Abstract: The chief aspect of solving **Optimal** **Reactive** **Power** Dispatch Problem (ORPD) is to minimize the real **power** loss and also to keep the voltage profile within the limits. In this paper, a new metaheuristic optimizing algorithm that is the simulation of “Grand Salmon Run” (GSR) is developed. The salmon run phenomena is one of the grand annual natural actions occurrence in the North America, where millions of salmons travel through mountain streams for spawn. The proposed GSR has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the simulation results reveals about the good performance of the proposed algorithm.

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The results obtained with the proposed approach are presented and compared favorably with results of other approaches, like the Linear Programming (LP) method. All data used this analysis is taken from the Iraqi Operation and Control Office, which belongs to the ministry of electricity [3]. An echolocation based algorithm known as the BAT search algorithm inspired by the behaviour of bats to **optimal** **reactive** **power** dispatch (ORPD) problem. The minimization of active **power** transmission losses through controlling a number of control variables is defined as the ORPD problem. The **optimal** **reactive** **power** dispatch is then developed as a non-linear optimization problem in regard to **power** transmission loss, voltage stability and voltage profile [4]. A Genetic Algorithm (GA) - based approach for solving **optimal** **Reactive** **Power** Dispatch (RPD) including voltage stability limit in **power** systems. The monitoring methodology for voltage stability is based on the L-index of load buses. Bus voltage magnitudes, transformer tap settings and **reactive** **power** generation of capacitor banks are the control variables. A binary-coded GA with tournament selection, two point crossover and bit-wise mutation is used to solve this complex optimization problem.

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This paper proposes Dolphin echolocation Algorithm (DEA) for solving the multi-objective **reactive** **power** dispatch problem. Echolocation is the genetic sonar used by dolphins and more than a few kinds of other animals for direction-finding and hunting in different environments. This aptitude of dolphins is mimicked in this paper to develop a new process for solving **optimal** **reactive** **power** dispatch problem. A detailed study has shown that meta-heuristic algorithms have certain overriding rules. These rules will facilitate to get enhanced results. Dolphin echolocation algorithm takes reward of these rules and outperforms many active optimization methods. The new approach DEA leads to outstanding results with little computational efforts. In order to evaluate the efficiency of the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other specified algorithms. Simulation results show that DEA is superior to other algorithms in tumbling the real **power** loss and enhancing the voltage stability.

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In **power** system operation, minimization of **power** loss in transmission lines and/or minimizing the voltage deviation at the load buses by controlling the **reactive** **power** is referred to as **optimal** **reactive** **power** flow (ORPF). ORPF is necessary for secured operation of **power** systems with regard to voltage stability. Here in this paper, the nature inspired Flower Pollination Algorithm (FPA) algorithm is introduced to solve multi constrained **optimal** **reactive** **power** flow problem in **power** system. Generator bus voltages, transformer tap positions and switchable shunt capacitor banks are used as variables to control the **reactive** **power** flow. Flower Pollination Algorithm was tested on standard IEEE 30 bus system and the results are compared with other methods to prove the effectiveness of the new algorithm. The results are quite emerging and the algorithm is found to be simple and easy to implement.

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_______________________________________________________________________________________________________ Abstract— **Optimal** **Reactive** **Power** Dispatch (ORPD) is required for **power** system control and proper operation. ORPD reduces the **power** system losses and improves the voltage profile, **power** system security, **power** transmission capability and overall system operation. The **reactive** **power** control variables like generator voltage, transformer tap-settings and switchable VAR sources are adjusted to solve ORPD problem. In this paper, the ORPD problem is solved as nonlinear constrained optimization problem with equality and inequality constraints for minimization of **power** losses and voltage deviation. The proposed approach employs Matpower Optimization toolbox of Matlab for the **optimal** setting of ORPD control variables. The Matpower Optimization toolbox has been implemented using generalized differential evolution on a standard IEEE 30-bus system to minimize **power** losses and voltage deviation. The simulation results of the proposed approach are compared with the results obtained from particle swarm optimization (PSO) based algorithm.

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ABSTRACT: In this paper, the recent metaheuristic algorithm of bat optimization algorithm(BOA), is implemented for solving **optimal** **reactive** **power** dispatch (ORPD) problems. BOA can outperform several robust and efficient metaheuristic algorithms in solving engineering problems. ORPD problem is taken as a multi-modal and constrained optimisation problem with large number of control variables. Real **power** loss minimization and sum of voltage deviation minimization are the objectives of ORPD problem. The proposed algorithm was tested on the standard IEEE 30 and 57 bus **power** systems. The simulation results prove the outperformance of BOA over other algorithms compared from the literatures.

