IUP Publications Online
Home About IUP Magazines Journals Books Archives
     
Recommend    |    Subscriber Services    |    Feedback    |     Subscribe Online
 
The IUP Journal of Electrical and Electronics Engineering:
Optimal Reactive Power Dispatch Using Grey Wolf Optimization Technique
:
:
:
:
:
:
:
:
:
 
 
 
 
 
 
 
 

The paper proposes Grey Wolf Optimization (GWO) technique to solve Optimal Reactive Power Dispatch (ORPD) problem. The proposed algorithm incorporates two objective functions: minimization of total power generation cost and L-index. The technique was tested on IEEE 30-bus system with quadratic cost function and total 23 control parameters. Favorable results were observed with multiobjective optimization formulation compared to single objective optimization problem.

 
 

The role of reactive power (VAr) is vital for reliable operation of power system. Nowadays, the demand for power is increasing exponentially and hence the existing transmission lines are forced to operate closer to their limits; in other words, they are at stressed condition. Sufficient reactive power is essential to maintain the line flow limits on transmission lines and voltage limits at bus bars. Also, since it is not desirable to transport reactive power over the network, it should be procured at different locations in the system depending upon perceived demand conditions, mix of the load and availability of reactive support devices. Inadequate reactive power supply in the transmission system lowers system voltage and may lead to a progressive and uncontrollable decline in voltage leading to blackout (Kothari and Nagrath, 2003). In this scenario, an optimum dispatch of reactive power can result in improved voltage profile, system security, power transfer capability and overall system operation. In general, the Optimal Reactive Power Dispatch (ORPD) problem is concerned with minimizing real power transmission losses and improving the system voltage profile by dispatching available reactive power sources in the system. Mathematically, ORPD problem is multi-objective, mixed integer, non-convex, non-differentiable and nonlinear optimization problem which aims to minimize the objectives while satisfying the various systems and unit constraints. Up to now, a number of solution methods have been proposed in the literature to solve ORPD, each with its particular mathematical and computational characteristics. The methods can be mainly classified into conventional techniques and global optimization techniques. Most conventional optimization techniques used in solving ORPD are based on nonlinear programming and linear programming.

Owing to the non-convexity nature of the ORPD problem, most general nonlinear programming techniques (Peschon et al., 1968; and Lee et al., 1985) might converge to a local minimum instead of a unique global minimum which may lead to an insecure convergence. Economic dispatch problem was solved using direct search method (Chen and Chen, 2001). The successive quadratic programming (Quintana and Santos-Nieto, 1989) method requires the computation of the second-order partial derivatives of the power flow equations and other constraints for solving the problem. The gradient and Newton methods suffer from the difficulty in handling inequality constraints. Linear programming (Mamandur and Chenoweth, 1981; and Palmer et al., 1983) requires the objective function and constraints to have a linear relationship, which may lead to loss of accuracy and inaccurate evaluation of system losses. Consequently, the conventional optimization methods are not reliable in solving the ORPD problem. In the last decade, there was an ever increasing interest in global optimization techniques. The global optimization techniques such as GA (Iba 1994; and Wu et al., 1998), ES (Gomes and Saavedra, 2002; and Bhagwan Das and Patvardhan, 2003), EP (Wu and Ma, 1995), PSO (Yoshida et al., 2000) and DE (Varadarajan and Swarup, 2008) have been proposed to solve the ORPD problem. These evolutionary algorithms have shown success in solving the ORPD problems since they do not need the objective function and constraints as differentiable and continuous. Multiobjective problem formulations of ORPD are reported in literature (Hsaio et al., 1994; and Chen and Ke, 2004), wherein the optimization algorithm used was not of multiobjective type. The multiobjective problem was converted into a single objective problem by linear combination of different objectives as weighted sum. One of the Multi-objective Evolutionary Algorithms (MOEAs) and Strength Pareto Evolutionary Algorithm (SPEA) has been applied to multiobjective ORPD problem (Abido and Bakhashwain, 2005) considering real power loss minimization and bus voltage deviation minimization as competing objectives. Duman et al. (2012) have solved ORPD problem using gravitational search algorithm. Medani and Sayah (2016) solved ORPD problem using particle swarm optimization with time varying acceleration coefficients.

 
 
 

Optimal Reactive Power Dispatch (ORPD), Grey Wolf Optimization (GWO), L-index