Published March 2021 | Version v1
Journal article

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

  • 1. Department of Electrical Engineering, State Key Laboratory of Power Systems, Tsinghua University, Beijing, 100084 (China)
  • 2. Qinghai Key Lab of Efficient Utilization of Clean Energy, Qinghai University, Xining, 810036 (China)
  • 3. Jiangsu Jiayi Thermoelectric Co., Ltd., Changzhou, 213000 (China)

Description

Highlights: • 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. 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.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.energy.2020.119525

Additional details

Identifiers

DOI
10.1016/j.energy.2020.119525;
PII
S0360544220326323;

Publishing Information

Journal Title
Energy (Oxford)
Journal Volume
218
Journal Page Range
vp.
ISSN
0360-5442
CODEN
ENEYDS

Optional Information

Copyright
Copyright (c) 2020 Published by Elsevier Ltd.