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In this paper, the strength of the hybrid version of BB-BC algorithm is demonstrated. The simple and easy to implement algorithm is enhanced by hybridization with PSO and successfully applied for **power** system optimization. The exploration capability of the basic BB-BC algorithm is improved by combining the exploration quality of PSO. The HBB-BC is an enhanced version of BB- BC and has good exploitation and exploration capabilities. This ensures the strength of the proposed algorithm in both local search and global search. The numerical results show that the hybrid version of the algorithm outperforms its basic form. Three different objectives are considered for verifying the effectiveness. In all the three objectives the performance is better. Further, optimization of **reactive** **power** by the proposed algorithm is highly encouraging. Moreover, the total Var requirement suggested by HBB-BC is much smaller against the one recommended by the basic algorithm. The reduction in Var requirement is equivalent to maximization of Var reserves in a **power** system. Therefore, the proposed algorithm in addition to **reactive** **power** optimization maximizes the Var reserves. The algorithm may be used for other **power** system optimization works like economic load dispatch, **optimal** **power** flow and voltage stability improvement.

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with different algorithms considering 1.1 p.u as the maximum setpoints of generators.. Best control variable settings reported for power loss minimization of the IEEE 30 bus test system [r]

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This paper presents Hybridization of chaos optimization algorithm with outlook algorithm (HCO),to solve **optimal** **reactive** **power** dispatch problem. The algorithm is organized in twofold phases. The first phase uses parallel chaos optimization grounded on tent map for global exploration, while outlook algorithm is engaged in the second phase for local exploration.The projectedHCO has been tested in standard IEEE 30 bus test system and simulation results show clearly the improved performance of the proposed algorithm in decreasing the real **power** loss.

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Both the gradient and Newton methods have the complexity in managing inequality constraints. If linear programming is applied then the input- output function has to be uttered as a set of linear functions which mostly lead to loss of accuracy. The problem of voltage stability and collapse play a major role in **power** system planning and operation [8]. Evolutionary algorithms such as genetic algorithm have been already proposed to solve the **reactive** **power** flow problem [9-11]. Evolutionary algorithm is a heuristic approach used for minimization problems by utilizing nonlinear and non-differentiable continuous space functions.In [12], Hybrid differential evolution algorithm is proposed to improve the voltage stability index. In [13] Biogeography Based algorithm is projected to solve the **reactive** **power** dispatch problem. In [14], afuzzy based method is used to solve the **optimal** **reactive** **power** scheduling method. In [15], an improved evolutionary programming is used to solvethe **optimal** **reactive** **power** dispatch problem. In [16], the **optimal** **reactive** **power** flow problem is solved by integrating a genetic algorithm with a nonlinearinterior point method. In [17], apattern algorithm is used to solve ac-dc **optimal** **reactive** powerflow model with the generator capability limits. In [18], F. Capitanescu proposes a two-step approach to evaluate **Reactive** **power** reserves with respect to operating constraints and voltage stability. In [19], a programming based approachis used to solve the **optimal** **reactive** **power** dispatch problem. In [20], A. Kargarian et al present aprobabilistic algorithm for **optimal** **reactive** **power** provisionin hybrid electricity markets with uncertain loads. This paper proposes Comprehensive Neighbourhood Algorithm (CNA) to solvereactive **power** dispatch problem. A set of arbitrarily generated solutions from the entire explore space are first produced and then the best of these solutions is selected. After that, the algorithm will iterate, and in every iteration there will be two sets of produced solutions, one from the global explore space and the other set of solutions will be produced from the neighbourhood of the most excellent solution [21,22]. The proposed algorithm CNA hasbeen evaluated in standard IEEE 30 and IEEE57, bus test systems. The simulationresults show that our proposed approach outperforms allthe entitled reported algorithms in minimization of real **power** loss.

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_______________________________________________________________________________________________________ Abstract— **Optimal** **reactive** **power** dispatch (ORPD) is a complex optimization problem in which we try to “optimally” set the values of control variables like **reactive** **power** output of generators (generator bus voltages), tap ratios of transformers and **reactive** **power** output of shunt compensators like capacitors etc. to minimize the total transmission active **power** losses while satisfying a given set of constraints. In this paper solution of ORPD problem is done by stochastic population based search algorithms like differential evolution (DE) and BAT algorithms. The numerical results clearly show that DE algorithm gives better results required to reach global best solution. In order to illustrate the effectiveness of the proposed algorithm, it has been tested on highly stressed modified IEEE 300-bus test system.

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to operating constraints and voltage stability. In [19], a programming based proposed approach used to solve the **optimal** **reactive** **power** dispatch problem. In [20], presents a probabilistic algorithm for **optimal** **reactive** **power** provision in hybrid electricity markets with uncertain loads. This paper proposes a new Improved Imperialist Competitive Algorithm (IICA) is used to solve the **optimal** **reactive** **power** dispatch problem. Recently, a new algorithm ICA has been proposed by Atashpaz-Gargari and lucas [21], and it is inspired from a socio-human phenomenon. In this paper, we have proposed a new algorithm called Acclimatized Imperialist Competitive Algorithm (AICA) that uses the probability density function to acclimatize the angle of colonies movement in the direction of imperialist’s position during iterations energetically. This method, augment the global search capability of the algorithm. This idea increases the performance of the ICA algorithm effectively in solving the optimization problems. The proposed algorithm AICA been evaluated in standard IEEE 57 bus test system & the simulation results shows that our proposed approach outperforms all reported algorithms in minimization of real **power** loss .

