Quantum Neural Networks for Microgrid Energy Dispatch.pdf

<p dir="ltr">This paper presents a novel hybrid quantum-classical approach for optimizing energy dispatch in microgrids. By leveraging Quantum Neural Networks (QNNs), the proposed method achieves a 32% reduction in operational costs compared to conventional techniques. The solution addresses key challenges such as renewable energy intermittency and the combinatorial complexity of unit commitment problems. Key results include 89% prediction accuracy for renewables and 40% faster convergence. The study includes a detailed case study with solar PV, wind turbines, batteries, and diesel generators. </p><p dir="ltr">This work is suitable for researchers in quantum computing, renewable energy systems, and smart grid optimization. The dataset includes simulation results and cost comparison charts for a 24-hour dispatch scenario. </p>