A power storage station placement algorithm for power distribution based on electric vehicle

Xu, Jianwen; Yi, Ping; Wang, Wei; Zhu, Ting

doi:10.1177/1550147717694169

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The rapid development and major achievements of battery technology have also provided a firm material foundation for the idea of an EV Energy Internet [18]- [21]. In previous work [22]- [31], we presented a concrete example to illustrate the usage of an EV Energy Internet, and then studied the optimization problem of how to deploy energy routers in an EV Energy Internet. We proved that the problem is NP-hard and developed a greedy heuristic solution.…”

Section: Related Workmentioning

confidence: 99%

Energy Transport Station Deployment in Electric Vehicles Energy Internet

Sui

Liu

et al. 2019

IEEE Access

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Energy Internet allows energy to flow flexibly for transmission and it can transport energy to every energy user via electric vehicles (EVs). The energy that EVs use is produced from renewable energy sources like solar power. However, under bad weather conditions, the power station will stop energy generation and will need to get power transported from other places. One solution is to set up some stations, which usually store excess energy. In special times, the energy stored by these stations can be used to supply electricity to all parts of the city. In this process, the position of the station that provides power should be considered carefully for the sake of reducing energy loss during transportation. The main idea of this paper focuses on how to choose positions for the stations. We put forward the concept of energy transport station (ETS) which can store and output energy, then we propose three algorithms, namely exhausted algorithm, greedy algorithm, and segmentation algorithm, to decide where to place ETSs so that the energy loss can be minimized. The algorithms will be simulated with data of bus route maps of Manhattan and the Pioneer Valley Transit Authority (PVTA), which will show the contrast of each algorithm and find their best application scenarios. INDEX TERMS Energy internet, electric vehicles, energy transmission.

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Section: Related Workmentioning

confidence: 99%

Energy Transport Station Deployment in Electric Vehicles Energy Internet

Sui

Liu

et al. 2019

IEEE Access

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“…1) Deployment location of V2G operation [65], [66], 2) Severity of CFEs (worst-case scenario, (N − 1 − 1) contingency) [67] Considering (1), the relationship of power system survivability against CFEs, when ESS in the form of V2G is deployed totally depends on its deployment location [65], [66]. The complete simulation study of how accurate ESS deployment in power grid will reduce the endangered, i.e., instability of ESS, when the system is faced with CFEs is performed in [65].…”

Section: ) Effect Of Cfes On the Stability Of Wind And V2g Powermentioning

confidence: 99%

Preventing Cascading Failure Through Fuzzy Co-Operative Control Mechanism Using V2G

et al. 2019

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The previously proposed algorithms for preventing cascading failures, which lead to a blackout event, involve specific load shedding schemes, which introduce incurring losses in the power system network. In this paper, a cooperative control based algorithm using a vehicle to grid (V2G) technology based on a fuzzy logic approach is proposed to prevent cascading failures without loss incurrence. The algorithm is implemented on a standard IEEE-30 bus system, and it uses mathematical combinations heuristically to identify the critical nodes through the use of a self-propagation graph to dispatch the optimum power from V2G. For the enhancement of computational speed, a network operator considers only those vulnerable nodes, which are identified by a self-propagating graph. Through this, a network operator can easily detect critical nodes by rooting straight to the vulnerable transmission lines in the IEEE-30 bus network. The probabilistic modeling in this paper is performed in such a way that network operators will mitigate cascading failures events (CFEs) after the occurrence of (N − 1) and (N − 1 − 1) contingencies/blackout events without performing load shedding. The detailed experimental analysis provides better visualization of the impact of CFEs on power grids to the power network operators and therefore significantly improves the accuracy of taking necessary actions to compensate these CFEs.INDEX TERMS Cascading failure events, vehicle to grid technology, IEEE-30 bus network, fuzzy controller.

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Optimization for charge station placement in electric vehicles energy network

Sui

Liu

et al. 2017

Proceedings of the Workshop on Smart Internet of Things

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A power storage station placement algorithm for power distribution based on electric vehicle

Cited by 3 publications

References 17 publications

Energy Transport Station Deployment in Electric Vehicles Energy Internet

Energy Transport Station Deployment in Electric Vehicles Energy Internet

Preventing Cascading Failure Through Fuzzy Co-Operative Control Mechanism Using V2G

Optimization for charge station placement in electric vehicles energy network

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