Optimal Scheduling of a Self-Healing Building Using Hybrid Stochastic-Robust Optimization Approach

Abstract

This article provides a two-stage robust energy management method for a self-healing smart building that can handle contingencies that occur during real-time operation. Aside from an electrical link with the distribution network, the smart building is equipped with a diesel generator and photovoltaic solar power generating systems. The energy management system should be smart enough to plan different resources based on the situation. At first, bilevel programming identifies critical faults for affected components based on mean time to repair. After identifying major failures, the faults are described in operational scenarios, and a two-stage hybrid robust-stochastic programming technique is used to determine the bid/offer in day-ahead and real-time energy markets, in which stochastic programming is responsible for considering the uncertainty of faults, and the robust optimization approach is used to cope with the uncertainty of real-time market prices. After linearization, the final optimization is modeled as mixed-integer linear programming in the GAMS optimization package. For the studied smart building, the daily operational cost is expected to increase from $25.794 (for the deterministic case) to $ 28.097 (for the most conservative case) due to the uncertainty of real-time market prices. Due to power shortages caused by the failure of components, the total expected not-supplied load is 6.72 kW (2.53%). A comparison between naive and self-healing scheduling indicated that naive energy management will charge an additional $ 2.75 without considering the probability of components failures under the deterministic case.