Abstract:
Load flow analysis is a significant tool for proper planning, operation and dynamic analysis of a
conventional power system which provides the steady state values of voltage magnitudes and
angles at fundamental frequency. However, due to the absence of slack bus in an autonomous
microgrid, modified load flow algorithms should be adopted considering the system frequency as
one of the solution variables. This work proposes the application of nature inspired hybrid
optimization algorithms for solving the load flow problem of autonomous microgrids. Several
nature-inspired algorithms such as, Genetic Algorithm (GA), Differential Evolution (DE)
algorithm, Flower Pollination Algorithm (FPA) and Grasshopper Optimization Algorithm
(GOA) are separately merged with Imperialistic Competitive Algorithm (ICA) to form four
hybrid algorithms named as ICGA, ICDE, ICFPA and ICGOA and their performances are tested
on a modified IEEE 37-Bus microgrid system as a case study. Particle swarm optimization
(PSO) algorithm is also employed to perform the load flow analysis of the same case study
system. Among the above-mentioned algorithms, to identify the algorithm with better
performance, independent samples t-tests have been conducted using SPSS statistical analysis
software. From the statistical analysis, it has been identified that ICDE exhibit better
performance compared to the other algorithms in terms of the number of iterations and the
execution time required to complete the optimization process for the load flow analysis.
