A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB001 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB002 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB003 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB004 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB005 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB006 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB007 </s> A review of meta-heuristic algorithms for reactive power planning problem <s> 4)] <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> BIB008
A Vector Evaluated PSO (VEPSO) method has been implemented on the multi-objective RPP problem Good efficiency A fuzzy based mechanism is employed to extract the best compromise solution over the trade-off front More computational burden and complexity to determine Pareto front that VEPSO generates two swarms where each one is based on an objective, and to extract the best compromise solution BIB001 A modified PSO method has been applied for scheduling of reactive power control variables to maximize the reactive power reserves. In this paper, the e-constraint approach has been used to assure desired static voltage stability margin based on a proximity indicator Better efficiency where, a fly-back mechanism has been applied to enable any violated particle to fly back to its previous position More computational burden to execute the flyback mechanism Artificial Bee Colony (ABC) algorithm BIB002 ABC was inspired by the foraging behavior of honey bee swarm. It has been executed for handling the ORPD problem in deregulated power systems after assuming an already established real power market It is as simple as PSO and DE with few control parameters such as colony size and maximum cycle number It is robust against initialization It has the ability to explore local solutions ABC has poor exploitation characteristics Its convergence speed is also an issue in some cases It may get stuck in local optimum Gravitational Search Algorithm (GSA) BIB005 BIB003 GSA was based on Newton's law of gravity and motion. In BIB005 , it has been applied to the RPP using FACTS to minimize the losses and bus voltage deviations. In BIB003 , opposition-based GSA for population initialization has been presented to solve the ORPD problem It is simple and easy to implement It has a high randomness of the individual moves. Thus, it provides the global exploration in the search space The local search ability of GSA is weak In BIB005 , it isn't robust against initialization. This feature is improved in BIB003 In BIB005 BIB003 , the considered problem was formulated as a single objective optimization problem Firefly Algorithm (FA) BIB006 FA was based on swarm behavior and has many similarities with PSO algorithm. It has been applied to minimize the real power loss or the voltage deviations FA is simple and easy to implement It is good at exploration It includes the self-improving process with the current space and it improves its own space from the previous stages FA often traps into local optima The minimization of power losses or voltage profile improvement is handled as a single goal optimization Its parameters were set fixed and they do not change with the time Teaching BIB004 TLA often converges to local optima TLA was based on the simulation of a classical learning process which composed of two phases: (i) learning through teacher and (ii) learning through interacting with the other learners. It has been applied to handle the ORPD problem considering only the power loss It has balanced global search ability and convergence rate. It has a good capability for global and local searching The exploration features need more support Power loss was the only considered objective Chemical Reaction Optimization (CRO) BIB008 CRO was based on the various chemical reactions occur among the molecules. It has been applied to the RPP using FACTS to minimize the transmission loss, improve the voltage profile and voltage stability CRO is easy to implement However, CRO behaves like a random search to traverse the whole solution space, which could confine the algorithm's search ability It is robust against initial seeds The local search needs more modifications since it may stick in local optima However, it has good robustness indices for solving the considered RPP in BIB008 , it is highly sensitive to the initial kinetic energy and the concerned loss rate Water Cycle Algorithm (WCA)  WCA is inspired from nature and based on the observation of water cycle and how rivers and streams flow downhill toward the sea in the real world. It has been applied to minimize the weighted sum of the losses and the voltage deviations It is simple and easy to use It has few control parameters It has a good exploration features Its local search ability of is weak It is often traps into local optima Its robustness and consistence need more uphold Differential Search Algorithm (DSA) BIB007 DSA was inspired by migration of super-organisms utilizing the concept of Brownian like motion. It has been applied to solve the non-feasibility problem solution of the fuel cost minimization problem (for a given operating point) by optimizing the RPP problem The candidate placements of VAR sources have been selected based on FVSI It has a good exploration feature in the search space to locate the region of global optimum Therefore, its convergence rate is fast but it is also a problem in some cases The minimization of fuel cost or load voltage deviations is handled as mono-objective optimization in two separate levels Transformer tap settings and VAR sources are treated as continuous variables Its exploitation of the optimal solution requires more support DSA is still novel and further researches are necessary to be developed and improved Hybrid techniques [16 (Ch.