Probabilistic Energy Flow of Integrated Electricity and Heat Systems with Quantile-based Quasi Monte Carlo Simulation

The strong uncertainty in both supply and demand poses acute challenge to the secure and economic operation of the integrated energy system, which requires a probabilistic energy flow (PEF) method with high accuracy to support various decision-making problems. However, nonparametric probabilistic forecasting of load and renewable energy generation has not been fully studied in the PEF problem. In this paper, an improved PEF method based on quasi Monte Carlo simulation (QMCS) and quantiles obtained from nonparametric probabilistic forecasting is proposed to enhance the accuracy and computational efficiency. The proposed method makes full use of the distribution information provided by quantiles of random variables including renewable energy generation and multi-energy demand, and significantly reduces the computational burden with low discrepancy sequences. Comprehensive numerical results on the integrated electricity and heat system demonstrate the superiority of the proposed method.