Accurate MILP-based battery cycle aging model for day-ahead energy and reserve scheduling in hybrid power plants

In this paper, we present an accurate mixed-integer linear programming formulation for modeling battery cycle aging in the short-term scheduling of a hybrid power plant participating in both day-ahead and secondary reserve markets. Our approach uses a slope-based piecewise linear approximation to estimate the battery cycle aging costs, closely based on the Rain-flow cycle counting algorithm (RFA). Our formulation accurately identifies battery aging cycles while accounting for the impact of the secondary regulation energy required from the battery in real-time on the cycle aging. After conducting a post-process to compute the real cycle aging cost by the RFA, the numerical results presented in the paper show that the proposed model increases profit by 1.80% and reduces capacity fade of the battery by nearly 50%, with respect to a similar formulation recently published, and that it provides an accurate estimation of the battery cycle aging. • Accurate MILP formulation to model cycle aging based on the Rain-flow algorithm. • Battery cycles accurately identified under electricity and reserve market commitments. • Battery reserve market participation and provision of regulation reserve/energy considered.