Energy & Network

Modern smart grids aim to dynamically balance energy supply and demand across distributed, often renewable-heavy, networks. This requires solving complex optimization problems in real time, such as load balancing, unit commitment, and fault-tolerant routing, under uncertainty and strict operational constraints.
A quantum computing for smart grid project explores how quantum algorithms could enhance decision-making across the grid. Applications include optimizing power dispatch, forecasting demand-response schedules, and reconfiguring grid topology in response to disruptions or fluctuations from solar and wind sources.
This involves formulating one or more of these challenges as a Quadratic Unconstrained Binary Optimization (QUBO) or similar problem type, then applying quantum or quantum-inspired solvers to identify efficient solutions. These are benchmarked against classical optimization routines in terms of speed, accuracy, and scalability.

For utility providers and grid operators, quantum computing offers a future pathway to more responsive, resilient, and sustainable energy networks.

Accurate forecasting of renewable energy generation such as wind or solar, is vital for grid stability, energy trading, and infrastructure planning. Ensemble models, which combine multiple forecasting methods, are commonly used to improve accuracy, but optimizing and calibrating these models across large datasets is computationally intensive.

A quantum computing proof-of-concept (PoC) in renewable forecast ensembles explores how quantum algorithms can enhance model selection, parameter tuning, or probabilistic aggregation. Quantum-enhanced regression, classification, or sampling methods may help capture nonlinear dependencies and reduce uncertainty in forecast outputs.

The use-case typically involves training ensemble models on historical weather and generation data, then applying quantum algorithms to optimize ensemble weights or identify the most informative model combinations. Performance is assessed against classical approaches in terms of forecast accuracy and computational efficiency.

Improved ensemble forecasting has direct benefits for energy providers and grid operators, enabling more reliable integration of renewables and smarter decision-making in dynamic power networks.