A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> The paper presents an integrated framework to analyze the issues of reactive power planning along with reactive power pricing. The planning problem involves optimal placement and sizing of capacitors in a network such that operating and investment costs are minimum. A simple bus-wise cost-benefit analysis (CBA) scheme is proposed which involves solving a modified optimal power flow problem (OPF) iteratively. The proposed CBA incorporates detailed hourly loading conditions at a bus and achieves a fairly accurate estimate of the benefits from capacitor placement. The formulation is directly handled by the well known MINOS code and is solved efficiently. It obviates the need to introduce integer variables and is thus suitable for large system applications. A two-part reactive power spot-pricing scheme is formulated, by which the investment and operational costs can be recovered by the utility. The proposed reactive power price has two parts-a fixed part to account for the investment costs of new capacitor at a bus and a variable spot price to account for the operating costs incurred in supplying the additional reactive power from generating units. <s> BIB001 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). Several techniques have been developed to make EP practicable to solve a real power system problem and other practical problems. The proposed approach has been used in the IEEE 30-bus system and a practical power system. For illustration purposes, only results for the IEEE 30-bus system are given. Simulation results, compared with those obtained by using a conventional gradient-based optimization method, Broyden's method, are presented to show that the present method is better for power system planning. In the case of optimization of noncontinuous and nonsmooth functions, EP is much better than nonlinear programming. The comprehensive simulation results show a great potential for applications of EP in power system economical and secure operation, planning and reliability assessment. <s> BIB002 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents a comparative study for three evolutionary algorithms (EAs) to the optimal reactive power planning (ORPP) problem: evolutionary programming, evolutionary strategy, and genetic algorithm. The ORPP problem is decomposed into P- and Q-optimization modules, and each module is optimized by the EAs in an iterative manner to obtain the global solution. The EA methods for the ORPP problem are evaluated against the IEEE 30-bus system as a common testbed, and the results are compared against each other and with those of linear programming. <s> BIB003 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB004 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper addresses some of the modeling and economic issues pertaining to reactive power planning. We present a new VAR planning model, propose an efficient solution algorithm, and discuss the economic issues. A contingency constrained OPF-based model incorporating (static) voltage stability constraints is developed to analyze VAR support decisions. DICOPT++, a state-of-the-art non-linear mixed integer programming (MINLP) algorithm, is proposed to solve the model efficiently. The sensitivity information elicited from the optimization model may also provide useful aid to network planning that are discussed. The VAR support cost allocation issue is addressed next. The Shapley value criterion of cooperative gaming is proposed to allocate VAR support costs among the loads contributing to voltage instability. The VAR planning model is implemented for the AEP 14-bus test system. The implications of the sensitivity information are discussed using the test system results. The result of the optimization approach is compared with two existing methods viz., Eigen Value (Modal) and Singular Value Decomposition (SVD) methods. Further, we discuss how the outcome of the Modal analysis could be integrated in the optimization model to form a hybrid approach. A numerical example of calculation of Shapley value is provided and, we also compare it with the conventional method of proportional allocation to gain better insight about this criterion. <s> BIB005 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> The paper proposes an application of hybrid evolutionary programming to reactive power planning. Reactive power planning is a nonsmooth and nondifferentiable optimization problem for a multiobjective function. The objective functions deals with the minimization of operating cost by reducing real power loss, improving the voltage profile and minimizing the allocation cost of additional reactive power sources. The proposed method is developed in such a way that a standard evolutionary programming (EP) method is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local optimization by direct search and systematic reduction in size of search region method is next employed to do fine tuning. The reactive power planning using hybrid evolutionary programming is demonstrated with the IEEE 30-bus system. The comprehensive simulation results show that hybrid evolutionary programming is a suitable method to solve the reactive power planning problem. <s> BIB006 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB007 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Evolutionary Programming (EP) optimization technique is recently applied in solving electric power system optimization problems. It is a fraction in the Evolutionary Computation (EC) optimization techniques under the artificial intelligence hierarchy. Optimization is an important issue in power system operation and planning particularly in the area of voltage stability studies. This paper presents transmission loss minimisation using optimal reactive power planning techniques (RPP). The problem involved optimization process; utilizing the ideas of EP to identify the optimal solution for RPP. In this study, EP engine was initially developed to implement the optimisation process considering two RPP procedures, namely optimal reactive power dispatch (ORPD) and optimal transformer tap changer setting (OTTCS). Comparative studies performed in this study aimed to identify the most suitable RPP technique for minimising transmission loss in power system, while maintaining the voltage profiles at reasonable voltage levels and avoiding overcompensating to the system. Repetitive load flow program was implemented for the fitness computation of the EP. Simulation results on a bulk IEEE Reliability System (RTS) are included to demonstrate the effectiveness of the proposed technique. Results indicated that ORPD outperformed OTTCS in minimising the system transmission loss with voltage profiles maintained within the acceptable limit. <s> BIB008 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper proposes an application of the Differential Evolution optimization algorithm to reactive power planning. This problem could be formulated mathematically as a nonlinear, non smooth, mixed-integer, multiobjective optimization problem. One of the objectives deals with the minimization of the real power losses that result in a reduction of the operational costs and improving the voltage profile across the system. The other objective minimizes the allocation of additional reactive power sources throughout the system. The proposed approach has been tested in a modified version of the IEEE 30-bus test system with satisfactory results. These results were compared with those obtained by another evolutionary computation technique (Evolutionary Programming). <s> BIB009 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB010 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Deregulation in the electricity supply industry has brought many new challenges to the problem of reactive power planning. Although the problem has been extensively studied, available standard optimization models and methods do not offer good solutions to this problem, especially in a competitive electricity market environment where many factors are uncertain. Given this background, a novel method for reactive power planning based on chance constrained programming is presented in this paper, with uncertain factors taken into account. A stochastic optimization model is first formulated under the presumption that the generator outputs and load demands can be modeled as specified probability distributions. A method is then presented for solving the optimization problem using the Monte Carlo simulation method and genetic algorithm. Finally, a case study is used to illustrate the validity and essential features of the proposed model and methodology. <s> BIB011 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB012 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract People have paid more attention to enhancing voltage stability margin since voltage collapses happened in some power systems recently. This paper proposes an optimal reactive power flow (ORPF) incorporating static voltage stability based on a multi-objective adaptive immune algorithm (MOAIA). The main idea of the proposed algorithm is to add two parts to an existing immune algorithm. The first part defines both partial affinity and global affinity to evaluate the antibody affinity to the multi-objective functions. The second part uses adaptive crossover, mutation and clone rates for antibodies to maintain the antibodies diversity. Hence, the proposed algorithm can achieve a dynamic balance between individual diversity and population convergence. The paper describes ORPF’s multi-objective functional mathematical model and the constraint conditions. The problems associated with the antibody are also discussed in detail. The proposed method has been tested in the IEEE-30 system and compared with IGA (immune genetic algorithm). The results show that the proposed algorithm has improved performance over the IGA. <s> BIB013 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract It is generally accepted that reactive power (or Var) compensation will bring benefits for utilities and industrial customers by providing local voltage and power factor support. However, there is a lack of a systematic approach to quantitatively identify the economic benefit. In addition, with the deregulation and restructuring, it is important to indicate the amount of benefit that each market participant may potentially receive given the right price signals. If such information can be easily obtained and presented, it will be more convenient for decision-markers to determine the cost benefit sharing of installing a Var compensator. This vision of this paper is to lay out a possible method for quantitatively evaluating the benefits from local reactive power compensation. The approach is to quantify the benefits into several categories such as reduced losses, shifting reactive power flow to real power flow, and increased transfer. The calculation of these benefits are illustrated with a simple two-bus power system model and then presented with a more complicated model using Optimal Power Flow to calculate the benefits. Simulation on the more complex system is conducted with seven buses in two areas. The simulation results show that the possible economic benefits can be significant,more » if compared with capacity payments to central generators or payment of power factor penalties applied by utilities. The potential economic value of local Var compensation may give various parties in electricity supply, delivery and end-use consumption a better understanding of the Var benefits to assist their cost-benefit analysis for Var compensation installation. Sensitivity analysis is also provided to illustrate that the benefits may not be monotonically increasing. Also, this paper suggests that the future reactive power market should consider local Var providers or other way to encourage load Var capability, since local Var benefit is significant.« less <s> BIB014 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB015 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents an algorithm for optimizing reactive power using Artificial Immune Ant Colony Algorithm. This hybrid algorithm uses artificial immune algorithm (AIA) to give pheromone to distribute and makes use of Ant Colony Algorithm (ACA) to give the optimal solution. The ACA has been improved. The improved ACA uses the best and worst ant to update the pheromone trails and pseudorandom proportional rule. Local pheromone update mechanism, pheromone limitation and adaptive pheromone decay coefficient are increased. The objective function of the proposed algorithm is to minimize the system active power loss. This algorithm has been applied to practical IEEE 30-bus system and shows better results and convergence rate as compared to other optimal methods. <s> BIB016 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents a new formulation of multi-objective reactive power and voltage control for power system. The objectives are active power loss, voltage deviation and the voltage stability index of the system. The load constrains and operational constrains are also taken into consideration. The multi-objective formulation of the problem requires a global performance index of the problem. A pseudogoal function derived on the basis of the fuzzy sets theory gives a unique expression for the global objective function, eliminating the use of weighing coefficients or penalty terms. Both objective functions and constraints are evaluated by membership functions. The inequality constrains are embedded into the fitness function by pseudogoal function which guarantees that the searched optimal solution is feasible. Moreover, a new type of evolutionary algorithm, particle swarm optimization (PSO), has been adopted and improved for this problem. To improve the performance of PSO, a fuzzy adaptive PSO (FAPSO) is proposed. A fuzzy system is employed to adaptively adjust the parameters of PSO, such as the inertia weight and learning factors, during the evolutionary process. The proposed approach has been examined and tested with promising numerical results of the IEEE 30-bus and IEEE 118-bus power systems. <s> BIB017 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB018 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper proposes an optimization based method of planning reactive power control for electric transmission systems to endow them with the capability of being reconfigured to a secure configuration under a list of contingencies. The overall objective function is to minimize the installation cost of new controls such as mechanically switched capacitors, while satisfying the requirements of voltage stability margin and voltage magnitude under a contingency list. The backward/forward search algorithm with linear complexity is used to select candidate locations for switched capacitors. Optimal locations and amounts of new switch controls are obtained by solving a sequence of mixed integer programming problems. The modified New England 39-bus system and a North American power system with 6358 buses are adopted to illustrate the effectiveness of the proposed method. <s> BIB019 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. 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 reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms. The simulation results show that the proposed approach is superior to the other listed algorithms and can be efficiently used for optimal reactive power dispatch. <s> BIB020 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB021 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP) considering the highest load buses, instead of considering the randomly selected load buses to install the reactive power sources. The proposed approach has been used in the IEEE 30-bus system. Simulation results, compared with those obtained by using the previous method of RPP using EP, considering the randomly selected load buses to install the reactive power sources are presented to show that, the present method is better for power system planning. <s> BIB022 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents a particle swarm optimization (PSO) approach for solving the contingency constrained optimal reactive power planning (CCORPP) problem. Generator bus voltage magnitudes, transformer tap settings and reactive power generation of capacitor bank are taken as the control variables. While solving the RPP problem using particle swarm optimization, the voltage magnitudes, the transformer tap setting and the reactive power generations of capacitor are taken as continuous variables. The proposed algorithm has been applied to find the optimal reactive power control variables in IEEE 30-bus system and is found to be more effective than the conventional methods and the Evolutionary Programming. <s> BIB023 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB024 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> A metaheuristic technique for solving the short-term transmission network expansion and reactive power planning problems, at the same time, in regulated power systems using the AC model is presented. The problem is solved using a real genetic algorithm (RGA). For each topology proposed by RGA an indicator is employed to identify the weak buses for new reactive power sources allocation. The fitness function is calculated using the cost of each configuration as well as constraints deviation of an AC optimal power flow (OPF) in which the minimum reactive generation of new reactive sources and the active power losses are objectives. With allocation of reactive power sources at load buses, the circuit capacity increases and the cost of installation could be decreased. The method is tested in a well known test system, presenting good results when compared with other approaches. <s> BIB025 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Voltage stability assessment and control form the core function in a modern energy control centre. This paper presents an improved Genetic algorithm (GA) approach for voltage stability enhancement. The proposed technique is based on the minimization of the maximum of L-indices of load buses. Generator voltages, switchable VAR sources and transformer tap changers are used as optimization variables of this problem. The proposed approach permits the optimization variables to be represented in their natural form in the genetic population. For effective genetic processing, the crossover and mutation operators which can directly deal with the floating point numbers and integers are used. The proposed algorithm has been tested on IEEE 30-bus and IEEE 57-bus test systems and successful results have been obtained. <s> BIB026 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract This paper presents a new approach for scheduling of reactive power control variables for voltage stability enhancement using particle swarm optimization (PSO). Cost function selected is maximization of reactive reserves of the system. To get desired stability margin a Schur’s inequality based proximity indicator has been selected whose threshold value along with reactive power reserve maximization assures desired static voltage stability margin. PSO has been selected because not only it gives global optimal solution but also its mechanization is very simple and computationally efficient. Reactive generation participation factors have been used to decide weights for reactive power reserve for each of generating bus. Developed algorithm has been implemented on 6-bus, 7-line and 25-bus 35-line standard test systems. Results have been compared with those obtained using Devidon–Fletcher–Powell’s (DFP) method. <s> BIB027 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> In this paper, a two-phase hybrid particle swarm optimization (PSO) approach is used to solve optimal reactive power dispatch (ORPD) problem. In this hybrid approach, PSO is used to explore the optimal region and direct search is used as local optimization technique for finer convergence. The performance of the proposed hybrid approach is demonstrated with the IEEE 30-bus and IEEE 57-bus systems and also the performance of this hybrid PSO is compared with that of PSO, Evolutionary Programming (EP) and hybrid EP. The performance of the proposed method is compared with the previous approaches reported in the literature. The performance of hybrid PSO seems to be better in terms of solution quality and computational time. (12), are some of the heuristic techniques that have been used, recently, to solve the ORPD problem. The EP is suitable for solving global optimization problems like ORPD. The only disadvantage of EP is that it takes more computation time (13). This paper proposes a hybrid approach to the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is one of the evolutionary computation (EC) technique based on swarm intelligence. It is sensitive to the tuning of its parameters and has a flexible mechanism to explore a global optimum point within a short calculation time (14). By employing the PSO initially the solution quality improves rapidly; later on obtaining the further improvement is very difficult and most of the computation time is spend over obtaining small improvements. To overcome this problem PSO is used for initial exploration and the local search (LS) technique is employed for finer convergence. The convergence of LS techniques depends on the initial search point and quickly finds the local optimum if the starting point is nearer to the optimum (15). This paper employs direct search (DS) (16) as a LS technique. The hybrid approach consists of two