压缩空气储能
储能
控制理论(社会学)
涡轮机
可再生能源
工程类
功率(物理)
计算机科学
机械工程
电气工程
物理
控制(管理)
量子力学
人工智能
作者
Jiayu Bai,Feng Liu,Xiaodai Xue,Wei Wei,Laijun Chen,Guohua Wang,Shengwei Mei
出处
期刊:Energy
[Elsevier BV]
日期:2020-12-06
卷期号:218: 119525-119525
被引量:53
标识
DOI:10.1016/j.energy.2020.119525
摘要
Advanced adiabatic compressed air energy storage (AA-CAES) is a scalable storage technology with a long lifespan, fast response and low environmental impact, and is suitable for grid-level applications. In power systems with high-penetration renewable generation, AA-CAES is expected to play an active role in flexible regulation. This paper proposes a state-space set-point control model of AA-CAES for the application in the power tracking mode considering off-design characteristics. The part-load features of the multi-stage turbine and heat exchanger are captured by simplified models, and then tailored for improving computational efficiency in the applications with a timescale of 1 min. The set-point control (power tracking) of AA-CAES entails the coordination of turbine inlet pressure, air mass flow rate and heat transfer fluid (HTF) mass flow rate, while ensuring the secure pressure at the throttle valve linking the air storage tank and the expansion train. The set-point control problem is cast to a differential-algebraic equation (DAE) constrained optimization problem, and is reformulated as a nonlinear program via the simultaneous collocation method. Case studies validate the accuracy and applicability of the proposed AA-CAES model for power tracking under off-design generating conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI