Termination Method for Distributed Optimization Algorithms

This paper proposes a fully distributed termination method for distributed optimization algorithms solved by multiple agents. The proposed method guarantees terminating a distributed optimization algorithm after satisfying the global termination criterion using information from local computations and neighboring agents. The proposed method requires additional iterations after satisfying the global terminating criterion to communicate the termination status. The number of additional iterations is bounded by the diameter of the communication network. This paper also proposes a fault-tolerant extension of this termination method that prevents early termination due to faulty agents or communication errors. We provide a proof of the method's correctness and demonstrate the proposed method by solving the optimal power flow problem for electric power grids using the alternating direction method of multipliers.

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arXiv2023

Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow

The future power grid may rely on distributed optimization to determine the set-points for huge numbers of distributed energy resources. There has been significant work on applying distributed algorithms to optimal power flow (OPF) problems, which require separate computing agents to agree on shared boundary variable values. Looser tolerances for the mismatches in these shared variables generally yield faster convergence at the expense of exacerbating constraint violations, but there is little quantitative understanding of how the convergence tolerance affects solution quality. To address this gap, we first quantify how convergence tolerance impacts constraint violations when the distributed OPF generator dispatch is applied to the power system. Using insights from this analysis, we then develop a bound tightening algorithm which guarantees that operating points from distributed OPF algorithms will not result in violations despite the possibility of shared variable mismatches within the convergence tolerance. We also explore how bounding the cumulative shared variable mismatches can prevent unnecessary conservativeness in the bound tightening. The proposed approach enables control of the trade-off between computational speed, which improves as the convergence tolerance increases, and distributed OPF solution cost, which increases with convergence tolerance due to tightened constraints, while ensuring feasibility.

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arXiv2023

PowerModelsADA: A Framework for Solving Optimal Power Flow using Distributed Algorithms

This paper presents PowerModelsADA, an open-source framework for solving Optimal Power Flow (OPF) problems using Alternating Distributed Algorithms (ADA). PowerModelsADA provides a framework to test, verify, and benchmark both existing and new ADAs. This paper demonstrates use cases for PowerModelsADA and validates its implementation with multiple OPF formulations.

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arXiv2022

Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms

Power system operators are increasingly looking toward distributed optimization to address various challenges facing electric power systems. To assess their capabilities in environments with nonideal communications, this paper investigates the impacts of data quality on the performance of distributed optimization algorithms. Specifically, this paper compares the performance of the Alternating Direction Method of Multipliers (ADMM), Analytical Target Cascading (ATC), and Auxiliary Problem Principle (APP) algorithms in the context of DC Optimal Power Flow (DC OPF) problems. Using several test systems, this paper characterizes the performance of these algorithms in terms of their convergence rates and solution quality under three data quality nonidealities: (1) additive Gaussian noise, (2) bad data (large error), and (3) intermittent communication failure.

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