Multiobjective daily Volt/VAr control in distribution systems with distributed generation using binary ant colony optimization

This paper presents a multiobjective daily voltage and reactive power control (Volt/VAr) in radial distribution systems, including distributed generation units. The main purpose is to determine optimum dispatch schedules for onload tap changer (OLTC) settings at substations, substation-switched capacitors, and feeder-switched capacitors based on the day-ahead load forecast. The objectives are selected to minimize the voltage deviation on the secondary bus of the main transformer, total electrical energy losses, the number of OLTCs, and capacitor operation and voltage fluctuations in distribution systems for the next day. Since this model is the weighted sum of individual objective functions, an analytic hierarchy process is adopted to determine the weights. In order to simplify the control actions for OLTC at substations, a time interval-based control strategy is used for decomposition of a daily load forecast into several sequential load levels. A binary ant colony optimization (BACO) method is used to solve the daily voltage and reactive control, which is a nonlinear mixed-integer problem. To illustrate the effectiveness of the proposed method, the Volt/VAr control is performed in IEEE 33-bus and 69-bus distribution systems and its performance is compared with the genetic, hybrid binary genetic, and particle swarm optimization algorithms. The simulation results verify that the BACO algorithm gives better performances than other algorithms.