Optimal management of microgrids under uncertainty using scenario reduction

This work addresses the optimal management of a microgrid through a two-stage Mixed Integer Linear Programming (MILP) approach. This approach uses a scenario-based mathematical formulation to deal with uncertain information. Sufficient representation of uncertainty usually requires increased number of scenarios, which result in large-scale optimisation models. This paper investigates the use of scenario reduction techniques to reduce computational requirements while maintaining good quality of the final optimal solution. The proposed approach is then applied to optimally manage the heat demand uncertainty within a microgrid so as to minimise the operational cost while considering electricity and heat generation, purchases, sales, storage and demand profiles.