Quantum for Energy and Utilities 2026

2.2 Renewable and nuclear energy As the global energy mix shifts toward renewables, the operational challenge moves from resource extraction to forecasting variable generation, integrating distributed assets and improving material efficiency. Solar Solar generation is driven by advances in photovoltaic materials, manufacturing and real-time forecasting to manage intermittency. The highest- impact computational bottlenecks are in materials discovery and in modelling micro-weather dynamics that determine near-term output. Material discovery (perovskites and organic photovoltaics) The efficiency of solar PV is fundamentally constrained by the bandgap properties of the absorber material. Discovering new candidates, such as perovskites, is slowed by the difficulty of accurately simulating electronic structure and charge-transport behaviour at scale. Quantum simulation can accelerate this discovery cycle by modelling exciton dynamics and charge-transfer pathways with higher fidelity, enabling faster screening of high-efficiency materials and reducing years of trial-and-error synthesis. Irradiance forecasting Accurate short-term forecasts of solar output are critical for grid stability, but performance is often limited by micro-weather variability that is difficult to capture with classical models. Quantum machine learning approaches (for example, quantum support vector machines and quantum neural networks) are being evaluated to process high-dimensional meteorological features more efficiently and improve short-horizon predictions of cloud cover and irradiance, supporting better reserve management and dispatch decisions.Wind Wind power performance depends on complex aerodynamics at the wind-farm scale and on high- availability operations in harsh environments. Key challenges include wake-aware layout optimization and condition monitoring to reduce downtime and improve yield. Wind farm layout optimization Within a wind farm, turbines create wake. Optimizing turbine placement to minimize wake losses is a high-dimensional, non-convex optimization problem. A near term approach is through quantum-inspired optimization to model wake interactions as a complex graph and search for layouts that increase energy yield without additional hardware. Predictive maintenance Wind turbines generate continuous streams of sensor data. Detecting subtle anomalies in vibration signatures before they progress into gearbox or blade failures is a core reliability challenge. Hybrid quantum classical machine learning approaches are being explored to improve early anomaly detection in vibration data, helping operators schedule maintenance during low-wind periods and reduce costly unplanned downtime. Hydro Hydropower sits at the intersection of physics- based constraints and market operations, requiring coordinated scheduling across cascaded assets. Operators must optimize water-value trade- offs while meeting environmental flow limits and safety requirements. In renewables, optimization for variable renewable integration emerges as the top near-term use case. Quantum for Energy and Utilities: Key Opportunities for Energy Transition 16