摘要：AbstractCoordinating the charging and discharging of a population of batteries with respect to load and generation forecasts decreases operating costs. However, in the case of asymmetric charging and discharging efficiencies the mathematical formulation of this problem is not straightforward. This paper compares the scheduling problem for a population of batteries in two formulations: mixed-integer (MINLP) and non-convex real-valued (NLP). In the case of the latter, the resulting separable NLP is solved using the Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN). In the case of the latter, the resulting MINLP is solved in hierarchical distributed fashion, we propose a mixed-integer extension of ALADIN. Real load and generation data is used to create an example of 54 batteries demonstrating improved performance for the MINLP formulation.
