ANFIS Based Smart Control of Electric Vehicles Integrated with Solar Powered Hybrid AC-DC Microgrid

Kaur, Sachpreet; Kaur, Tarlochan; Khanna, Rintu

doi:10.1080/15325008.2020.1854386

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…Here, the proposed system is used as an ANFIS controller interface with supervisory control in order to bi-directionally communicate with the voltage source inverter (VSI) over the MOD/controller area network bus. From Figure 2, the coordinated system controls the PV-based DGs power and monitors the BSS status, EVs charging load, and grid availability/unavailability status in order to control the active power flow between other power system components based on the mode of operation [44]. To optimize the storage system, a coordinated control strategy must require information on SoC status, BSS flexible capacity, and dis(charging) rate with respect to load.…”

Section: Coordinated Control Strategymentioning

confidence: 99%

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

Amir

Din

Haque

et al. 2023

IET Generation Trans & Dist

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Solar‐based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)‐based coordinated control for photovoltaic (PV)‐based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real‐time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro‐based fuzzy control approach includes forecasting solar‐based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real‐time hardware‐in‐loop experimental setup.

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Section: Coordinated Control Strategymentioning

confidence: 99%