Combining quantum algorithms with grid operations to boost forecasting, efficiency, and resilience—advancing toward quantum-intelligent power systems. Industry Background Dual-Carbon Goals Driving Grid Upgrades Energy restructuring calls for smarter, greener computing. Growing Grid Complexity Rising demand and renewables increase computing pressure. Need for Breakthrough Technologies Classical limits emerge; quantum offers new solutions. Industry Pain Points Low Algorithm Efficiency Conventional methods are slow and fail to meet real-time demands like power flow and load forecasting. Limited Accuracy Struggle with complex, uncertain data, reducing decision precision. Data Overload Exploding data volumes expose classical computing limits, hindering analysis and application. Solution Architecture & Advantages Full-Stack Integration Covers applications, algorithms, frameworks, and hardware for end-to-end quantum-power grid integration. Multi-Algorithm Collaboration Utilizes QLSTM, QTransformer, QMLP, etc., for time series, stream, and large-model computing. Platform-Hardware Synergy Supports encryption, structuring, and simulation with flexible backend quantum and hybrid clusters. Application Scenarios Power Forecasting Quantum LSTM improves short-term PV prediction for better supply-demand balance. Load Forecasting Quantum attention models fuse multi-source data to enhance load accuracy. Power Flow Calculation Quantum algorithms speed up flow computations for real-time scheduling. Fault Diagnosis Quantum models boost fault detection accuracy and response speed. Collaboration Case State Grid Corporation of China Exploring quantum-based solutions for PV power forecasting, power flow calculation, and load prediction.
Compiled from https://originqc.com.cn/en
