In this paper, an Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA)
has been applied for minimizing the power losses in the distribution network
by suitable placement, sizing and subsequent allocation of load on
Distributed Generators (DG) for a varying load with a time horizon of
twentyfour hours. Many efforts have been reported in the literature to
minimize power losses. However, they have mostly used a fixed load, i.e.,
nonvarying load, whereas it is well known that load in distribution network
varies during the day. An investigation was undertaken to find the reduction
in power losses on a timevarying load. It has been found that the average
power losses for dynamic load allocation on DGs for every hour have a
maximum reduction in power loss as compared with other well-known cases in
the literature. Optimal location and size of DG is a difficult nonlinear,
non-differentiable combinatorial optimization problem. AQiEA is used to find
the appropriate location and capacity of DG for a varying load with a time
horizon of twenty-four hours to minimize the power losses. AQiEA doesn?t
require additional operators like local search and mutation to improve the
convergence rate and avoid the premature convergence. A Quantum Rotation
inspired Adaptive Crossover operator is used as a variation operator, which
is parameter free. The effectiveness of AQiEA is demonstrated on two test
bus systems viz., 33 bus system and 69 bus system, which are used as
benchmark problems for validating the proposed methodology as well as for
comparative testing amongst existing techniques. Wilcoxon signed rank test
is also used to demonstrate the effectiveness of AQiEA. The experimental
results show that AQiEA has better performance as compared to some existing
?state of art? techniques.
Proportional Power-Sharing Control of Distributed Generators in Microgrids
