A mixed-integer linear programming model for microgrid optimal scheduling considering BESS degradation and RES uncertainty

The integration of renewable energy sources (RES) and battery energy storage systems in microgrid offers significant advantages but also presents challenges, such as the variable nature of RES and high battery costs. This paper introduces an innovative battery degradation model using the rain-flow counting algorithm to address both complete and incomplete cycles. We also propose a degradation cost model based on battery capacity loss and engineering economics principles. Additionally, we present a new metric, the probability of reserve adequacy (PRA), which indicates the likelihood of maintaining sufficient spinning reserves to meet local demand. The PRA is converted into deterministic constraints using properties of the normal distribution. To minimize operating costs while ensuring a specified PRA, the microgrid scheduling problem is formulated and solved using mixed-integer linear programming (MILP). The numerical simulation results indicate that the proposed model outperforms previous linear battery degradation models, achieving a 33.33 % reduction in cycle aging, a 24.11 % decrease in aging costs, and a 92 % reduction in computational load and processing time.