Modelling and control of advanced adiabatic compressed air energy storage under power tracking mode considering off-design generating conditions

• Published in: Energy (Oxford) 2021-03, Vol.218, p.119525, Article 119525
• Main Authors: Bai, Jiayu, Liu, Feng, Xue, Xiaodai, Wei, Wei, Chen, Laijun, Wang, Guohua, Mei, Shengwei
• Format: Article
• Language: English
• Subjects: AA-CAES; Adiabatic; Adiabatic flow; Air masses; Collocation methods; Compressed air; Computer applications; DAE constrained Optimization; Design; Differential equations; Energy storage; Environmental impact; Flow rates; Fluid flow; Gas expanders; Heat exchangers; Heat transfer; Inlet pressure; Life span; Mass flow rate; Off-design performance; Optimization; Set-point control; State-space model; Storage tanks; Throttles; Tracking control; Turbines
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2020.119525

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. •A state-space model of AA-CAES for real-time power tracking control is proposed.•Turbine inlet pressure, air mass flow rate and heat capacity ratio are coordinated.•The part-load features of turbine and heat exchanger are captured by simplified model.•The set-point control of AA-CAES is cast to a DAE constrained optimization problem.•Simultaneous collocation method is adopted for NLP reformulation.