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In a typical power system network, transmission losses are considered as one of the important parameters for economic operation. To concern this problem, researchers have proposed many techniques to minimize the transmission losses based on the cost benefit analysis which is associated with the optimal placement of the reactive power sources. In the present work, a novel technique, namely oppositional crow search algorithm (CSA), is proposed for Var planning by utilizing the fuzzy logic technique to determine capacitor placement positions. In this approach, fuzzy membership value is calculated based on the loss sensitivity factor of each bus of the test networks. Then, shunt capacitors placement positions are assigned to buses having the higher membership values. Once the capacitor placement positions are evaluated, the CSA and oppositional CSA are executed to obtain the optimal setting of transformer tap positions, reactive power generation of the generators, and magnitude of shunt capacitors placed at the weak nodes. The proposed method is performed on standard IEEE 30 and IEEE 57 bus networks, and the obtained results are compared with several other established methods for Var planning. From the obtained results, it is found that the proposed method shows better performance when compared to other techniques suggested in the literature in terms of reduced active power loss and system operating cost. [ABSTRACT FROM AUTHOR]

This paper formulates and solves a techno-economic planning problem of reactive power (VAR) in power transmission systems under loadings. The objective of the proposed research work is to minimize the combination of installation cost of reactive power sources, power losses and operational cost while satisfying technical constraints. Initially, the positions for the placement of reactive power sources are determined technically. Different cost components such as VAR generation cost, line charging cost etc. are then added in the total operating cost in a most economical way. Finally, the optimal parameter setting subjected to reactive power planning (RPP) is obtained by taking advantages of hybrid soft computing techniques. For the justification of the efficiency and efficacy of the proposed approach the entire work is simulated on two inter-regional transmission networks. To validate the robustness and ease of the soft computing techniques in RPP the responses of benchmark functions and statistical proof are provided simultaneously. [ABSTRACT FROM AUTHOR]

This research work proposes a planning strategy pertaining to the techno-economic operation of Indian power systems. The proposed strategy focused on the reactive power (VAr) planning (RPP) subject to the system operating cost minimization ensuring system security. To mitigate the RPP issue, unique meta-heuristic hybridized techniques are adopted to size the optimal parameter settings such as alternator's reactive power, tap settings of transformers etc. instigated by power flow analysis satisfying all equality and inequality constraints. The controlling variables are optimally determined to solve this non-linear RPP problem. The bottleneck for the installation of static VAr compensators at weak buses is also wiped out by different analytical techniques viz. loss sensitivity, power flow and modal analysis. Two different inter-regional transmission networks prevailing in India are considered to measure the adaptability, efficacy and efficiency of the proposed approach. The results obtained by applying the proposed approach have confirmed that network loss-minimization procedures produce an acceptable solution and reduce the operating cost which is especially important in RPP. The responses of bench mark functions and statistical analysis of the outcomes from both systems allow us to assess the overall efficiency and efficacy of the proposed techno-economic planning approach. [ABSTRACT FROM AUTHOR]

This study introduces a techno-economic planning strategy of reactive power (VAr) in power transmission systems. This planning strategy primarily has been focused on reactive power planning (RPP) through operating cost minimisation ensuring cost-oriented system security. The objective function (operating cost) is blended with four different components viz. cost due to real power loss, VAr generation cost, additional VAr compensation devices cost and line charging cost. FACTS (flexible AC transmission system) devices are installed at weak positions through different echelons to improve the system voltage stability. The authors also implemented the probabilistic hybridisation of crow search algorithm and JAYA to find out the optimal set of controlling parameters related to VAr. Finally, a sharp and minute analysis of the results has been done to validate the proposed strategy under multi-loading conditions. To understand the efficiency and efficacy the code profiling and simulation has been rendered on IEEE 30 bus and UPSEB 75 bus test system. [ABSTRACT FROM AUTHOR]

