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Sensors 2016, 16(6), 907; doi:10.3390/s16060907

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

1,†,* , 2,†
,
3,†,* , 2,†
and
2
1
SKLMSE Lab, School of Electronic & Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
School of Electronic & Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3
Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editors: Dongkyun Kim, Houbing Song, Juan-Carlos Cano, Wei Wang, Waleed Ejaz and Qinghe Du
Received: 18 April 2016 / Revised: 30 May 2016 / Accepted: 1 June 2016 / Published: 17 June 2016
View Full-Text   |   Download PDF [545 KB, uploaded 17 June 2016]   |  

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. View Full-Text
Keywords: archipelago microgrid; electric vehicles (EVs); two-stage stochastic programming; scheduling archipelago microgrid; electric vehicles (EVs); two-stage stochastic programming; scheduling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, Q.; An, D.; Yu, W.; Tan, Z.; Yang, X. Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid. Sensors 2016, 16, 907.

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