An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems

The large uncertainties in wind power generation will bring great challenges to the analysis of optimal reactive power dispatch (ORPD). This paper considers a multi-objective ORPD strategy solved by a heuristic search algorithm that combines the elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and a roulette wheel selection to optimize the operation of wind power integrated systems. The proposed ORPD strategy employs day-ahead predicted wind energy and load demand data to optimally set of the following control variables: i) optimal tap positions of on-load tap changers (OLTCs), ii) reactive demand set point of reactive power compensators and iii) active and reactive power outputs of wind farms (WFs) with the objectives to minimize: a) voltage deviations, b) active power loss, c) wind turbine harmonic distortions and d) number of switching operations of OLTCs. Because of the uncertainties of wind energy and load demand, hourly modifications of the day-ahead optimal results are also formulated to determine the real-time optimal reactive power
dispatch. The proposed new ORPD strategy has been rigorously tested using IEEE 33-bus test system, PG&E 69-bus test system and modified real GB network. Results obtained confirmed the efficacy and applicability of the proposed strategy in both distribution and transmission networks.