Abstract
Reactive power optimization is a major concern in the operation and control of power systems. In this paper a new multi-objective differential evolution method is employed to optimize the reactive power dispatch problem. It is the mixed–integer non linear optimization problem with continuous and discrete control variables such as generator terminal voltages, tap position of transformers and reactive power sources. The optimal VAR dispatch problem is developed as a nonlinear constrained multi objective optimization problem where the real power loss and fuel cost are to be minimized at the same time. A conventional weighted sum method is inflicted to provide the decision maker with a example and accomplishable Pareto-optimal set. This method underlines non-dominated solutions and at the same time asserts diversity in the non-dominated solutions. Thus this technique treats the problem as a true multi-objective optimization problem. The performance of the suggested differential evolution approach has been tested on the standard test system IEEE 30-bus.
Highlights
Optimal reactive power expedition problem is one of the difficult optimization worries in power systems
The goal of this paper is to develop the RPD problem as a multi-objective optimization and exemplify its solution using Pareto based multi-objective optimization Differential evolution
The problem was handled as a multi-objective optimization problem where both power loss PL and Fuel cost were optimized simultaneously by converting it into a single objective optimization problem by linear combination of PL and Fuel cost objectives using (19)
Summary
Optimal reactive power expedition problem is one of the difficult optimization worries in power systems. The problem of voltage stability and voltage collapse has become a major worry in power system designing and procedure It is a non- linear optimization problem and several mathematical techniques have been followed to solve this optimal reactive power dispatch problem. These admit the gradient method [1,2], Newton method [3] and linear programming [4]. Contrariwise, the studies on evolutionary algorithms, over the past few years, have shown that these methods can be expeditiously used to wipe out most of the difficulties of classical methods [12,13] Since they use a population of solutions in their search, multiple Pareto-optimal solutions can, in principle, be found in one single run. The strength of the proposed approach to solve multi-objective VAR management problem has been established on the standard IEEE-30 bus system [14]
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