Shunt Capacitors In Distribution Networks Research Articles

A flexible loss reduction formulation for simultaneous capacitor placement and network reconfiguration in distribution grids

Efficiently reducing power losses involves various strategies such as opening normally closed switches and closing normally open tie-lines in distribution feeders, and optimally placing shunt capacitors in distribution networks. In this process, the accurate modeling of the demand side is crucial in understanding the behavior of electricity consumers appropriately. It is important to note that loads are not constant; they vary with changes in voltage magnitude, which in turn depend on the type of consumer. Each load can be represented by its constant power, current, and impedance components. In this regard, establishing a clear relationship between these components and consumer types can significantly enhance the flexibility of the network switching or capacitor placement strategy. Therefore, this study aims to mathematically formulate the correlation between load components and consumer types, aiming at establishing an efficient reconfiguration and capacitor allocation formulations. This is achieved by transforming polynomial load formulations into quadratic ones, while establishing mathematical relationships between the quadratic and polynomial models for various load types. The accuracy and convergence time of the proposed model was tested through its application to 16- and 33-bus distribution networks, and the results have been compared with the nonlinear metaheuristic approach. The results demonstrated that the proposed framework can efficiently provide optimal solutions within a shorter computational time as compared to the nonlinear and metaheuristic approaches.

Simultaneous optimal allocation and sizing of distributed generations and shunt capacitors in distribution networks using fuzzy GA methodology

This paper presents a combination of fuzzy and genetic algorithm (GA)-based methodology for simultaneous optimum allocation and sizing of distributed generations (DGs) and shunt capacitors (SCs) together in distribution systems. The objectives of reduction of active power and reactive power supply, reduction of real power loss and improvement of branch current capacity, voltage profile and voltage stability are considered. The combination of shunt capacitors with both unity power factor DGs and lagging power factor DGs also considered for analyzing the performance of the distribution systems. Simulation results are demonstrated to show the advantage of proposed fuzzy genetic algorithm-based technique over conventional multiobjective approach and loss sensitivity-based optimization techniques reported in the literature.

Gbest-Guided Artificial Bee Colony Optimization Algorithm-Based Optimal Incorporation of Shunt Capacitors in Distribution Networks under Load Growth

Abstract In this work, a new technique is introduced for optimal incorporation of shunt capacitors (SCs) in distribution networks. This technique has been compared to other sensitivity-based approaches such as loss sensitivity factor, index vector method, power loss index, and index of voltage stability. In the proposed technique, the optimal positions as well as the ratings of SCs are identified through an optimization algorithm. In sensitivity-based approaches, the positions of SCs are determined through a sensitivity approach and the optimal ratings of SCs are computed through an optimization algorithm. The main target of this study is to minimize the total annual cost and power loss of the network under load growth. This has been done through the population-based Gbest-guided artificial bee colony (GABC) optimization technique. Furthermore, the outcomes obtained through the GABC algorithm are compared to those from the iteration particle swarm optimization algorithm. The whole work along with the proposed methodology has been demonstrated on standard 34-bus and 118-bus distribution networks for SC placement. The results show that it reduces the total annual expense of the network to a great value. Consequently, it improves the total power loss reduction, enhances the voltage profile and power factor, and reduces the total voltage deviation. The obtained numerical outcomes through the proposed technique have been compared with the published literature outcomes to show the viability and superiority of the algorithm.

Optimal capacitor placement in distribution networks regarding uncertainty in active power load and distributed generation units production

The paper proposes a new method for optimal allocation of shunt capacitors in distribution network with renewable distributed generation units such as wind turbines and/or solar power plants. The proposed method, based on application of the Monte Carlo simulation methods, respects the uncertainties associated with load demand and renewable sources power production. The optimisation problem is modelled by two criterion functions with the aim of improving voltage profile in a network by optimal selection of the locations and installed powers of the shunt capacitors. The suggested criterion functions have been optimised simultaneously by applying the non-dominated sorting genetic algorithm. On the basis of the measured data on wind speed and solar radiation, the method has been tested by the example of a real distribution network.

Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique

Abstract This paper attempts to minimize power losses in radial distribution networks and facilitates an enhancement in bus voltage profile by determining optimal locations, optimally sized distributed generators and shunt capacitors by hybrid Harmony Search Algorithm approach. To overcome the drawback of premature and slow convergence of Harmony Search Algorithm (HSA) over multi model fitness landscape, the Particle Artificial Bee Colony algorithm (PABC) is utilized to enhance the harmony memory vector. In the first approach, the formulation echoes the determination of loss sensitivity factor to decide the sensitive nodes and thereafter decides on the optimal rating through the use of hybrid Algorithm. The second approach encircles the role of hybrid Algorithm to search for both the optimal candidate nodes and sizing of compensating devices by significant increase in loss reduction with the former approach. The procedure travels to examine the robustness of the proposed hybrid approach on 33 and 119 node test systems and the result outcomes are compared with the other techniques existing in the literature. The simulation results reveal the efficiency of the proposed hybrid algorithm in obtaining optimal solution for simultaneous placement of distributed generators and shunt capacitors in distribution networks.

New Sensitivity based Approach for Optimal Allocation of Shunt Capacitors in Distribution Networks Using PSO

This paper proposes a new methodology for optimal allocation of shunt capacitors in distribution systems. The proposed method combines various objectives and constraints into a Comprehensive Constraint Multi-objective Function. The function has been optimized using a Particle Swarm Optimization (PSO) based method. Attempts have also been made to improve the performance of PSO. The search space of PSO is reduced by introducing a new reactive power flow sensitivity approach that determines the set of candidate nodes suitable for capacitor placement and also by employing a new constrained particle structure. The proposed method is tested on a 69-bus test distribution system and the application results are promising.

New Evolutionary Technique for Optimization Shunt Capacitors in Distribution Networks

New Evolutionary Technique for Optimization Shunt Capacitors in Distribution NetworksThe paper presents a new evolutionary technique for optimizing on one part the numbers, the sizes and locations of shunt capacitors in radial distribution network in away to minimize the annual cost of active power losses with the improvement of voltage profiles of different buses. The technique is applied on 10 buses network and on IEEE 34 buses network test. The results are compared with ones of previous studies using heuristiques methods and the same network tests.