Clearing local transactive energy markets while enforcing non-convex AC optimal power flow constraints becomes computationally intractable as the number of distributed energy resources scales into the millions, and classical interior-point and branch-and-bound solvers cannot meet real-time deadlines at this scale. We propose a hybrid quantum-classical framework that maps the joint market-clearing and power-flow problem onto a quadratic unconstrained binary optimization form with dynamic penalty weighting and solves it with a customized Quantum Approximate Optimization Algorithm. On synthetically expanded IEEE 8500-node networks with up to 100,000 distributed energy resources, the framework exhibits empirical (N N) scaling, clears the largest network in 72.8 seconds, and maintains an optimality gap below 3.5%, compared with hours-long or non-convergent classical baselines.
Clearing local transactive energy markets while enforcing non-convex AC optimal power flow constraints becomes computationally intractable as the number of distributed energy resources scales into the millions, and classical interior-point and branch-and-bound solvers cannot meet real-time deadlines at this scale. We propose a hybrid quantum-classical framework that maps the joint market-clearing and power-flow problem onto a quadratic unconstrained binary optimization form with dynamic penalty weighting and solves it with a customized Quantum Approximate Optimization Algorithm. On synthetically expanded IEEE 8500-node networks with up to 100,000 distributed energy resources, the framework exhibits empirical (N N) scaling, clears the largest network in 72.8 seconds, and maintains an optimality gap below 3.5%, compared with hours-long or non-convergent classical baselines.
• We propose a new hybrid quantum-classical algorithm for solving the UC problem. • The hybrid model successfully optimizes 24-hour UC systems from 10 to 1000 units. • The model outperforms classical metaheuristics (GA and SA) and classical MINLP. • This represents an improvement over previous QCUC methods, a step to real-world use. Resource scheduling is critical in many industries, especially in power systems where the Unit Commitment (UC) problem determines the on/off status and output levels of generators under physical and economic constraints. Traditional exact methods, such as Branch‑and‑Bound, Branch‑and‑Cut, dynamic programming and mixed‑integer linear programming (MILP), 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 other resource scheduling problems by extension, 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 (QUBO) 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 (MINLP) 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.
Optimization of electricity surplus is a crucial element for transmission power networks since it leads to reducing costs as well as increasing efficiency across the network as a whole. In this paper, we show how to optimize such network costs using a quantum annealing approach. First, we define the Quadratic Unconstrained Binary Optimization (QUBO) problem for network partitioning. To achieve this, we introduce a method to translate inequality constraints with real-valued coefficients into approximate penalty functions. Next, we test the implementation on purely quantum and quantum-classical hybrid architectures. We then solve the problem using the D-Wave hybrid Constrained Quadratic Model (CQM) solver, the D-Wave hybrid Binary Quadratic Model (BQM) solver, as well as classical solvers available on Azure Quantum Cloud. Finally, we find that the value of the objective function obtained with the quantum-classical hybrid solvers is always lower compared to the classical approaches across a range of different problem sizes. This demonstrates that the quantum-classical hybrid methods outperform the classical methods in terms of solution quality.
Modern distribution grids face unprecedented challenges from intermittent renewable integration, extreme weather events, and the need for real-time optimal power flow (OPF) solutions that preserve physical constraints while minimizing operational costs. This paper introduces Constraint-Aware QAOA for Real-Time Quantum Smart Grid Optimization, a novel hybrid quantum-classical framework that directly addresses renewable dispatch, battery scheduling, and demand response in distribution networks. We formulate the stochastic OPF problem with renewable integration as a constrained optimization challenge and propose three innovative solution approaches: (1) Constraint-Preserving QAOA (CP-QAOA) with a novel Power-Flow Preserving Mixer (PF-Mixer) that enforces Kirchhoff-style flow conservation during quantum evolution, (2) Phase-Encoded Power-Flow (PEPF) mapping that reduces qubit requirements by 3× through mixed binary-phase representation, and (3) Hybrid Receding-Horizon Quantum Controller (HRQC) enabling subminute reoptimization via intelligent problem decomposition. For renewable integration modeling, we develop multimodal fusion techniques combining real-time generation forecasts, battery state-of-charge dynamics, and demand response signals with quantum superposition states. Our experimental evaluation on IEEE test systems with realistic renewable penetration scenarios demonstrates significant improvements: the CP-QAOA approach achieves 18.3% reduction in operational costs compared to classical OPF baselines, while quantum methods provide > 95% constraint feasibility compared to < 31% for standard QAOA with penalty encoding. The hybrid framework shows strong promise for scalable deployment in real smart grid infrastructure, with practical implications for quantum-enhanced grid operations under high renewable penetration.
Optimal Power Flow (OPF) is a fundamental optimisation problem in power systems, aiming to minimize the generation cost dispatch while satisfying operational constraints. Traditional optimisation methods, especially the AC-OPF formulation, struggle to converge or find optimal solutions due tothenon-linear andnon-convexnatureoftheproblem. Thisstudyaddressesthe AC-OPF using a hybrid iterative algorithm that combines classical and quantum computing, where Quantum Annealing assists in optimizing decision variables. ThemethodologysplitstheAC-OPFintotwointerrelatedproblems. Thefirstsub-problemisthe classical OPFformulation, whichincorporatesallelectricalnetworkcomponentsandminimizes the objective function under technical and operational constraints. The second sub-problem, also known as the master problem, is a binary problem that assumes activation states of the generators, while minimizingcostsofgenerationandstart-upcostsfordifferenttimesteps. This forms aBinary Integer Linear Programming (BILP) problem, which is well-suited for quantum computing due to its discrete structure and combinatorial complexity. Once the BILP is defined, it is converted into a Hamiltonian and evolved using Quantum Annealing throughtheQiboframework. Theresultingoptimalbinarycombinationisthenapplied to the network model and solved classically. If the solution is feasible and optimal, the process ends. If not, the combination is excluded from the BILP and added as a constraint to guide the search for the next best feasible solution. This methodology is tested on two IEEE networks of 9 to evaluate computational speedup and assess the limitations of actual quantum hardware. Once this is done, future work will explore the use of Quantum Machine Learning to generate 24-hour load and generation profiles, while in this work, an LSTM is used, enabling the AC-OPF solutions.
Abstract This work presents a novel implementation of the Quantum Approximate Optimization Algorithm (QAOA) for resolving the unit commitment and loss reduction problem in complex 500-node electrical networks. By leveraging the ARK5Q-200K protocol, we map the grid topology into a Quadratic Unconstrained Binary Optimization (QUBO) formulation. Our results demonstrate that phase-synchronized transcendental mixers achieve a ground-state energy approximation with a residual gap of <1% relative to the Held-Karp lower bound, maintaining a Hamiltonian precision of 1.48 mHa. We show that the sub-femto scale resolution of the CCristo infrastructure effectively mitigates classical convergence failures in NP-hard logistical manifolds. This framework provides the necessary stability for large-scale energy distribution in high-density orbital or planetary infrastructures, establishing a new ontological baseline for interstellar resource management.
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.