Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilization.
Microgrids enable distribution of electricity with higher shares of variable renewables, higher power quality, greater reliability and higher efficiency. There are a large number of factors in addition to the technology, which affect their shift towards market competitiveness and widespread adoption. The PESTEL framework, covering Political, Economic, Social, Technical, Environmental and Legislative factors, is used to identify and describe the drivers and barriers for microgrid development at the global level. The framework enables a broader approach to describe potential for microgrid applications. The results aim to provide engineers, project developers and microgrid specialists with an overview of the prospects for microgrid deployment.
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