arXiv subjects

Md Habib Ullah

Publications and source records attributed to Md Habib Ullah.

3 recordsLinked to original sources

arXiv2026

Quantum Computing in Next-Gen Smart Grid Operations: A Comprehensive Review

The rapid proliferation of grid-edge distributed energy resources has significantly increased the operational complexity of modern power systems. Consequently, conventional computational techniques face growing scalability and computational-efficiency challenges in addressing large-scale optimization and control, uncertainty management, nonlinear dynamics, and combinatorial decision-making in smart grid operations. Quantum computing has therefore emerged as a promising computational paradigm that can complement classical methods in addressing selected computationally intensive problems. In this context, this paper presents a comprehensive structured review of quantum computing applications in smart grid operations. Following a transparent keyword-based literature search, the paper classifies and assesses existing studies across monitoring and estimation, system planning, operation and control, security, reliability and resilience, stability assessment, data-driven intelligence, and digital twin technologies. The paper also describes fundamental quantum-computing concepts and key algorithms, highlighting their relevance for power system applications. Furthermore, it reviews the current state of quantum hardware, software frameworks, simulators, cloud services, and emerging hardware-agnostic ecosystems that support cross-platform application development and deployment. The reviewed studies are examined by implementation environment, benchmarking practices, application scale, and evidence of computational advantage. Finally, the principal challenges associated with practical implementation are discussed, and future research directions are outlined. This paper provides a consolidated and evidence-calibrated perspective on the current state and future potential of quantum computing for next-generation smart grid operations.

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arXiv2025

Hybrid Quantum-Classical Optimization of the Resource Scheduling Problem

Resource scheduling is critical in many industries, especially in power systems. The Unit Commitment problem determines the on/off status and output levels of generators under many constraints. Traditional exact methods, such as mathematical programming methods or dynamic programming, remain the backbone of UC solution techniques, but they often rely on linear approximations or exhaustive search, leading to high computational burdens as system size grows. Metaheuristic approaches, such as genetic algorithms, particle swarm optimization, and other evolutionary methods, have been explored to mitigate this complexity; however, they typically lack optimality guarantees, exhibit sensitivity to initial conditions, and can become prohibitively time-consuming for large-scale systems. In this paper, we introduce a quantum-classical hybrid algorithm for UC and, by extension, other resource scheduling problems, that leverages Benders decomposition to decouple binary commitment decisions from continuous economic dispatch. The binary master problem is formulated as a quadratic unconstrained binary optimization model and solved on a quantum annealer. The continuous subproblem, which minimizes generation costs, with Lagrangian cuts feeding back to the master until convergence. We evaluate our hybrid framework on systems scaled from 10 to 1,000 generation units. Compared against a classical mixed-integer nonlinear programming baseline, the hybrid algorithm achieves a consistently lower computation-time growth rate and maintains an absolute optimality gap below 1.63%. These results demonstrate that integrating quantum annealing within a hybrid quantum-classical Benders decomposition loop can significantly accelerate large-scale resource scheduling without sacrificing solution quality, pointing toward a viable path for addressing the escalating complexity of modern power grids.

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arXiv2020

Distributed Dynamic Pricing in Peer-to-Peer Transactive Energy Systems in Smart Grid

The rapid growth of proactive consumers with distributed power generation and storage capacity, empowered by Internet of Things (IoT) devices, is transforming modern power markets into an independent, flexible, and distributed structure. In particular, the recent trend is peer-to-peer (P2P) transactive energy systems, wherein the traditional consumers became prosumers (producer+consumer) and can maximize their energy utilization by sharing with neighbors without any conventional intermediary intervention in the transactions. However, the competitive dynamic energy pricing scheme is inevitable in such systems to make the optimal decision. It is very challenging when the prosumers have limited access to the fellow prosumer's system information (i.e., load profile, generation, and so on). This paper presents a privacy-preserving distributed dynamic pricing strategy for P2P transactive energy systems in the smart grid using Fast Alternating Direction Method of Multipliers (F-ADMM) algorithm. The result shows that the algorithm converges very fast and facilitates easy implementation. Moreover, a closed-form solution for a P2P transactive energy system was presented, which accelerate the overall computation time.

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