arXiv subjects

Hussein Sharadga

Publications and source records attributed to Hussein Sharadga.

4 recordsLinked to original sources

arXiv2026

Rapid Concurrent GPU-CPU Solvers for Scalable Unit Commitment in Large Power Grids

This paper presents an accelerated solver for the unit commitment problem in large-scale power systems. The approach is based on the concurrent execution of GPU- and CPU-based optimization solvers on a single machine, with the solver that converges first terminating the other to minimize overall runtime. This strategy effectively harnesses the complementary strengths of different solvers. Convergence is further accelerated through a systematic and aggressive presolve approach. Numerical experiments on a 6,049-bus system with millions of decision variables and constraints demonstrate speedups ranging from 2.14x to 5.61x, reducing the maximum runtime from 42.12 minutes to 5.77 minutes across 45 test cases. These results highlight the scalability and computational efficiency of the proposed GPU-CPU concurrent solver framework.

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arXiv2025

GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids

This work presents a GPU-accelerated solver for the unit commitment (UC) problem in large-scale power grids. The solver uses the Primal-Dual Hybrid Gradient (PDHG) algorithm to efficiently solve the relaxed linear subproblem, achieving faster bound estimation and improved crossover and branch-and-bound convergence compared to conventional CPU-based methods. These improvements significantly reduce the total computation time for the mixed-integer linear UC problem. The proposed approach is validated on large-scale systems, including 4224-, 6049-, and 6717-bus networks with long control horizons and computationally intensive problems, demonstrating substantial speed-ups while maintaining solution quality.

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arXiv2024

Real-time Building Energy Storage Scheduling under Electrical Load Uncertainty: A Dynamic Markov Decision Process Approach with Comprehensive Analysis of Different Pricing Policies

In response to the increasing deployment of battery storage systems for cost reduction and grid stress mitigation, this study presents the development of a new real-time Markov decision process model to efficiently schedule battery systems in buildings under electrical load uncertainty. The proposed model incorporates quantile Fourier regression for load fitting, leading to a large-scale optimization problem with approximately a million decision variables and constraints. To address this complexity, the problem is formulated as a linear program and solved using a commercial solver, ensuring effective navigation and identification of optimal solutions. The model's performance is evaluated by considering different pricing policies and scenarios, including demand peak shaving. Validation of the Markov model is conducted using one year of historical demand data from a school. Findings indicate that MDP performance in adapting to uncertain loads can range from 30 to 99% depending on the pricing policy.

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arXiv2023

Optimizing Multi-Timestep Security-Constrained Optimal Power Flow for Large Power Grids

This work proposes a novel method for scaling multi-timestep security-constrained optimal power flow in large power grids. The challenge arises from dealing with millions of variables and constraints, including binary variables and nonconvex, nonlinear characteristics. To navigate these complexities, techniques such as constraint relaxation, linearization, sequential optimization, and problem reformulation are employed. By leveraging these methods, complex power grid problems are solved while achieving high-quality solutions and meeting time constraints. The innovative solution approach showcases great robustness and consistently outperforms benchmark standards.

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