phases. In phase-1, PSO is employed to obtain the optimal region quickly and in phase-2, the DS with systematic reduction of the size of the search region (16) is used to find the local optimum. To validate the proposed hybrid method, it is tested on two IEEE standard test systems having non-linear characteristics. The results of the proposed hybrid approach are compared with PSO, EP and hybrid EP. The comparison exhibits the effectiveness of the proposed approach in terms of solution quality and computation time. <s> BIB028 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper proposed a procedure to solve the optimal reactive power dispatch (ORPD) problem using ant colony optimization (ACO) algorithm. The objective of the ORPD problem is to minimize the transmission power losses under control and dependent variable constraints. Proposed sensitivity parameters of reactive power at generation and switchable sources are derived based on a modified model of fast decoupled power flow. The proposed ACO-based algorithm is applied to the IEEE standard 14-bus, 30-bus systems, and a real power system at West Delta Network as a part of the Unified Egyptian Network. The obtained simulation results are compared with those of conventional linear programming, genetic algorithm, and particle swarm optimization technique. Simulation results show the capability of the proposed ACO-based algorithm for solving the ORPD problem, especially with increasing the system size. <s> BIB029 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract This paper addresses an application of modified NSGA-II (MNSGA-II) by incorporating controlled elitism and dynamic crowding distance (DCD) strategies in NSGA-II to multiobjective optimal reactive power dispatch (ORPD) problem by minimizing real power loss and maximizing the system voltage stability. To validate the Pareto-front obtained using MNSGA-II, reference Pareto-front is generated using multiple runs of single objective optimization with weighted sum of objectives. For simulation purposes, IEEE 30 and IEEE 118 bus test systems are considered. The performance of MNSGA-II, NSGA-II and multiobjective particle swarm optimization (MOPSO) approaches are compared with respect to multiobjective performance measures. TOPSIS technique is applied on obtained non-dominated solutions to determine best compromise solution (BCS). Karush–Kuhn–Tucker (KKT) conditions are also applied on the obtained non-dominated solutions to substantiate a claim on optimality. Simulation results are quite promising and the MNSGA-II performs better than NSGA-II in maintaining diversity and authenticates its potential to solve multiobjective ORPD effectively. <s> BIB030 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> The optimal Reactive Power Dispatch (RPD) problem is a nonlinear constrained optimization problem. This paper presents true multi-objective solution set for multi-objective RPD problem. The objective functions are real power loss minimization and control variable adjustment costs. In this paper, a Modified Non-Dominated Sorting Genetic Algorithm version II (MNSGA-II) is proposed for solving RPD problem. For maintaining good diversity in the performance of NSGA-II, the concept of Dynamic Crowding Distance (DCD) is implemented in NSGA-II algorithm and given name as MNSGA-II. The standard IEEE 30-bus and IEEE 118 bus test systems are used. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference Pareto front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures.The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPD problem. <s> BIB031 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Reactive power dispatch (RPD) is one of the important tasks in the operation and control of power system. This paper presents an efficient and reliable evolutionary-based approach to solve the RPD problem. The proposed approach employs differential evolution (DE) algorithm for optimal settings of RPD control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect power losses minimization, voltage profile improvement, and voltage stability enhancement. The simulation results of the proposed approach are compared to those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the RPD problem. <s> BIB032 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper addresses an optimal Reactive Power Planning (RPP) of power system. The Static Var Compensator (SVC) is introduced into power system in order to reactive power support and voltage control. The locations and the outputs of SVCs are determined using our proposed optimal reactive power planning model. The proposed method optimizes several objective functions at the same time within one general objective. The optimized objectives are minimization of total investment in reactive power support, average voltage deviation and minimization of total system loss. These objective functions are one of the most important objectives for every transmission and distribution systems. Harmony Search Algorithm (HS) is used to solve the optimization problem. The validity of the proposed method is tested on a typical power system. <s> BIB033 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <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> BIB034 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents a harmony search algorithm for optimal reactive power dispatch (ORPD) problem. Optimal reactive power dispatch is a mixed integer, nonlinear optimization problem which includes both continuous and discrete control variables. The proposed algorithm is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices to optimize a certain object. The objects are power transmission loss, voltage stability and voltage profile which are optimized separately. In the presented method, the inequality constraints are handled by penalty coefficients. The study is implemented on IEEE 30 and 57-bus systems and the results are compared with other evolutionary programs such as simple genetic algorithm (SGA) and particle swarm optimization (PSO) which have been used in the last decade and also other algorithms that have been developed in the recent years. <s> BIB035 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Reactive Power Optimisation (RPO) is an important issue in deregulated electricity market. This paper presents a Hybrid Particle Swarm Optimisation (HPSO) approach for solving the RPO problem in electricity market. Particle Swarm Optimisation (PSO) is a population-based search algorithm for global optimisation and has a minimum number of control parameters. In the proposed HPSO, to avoid the possibility of trapping in local optimum, genetic operators namely crossover and mutation are applied to the PSO algorithm. The proposed algorithm has been applied to find the optimal reactive power control variables in IEEE 30-bus system and promising results have been obtained. <s> BIB036 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents the application of Covariance matrix adapted evolution strategy (CMAES) to reactive power planning (RPP) problem by minimizing the combined total cost of energy loss and the allocation cost of additional reactive power sources. Voltage stability index (L-index) is