Multi cases optimal reactive power dispatch using evolutionary programming

Abstract

Evolutionary Programming (EP) is one of many types in Evolutionary Computation (EC) that used for optimization process. EP technique is used to find the optimal reactive power dispatch (ORPD) since it is one of the accessible options schemes that can be used on the system as a reactive power support. Sometimes, it is not necessary to operate all generators in order to perform ORPD to in achieve the objectives. Also, increment of reactive power load to the system will cause voltage decomposes with the increase in transmission loss in the system. Therefore, the proposed method decides the best grouping of generators that should be operated in system by bearing in mind the transmission loss reduction. ORPD will be used to minimize the transmission loss as well the increasing reactive power loading. This method conducted on IEEE 30-bus Test System with multi cases scenario. The best combination of operating generators determined and the transmission loss after optimization is smaller compared to the transmission loss before optimization resulted.

A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources

“…To accomplish these objectives, the optimal adaptation of specified control variables such as transformer tapping settings, generator buses' voltages, and reactive power resource distribution, such as the VAR shunt compensator, is needed. Once the problem of ORPD is resolved, the equality constraints and the inequality constraints are kept inside their permissible operational bounds [3,4].…”

A novel hybrid approach based artificial bee colony and salp swarm algorithms for solving ORPD problem

“…Linear programming (LP), nonlinear programming (NLP) [3] and Newton method [4] were among the presented techniques in the literature. Unfortunately, these conventional methods present some drawbacks in dealing with non-convex and MINLP optimization problems considering non-differentiable objective functions and constraints, additionally to their premature convergence by trapping in local optima when solving complex optimization problems [5], [6]. Recently, computational intelligence methods have been imposed as an alternative to the classical optimization techniques called meta-heuristics, which are based on mimicking physical or biological phenomena and their main advantage concerns the ability in dealing with combinatorial and non-convex optimization problems.…”