Predictive Scheduling Framework for Electric Vehicles Considering Uncertainties of User Behaviors

Battery life-cycle optimization and runtime control for commercial buildings demand side management: A New York City case study

“…Besides passively shifting the loads, many researchers have addressed active DSM through integrating battery based energy storages. For instance, batteries on Electric Vehicles (EVs) can be used for peak shaving and load balancing [19,20]. Stationary battery pack can provide islanding capability, grid supports and economic operation [21][22][23].…”

Hierarchical distributed framework for EV charging scheduling using exchange problem

“…Nevertheless, EV load management is challenging due to the uncertainty in arrival time, departure time and energy demand (Khaki et al [6], Chung et al [7]), limited capacity of the energy resources and distribution grid equipment (Khaki et al demand. Then, the optimal charging profiles are sent back to EVs: By Clement-Nyns et al [3], it is shown that the uncoordinated EV charging increases power loss and voltage deviation significantly, therefore the authors propose a centralized method where the EV owners have no control over the charging profile, and it is decided by DNO; a model predictive based algorithm is proposed by Tang and Zhang [9] for total charging cost reduction, where the authors use the truncated sample average approximation to reduce the complexity of their centralized method at the cost of performance degradation; Wang et al [10] introduce a centralized event-triggered receding horizon method to reduce EV charging cost in a campus parking; an optimal strategy for V2G aggregator is designed by Peng et al [11] to maximize the economic benefit by participation in frequency regulation while satisfying EV owners' demand; a centralized algorithm is designed by Bilh et al [12] to flatten the netalod fluctuations due to renewable energy resources using EV charging control; Zheng et al [13] propose a real-time EV charging scheduling where the computational complexity is reduced by introducing a capacity margin and the charging priority indices; a centralized mechanism is proposed by Perez-Diaz et al [14] in which a thirdparty entity coordinates a day-ahead bidding system to optimize the global bid; a transactive EV charging management is presented in Liu et al [15] to maximize the real-time profit based on the net electricity exchange with the grid; and a two-layer centralized EVCS is proposed by Mehta et al [16] where each aggregator optimizes active power of the EVs in the first layer, and the second layer provides reactive power management for loss reduction in the grid. The main issues with the centralized approaches are: (I) EV owners' privacy as they have to communicate their sensitive charging information (e.g.…”

“…It is worthwhile to mention that the convergence rate can be improved by the adaptive penalty term which is not used in this paper. of the EVs are generated as follows: the initial and designated EVs' battery energies are normally distributed over [8,10] kWh and [22,25] kWh, respectively;…”

Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

“…Deep learning offers a scalable data-driven discriminative paradigm to understand, model and predict the behavior of complex systems by extracting the deep collective knowledge. With the new wave of the Internet-of-Things (IoT) and the feasibility of using the internet almost everywhere, there is a big chance for scalable device-specific real-time monitoring and analysis by pushing deep learning and advanced analytic computations from the cloud next to IoT devices (which also called edge computing) [6,7]. In particular, the benefits of edge computing are much more pronounced for real-time reliability modeling and prediction of sophisticated physical and engineering systems such as power electronic converters.…”