Abstract
In this paper, a novel hybrid firefly-bat algorithm with constraints-prior object-fuzzy sorting strategy (HFBA-COFS) is put forward to solve the strictly-constrained multi-objective optimal power flow (MOOPF) problems. The hybrid firefly-bat algorithm (HFBA) integrates the dimension-based firefly algorithm and the modified bat algorithm to improve the population-diversity and global-exploration ability of original algorithm. To handle the unqualified state variables and overcome the deficiency of traditional penalty function approach (PFA), the constraints-prior Pareto-dominant rule (CPR) which takes constraints-violation and objectives-value into consideration is proposed in this paper. Furthermore, an effective constraints-prior object-fuzzy sorting (COFS) strategy based on CPR rule is presented to seek the well-distributed Pareto optimal set (POS) in solving the MOOPF problems. To validate the great advantages of HFBA-COFS algorithm, ten MOOPF cases optimizing active power loss, total emission and fuel cost are simulated on the IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems. In addition, the generational distance and SPREAD evaluation indexes powerfully demonstrate that the proposed HFBA-COFS algorithm can achieve high-quality POS, which has great significance to realize the safe and economic operation of large-scale power systems.
Highlights
The optimal power flow (OPF), as a predominant tool to realize the economic and stable operation of electrical systems, is very vital for the enhancement of power quality
The convergence analysis for these multi-objective optimal power flow (MOOPF) cases of IEEE 118-bus system is not carried out because the DE-penalty function approach (PFA) and non-dominated sorting genetic algorithm-II (NSGA-II) methods cannot obtain the uniformly-distributed Pareto fronts (PFs) with zero constraints-violation
1) The basic bat algorithm is modified by nonlinear weight coefficient and novel monotone random filling model (MRFM) model
Summary
The optimal power flow (OPF), as a predominant tool to realize the economic and stable operation of electrical systems, is very vital for the enhancement of power quality. B. CONTRIBUTIONS To realize the safe and economical operation of power system, a hybrid firefly-bat algorithm with constraints-prior objectfuzzy sorting strategy (HFBA-COFS) is proposed to solve the MOOPF problems. Simulation results clearly state that the HFBA-COFS algorithm has incomparable advantages over other published methods in dealing with the many-objective optimizations of large-scale power systems. The suggested COFS sorting rule, which has great superiorities in solving the multi-dimensional MOOPF problems, comprehensively takes the Rank index based on objective values. In contrast to the typical non-dominated sorting genetic algorithm-II (NSGA-II) and DE-PFA algorithms, the applicability and superiority of presented HFBA-COFS algorithm in solving the strictly-constrained MOOPF problems are validated. To verify the availability and superiority of HFBA-COFS algorithm, Section VI gives a comprehensive analysis of experiment results mainly based on the dominance rate, performance metrics and computational complexity.
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