Optimal bidding and scheduling of AA-CAES based energy hub considering cascaded consumption of heat. (15th October 2021)

• Record Type: Journal Article
• Authors: Wu, Danman; Bai, Jiayu; Wei, Wei; Chen, Laijun; Mei, Shengwei
• Is Part Of: Energy. Volume 233(2021)
• Publication Date: 2021-10-15
• Subjects: Advanced-adiabatic compressed air energy storage (AA-CAES) -- Energy hub -- Multi-temperature heat demands -- Optimal bidding -- Surrogate optimization method; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.121133
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The increasing level of uncertainty caused by high penetration of renewable energy and the widening gap of peak-valley demands call for the deployment of energy storage in power systems. Advanced-adiabatic compressed air energy storage (AA-CAES) is a large-scale physical energy storage technology with the merits of long lifetime, low environmental impact, and no emission. Moreover, AA-CAES works with electricity and heat, making it an excellent choice to realize the framework of energy hub. This paper proposes a cogeneration and storage architecture of an AA-CAES based energy hub in an industrial park. Particularly, the cascaded use of thermal energy to supply heat demands with different temperatures is modeled. A bi-level optimization model is established to study the optimal bidding and scheduling of AA-CAES based energy hub in the day-ahead market. The upper level is the power purchase and self-scheduling of the energy hub, aiming at minimizing the daily operation cost; the lower level represents the market clearing problem which determines the electricity price based on alternating-current optimal power flow. A radial basis function based surrogate optimization method is developed to solve the bi-level model with a nonlinear lower level problem. The problem is decomposed into second-order cone programs and a nonlinear surrogate model which can be solved without much computational effort. Numerical examples verify the effectiveness of the proposed method. Highlights: AA-CAES based energy hub model with cascaded consumption of heat. A bi-level program for optimal bidding and scheduling of the energy hub. A radial basis function based surrogate method to solve the bi-level program. Comparison with Bayesian optimization approach.