Model Predictive Control Optimization Strategy for Integrated Energy Systems: A Two-stage Dual-loop Optimization Framework
Xing Dong, Chao Jiang, Jibin Liu, Daduan Zhao, Bo Sun
Abstract
Integrated Energy Systems (IESs) are important vehicles for achieving energy conservation and emission reduction. However, operating an IES smoothly is difficult due to source–load fluctuations and the complexity of the multiple timescales of different energy flows. To tackle the challenges, this paper proposes a two-stage dual-loop optimization framework for IESs, where the two stages comprise the first stage: day-ahead cooperative optimization of source-storage-demand (DCOS), and the second stage: intraday dual-loop rolling optimization control (IDRO). In DCOS, energy storage, and integrated demand response models are established, and a carbon emission trading mechanism is introduced to achieve an economically low-carbon operating plan. In IDRO, an electric power rolling optimization model based on model predictive control is established in the inner loop, and a cooling and heating power output adjustment strategy based on user comfort event-trigger mechanism is developed in the outer loop. The proposed optimization strategy enables the coordinated operation of multiple energy flows across various time scales, effectively mitigating the imbalance between production and demand during intraday operations under source–load fluctuations scenario. In case studies, this strategy is applied to a typical IES, with simulations conducted to evaluate its performance during typical summer and winter seasons.