Solution of optimal power flow using nondominated sorting multi objective gravitational search algorithm

A modified MOEA/D approach to the solution of multi-objective optimal power flow problem

“…The optimal power flow problem as one of the famous multi-objective optimizations also attracts many scholars. Some MOEAs such as multi-objective differential evolutionary [13][14][15][16][17][18], artificial bee colony algorithm [5,[19][20][21][22], multi-objective adaptive immune algorithm [10], enhanced genetic algorithm [23], NSGA-II [7], multi-objective PSO [24,25], quasi-oppositional biogeography-based optimization [26], multi-objective harmony search algorithm [27], modified shuffle frog leaping algorithm [28,29], gravitational search algorithm [1,[30][31][32][33][34], multi-objective modified imperialist competitive algorithm [35,36], multi-hive bee foraging algorithm [37], teaching-learning based optimization algorithm [2,38], multiobjective solution Q() learning [39], etc., have been proposed aiming at the solution of MOOPF problems. However, the above methods have to do some more efforts in order to approach to the true Pareto-Optimal Front and obtain the diversity of the solutions.…”

“…The objective function considered here is to minimize the voltage magnitude deviations (VMD) of all the load bus from 1 per unit (p.u.) [1]. The objective function can be defined as follows:…”

“…The optimal power flow (OPF) problem plays one of the most important roles in the operation of modern power systems and has received a lot of interest over the years [1]. It involves the dispatching or setting for all the generator real powers, the generator bus voltages, the tap ratios of transformers and the reactive power generations of VAR sources.…”

A directional control method for interface flow considering static voltage stability

“…The vector of interface power flow at the base case is shown in (9), and the power control objective is depicted in (10). The corresponding power variation on the each interface line is depicted in (11).…”

“…As a result, the precision of this method is limited, especially when the reactive power has to be considered. The optimal power flow or artificial intelligence based methods were developed in [4,9,10]. The main principle is to formulate an optimization problem such that the dominant elements are the equality and inequality constraints of power flow.…”