NDLI: Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid

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Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid

Content Provider	MDPI
Author	Yang, Qingyu An, Dou Yu, Wei Tan, Zhengan Yang, Xinyu
Abstract	Due to the advantage of avoiding upstream disturbance and voltage fluctuation from a power transmission system, Islanded Micro-Grids (IMG) have attracted much attention. In this paper, we first propose a novel self-sufficient Cyber-Physical System (CPS) supported by Internet of Things (IoT) techniques, namely “archipelago micro-grid (MG)”, which integrates the power grid and sensor networks to make the grid operation effective and is comprised of multiple MGs while disconnected with the utility grid. The Electric Vehicles (EVs) are used to replace a portion of Conventional Vehicles (CVs) to reduce CO 2 emission and operation cost. Nonetheless, the intermittent nature and uncertainty of Renewable Energy Sources (RESs) remain a challenging issue in managing energy resources in the system. To address these issues, we formalize the optimal EV penetration problem as a two-stage Stochastic Optimal Penetration (SOP) model, which aims to minimize the emission and operation cost in the system. Uncertainties coming from RESs (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by the Monte Carlo-based method. To enable the reasonable deployment of EVs in each MGs, we develop two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. An extensive simulation study based on a modified 9 bus system with three MGs has been carried out to show the effectiveness of our proposed schemes. The evaluation data indicates that our proposed strategy can reduce both the environmental pollution created by CO 2 emissions and operation costs in UCS and LCS.
File Size	558080
File Format	PDF
e-ISSN	14248220
DOI	10.3390/s16060907
Journal	Sensors
Issue Number	6
Volume Number	16
Language	English
Publisher Date	2016-06-17
Access Restriction	Open
Subject Keyword	archipelago microgrid electric vehicles (EVs) two-stage stochastic programming scheduling
Content Type	Text
Resource Type	Article

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