Look-Ahead Optimal Participation of Compressed Air Energy Storage in Day-Ahead and Real-Time Markets

Abstract

Compressed air energy storage (CAES) is a large-scale storage technology that offers a valuable source of flexibility to power systems operation. This paper proposes novel look-ahead optimization models for CAES participation in day-ahead and real-time energy, regulation up and down, spinning reserve up and down, and non-spinning reserve markets. The proposed market participation models, formulated as mixed-integer linear programming problems, integrate a detailed CAES model that takes into account the physical characteristics and simultaneous operation of the compressor and expander as well as the dynamics of the air storage. The proposed look-ahead models extend the scheduling horizon of the day-ahead and real-time market participation problems into future that would maximize the utilization of CAES air storage and optimize the market participation decisions, given the expected information about profit opportunities in the future. The day-ahead model secures adequate compressed air to meet the likely deployment of the ancillary services in real time. The real-time look-ahead model enables additional profits through energy arbitrage and by modeling the potential revenues from substituting lower quality services (with possibly higher deployment) with excess higher quality services in the real-time market. The proposed look-ahead model, demonstrated on a sample CAES using real price data of california independent system Operator (CAISO), enables achieving higher profits for the CAES, paving the way for investment in and market integration of this technology.