In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned with the installation or removal of reactive power equipment in power system. This article proposes an effective way to find out the optimal parameter settings related to the system active power loss as well as operational cost. Genetic Algorithm (GA)-based strategy is applied to determine the optimal values of reactive power generation of generators, size of shunt capacitors, transformer tap settings prior to minimizing the system operating cost due to active power loss, installation cost of shunt capacitors at the weak nodes, and the cost of line-charging elements. The proposed approach is applied to IEEE 30 and IEEE 57 bus test system. Finally, a comparative analysis has been done to validate the efficacy of GA-based approach with some well-established meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a new meta‐heuristic optimization algorithm named grey wolf optimization algorithm based on leadership and hunting behavior of grey wolf for the reactive power planning of a connected power network. The performance of the proposed opposition‐based grey wolf optimization (OGWO) and grey wolf optimization (GWO) is examined and tested successfully in standard IEEE 14, IEEE 30, and IEEE 57 bus test systems for the minimization of active power loss and the total operating cost while maintaining voltage profile of the buses within permissible limit. Active power loss and total operating cost are minimized by optimal planning of the reactive generation of generators, transformer tap setting arrangements, and reactive output of shunt capacitors as installed at weak nodes under different loading conditions. The weak nodes are detected by power flow analysis. The results obtained by GWO algorithm is compared to other popular techniques recently reported in recent state‐of‐the‐art literature. It is observed that proposed OGWO and GWO algorithm yields much better result in terms of reducing operating cost and minimizing active power loss. Merit lies with GWO is that its simple structure for implementation and its ability not to be trapped in local minima, thus exploring wider search area. [ABSTRACT FROM AUTHOR]

In the present work, Whale optimization algorithm (WOA), Differential evolution (DE), Grey wolf optimization (GWO), Quasi-opposition based Differential Evolution (QODE) and Quasi-opposition based Grey wolf optimization (QOGWO) algorithm has been applied for the solution of reactive power planning with FACTS devices i.e., Thyristor controlled series compensator (TCSC) and Static Var compensator (SVC). WOA is a recently developed nature-inspired meta-heuristic algorithm based on hunting behaviour of Humpback Whales; DE is a stochastic real-parameter optimization technique comprising of genetic parameters namely - mutation & cross-over; and GWO is a nature-inspired meta-heuristic algorithm based on hunting behaviour of Grey wolf. Standard IEEE 30 and IEEE 57 bus test system has been adopted for the testing purposes. Location of TCSC has been determined by the power flow analysis method and location of SVC has been determined by the voltage collapse proximity indication (VCPI) method. Further, WOA, GWO, DE, QODE and QOGWO algorithms have been applied to find the optimal setting of all control variables including TCSC, the series type and SVC, the shunt kind of FACTS device in the test system which minimizes active power loss and system operating cost while maintaining voltage profile within permissible limit. The superiority of the proposed WOA technique has been illustrated by comparing the results obtained with all other techniques discussed in the present problem. ANOVA test has also been conducted to show the statistical analysis between different techniques. The proposed approach shows lesser number of iterations which does not gets trapped in the local minima and offers promising convergence characteristics. [ABSTRACT FROM AUTHOR]

In the present work, reactive power planning problem along with voltage stability margin is addressed by effective co-ordination of reactive power sources. Modal analysis and L-index methods are used to detect weak nodes of the system accordingly. Differential Evolution (DE) and Genetic Algorithm (GA)-based optimization techniques are applied for the proper co-ordination of Var sources under base and increased loading conditions maintaining voltage stability of the connected power network. The problem is multi-objective and IEEE 30 bus system is taken as the standard system. It is observed that modal analysis based detection of weak nodes are more effective than the L-index-based detection. Moreover, the DE-based optimization algorithm gives better result compared to GA-based approach in maximizing reactive power reserves. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for reactive power planning of a connected power network. Reactive power planning is nothing but the optimal usage of all reactive power sources i.e., transformer tap setting arrangements, reactive generations of generators and shunt VAR compensators installed at weak nodes. Shunt VAR compensator placement positions are determined by a FVSI (Fast Voltage Stability Index) method. Optimal setting of all reactive power reserves are determined by a GA (genetic algorithm) based optimization method. The effectiveness of the detection of the weak nodes by the FVSI method is validated by comparing the result with two other wellknown methods of weak node detection like Modal analysis and the L-index method. Finally, FVSI based allocation of VAR sources emerges as the most suitable method for reactive power planning. [ABSTRACT FROM AUTHOR]

