Volume 50

Adaptive Scheduling Strategies for Integrated Energy Systems under Renewable Energy Uncertainties Xinpei Yang, Jiong Shen, Honghai Niu, Yiguo Li, Junli Zhang

Abstract

The efficient operation of Integrated Energy Systems (IES), which combine electricity, heat, and cooling, is challenged by the low predictability inherent in renewable energy sources like wind and solar power. Traditional scheduling methods, relying on day-ahead and intra-day forecasts, often fail to accommodate the deviations between forecasted and actual energy generation, leading to suboptimal performance and increased operational costs. This paper proposes an enhanced scheduling method that incorporates multiple scenarios outside the probabilistic prediction intervals to address forecast deviations. By calculating optimal scheduling schemes for these extra scenarios, the proposed method ensures that IES can adapt dynamically to real-time conditions, maintaining closer proximity to the optimal operating point. Simulation results using real power grid data from Belgium demonstrate that this method significantly reduces system costs compared to traditional scheduling approaches, effectively mitigating the economic impact of forecast inaccuracies. The study highlights the potential of scenario-based scheduling in improving the reliability and efficiency of IES under uncertainty.

Keywords Integrated Energy Systems, Renewable Energy, Forecast Deviations, Scenario-based Scheduling, Stochastic Model Predictive Control, Operational Efficiency