模型预测控制
可再生能源
工程类
控制理论(社会学)
系统动力学
控制器(灌溉)
控制工程
计算机科学
控制(管理)
农学
生物
电气工程
人工智能
作者
Chunming Liu,Chunling Wang,Yitong Yin,Pengfei Yang,Huiling Jiang
出处
期刊:Applied Energy
[Elsevier]
日期:2022-03-01
卷期号:310: 118641-118641
被引量:18
标识
DOI:10.1016/j.apenergy.2022.118641
摘要
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.
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