Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles

336 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects (2016, neutral): “Another study by the same authors [48] proposes a particle swarm optimization algorithm to solve the same optimization problem including uncertainties related to load consumption, renewables’ generation power and number of EVs connected to the network.”
Big Data Analytics for Dynamic Energy Management in Smart Grids (2015, background): “Efficient data mining is crucial towards the optimized operation of the SG, since it strongly affects the decision-making of power producers and consumers, and the reliability of the grid.”
A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads (2014, background): “J Mod Power Syst Clean Energy 1(3):231–240 [5] Saber AY, Venayagamoorthy GK (2012) Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles.”
A Review of Charge Scheduling of Electric Vehicles in Smart Grid (2015, methodology): “Some other works have focused on optimizing EV-side benefits such as minimizing charging cost [35], [37], [39], [45], minimizing both CO2 emission and charging cost [37], [39], maximizing the average SOC of EVs [48], and maximizing user convenience [49].”