Data-driven energy mix optimization: forecasting, risk-aware planning and quantum-enhanced methods
We propose a unified, risk-aware framework for renewable energy mix planning integrating meteorological parameterization, probabilistic machine learning forecasts and optimization. Using NASA POWER reanalysis, wind- and solar-relevant features are derived. Gaussian Process Regression provides point predictions with predictive covariances, capturing both variability and interdependence of renewable resources. These forecasts feed into a portfolio-theoretic optimization model: short-term scheduling is solved as mixed-integer quadratic programs with Alternating Direction Method of Multipliers (ADMM)-based decomposition, while long-term planning is formulated as binary quadratic programs with variance and lagged-covariance penalties. The exponential scaling of classical solvers motivates hybrid quantum–classical approaches such as quantum annealing for multi-period, high-resolution planning. The framework reduces exposure to scarcity events, enhances security of supply and directly supports decarbonization targets, while also offering a transferable methodology for risk-informed planning in other sectors facing stochastic variability. The results obtained from the implementation of the proposed framework for Germany indicate that endowed with quantum computing techniques the short-term optimization can facilitate robust integration of the renewables in the grid without compromising grid reliability and the long-term optimized planning (not feasible with classical computing resources) can avoid overproduction by about 30% at certain times, reducing storage costs and detrimental impact on grid frequency.