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A novel bio-heuristic algorithm called Refined Bacterial Foraging Algorithm (RBFA) is proposed in the paper to solve the **optimal** **power** dispatch of deregulated electric **power** systems. The **Optimal** **reactive** **power** dispatch (OPD) problem has growing effect in modern electric **power** systems and the problem in need of to address the secure operation and **optimal** operation, with **optimal** location of FACTS devices, control of **power** flow based on stability indices and reliability analysis. So, this problem is well known as a multi-disciplinary and multi objective problem where in need of exact formulation and solution of the problem. The basic BFA is based on a metaphor of social interaction of E-coli bacteria the self adaptability of individuals in the group searching activities has attracted a great deal of interests in real word problems but it gives poor performance in global points when its applied to high dimensional and multi objective problems. To avoid the local optima and in order to track the good tracking ability of global solutions, the improved version of BFA algorithm is proposed and the proposed algorithm is called Refined Bacterial Foraging Algorithm (RBFA).The RBFA is improved version of the basic BFA with search direction phenomenon, variation of step sizes in chemotaxis behavior and variation of position updating process. The **optimal** dispatch problem consist of **reactive** **power** dispatch, **optimal** location of FACTS devices with transformer taps, real **power** loss and voltage stability margin are simultaneously optimized with effective controls and limits. The evaluation analysis of proposed work carried out with Standard IEEE systems. The simulation result shows the performance of RBFA is superior or comparable to that of the other algorithms and is greatly in terms of speed of convergence, optimization quality, robustness and fast convergence ability.

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advantages of Fuzzy method are: Accurately represents the operational constraints and fuzzified constraints are softer than traditional constraints. The advantages of GA methods are: It only uses the values of the objective function and less likely to get trapped in a local optimum. Higher computational time is its disadvantage. The advantages of the EP are adaptable to change, ability to generate good enough solutions and rapid convergence. ACO and PSO are the latest entry in the field of optimization. The main advantages of the ACO are positive feedback for recovery of good solutions, distributed computation, which avoids premature convergence. It has been mainly used in finding the shortest route in the transmission network, short-term generation scheduling and **optimal** unit commitment. PSO can be used to solve complex optimization problems, which are non-linear, non-differentiable and multi-model. The main merits of PSO are its fast convergence speed and it can be realized simply for less parameters need adjusting. PSO has been mainly used to solve Bi-objective generation scheduling, **optimal** **reactive** **power** dispatch and to minimize total cost of **power** generation. Yet, the applications of ACO and PSO to solve Security constrained OPF, Contingency constrained OPF, Congestion management incorporating FACTS devices etc. Of a deregulated **power** system are to be explored out.

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Interconnected electrical network comprises of numerous generators, transmission lines, variety of loads and transformers. The term Flexible Alternating Current Transmission System (FACTS) devices or controllers describe a wide range of controllers, many of which incorporate large **power** electronic converters that can increase the flexibility of **power** systems making them more controllable and stable. FACTS devices stabilize transmission systems with increased transfer capability and reduced risk of line trips [A. Kumar and S. B. Dubey 2013]. The major problem in **power** system is upholding steady acceptable system parameters like transients and voltage under normal operating and anomalous conditions, which is usually referred as voltage regulation problem and regaining synchronism after a major fault [C. Makkar and L. Dewan 2010]. This results in system overloading. Overloading may also due to faults, heavy loading, long transmission lines with uncontrolled buses at the receiving end, radial transmission lines, and shortage of local **reactive** **power**, intrinsic factors, and small generation reserve margins[A. Satheesh and T. Manigandan 2013] .

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The IEEE 30 bus system has 6 generator buses,24 load buses and 41 transmission lines of which four branches are (6-9),(6,10),(4,12) and (28-27) are with tap setting transformers. This is shown in figure: 4, the upper and lower voltage limits at all buses except slack bus are taken as 1.10 p.u and 0.95 p.u respectively. The slack bus voltage is fixed to its specified value of 1.06 p.u. Generator terminal voltages, transformer tap settings and **reactive** **power** sources were taken as the optimization variables. The possible locations for **reactive** **power** sources are buses 10, 12, 15, 17, 20, 21, 23, 24 and 29. The optimization variables are represented as a mixture of floating point numbers in the MOGA population. The initial population was randomly generated between the variable’s lower and upper limits. Tournament selection was applied to select the members of the new population. Blend crossover and uniform mutation were applied on the selected individuals. The performance of MOGA generally depends on the MOGA parameter used, in particular, the crossover and mutation probabilities respectively. The performance of MOGA for various crossover and mutation probabilities in the range 0.6-0.9 and 0.001-0.01 respectively was therefore evaluated. It was applied by considering several sets of parameters inorder to prove its capability to provide acceptable trade-offs close to the Pareto **optimal** front. The **optimal** settings of the MOGA were obtained by the following parameters are given below:

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