considered as an additional constraint in order to include the system stability into account. To improve the search efficiency of CMAES, penalty parameter-less scheme is employed for constraint handling. To determine the performance of the CMAES method on reactive power planning problem, IEEE-30 bus test system and Tamil Nadu Electricity Board (TNEB)-69 bus system (a practical system in India) are taken. For comparison purposes, the results of real-coded GA (RGA), particle swarm optimization (PSO), sequential quadratic programming (SQP), and Broyden–Fletcher–Goldfarb–Shanno (BFGS) methods are considered. The simulation results reveal that the CMAES algorithm performs better in terms of energy cost, investment cost, and consistency in getting optimal solution. Karush–Kuhn–Tucker (KKT) conditions are also applied on the best solution obtained using CMAES algorithm to substantiate a claim on optimality. Copyright © 2010 John Wiley & Sons, Ltd. <s> BIB037 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents a multi-objective optimization methodology to solve the Optimal Reactive Power Flow (ORPF) problem. The e-constraint approach is implemented for the Multi-objective Mathematical Programming (MMP) formulation. The solution procedure uses Mixed Integer Non-Linear Programming (MINLP) model due to discrete variables, such as the tap settings of transformers and the reactive power output of capacitor banks. The optimum tap settings of transformers are directly determined in terms of the admittance matrix of the network since the admittance matrix is constructed in the optimization framework as additional equality constraints. The optimization problem is modeled in General Algebraic Modeling System (GAMS) software and solved using DICOPT solver. Simulation results are implemented on the IEEE 14-, 30-, and 118-bus test systems to simultaneously optimize the total fuel cost, power losses and the system loadability as objective functions. The simulation results show that the proposed algorithm is suitable and effective for the reactive power planning. <s> BIB038 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract This work proposes a two-phase iterative simulation optimization approach to solve the optimal volt-ampere reactive (VAR) sources planning problem. In the upper phase, an Ordinal Optimization (OO)-based method is employed as the optimization methodology, which performs ranking and selection to choose better solutions from a candidate solution set based on the structural information of system extracted from the simulation methodology performed in the lower phase. The simulation methodology, which is based on the Dual Projected Pseudo Quasi-Newton (DPPQN) method, is employed in the lower phase. The proposed OO-based approach consists of four levels. The size and solution quality of the resulting candidate solution set is reduced and improved level by level, and the best solution obtained in the final level is the good enough capacitor placement pattern that we seek. Via numerous tests, this work demonstrates the efficiency of the proposed approach with respect to the genetic algorithm (GA) and tabu search (TS) on the IEEE 118-bus system. Test results show that the proposed approach is an excellent alternative for the optimal VAR sources planning problems. <s> BIB039 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents a new method for VAr planning under multiple operating scenarios, optimizing sizes and locations of new reactive compensation equipment to ensure that both the system voltage profile and voltage stability requirements are met. The approach is based on mbi L1-norm regularization for finding a solution with minimum VAr installation sites and on the mbi L2-norm penalty function for satisfying the multiple state constraints. The mbi L2-norm penalty function is exact in the sense that a finite penalty parameter is required to establish equivalence with the VAr planning problem, thus avoiding numerical ill-conditioning. The solution is obtained from a successive conic programming algorithm which makes use of adaptive trust-region control. The results of the new method are compared with those of an optimal power flow-based program for VAr planning. <s> BIB040 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> In deregulated electric power systems, one of the most important topics is to augment the total system social welfare. Many different methods have been carried out for this problem. One of the most viable methods to increasing system social welfare is to supply the reactive demands as locally. In this way, the reactive demands are supplied via locally reactive sources and the capacity of transmission lines is not used to transfer reactive power of demands. Thus, the congestion of lines is not reached and the system social welfare which is related to the congestion of lines is increased. In this scope, this study addresses an optimal Reactive Power Planning (RPP) in a deregulated power system. The proposed method optimizes two objective functions at the same time within one general objective. The optimized objectives are minimization of total investment in reactive power support and maximizing of total system social welfare. Genetic Algorithm (GA) is used to solve the optimization problem. The validity of the proposed method is verified on a typical power system. <s> BIB041 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper discusses the application of Modified Non-Dominated Sorting Genetic Algorithm-II (MNSGA-II) to multi-objective Reactive Power Planning (RPP) problem. The three objectives considered are minimization of combined operating and VAR allocation cost, bus voltage profile improvement and voltage stability enhancement. For maintaining good diversity in nondominated solutions, Dynamic Crowding Distance (DCD) procedure is implemented in NSGA-II and it is called as MNSGA-II. The standard IEEE 30-bus test system, practical 69-bus Indian system and IEEE 118-bus system are considered to analyze the performance of MNSGA-II. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference pareto-front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures namely gamma, spread, minimum spacing and Inverted Generational Distance (IGD) in 15 independent runs. The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPP problem. A decision-making procedure based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used for finding best compromise solution from the set of pareto-solutions obtained through MNSGA-II. <s> BIB042 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract In this paper a mathematical model for solving simultaneous transmission network expansion and reactive power planning problem (TEPRPP) via an AC model has been presented. A Real Genetic Algorithm (RGA) combined an Interior Point Method (IPM) aimed to obtaining a significant quality solution for such a problem has been employed. The proposed algorithm is tested on three systems; IEEE 24-bus system, 46-bus south Brazilian and the Southeast Network of Iran (SNI). The obtained results show the capability and the viability of the proposed methodology incorporating the AC model of the TEPRPP problem even in real world. <s> BIB043 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper presents a model for long-term reactive power planning where a deterministic nonlinear model is expanded into a multi-stage stochastic model under load uncertainty and an N-k contingency analysis. Reactive load shedding is introduced in the objective function to measure the reactive power deficit after the planning process. The objective is to minimize the sum of investment costs (IC), expected operation costs (EOC) and reactive load shedding costs optimizing the sizes and locations of new reactive compensation equipment to ensure power system security in each stage along the planning horizon. An efficient scenario generation and reduction methodology is used for modeling uncertainty. Expected benefits are calculated to establish the performance of the expected value with perfect information (EVPI) and the value of the stochastic solution (VSS) methodologies. The efficacy of the proposed model is tested and justified by the simulation results using the Ward-Hale 6-bus and the IEEE 14-bus systems. <s> BIB044 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract Reactive power dispatch (RPD) is one of the major issues of modern energy management system. This article presents an efficient genetic algorithm (GA) approach for modelling and solving RPD problem of power system in the framework of fuzzy goal programming (FGP) in uncertain environment. In the proposed approach, the objectives of the problem concerned with RPD problem are fuzzily described. In the solution process, the proposed GA method is used in the framework of FGP model in an iterative manner to reach a satisfactory decision. The proposed approach is tested on the standard IEEE 6-Generator 30-Bus System and compared with the solutions obtained in previous study. <s> BIB045 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper proposes an efficient differential evolution (DE) algorithm for the solution of the optimal reactive power dispatch (ORPD) problem. The main objective of ORPD is to minimize the total active power loss with optimal setting of control variables. The continuous control variables are generator bus voltage magnitudes. The discrete control variables are transformer tap settings and reactive power of shunt compensators. In DE algorithm the other form of differential mutation operator is used. It consists to add the global best individual in the differential mutation operator to improve the solution. The DE algorithm solution has been tested on the standard IEEE 30-Bus test system to minimize the total active power loss without and with voltage profile improvement. The results have been compared to the other heuristic methods such as standard genetic algorithm and particle swarm optimization method. Finally, simulation results show that this method converges to better solutions. <s> BIB046 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Reactive Power Dispatch is one of the important tasks in the operation and control of power systems. It is a section of optimization problems in power system that minimizes the objective functions by satisfying a set of constraints and using a set of controllable variables. As the Reactive Power Dispatch is a nonlinear problem, it has multiple minima. So the conventional techniques and mathematical programming methods are not suitable to solving these problems. In this paper, the Seeker Optimization Algorithm (SOA) is considered to find a global optimum reactive power dispatch by minimizing the different objective functions. In this work, the objective functions are reducing active power losses, improving voltage deviation and increasing voltage stability. To show efficiency and powerful performance of SOA, it is applied to optimal reactive power dispatch on standard IEEE-30 bus power system. Finally, the obtained results of SOA are compared with Particle Swarm Optimization (PSO) algorithm, Multi Agent Particle Swarm Optimization (MAPSO) and Genetic Algorithm (GA) methods. <s> BIB047 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Reactive power planning is vital for maintaining the voltage stability of power systems and evolutionary algorithms are highly useful for achieving this task. This paper compares the eectiveness of the dierential evolution (DE) and evolutionary programming (EP) algorithms in optimizing the reactive power planning of power systems under line outage contingency conditions. DE is ecient in exploration through the search space of the problem, while EP is simple and easy to implement. The low cost but fast response thyristor-controlled series capacitor (TCSC) exible alternating current transmission system (FACTS) device is incorporated to control the power ows. The optimal settings of the control variables of the generator voltages, transformer tap settings, and location and parameter settings of the TCSC are considered for reactive power planning and the resultant reactive power reserves. The eectiveness of the <s> BIB048 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This study proposes a multi-objective fuzzy-based procedure for solving reactive power management in competitive environment. The proposed procedure incorporates both economical and technical aspects of reactive power support. The economical aspect aims to minimise the total costs of reactive power purchase from service providers as primary objective function, whereas the technical aspect is taken care of through the secondary objective function that minimises the total transmission losses. The proposed procedure achieves security constraints such as the bus voltage limits, reactive power capability limits and transmission line reactive transfer limits. The proposed procedure is applied to the west-delta region system as a part of the Egyptian Unified network. The numerical results show that the proposed procedure achieves a minimum real power loss with maximal reactive reserve for power systems for different operating conditions. <s> BIB049 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> In this paper, a new methodology for Transmission Expansion Planning (TEP) in deregulated electricity market is presented. The proposed TEP is associated with Reactive Power Planning (RPP), reliability assessment and also consideration of wind and load uncertainties. The proposed planning aims at investment cost minimization, social welfare maximization and satisfying reliability constraint at the same time with taking into account wind and load uncertainties. Expected Energy Not Supplied (EENS) is used as an index for reliability evaluation. At first, Monte-Carlo simulation is used to obtain the Probability Density Function (PDF) of Wind Turbine Generator (WTG) output. Then, the WTG and load uncertainties are considered in TEP formulation. Particle Swarm Optimization (PSO) method is considered to solve the proposed planning problem which is a constrained nonlinear mixed integer optimization programming. Simulation results on two standard test systems (Garver and RTS systems) verify the effectiveness of the proposed planning for consideration of wind and load uncertainties in TEP problem under electricity market environment. Also, the proposed method leads to reduction of the total investment cost, the reliability improvement and the social welfare maximization. <s> BIB050 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This study proposes an optimisation-based method of planning static and dynamic VAR sources for the enhancement of electric power transmission systems under a set of contingencies. The overall objective function is to minimise the total installation cost of static and dynamic VAR sources, while satisfying the requirements of long-term voltage stability margin and transient voltage dip under a contingency list. The backward/forward search algorithm with linear complexity is used to select candidate locations for reactive power compensation. Optimal locations and amounts of static and dynamic VAR sources are obtained by solving a sequence of mixed integer programming problems. The New England 39 bus system is adopted to illustrate the effectiveness of the proposed method. The systematic applicability of the proposed method to a large scale practical system is also illustrated using a 16173-bus U.S. Eastern Interconnection system. <s> BIB051 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Power systems are mostly run under stressed operating conditions due to rapidly increasing demand but inadequate expansion in generation and transmission capacity. Thus voltage stability has become a major concern in present day power system operation which depends on the availability and distribution of reactive power. It becomes very important to ensure the presence of sufficient amount of reactive power source in the network for secure and reliable operation of electrical power system. This paper mainly deals with short-term reactive power procurement (RPP) problem assuming an already established real power market. Contributions of this paper are three fold. First, this paper presents three new RPP methods which are formulated as optimum power flow (OPF) problems. OPFs are solved using Artificial Bee Colony (ABC) algorithm as population based stochastic solution methods are more suitable for solving reactive power optimization problem. Second, a detailed analysis of voltage stability for proposed RPP problems are performed for both normal and contingency condition to find out how these new formulations affect voltage stability. Third, proposed methods are compared with the classical RPP formulations, e.g., Minimization of L-index and loss Minimization. Finally results are reported for both test system and a large practical system. <s> BIB052 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract Gravitational search algorithm (GSA) is based on law of gravity and the interaction between masses. In GSA, searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. In this paper, to further improve the optimization performance of GSA, opposition-based learning is employed in opposition-based gravitational search algorithm (OGSA) for population initialization and also for generation jumping. In the present work, OGSA is applied for the solution of optimal reactive power dispatch (ORPD) of power systems. Traditionally, ORPD is defined as the minimization of active power transmission losses by controlling a number of control variables. ORPD is formulated as a non-linear constrained optimization problem with continuous and discrete variables. In this work, OGSA is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation to optimize certain objectives. The study is implemented on IEEE 30-, 57- and 118-bus test power systems with different objectives that reflect minimization of either active power loss or that of total voltage deviation or improvement of voltage stability index. The obtained results are compared to those yielded by the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature including basic GSA. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving ORPD problems of power systems. <s> BIB053 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> One of the major optimization problems in power systems is the optimal reactive power dispatch (ORPD) problem. ORPD problem is a multi-variable problem with non-linear characteristics and has established significant emphasize on secure and economical operation of power systems. Therefore, finding an effective algorithm to solve this problem is absorbing a growing attention among the academics. In this paper, a reliable and effective algorithm is introduced by combining modified teaching learning algorithm (MTLA) and double differential evolution (DDE) algorithm in order to handle the ORPD problem. Moreover, the proposed algorithm is applied to ORPD problem on IEEE 14-bus, IEEE 30-bus and IEEE 118-bus power systems for performance assessment and validation purposes. The obtained results demonstrate the efficiency of the proposed hybrid algorithm and show faster convergence and better solutions in comparison with previously reported algorithms. <s> BIB054 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> This paper proposed an application of New Voltage Stability Index(NVSI) to Reactive Power Planning(RPP) using Soft Computing Technique based Differential Evolution(DE).NVSI is used to identify the weak buses for the Reactive Power Planning problem which involves process of experimental by voltage stability analysis based on the load variation. The Formulation of a New Voltage Stabilty Index(NVSI),which is originates from the equation of a two bus network,neglecting the resistance of (...) <s> BIB055 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract In this paper, Gravitational Search Algorithm (GSA) is used as optimization method in reactive power planning using FACTS (Flexible AC transmission system) devices. The planning problem is formulated as a single objective optimization problem where the real power loss and bus voltage deviations are minimized under different loading conditions. GSA based optimization algorithm and particle swarm optimization techniques (PSO) are applied on IEEE 30 bus system. Results show that GSA can also be a very effective tool for reactive power planning. <s> BIB056 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract This paper presents a novel hybrid algorithm combining Firefly Algorithm (FA) and Nelder Mead (NM) simplex method for solving power system Optimal Reactive Power Dispatch (ORPD) problems. The ORPD is a very important aspect of power system operation and is a highly nonlinear, non-convex optimization problem, consisting of both continuous and discrete control variables. Like many other general purpose optimization methods, the original FA often traps into local optima and in order to overcome the shortcoming, in this paper, an efficient local search method called NM simplex subroutine is introduced in the internal architecture of the original FA algorithm. The proposed Hybrid Firefly Algorithm (HFA) avoids premature convergence of original FA by exploration with FA and exploitation with NM simplex. The proposed method is applied to determine optimal settings of generator voltages, tap positions of tap changing transformers and VAR output of shunt capacitors to optimize two different objective functions; such as minimization of real power loss and voltage deviations. The program is developed in Matlab and the proposed hybrid algorithm is examined on two standard IEEE test systems for solving the ORPD problems. For validation purpose, the results obtained with the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version of FA and other existing methods. It is revealed that the proposed hybrid method is able to provide better solutions. <s> BIB057 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract This paper proposes a new multi-level methodology based on the optimal reactive power planning. The developed methodology is designed to solve the problem of the non-feasibility solution of the fuel cost minimization problem (for a given operating point) where the classical method such as interior point method (IPM) is applied. The proposed solution to solve this problem is based on the application of the optimal reactive power planning problem considering voltage stability as the initial solution of the fuel cost minimization problem. To improve the latter the load voltage deviation problem is applied to improve the system voltage profile. For a good result improvement, the reactive power planning problem and the load voltage deviation minimization problems are solved using a new optimization method namely the Differential Search Algorithm (DSA). Moreover, the fuel cost minimization problem is solved using IPM. To identify the candidate placements of compensation devices for the optimal reactive power planning problem, a new voltage stability index namely: The Fast Voltage Stability Index (FVSI) is used. The methodology has been tested with the equivalent Algerian power system network, and the simulation results show the effectiveness of the proposed approach to improve the reactive power planning problem and to minimize the system voltage deviation. <s> BIB058 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Reactive power optimization is one of the efficient ways of improving power system reliability and efficiency which is beneficial in power planning studies. ORPD problems are best solved using different algorithms to achieve optimization. This paper deals a comparative study of three algorithms PSO, GSA and hybrid PSOGSA algorithm. The ORPD multiobjective problem is tested over standard IEEE system. The test is performed under two conditions with and without penalty function added to the objective function. And all results obtained are summarized and best optimal solution is obtained from the hybrid PSOGSA algorithm. <s> BIB059 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> Introduction <s> Abstract Optimal reactive power dispatch (ORPD) problem has a significant influence on optimal operation of power systems. However, getting optimal solution of ORPD problem is a strenuous task for the researchers. The inclusion of flexible AC transmission system (FACTS) devices in the power system network for solving ORPD problem adds to its complexity. This paper presents the application of chemical reaction optimization (CRO) for optimal allocation of a static synchronous compensator (STATCOM) to minimize the transmission loss, improve the voltage profile and voltage stability in a power system. The proposed approach is carried out on IEEE 30-bus and IEEE 57-bus test systems and the simulation results are presented to validate the effectiveness of the proposed method. The results show that the proposed approach can converge to the optimum solution and obtains better solutions as compared to other methods reported in the literature. <s> BIB060
Nowadays, reactive power planning (RPP) problem has become one of the most challenging problems in power systems. It has been an important stage of transmission expansion planning (TEP) problem in recent years BIB025 BIB050 BIB043 . In addition, reactive power control/dispatch is an important function in the planning process for the future of power systems. It aims to utilize all the reactive power sources efficiently, which are suitably located and sized in the planning process BIB038 BIB026  BIB029 BIB013 BIB027 BIB004 . Generally, the various RPP solutions are divided into three groups which are analytical approaches BIB001  BIB014 , arithmetic programming approaches BIB043 BIB038 BIB001  BIB014 BIB015 BIB007  (Ch. 2),17(Ch. 3), BIB005 BIB044 BIB039 BIB019 BIB040 BIB051 , and meta-heuristic optimization techniques. Various Meta-heuristic Optimization Algorithms (MOA) have been applied to the RPP problem such as Genetic Algorithms (GA) BIB026   BIB010 BIB041 BIB011 BIB045  BIB042 BIB030 BIB031 , Differential Evolution (DE)    BIB009   BIB055   BIB046 BIB032  , Harmony Search (HS) BIB033 BIB034 BIB035 , Seeker Optimization Algorithm (SOA) BIB020 BIB021 BIB047 , Evolutionary Programming (EP) BIB002 BIB003 BIB022 BIB048 BIB006 BIB008 , Ant Colony Optimization (ACO) BIB029 BIB016 , Immune Algorithm (IA) BIB013 , Particle Swarm Optimization (PSO) BIB050 BIB027  BIB023   , Artificial Bee Colony (ABC) BIB052 , Gravitational Search Algorithm (GSA) BIB056 BIB053 , Firefly Algorithm (FA) BIB057 , Teaching Learning Algorithm (TLA) BIB054 , Chemical Reaction Optimization (CRO) BIB060 , Water Cycle Algorithm (WCA)  , and Differential Search Algorithm (DSA) BIB058 . Hybrid techniques have been suggested in some researches that make use of advantages of different algorithms simultaneously to improve the quality of solution BIB026 BIB004 16(Ch. 5) , BIB006 BIB016 BIB017 BIB036 BIB018 BIB028 BIB012 BIB049 BIB024  BIB059 . Also, multi-objective formulation of optimization problems for reactive power planning and operation has been treated using the mathematical sum approach [1,11,24,25,28,35-38,5 0,51,53,56,68] , weighting functions BIB041 BIB045 BIB046 BIB033 BIB034 BIB021 BIB018 , e-constraint approach  BIB005 BIB039 BIB033  BIB037 , fuzzy goal programming techniques BIB011  , and Pareto concept BIB038 BIB013 16(Ch. 4) ,  BIB010 BIB042 BIB030 BIB031 BIB009  . Various conventional methods have been presented to solve the RPP problem and assured their incompetence in handling multi-objective nonlinear problems and they may converge to a local optimum. MOAs that mimic the nature opened a new era in computation. For the past decades, numerous research applications of MOAs have been concentrated for solving the RPP problem. In this particular area, the research is still young which broadens the scope and viability of MOAs exploring new modifications and developments in solving the RPP problem. This paper presents a broad overview of solution methods for solving the RPP problem which are analytical approaches, arithmetic programming approaches, and metaheuristic optimization techniques. Also, the different applications of meta-heuristic algorithms for solving the RPP problem are extensively reviewed and thoroughly discussed. Furthermore, the multi-objective formulations of reactive power planning and operations are studied to clarify their merits and demerits. This paper is organized as follows. The formulation of the RPP problem is presented in Section 2. Section 3 discusses the different methods applied to solve the RPP problem. The multi-objective formulations of the RPP problem are discussed in Section 4. The concluding remarks are highlighted in Section 5.