Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Abstract

Abstract-This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. An improved genetic algorithm which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimization problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point number and integers are used. The proposed method has been tested on IEEE 30 bus system and has resulted in loss which is less than the value reported earlier and is well suited for solving the mixed integer optimization problem.Index Terms-Modal analysis, optimal reactive power dispatch, loss minimization, genetic algorithm. I. INTRODUCTIONOptimal reactive power dispatch problem is one of the difficult optimization problems in power systems. The sources of the reactive power are the generators, synchronous condensers, capacitors, static compensators and tap changing transformers. The problem that has to be solved in a reactive power optimization is to determine the required reactive generation at various locations so as to optimize the objective function. Here the reactive power dispatch problem involves best utilization of the existing generator bus voltage magnitudes, transformer tap setting and the output of reactive power sources so as to minimize the loss and to enhance the voltage stability of the system. It involves a non linear optimization problem. Various mathematical techniques have been adopted to solve this optimal reactive power dispatch problem. These include the gradient method [1-2], Newton method [3] and linear programming [4][5][6][7].The gradient and Newton methods suffer from the difficulty in handling inequality constraints. To apply linear programming, the input-output function is to be expressed as a set of linear P. Aruna Jeyanthy is with the Electrical and Electronics Engineering Department, N.I. College of Engineering Kanyakumari District, India (corresponding author to provide phone: 91 9443174253. email: arunadarwin@yahoo.com) D.Devaraj is with the Electrical and Electronics Engineering Department, Kalasalingam University, Srivilliputhur, India.functions which may lead to loss of accuracy. Recently global optimization techniques such as genetic algorithms have been proposed to solve the reactive power flow problem [8,9]. A genetic algorithm is a stochastic search technique based on the mechanics of natural selection. In this paper, genetic algorithm is used to solve the voltage constrained reactive power dispatch problem. The proposed algorithm identifies the optimal values of generation bus voltage magnitudes, transformer tap setting and the output of the reactive p...

Study of Different Methods of Voltage Stability Analysis

“…Reactive power dispatch problem include the maximum use of efficiency of existing generator bus voltage magnitude, transformer tap setting and output of reactive power sources to reduce the losses and maintain the voltage stability [24]. These parameters are taken as optimization variables.…”

A Review on Optimal Reactive Power Dispatch

“…In [21], The optimal power flow solution is obtained with fuel cost minimization as the objective function, Optimal power flow can be formulated for reactive power optimization. The proposed method was applied in the IEEE 30-bus system to find out the optimal power flow.…”

Multi cases optimal reactive power dispatch using evolutionary programming

“…LP method requires that the objectives function and constraints have linear relationship, which may lead to loss of accuracy [1]. Besides that, the Gradient and Newton methods which also the methods of mathematical optimization techniques suffer from the difficulty in handling inequality constraints [1,8,9]. Zhu and Xiaong proposed a new approach to study the ORPD using a Modified Interior Point (MIP) method to minimize the system power losses and to penalize any new ORPD utilization while Granade et al approached a decentralized approach based on Lagragian decomposition method for solving ORPD problem in multi-area power systems however still these classical methods suffer from many drawbacks [10,11].…”