Dynamic Load Balancing Applying Water-Filling Approach in Smart Grid Systems

To enhance the reliability of the power grid, further processing of the power demand to achieve load balancing is regarded as a critical step in the context of smart grids with Internet of Things technology. In this paper, dynamic offline and online scheduling algorithms are proposed to minimize the power fluctuations by applying a geometric water-filling approach. For the offline approach, full information in the power demand is available, possibly by predicting from the power utilities. We present an exact approach in order to allocate the elastic loads based on the inelastic load's information considering the group-and node-power upper constraints. For the online approach, the reference level is computed dynamically using historical demand data to minimize the fluctuation in the grid, and the elastic loads can only be scheduled in the future time slots. Two dynamic algorithms are investigated to achieve load balancing in the power grid without influencing user experience by real-time reference level adjustment. Facilitated by the proposed methodologies, the power utilities can significantly reduce the cost of improving the power capacity, and the consumers are able to enjoy more stable electrical power.