Risk Constrained Self-Scheduling of AA-CAES Facilities in Electricity and Heat Markets: A Distributionally Robust Optimization Approach

Abstract

Advanced adiabatic compressed air energy storage (AA-CAES) has the advantages of large capacity, long service time, combined heat and power generation (CHP), and does not consume fossil fuels, making it a promising storage technology in a low-carbon society. An appropriate self-scheduling model can guarantee AA-CAES's profit and attract investments. However, very few studies refer to the cogeneration ability of AA-CAES, which enables the possibility to trade in the electricity and heat markets at the same time. In this paper, we propose a multi-market self-scheduling model to make full use of heat produced in compressors. The volatile market price is modeled by a set of inexact distributions based on historical data through ø-divergence. Then, the self-scheduling model is cast as a robust risk constrained program by introducing Stackelberg game theory, and equivalently reformulated as a mixed-integer linear program (MILP). The numerical simulation results validate the proposed method and demonstrate that participating in multi-energy markets increases overall profits. The impact of uncertainty parameters is also discussed in the sensibility analysis.

Keywords

• Advanced adiabatic compressed air energy storage (AA-CAES)
• conditional value at risk (CVaR)
• distributionally robust optimization (DRO)
• heat market
• self-scheduling
• Stackelberg game