Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids

Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization Approach

Generally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction.