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Journal of Applied Automation Technologies

Distributed Quantum Computing Framework for Smart Grid Optimization

Agata Jolanta Jachowa, Ludwik Kłos

Published Apr 14, 2024• 0 citations
Publisher

Abstract

Optimization models for large smart grids need to consider network constraints, renewable energy uncertainty, storage dynamics and flexible demand simultaneously. The mixed discrete-continuous nature of these problems is not suitable for the operational time constraints, and a single quantum processor cannot yet handle real grid instances. A distributed quantum computing framework for an electrical network is proposed in this paper, which divides the network into coupled regions, assigns compact quadratic unconstrained binary optimisation subproblems to heterogeneous quantum workers, and employs an asynchronous classical coordinator to enforce boundary consensus. The four components of the framework are adaptive penalty encoding, warm-started variational circuits, confidence-aware result aggregation and projection-based feasibility repair. A reproducible simulation study is constructed for modified 33-bus, 118-bus, 300-bus and 1354-bus systems with renewable generation, batteries and responsive loads. In all the test cases, the proposed method reduced the operating cost and constraint violation compared with centralised QAOA, quantum annealing, ADMM, and mixed-integer programming under the same time budget. It still performs reasonably well under high worker latency and depolarising noise. Based on the above results, near-term quantum resources will be more credible as coordinated regional accelerators than as a single grid optimizer. Therefore, it will be presented as an engineering architecture, an explicit optimisation map and a multi-metric evaluation system for scalable quantum-assisted smart grid operation.

DOI
10.64972/jaat.2024v2.269p14e:192-206

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Optimization models for large smart grids need to consider network constraints, renewable energy uncertainty, storage dynamics and flexible demand simultaneously. The mixed discrete-continuous nature of these problems is not suitable for the operational time constraints, and a single quantum processor cannot yet handle real grid instances. A distributed quantum computing framework for an electrical network is proposed in this paper, which divides the network into coupled regions, assigns compact quadratic unconstrained binary optimisation subproblems to heterogeneous quantum workers, and employs an asynchronous classical coordinator to enforce boundary consensus. The four components of the framework are adaptive penalty encoding, warm-started variational circuits, confidence-aware result aggregation and projection-based feasibility repair. A reproducible simulation study is constructed for modified 33-bus, 118-bus, 300-bus and 1354-bus systems with renewable generation, batteries and responsive loads. In all the test cases, the proposed method reduced the operating cost and constraint violation compared with centralised QAOA, quantum annealing, ADMM, and mixed-integer programming under the same time budget. It still performs reasonably well under high worker latency and depolarising noise. Based on the above results, near-term quantum resources will be more credible as coordinated regional accelerators than as a single grid optimizer. Therefore, it will be presented as an engineering architecture, an explicit optimisation map and a multi-metric evaluation system for scalable quantum-assisted smart grid operation.
In addressing the complexity and real-time demands of modern grid optimization in quantum technology, advancements in computing and other fields have provided new avenues for meeting these needs. This article proposes a structured hybrid quantum-classical framework that uses adiabatic quantum computing to plan the operation of large-scale power networks. A large number of experiments were conducted on benchmark power system cases with 14 to 300 nodes, including various topologies and random operating conditions. The aforementioned method first performs preprocessing and problem partitioning. Then, using spatial and temporal partitioning schemes, the most difficult subproblems are assigned to quantum subroutines. The results show that compared to the state-of-the-art traditional algorithms, the hybrid method has a constraint violation rate of less than 0.03 in all test cases, an average operational cost reduction of 6%, and energy savings of 18%. Runtime evaluation shows that the hybrid solver outperforms pure classical and purely quantum methods, and the computation time exhibits sublinear scaling as the grid size increases. The structure can be flexibly adjusted and can still function normally in emergencies, even if parameters and resources are modified or the topology changes. Research indicates that combining adiabatic quantum computing with hybrid optimization systems can enhance the system's scalability, solution quality, and computational efficiency. This method is currently being applied to the construction of new smart grids.
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Growing integration of distributed energy resources increases power-system variability and uncertainty. During disturbances, these effects can intensify generation-load imbalances and cascading failures. Controlled islanding limits their propagation by partitioning a compromised grid into connected, electrically sustainable islands. However, classical methods face rapidly growing computational costs as network size and island count increase. Quantum optimization offers an alternative for exploring this combinatorial partition space. Yet monolithic quantum formulations encode all assignment decisions in one circuit, causing qubit demand and circuit complexity to scale with network size. In this study, a qubit-bounded sequential distributed quantum approximate optimization algorithm (QAOA) framework is proposed to tackle coherent controlled islanding under limited quantum resources. It formulates the optimization as boundary-conditioned regional quadratic unconstrained binary optimization (QUBO) subproblems that are solved sequentially within a fixed qubit budget. Thus, circuit width remains independent of network size, with aggregate quantum workload scaling linearly on bounded-degree networks. Evaluation covers eleven IEEE systems from 9 to 300 buses using IBM quantum computing resources, with Gurobi and monolithic QAOA as references. Across all systems, the framework recovers feasible Gurobi-optimal partitions under noise, confirming the resilience of its solution quality. The results further show that the proposed method substantially reduces quantum-resource demand and circuit complexity relative to monolithic QAOA, allowing large islanding problems to be addressed within current hardware limits. The proposed framework provides a feasible and scalable pathway for quantum optimization in large-scale power systems.
The modernization of power grids, transitioning into smart grids, demands innovative technologies to manage and optimize energy distribution effectively. As power systems continue to evolve into smart grids with distributed and dynamic architectures, efficient load balancing becomes critical. Classical methods often prove inadequate in handling the complexity and variability of smart grid data. Increasing popularity of renewable energy sources adds another layer of complexity. The integration of renewable energy sources and increasing demand volatility pose significant challenges for smart grid load balancing. Traditional machine learning (ML) methods, while promising, face limitations in real-time optimization and scalability. Quantum Machine Learning (QML), which leverages quantum computing principles like superposition and entanglement, offers a promising frontier for optimizing energy distribution and load balancing by enhancing established ML algorithms. This paper surveys and compares current research on the application of QML to smart grid load balancing, exploring quantum neural networks (QNNs), quantum support vector machines (QSVM), as well as quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA), to improve various aspects of load-balancing efficiency in smart grids. By encoding classical grid data into quantum states, QML models exploit superposition and entanglement to process high-dimensional datasets faster than classical methods.
As more and more controllable devices equiped in the smart grid, the inceasing scale of binary virables brings time-consuming dimension explosion problems to traditional optimizations problems. As a parallel computing method, quantum computing is valuable for exploration to accelerate the optimization computation. The classic quantum annealer (QA) only can be used for quadratic unconstraint binary optimization (QUBO) problems. This paper is to explore a novel algorithm based on the quantum annealer for grid partition problems. It is a typical large-scale Integer Quadratic Programming (IQP) problems. The proposed quantum algorithm attemped to apply Lagrangian relaxation (LR) and alternating direction method of multipliers (ADMM), to transform an IQP problem to general format of QUBO problems. Some numerical cases of the grid partition are discussed to compare the performance of the proposed algorithm with some typical methods, thus verifing the feasibility and performance of this ADMM-QA algorithm.