Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Optimal scheduling of load tap changer and switched shunt capacitors in smart grid with electric vehicles and charging stations

Random charging of plug-in electric vehicles (PEVs) particularly during the peak load hours could impairment the performance of future smart grids. This paper presents genetic algorithms (GAs) for optimal scheduling of LTC and switched shunt capacitors (SSCs) to improve the performance of smart grid with PEV charging at consumer premises in residential feeders and PEV charging stations (PEV-CSs) in distribution networks. The forecasted daily load curves associated with PEV-CSs and residential feeders populated with PEVs are first generated and then incorporated in the GA-based optimal LTC and SSC scheduling solution. Simulation results without and with optimal scheduling are presented for a 449 node smart grid system with 5 PEV-CSs considering random and coordinated charging of 264 PEVs in 22 low voltage residential networks

Overnight coordinated charging of plug-in electric vehicles based on maximum sensitivities selections

The future smart grid (SG) will be populated with high penetrations of plug-in electric vehicles (PEVs) that may deteriorate the quality of electric power. The consumers will also be seeking economical options to charge their vehicles. This paper proposes an overnight maximum sensitivities selection based coordination algorithm (ON-MSSCA) for inexpensive overnight PEV charging in SG. The approach is based on a recently implemented online algorithm (OL-MSSCA) that charges the vehicles as soon as they are randomly plugged-in while considering SG generation, demand and voltage constraints. In contrast to the online approach, ON-MSSCA relies on inexpensive off-peak load hours charging to reduce the cost of generating energy such that SG constraints are not violated and all vehicles are fully charged overnight. Performances of the online and overnight algorithms are compared for the modified IEEE 23kV distribution system with low voltage residential feeders populated with PEVs

Optimal scheduling of LTC and switched shunt capacitors in smart grid concerning overnight charging of Plug-in Electric Vehicles

It is well-known that load variation and nonlinearity have detrimental impacts on the operation and performance of the conventional power systems and future smart grids (SGs) including their voltage profiles, power quality, losses and efficiency particularly during the peak load hours. This paper will perform optimal scheduling of transformer load tap changer (LTC) and switched shunt capacitors (SSCs) in smart grid with nonlinear loads and plug-in electric vehicle (PEV) charging activities to improve voltage profile, reduce grid losses and control the total harmonic distortion (THD). An established genetic algorithm (GA) for the dispatch of LTC/SSC and a recently implemented algorithm based on maximum sensitivity selections (MSS) optimization for coordination of PEVs are used to perform detailed simulations and analyses