A novel load distribution strategy for aggregators using IoT-enabled mobile devices

TL;DR

The paper tackles the problem of unpredictable grid load from IoT devices and mobile entities (e.g., EVs) by modeling a two-tier DSM system with multiple aggregators and mobility-enabled devices. It formulates a MINLP that incorporates mobility, multiple power modes, deadlines, and aggregator capacity, and then proposes an online distributed heuristic to minimize the cumulative utility loss $\\sum_t \\sum_k u_k(t)$ using decision variables $\\gamma_{pk}[i,a_j,t]$ and $\\gamma_{mk}[a_j,a_{\bar{j}},t]$. Key contributions include a generic device model with mobility, a priority-based scheduling algorithm, and experimental validation on synthetic data and a real-world EV dataset, showing a significant improvement (\\geq 57.23 ext{\%}) over baselines and solver runtimes. The work demonstrates practical DSM across multiple aggregators with load migration, delivering scalable decisions and improved grid utilization in mobility-rich environments.

Abstract

The rapid proliferation of Internet-of-things (IoT) as well as mobile devices such as Electric Vehicles (EVs), has led to unpredictable load at the grid. The demand to supply ratio is particularly exacerbated at a few grid aggregators (charging stations) with excessive demand due to the geographic location, peak time, etc. Existing solutions on demand response cannot achieve significant improvements based only on time-shifting the loads without considering the device properties such as charging modes and movement capabilities to enable geographic migration. Additionally, the information on the spare capacity at a few aggregators can aid in re-channeling the load from other aggregators facing excess demand to allow migration of devices. In this paper, we model these flexible properties of the devices as a mixed-integer non-linear problem (MINLP) to minimize excess load and the improve the utility (benefit) across all devices. We propose an online distributed low-complexity heuristic that prioritizes devices based on demand and deadlines to minimize the cumulative loss in utility. The proposed heuristic is tested on an exhaustive set of synthetic data and compared with solutions from a solver/optimization tool for the same runtime to show the impracticality of using a solver. A real-world EV testbed data is also tested with our proposed solution and other scheduling solutions to show the practicality of generating a feasible schedule and a loss improvement of at least 57.23%.

Figures (3)

• Figure 1: The grid aggregator and the devices (including mobile devices formed into clusters)
• Figure 2: The difference of utility loss achieved with and without mobility
• Figure 3: Comparison of solver performance with the heuristic for the same runtime.