To enhance the economic efficiency and operational security of distribution
grids, this paper develops a reactive power optimization model that incorporates
distributed power sources. The model aims to minimize the costs of reactive-load
compensation equipment, reduce voltage deviations, and lower network losses
while satisfying operational constraints. To overcome the common drawbacks of
the standard genetic algorithm—such as limited optimization precision and a
tendency to converge to local optima—four improvement strategies are introduced.
These include an enhanced encoding scheme, an initial population generated via
opposition-based learning, an elite retention strategy, and the adaptive
adjustment of crossover and mutation rates. Together, these modifications
strengthen the algorithm’s global search capability.
The proposed approach is validated using the IEEE30 node system. Compared with
both the conventional genetic algorithm (GA) and an adaptive genetic algorithm,
the improved method demonstrates faster convergence and a more robust ability to
escape local optima. Simulation results indicate that the suggested algorithm
effectively reduces voltage fluctuations and power losses in the network while
improving the overall cost-efficiency of grid operation.