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.
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.