A novel load distribution strategy for aggregators using IoT-enabled mobile devices
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| Publicat a: | arXiv.org (Dec 9, 2024), p. n/a |
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| Altres autors: | , , , |
| Publicat: |
Cornell University Library, arXiv.org
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| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3108869985 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3108869985 | ||
| 045 | 0 | |b d20241209 | |
| 100 | 1 | |a Shivaraman, Nitin | |
| 245 | 1 | |a A novel load distribution strategy for aggregators using IoT-enabled mobile devices | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 9, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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%. | |
| 653 | |a Heuristic | ||
| 653 | |a Solvers | ||
| 653 | |a Scheduling | ||
| 653 | |a Electric power demand | ||
| 653 | |a Internet of Things | ||
| 653 | |a Mixed integer | ||
| 653 | |a Electronic devices | ||
| 653 | |a Electric vehicle charging | ||
| 653 | |a Geographical locations | ||
| 653 | |a Load distribution (forces) | ||
| 653 | |a Synthetic data | ||
| 700 | 1 | |a Fittler, Jakob | |
| 700 | 1 | |a Ramanathan, Saravanan | |
| 700 | 1 | |a Easwaran, Arvind | |
| 700 | 1 | |a Steinhorst, Sebastian | |
| 773 | 0 | |t arXiv.org |g (Dec 9, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3108869985/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2409.14293 |