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 grid with Internet of Things (IoT) technology. In this paper, dynamic offline and online scheduling algorithms are proposed to minimize the power fluctuations by applying geometric water-filling approach. For 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 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 realtime 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 electricity power.
The main contribution of this paper is to dynamically implement the optimal elastic load scheduling to achieve load balancing in IoT environment. Based on WF algorithm, we propose dynamic approaches to schedule the elastic load both offline and online to flatten the overall power consumption considering the peak power constraints. Firstly, we inherit the basic concept and the problem formulation for computing the general exact solution for load balancing applying WF approach from our previous work , where the offline approach with full given load information was investigated. A Smart Grid Operator can generate a constant reference level and the solution from the one-time computation. In this paper, we implement the new online load balancing approaches on top of the offline approach using the load prediction model from , considering the group and node power upper constraints for elastic loads.
In this paper, we discuss both offline and online solutions for the load balancing problem. For offline operation, it is assumed that all the load demand is known. The reference level L is a constant, and it is computed once. For online operation, the available load information is the load demand from the past time slots. The reference level L is a vector which is different by time slots. We shall use a moving window to solve reference level Lk for next time slot scheduling. Fig. 3(a) shows the model of moving window approach with window size N. When the algorithm runs to time k, we apply a reference level Lk determined by the parameters from the time slots k (N 1) to k in order to schedule the loads for the (k + 1)th time slot. With a smaller window size, the reference level is shaped more likely to the unscheduled power profile. In contrast, with a larger window size, the variation of the reference level is slow. Furthermore, we propose a computation reduced online approach. We predict inelastic load information for the mth time slot in the future, as shown in Fig. 3(b). Then we shift the starting point of the window by m slots, so that the window size is kept at N but we analyze the data which includes both the past and future information.
In this paper, we presented three algorithms to solve the load balancing problem in the smart grid. Firstly, we reviewed the load balancing model and its optimal offline solution investigated in our earlier work. Then we extend the solution to its online algorithm to compute and distribute the elastic load among the time slots and individuals based on the water-filling concept and load prediction. Furthermore, we proposed computation efficient online algorithm making use of prediction and extrapolation. Simulation results are presented to show that both online algorithms significantly reduce the load fluctuation. We also extended our work by enabling different levels of constraints to play roles in the algorithms in order to improve the controllability in the smart grid. For future works, firstly, we need to consider the tolerance of the elastic load flexibility. Secondly, the research needs to be extended to get the precise prediction for reference levels in the online algorithm. The influence of the system when error prediction occurred is another scenario in our further study.
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