Abstract
Optimal power flow (OPF) problems are important optimization problems in power systems which aim to minimize the operation cost of generators so that the load demand can be met and the loadings are within the feasible operating regions of the generators. This brief paper emphasizes two essential issues related to solving the OPF problems and which are rarely addressed in recent research into power systems: 1) the necessity to validate operational constraints on OPF, which determine the feasibility of power systems designed for the OPF problems; and 2) and the necessity to develop conventional methods for solving OPF problems which can be more effective than the commonly-used heuristic methods.
Highlights
For the past few decades, modern control centers of power systems have been equipped with computational tools to perform complex and extensive off-line studies in order to provide electrical power with minimum costs and minimum power interruptions, since the amount of power supply is more demanding and more stable supplies are required in the competitive environment [Wood and Wollenberg 1996]
Experimental results The effectiveness of the proposed Sequential Quadratic Programming (SQP) algorithm was evaluated by solving the optimal power flow (OPF) problems which are involved in the design of small, medium and large scale power systems, namely WSCC 9 bussystem, IEEE 30 bus-system and Poly-system
The results obtained by the SQP algorithm were compared with those obtained by an effective heuristic method, namely advanced particle swarm optimization (PSO) [Chatuervedi 2008], which has been developed to solve OPF problems and non-convex parametrical problems
Summary
For the past few decades, modern control centers of power systems have been equipped with computational tools to perform complex and extensive off-line studies in order to provide electrical power with minimum costs and minimum power interruptions, since the amount of power supply is more demanding and more stable supplies are required in the competitive environment [Wood and Wollenberg 1996]. Research has mostly focused on the minimization of the cost functions, but very little attention has been paid to satisfying the constraint functions which aim to ensure the generated power satisfactorily meets the user demands and the power losses [Bakirtzis et al 2002, Devaraj and Yegnanarayana 2005, Paranjothi and Anburaja 2002, Todorovski and Rajicic 2006, Chiang 2005, Yuryevich and Wong 1999, Mo et al 2007, Chatuervedi et al 2008, Esmin 2005, Meng 2010] This can lead to an undesirable situation, where a small generation cost can be achieved but unsustainable power is produced.
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