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

Abstract

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.

Enhancing smart charging in electric vehicles by addressing paused and delayed charging problems

“…This could harm the roll-out of different smart charging applications, including renewablebased charging, in which the EV charging power depends on the output of a PV system or wind turbine. Implementing renewable-based charging systems has the potential to boost the self-consumption of renewable energy and enhance the integration of renewable energy technologies into the grid [22][23][24] . This approach helps mitigate the intermittency of renewable energy generation and reduces dependence on fossil fuels to fulfill electricity demand.…”

“…Lastly, the roll-out of smart charging can accelerate the energy transition by shifting the charging demand of EVs to moments with excess renewable generation [22][23][24] , thereby reducing the dependency on fossilbased energy resources and mitigating the intermittency challenges associated with renewable energy sources. If vehicle-to-grid (V2G) functions are considered, EVs could even act as a mobile storage medium for excess renewable energy, which can be utilized to meet the electricity demand during periods of renewable energy shortage.…”

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

“…The hardware controller is more convenient, which has a faster speed. The disadvantage is that it requires variable processing (Amir et al, 2023;Zhang G. et al, 2023b;Chobe et al, 2023). Therefore, for the fuzzy algorithm of optimal efficiency point system control, the process is shown in Figure 5.…”

Optimizing Cost and Emission Reduction in Photovoltaic–Battery‐Energy‐Storage‐System‐Integrated Electric Vehicle Charging Stations: An Efficient Hybrid Approach

“…A hybrid optimization strategy that addressed the aforementioned model integrated the order preference by similarity to optimum solution multi‐objective particle swarm optimization (PSO) approach. Amir et al [ 11 ] developed the IEMS‐based synchronized management system for PV‐based EVCS. To analyze concurrent meteorological and load demand information, the presented intelligent energy management algorithm method was optimized.…”