A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <s> This paper presents a new optimization model EPSO, evolutionary particle swarm optimization, inspired in both evolutionary algorithms and in particle swarm optimization algorithms. The fundamentals of the method are described, and an application to the problem of loss minimization and voltage control is presented, with very good results. <s> BIB001 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <s> Evolutionary particle swarm optimization (EPSO) is a robust optimization algorithm belonging to evolutionary methods. EPSO borrows the movement rules from particle swarm optimization (PSO) and uses it as a recombination operator that evolves under selection. This paper presents a reactive power planning approach taking advantage of EPSO robustness, in a model that considers simultaneously multiple contingencies and multiple load levels. Results for selected problems are summarized including a trade-off analysis of results. <s> BIB002 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <s> Increased load forecasts can severely deteriorate the performance of a power system. Reactive compensation devices are a common method to allow a power system to return to an acceptable performance level for an expected load. Reactive power planning (RPP) is used to determine the optimal placement of reactive devices for a set of objectives. RPP is a large scale multiple objectives highly constrained and partially discrete optimization problem that is very difficult to solve. Evolutionary algorithms have been used to solve RPP problems. However, new multi-objective evolutionary computational techniques have shown the ability to consider an optimization problem's objectives independently for the determination of Pareto Optimal solutions. This paper aims at applying the Non-Dominated Sorting Genetic Algorithm II (NSGAII) to a multi-objective RPP. The results from the case study presented show that there is great potential in the use of evolutionary computation for solving the multi-objective RPP. <s> BIB003 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <s> The particle swarm optimization (PSO) has been shown to converge rapidly during the initial stages of a global search, but around global optimum, the search pro~ess becomes very slow. On the other hand, the genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient and can find the optimal solution more accurately and with less computational time. Optimal location of SVC using this hybrid PSO-GA algorithm is found. We have also found the optimal place of SVC using GA and PSO separately and have compared the results. It has been shown that the new algorithm is more effective and efficient. An IEEE 68 bus test system is used for simulation. <s> BIB004 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <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> BIB005 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <s> This paper presents a new approach to the optimal reactive power planning based on fuzzy logic and particle swarm optimization (PSO). The objectives are to minimize real power loss and to improve the voltage profile of a given interconnected power system. Transmission loss is expressed in terms of voltage increments by relating the control variables i.e. reactive var generations by the generators, tap positions of transformers and reactive power injections by the shunt capacitors. The objective function and the constraints are modeled by fuzzy sets. A term ‘sensitivity’ at each bus is defined which depends on variation of real power loss with respect to the voltage at that bus. Based on the Fuzzy membership values of the sensitivity, corrective action at a particular bus is taken i.e. shunt capacitors are installed at the candidate buses based on real power loss and sets of solution. Then, PSO is applied to get final solution. PSO is used for optimal setting of transformer tap positions and reactive generations of generators. The solutions obtained by this method is compared with the solutions obtained by other evolutionary algorithms like genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). <s> BIB006 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <s> Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem. <s> BIB007 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 5),68] <s> Static Var compensator (SVC) is normally used in power system to improve voltage profile and reduce system power losses. In this paper, a relatively new optimization technique named as the improved harmony search algorithm (IHS) is applied to determine optimal location and size of SVC devices in a transmission network. A multi-criterion objective function comprising of both operational objectives and investment costs is considered. The results on the 57-bus test system showed that the IHS algorithm give lower power loss and better voltage improvement compared to the particle swarm optimization technique in solving the SVC placement and sizing problem. <s> BIB008
A hybrid PSO-DE algorithm has been implemented for solving the reactive power control problem in electricity market PSO-DE algorithm carried out a differential operator from DE in the update of particle velocity of PSO A selection strategy has been added that a particle is moved to a new location only if the new location yields a better fitness value Slow convergence rate More computational burden and complexity Both algorithms are very sensitive to the setting of the control parameters Using trial and errors to parameters initializations A hybrid PSO-GA algorithm has been implemented to minimize the cost of reactive power generation, reactive power compensators and active power losses. BLX-a, and uniform mutation operators from GA algorithm are applied on the PSO particles Crossover and mutation are done if there is no change in the global position for a number of iterations to avoid premature convergence BIB004 Another model of hybrid PSO-GA has been performed to search for the optimal placement of SVC. PSO algorithm is implemented firstly until Simple hybrid model and easy to implement Good diversity Slower convergence performance More control parameters which needed to be tuned Using trial and errors to parameters initializations (continued on next page) Handling a single objective optimization problem which is the real losses BIB001 BIB002 Evolutionary Particle Swarm Algorithm (EPSO) method has been applied to the reactive power control and planning. EPSO formulation is based on the particle movement like the classical PSO where, the weights are mutated using EP mutation factor More diversity of solutions Considering different contingencies and load levels in BIB002 Such a complexity due to EP mutations Parameter tuning is needed BIB006 A hybrid between fuzzy reasoning approach and PSO method has been introduced. Fuzzy membership of loss sensitivity at each bus has been evaluated to determine candidate buses to install shunt capacitors. PSO has been used immediately to minimize the investment costs and transmission losses as well number of objective functions. A normalization process has been incorporated to the weighted sum approach in BIB008 BIB007 . In BIB008 , each objective function (real power losses, voltage deviation, and VAR investment cost) has been normalized in a comparative manner with its base case value. Also, the normalization process can be done as a fuzzification process BIB007 to map all objectives within the range of [0, 1]. Then it is generally modeled as weighted sum defined in Eq. BIB005 . The normalization process enables comparing the different objectives in a fairly manner. The optimal solution is greatly affected by the selection of the weights. Another problem associated with this approach is that it may find solutions that are close to one or more operating constraint violations BIB003 .