Multi-Objective Energy Management in a Microgrid with Smart Buildings Based on Cloud-Fog Computing

With the increase in the number of locally distributed generation resources and the increase of smart buildings in today's microgrids, the computational volume and data exchanged between units have increased. Therefore, it is necessary to perform on-site calculations and send the final information to the upstream layer for decision-making and planning. This paper presents a cloud-fog computing structure that reduces the computational burden and time of calculations for energy management of smart buildings in the smart microgrids. The proposed system has several smart buildings, each with fixed and shiftable loads, an energy storage system, and a photovoltaic system. In the proposed energy management of smart buildings, the cost and peak-to-average ratio are presented as objective functions in a multi-objective problem. Every smart building can buy and sell energy with the upstream network. Whale optimization algorithm has been used to solve the optimization problem and the cost and peak-average ratio have been reduced and optimized simultaneously. By implementing cloud-fog computing and solving the optimization problem of each smart building locally, the computational burden and computing time have been reduced from 465.46 seconds to 48.52 seconds compared to the case without cloud-fog structure. Therefore, cloud-fog computing has reduced the computational burden and the time of calculations in the proposed energy management system of each smart building, after which the planning of the units is done locally.