This study compares classical and quantum optimization algorithms for maximizing total power output from a hybrid renewable energy system comprising PV panels and Bl-WT. Classical methods include PSO, SA, CRO, FTMA, CSA, JA, GA, and FL. PSO achieved the fastest convergence at 19 iterations with a peak of 7700 W, while JA reached the highest output of 7820 W in 81 iterations. GA and CSA converged at 99 iterations with 7730 W and 6900 W respectively, FTMA achieved 7750 W in 119 iterations, and SA matched JA’s maximum of 7820 W but required 999 iterations. FL delivered 7250 W without a defined convergence profile. Quantum approaches: VQE, VQD, QAOA, QAE, and QPMC-QAE have shown distinctive capabilities. QAOA with SLSQP converged in 19 iterations to a Hamiltonian minimum of −4.3, while AQGD reached convergence in 3 iterations at −1.0. VQE attained minima near −8.0 with NELDER-MEAD in 125 iterations, and VQD produced excited states with iteration counts from 378 (SLSQP) to 2569 (AQGD). QAE and QPMC-QAE stably predicted power outputs up to ∼7200 W, with QPMC-QAE closely matching actual data and demonstrating scalability across datasets. Results indicate PSO and QAOA as the fastest in classical and quantum domains, JA and SA as delivering the highest classical outputs, and QPMC-QAE as the most reliable quantum estimation method for complex hybrid energy optimization.
This study compares classical and quantum optimization algorithms for maximizing total power output from a hybrid renewable energy system comprising PV panels and Bl-WT. Classical methods include PSO, SA, CRO, FTMA, CSA, JA, GA, and FL. PSO achieved the fastest convergence at 19 iterations with a peak of 7700 W, while JA reached the highest output of 7820 W in 81 iterations. GA and CSA converged at 99 iterations with 7730 W and 6900 W respectively, FTMA achieved 7750 W in 119 iterations, and SA matched JA’s maximum of 7820 W but required 999 iterations. FL delivered 7250 W without a defined convergence profile. Quantum approaches: VQE, VQD, QAOA, QAE, and QPMC-QAE have shown distinctive capabilities. QAOA with SLSQP converged in 19 iterations to a Hamiltonian minimum of −4.3, while AQGD reached convergence in 3 iterations at −1.0. VQE attained minima near −8.0 with NELDER-MEAD in 125 iterations, and VQD produced excited states with iteration counts from 378 (SLSQP) to 2569 (AQGD). QAE and QPMC-QAE stably predicted power outputs up to ∼7200 W, with QPMC-QAE closely matching actual data and demonstrating scalability across datasets. Results indicate PSO and QAOA as the fastest in classical and quantum domains, JA and SA as delivering the highest classical outputs, and QPMC-QAE as the most reliable quantum estimation method for complex hybrid energy optimization.
This study compares seven quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), Quantum Amplitude Estimation (QAE), Quantum Neural Networks (QNN), a novel integration of Quantum Parallel Multi-layer Monte Carlo (QPMC) with QAE proposed in this work, and Quantum-Inspired Evolutionary Optimization (QIEO) to optimize power extraction from photovoltaic (PV) panels and bladeless wind turbines (BI-WT). The analysis considers key parameters, including effective open-circuit voltage (VOC) of the series-connected PV modules ranging from 85.54V to 89.74V, short-circuit current (ISC) between 2.81A and 21.47A, wind speed (v) spanning from 0.6m/s to 22.3m/s, and solar irradiance (Gθ) varying from 119W/m2to 924W/m2. VQE and VQD optimize power output effectively, with VQE converging faster. QAOA remains stable but does not always reach optimal power values. QAE estimates power output with high accuracy, aligning with experimental data. QNN achieves lower validation loss when trained for 200 epochs, demonstrating improved generalization. The QPMC with QAE method estimates power output fluctuations with minimal deviation. QIEO exhibits enhanced optimization, with population sizes above 250 individuals leading to stable power output maximization.
The researched data shows that quantum algorithms have been mostly used for battery’s chemistry and conventional grid systems. These algorithms have proved to be computationally cheaper as a common meritorious aspect in almost all implementations. However, usage of quantum algorithms to integrate two renewable energy systems is yet to be explored. This study investigates the application of quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Amplitude Estimation (QAE)—to optimize energy extraction from solar panels and bladeless wind turbines. Nine different optimizers are used on 3066 and 11547 datapoints of wind and solar respectively, to comment about optimality between QAOA, VQD and VQE. Results demonstrate that optimizers like NELDER_MEAD and COBYLA excel in convergence speed, whereas in case of energy minimization SPSA and SLSQP for VQE, COBYLA and P_BFGS for VQD have outperformed other optimizers. QAOA effectively tackles combinatorial optimization tasks as convergence speed for all optimizers has been relatively rapid. Since VQE, VQD and QAOA have not been accurate in finding optimal data point, hence QAE is utilized. Additionally, QAE’s precision in amplifying high-probability quantum states aligns well with the periodic and structured nature of wind and solar energy datasets.
The design and operation of renewable energy conversion systems—such as wind turbines, solar photovoltaic arrays, and hybrid storage networks—require balancing multiple, often conflicting objectives, including energy efficiency, cost minimization, environmental impact, and system reliability. Traditional optimization techniques, while effective for isolated parameters, struggle with the combinatorial complexity and nonlinear interdependencies inherent to integrated energy systems. This study explores the application of quantum optimization algorithms in multi-objective renewable energy design, emphasizing how quantum annealing, Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE) frameworks can accelerate solution discovery and improve design precision. By leveraging the principles of superposition and entanglement, quantum systems can process vast design spaces in parallel, enabling the rapid evaluation of trade-offs among competing objectives. A hybrid quantum–classical workflow is proposed to integrate quantum solvers into existing computational pipelines for system simulation, control optimization, and energy forecasting. Preliminary modeling results and conceptual simulations indicate that quantum-enhanced optimization not only reduces computation time but also achieves higher Pareto-optimal efficiency compared to conventional multi-objective genetic algorithms and particle swarm optimizers. This interdisciplinary approach demonstrates the transformative potential of quantum computation in sustainable energy system design, supporting the global transition toward carbon neutrality and energy equity. By embedding quantum optimization into renewable infrastructure development, the study provides a foundation for next-generation decision-making frameworks that harmonize performance, sustainability, and scalability.
When talking about rising demand for electricity, shifting to renewable energy sources, and prioritizing grid stability, one thing that is crucial is optimizing the scheduling of electricity load based on the demand. There are various classical machine learning (ML) and meta-heuristic optimization methods (Particle Swarm Optimization, Genetic Algorithms, Differential Evolution, Simulated Annealing) used to forecast load, and then schedule accordingly to minimize cost, reduce the peak load of system, and improve the overall efficiency. However, these methods struggle in handling large-scale combinatorial complexity and multiple constrained systems in real time. This paper presents a comparative study of classical ML-based optimization techniques and introduces a novel quantum-machine-learning approach using the Quantum Approximate Optimization Algorithm (QAOA) for electricity load scheduling. Initially, a survey was conducted and several classical techniques for optimization and forecasting were evaluated on realistic demand data. The paper highlights their strengths and drawbacks in terms of solution quality, computational time, and scalability. Then, the paper proposes and apply QAOA to the same scheduling problem, formulating load scheduling as a quadratic unconstrained binary optimization (QUBO) model suitable for quantum optimization. We assess the performance of QAOA against classical methods under identical constraints. Thus, Simulated Annealing produces a normalized cost of 0.00029 (after 37 seconds) whereas the cost found by Differential Evolution is 0.1314 and by the Genetic Algorithm 0.4379. This tends to confirm the validity of classical heuristics for smaller problems [16]. Our results show that while classical methods perform well for small to medium scale problems, the QAOA approach offers competitive or superior performance in terms of optimization quality and scalability for larger, more complex instances, with potential reductions in computation time under certain conditions. We conclude by discussing the practical challenges of implementing QAOA (e.g. noise, parameter tuning) and suggest directions for future research to advance quantum-assisted optimization in electricity scheduling.
Quantum computing is a big change in how to do calculations, especially good for solving complex problems that are hard for traditional computers. These problems often get worse quickly as they grow bigger, making them very difficult to solve efficiently. Although quantum computers have some big advantages, they still have some issues like limited number of qubits, noise, and problems with staying stable for long periods. To deal with these problems and make the best use of both quantum and classical methods, this research proposes a new system that mixes classical methods such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with quantum methods like Quantum Approximate Optimization Algorithm (QAOA) and different versions of Grover's search. [14] [15] [1] [5]The classical methods are good at finding good solutions and working reliably, while the quantum parts can help in certain tasks, like searching or improving calculations. The new system is tested on real problems in areas like logistics (like planning delivery routes), network routing (like finding the fastest paths), and job scheduling (like assigning tasks with limited resources). A way is developed to turn these problems into a form that quantum computers can understand, called QUBO. [10]The tests use real data and simulated quantum setups (like Qiskit and D-Wave Ocean SDK). The results show that this new system works better than using only classical or only quantum methods in terms of the quality of the solutions, how fast they find the answers, and how long they take. This study shows the potential of using a mix of quantum and classical methods to solve problems more effectively and sets up a foundation for future research in using quantum methods to help make better decisions. [12] [18]
• Quantum and classical algorithms are benchmarked for building performance optimization. • A surrogate model is trained on 15,000 IDA ICE simulations for prediction. • NSGA-II achieves the best trade-offs between energy use and thermal comfort. • Quantum methods deliver faster results but with reduced solution quality. • Quantum computing shows potential for future real-time building optimization. Optimizing building performance is essential for enhancing energy efficiency and occupant comfort. This study evaluates the applicability of quantum computing–based optimization methods in the Architecture, Engineering, and Construction (AEC) industry by comparing the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) with classical multi-objective optimization algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A dataset of 15,000 building simulations was used to train an Extreme Gradient Boosting (XGBoost) model for predicting total energy consumption (kWh/m 2 /year) and Predicted Percentage of Dissatisfied (PPD) occupants. These predictions were then used in the optimization phase. NSGA-II produced the best trade-offs, achieving energy consumption between 17.84 and 19.84 kWh/m 2 /year and PPD below 5.2 %, with strong diversity and convergence. QAOA executed faster (0.54 min) than NSGA-II (18.9 min) but resulted in higher energy values (31.85–55.62 kWh/m 2 /year) and weaker solution quality. Quantum Annealing completed in 0.37 min but returned clustered solutions near 45.88 kWh/m 2 /year. While the current limitations of quantum methods constrain their effectiveness, the findings indicate their potential as fast solvers in future building performance optimization workflows, particularly as hardware and algorithmic capabilities mature.