Reactive power planning is one of the most challenging problem for efficient and source operation of an interconnected power network. It requires effective and optimum co-ordination of all the reactive power sources present in the network. Recently, Teaching Learning Based Optimization (TLBO) algorithm is evolved and finds its application in the field of engineering optimization. In the proposed work TLBO based optimization algorithm is used for reactive power planning and applied in IEEE 30 and IEEE 57 bus system. The results obtained by this method are compared with the results obtained by other optimization techniques like PSO (Particle swarm optimization), Krill heard, HSA (Harmony search algorithm) and BB-BC (Big Bang-Big Crunch). At the end, TLBO appears as the most effective method for reactive power planning among all the methods discussed and can be considered as one of the standard method for reactive power optimization. [ABSTRACT FROM AUTHOR]

This study presents gravitational search algorithm (GSA)-based optimisation technique for the optimum coordination of multi-type flexible AC transmission system (FACTS) devices with the existing reactive power sources present in a connected power network. Three types of FACTS devices: namely, unified power flow controller, static var compensator and thyristor controlled series capacitor are used in the present problem and IEEE-30 and IEEE-57 bus systems are taken as standard test systems. The existing reactive power sources are reactive generations of the generators and transformer tap setting arrangements. The main objective of the present study is to reduce active power loss, system operating cost including the cost of FACTS devices and congestion in transmission network. The result obtained with the proposed GSA-based approach is compared with the results obtained by genetic algorithm, differential evolution and particle swarm optimisation techniques. It is observed that by optimal placement of FACTS devices along with the optimal setting of the transformer tap arrangements and control of reactive generations of the generators line loss, system operating cost and transmission congestion is reduced significantly by the GSA-based method compared with other methods. [ABSTRACT FROM AUTHOR]

In the proposed work, authors have applied swarm intelligence based algorithms for the effective Co-ordination of Flexible AC transmission system (FACTS) devices with other existing Var sources present in the network. IEEE 30 and IEEE 57 bus systems are taken as standard test systems. SPSO (Simple Particle Swarm Optimization) and other two swarm based intelligence approaches like APSO (Adaptive Particle Swarm Optimization) and EPSO (Evolutionary Particle Swarm Optimization) are used for the optimal setting of the Var sources and FACTS devices. The result obtained with the proposed approach is compared with the result found by the conventional RPP (Reactive power planning) approach where shunt capacitors, transformer tap setting arrangements and reactive generations of generators are used as planning variables. It is observed that reactive power planning with FACTS devices yields much better result in terms of reducing active power loss and total operating cost of the system even considering the investment costs of FACTS devices. [ABSTRACT FROM AUTHOR]

Reactive power source planning problem has significant role for secure and economic operation of power system. Reactive power planning problem is nothing but the proper coordination of existing Var sources which lead to loss minimization and cost economic operation of the system. In the proposed approach several bio-inspired algorithms like Particle Swarm Optimization (PSO), Evolutionary Particle Swarm Optimization (EPSO), Adaptive Particle Swarm Optimization (APSO), Hybrid Particle Swarm Optimization (HPSO) and Bacterial Foraging Algorithm (BFA) are used for the reactive power planning problem. Finally a comparison of all these techniques are made on the basis of the results obtained when applied to different standard test system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Fuzzy based evolutionary algorithm. [•] FACTS devices for reactive power optimization. [•] FACTS devices for minimum operating cost of a power system. [Copyright &y& Elsevier]