Distributionally Robust Scheduling of Electrified Heating Under Heat Demand Forecast Uncertainty
Researchers' distributionally robust framework reduces demand violations by 40% and operating costs by 34%.
A team led by Alessandro Quattrociocchi developed a distributionally robust chance-constrained optimization framework for scheduling multi-MW electric boilers and heat pumps. Using limited forecast samples, their model reduces heat-demand balance violations by 40% compared to deterministic schedulers. By modeling real-time rebound costs, it cuts daily operating costs by up to 34%, helping balance responsible parties (BRPs) hedge against volatile electricity prices and forecast uncertainty.
Why It Matters
Enables more reliable and cost-effective integration of renewable energy by optimizing large-scale electrified heating, a major source of grid flexibility.