Review of reactive power planning: objectives, constraints, and algorithms

Abstract—The key of reactive power planning (RPP), or Var planning, is the optimal allocation of reactive power sources con-sidering location and size. Traditionally, the locations for placing new Var sources were either simply estimated or directly assumed. Recent research works have presented some rigorous optimiza-tion-based methods in RPP. This paper will first review various objectives of RPP. The objectives may consider many cost func-tions such as variable Var cost, fixed Var cost, real power losses, and fuel cost. Also considered may be the deviation of a given voltage schedule, voltage stability margin, or even a combination of different objectives as a multi-objective model. Secondly, different constraints in RPP are discussed. These different constraints are the key of various optimization models, identified as optimal power flow (OPF) model, security-constrained OPF (SCOPF) model, and SCOPF with voltage-stability consideration. Thirdly, the optimization-based models will be categorized as conventional algorithms, intelligent searches, and fuzzy set applications. The conventional algorithms include linear programming, nonlinear programming, mixed-integer nonlinear programming, etc. The intelligent searches include simulated annealing, evolutionary algorithms, and tabu search. The fuzzy set applications in RPP address the uncertainties in objectives and constraints. Finally, this paper will conclude the discussion with a summary matrix for different objectives, models, and algorithms. Index Terms—Evolutionary algorithms (EAs), fuzzy sets, linear programming (LP), mixed integer nonlinear programming (MINLP), multi-objective optimization, nonlinear programming (NLP), OPF, reactive power optimal allocation, reactive power planning, simulated annealing (SA), security-constrained OPF (SCOPF), security constrained OPF with voltage-stability consid-eration (SCOPF-VS), tabu search (TS), voltage stability margin. I