Development of a Data Generation System with Various Loading Conditions for Capacitor Placement and Sizing Studies

Power efficiency can be achieved in distribution systems by maintaining voltage stability and reducing transmission line losses. One of the solutions is through the installation of capacitor banks to compensate for the lagging power factor at the load. This installation improves the power factor. In improving the power factor, the volt-ampere demand is reduced, hence reducing current demand and thereby reducing line losses and voltage drops. The research on optimal capacitor placement and sizing is rich considering different techniques, from mathematical-based techniques to the recent application of artificial intelligence-based solutions. While previous research has yielded good results that have contributed to this area of power system efficiency, most optimal capacitor studies assume rated conditions or at steady-state conditions. While steady state conditions are acceptable, it does not provide an accurate representation of the state of the power system. There has been research where optimal capacitor placement and sizing studies have considered different loading conditions, however, some of these studies either employ capacitor switching, or use insufficient loading conditions without basis. This paper provides a basis for the development of power system loading conditions to aid for a more comprehensive solution methodology of optimal capacitor studies. It is based on the principle that the load demand of a power system can be simplified into discrete approximations that still provide accurate representation of the power demand in the power system through achieving the intended percent loading of the system with a negligible ± 0.00001 or ± 0.5% error.