A review of meta-heuristic algorithms for reactive power planning problem <s> Arithmetic programming approaches <s> Reactive power planning (RPP) involves optimal allocation and determination of the types and sizes of the installed capacitors. Traditionally, the locations for placing new VAr sources were either simply estimated or directly assumed. Essentially, it is a large-scale nonlinear optimization problem with a large number of variables and uncertain parameters. there are no known ways to solve such nonlinear programming problems (NLP) exactly in a reasonable time. This paper introduces mathematical formulations and the advantages and disadvantages of nine categories of methods, which are divided into two groups, conventional and advanced optimization methods. The first group of methods is often trapped by a local optimal solution; the second one can guarantee the global optimum but needs more computing time. The corresponding development history period is also split into a conventional period and an artificial intelligence period. In the second period, there is a tendency to integrate several approaches to solve the RPP problem. <s> BIB001 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Arithmetic programming approaches <s> The key of reactive power planning (RPP), or Var planning, is the optimal allocation of reactive power sources considering 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 optimization-based methods in RPP. This paper will first review various objectives of RPP. The objectives may consider many cost functions 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. <s> BIB002
Arithmetic programming approaches are also called Conventional Optimization Algorithms (COAs). A variety of conventional methods have been widely used to solve the reactive power operation and planning for years BIB002 BIB001  ,17(Ch. 3)]. COAs have been developed and implemented to solve the RPP problem. Table 1 shows a comparison between various COAs that have been applied to the RPP problem.