Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance. (15th March 2022)

• Record Type: Journal Article
• Authors: Liu, Chunming; Wang, Chunling; Yin, Yujun; Yang, Peihong; Jiang, Hui
• Is Part Of: Applied energy. Volume 310(2022)
• Publication Date: 2022-03-15
• Subjects: Community integrated energy system -- Model predictive control -- Multiple time scales -- Dynamic performance optimization -- Renewable energy generation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118641
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel MPC-based optimal dispatch and control strategy is proposed for CIES. Rolling forecast and feedback correction are developed to handle the uncertainties. A closed-loop dynamic performance optimization control model is presented. The approach manages to optimize the system dispatch and dynamic response performance. Abstract: Integrated energy systems have recently attracted interest for the purpose of energy development and utilization owing to the increase in environmental pollution and the shortage of fossil energy. However, the integration of a high proportion of renewable energies and controllable loads in the system increases the uncertainty of system operation significantly. In order to rapidly track the system fluctuations and accurately control the operating equipment, a bi-level and multi-timescale dispatch and control strategy based on model predictive control is proposed for a community integrated energy system (CIES) considering dynamic response performance. The upper-level optimizer completes the rolling forecast of renewable outputs and loads over a long-time horizon, and builds an economic rolling optimal scheduling model with consideration of feedback correction. The lower-level controller establishes a closed-loop dynamic performance optimization control model over a short time scale by real-time controlling the system's upstream energy equipment. The simulation results on a modified CIES located in Beijing, China demonstrate that the proposed bi-level strategy increases the prediction accuracy while ensuring the economic efficiency of the system operation, and improves the dynamic tracking ability of the equipment and the overall energy supply dynamic response rate of the system, which provides a fundamental way for efficient application of CIES.