Impact of communication delays on PEVs charging power control for frequency stabilization in remote microgrid

Jamroen, Chaowanan; Kesorn, Natchanan; Pichetjamroen, Achara; Dechanupaprittha, Sanchai

doi:10.1109/appeec.2017.8308954

Cited by 2 publications

(3 citation statements)

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“…Moreover, a self-learning particle swarm optimization (SLPSO) was applied to optimally tune the membership functions in the fuzzy logic system based on the multi-objective optimization problem, minimizing frequency deviations and impact on EV owner in terms of charging power deviation [18]. In addition, the impact of communication delays on the EV charging controller performance was also studied and explored in [19]. In [20], EVs charging power control was also employed in the coordination of the energy storage systems to support frequency regulation and energy management.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…η ev is the EV energy efficiency. From (19), the SoC results from stabilized energy created by the controlled active power. Therefore, the SoC may deviate from the expected SoC assigned by the EV owner.…”

Section: B Analysis On Expected Ev Energymentioning

confidence: 99%

“…Surprisingly, the three parameters have a high contribution to the total effect of variance in the expected EV energy perspective. This implies that these three parameters have strong interaction with each other in nonlinear combinations in (19). The difference between S T i and S i can represent the interaction of i-th parameter with all other parameters.…”

Section: ) Expected Ev Energy Aspectmentioning

confidence: 99%

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EVs Charging Power Control Participating in Supplementary Frequency Stabilization for Microgrids: Uncertainty and Global Sensitivity Analysis

2021

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Electric vehicle (EV) potential has broadly highlighted in providing ancillary services in a microgrid, such as grid reserve and regulation support. However, uncertain behaviors of EV charging raise crucial concerns for both the utilities and EV owners. In this paper, the impacts of EV charging uncertainties for EV charging power control participating in supplementary frequency stabilization are assessed separately based on the two perspectives, i.e., power capacity for the utility perspective and expected EV energy for the EV owner perspective. On one hand, the power capacity accessed by the utility directly relates to the stabilization capability, which depends on the number of EVs that are willing to participate in the frequency stabilization program and the rated charging power of EVs. On the other hand, the variance of expected EV energy realized by the EV owners is considered in terms of the remaining state of charge (SoC), energy capacity, and available charging time. Besides, a variance-based global sensitivity analysis (GSA) is essentially applied to identify the influential parameters of these uncertainties. The simulation studies are conducted using a microgrid environment through DIgSILENT Powerfactory software to reveal such impacts of EV charging uncertainties based on the two perspectives. The results indicate that the number of participating EVs is the most influential parameter for frequency stabilization capability, followed by the rated charging power of EV. From the EV owner perspective, the energy capacity is the dominant parameter affecting the expected EV energy variance, followed by the remaining energy and the available charging time, respectively.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: B Analysis On Expected Ev Energymentioning

confidence: 99%

Section: ) Expected Ev Energy Aspectmentioning

confidence: 99%

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