Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topi

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Critical Review of Different Methods for Siting and Sizing Distributed-generators

Due to several benefits attached to distributed generators such as reduction in line losses, improved voltage profile, reliable system etc., the study on how to optimally site and size distributed generators has been on the increase for more than two decades. This has propelled several researchers to explore various scientific and engineering powerful simulation tools, valid and reliable scientific methods like analytical, meta-heuristic and hybrid methods to optimally place and size distributed generator(s) for optimal benefits. This study gives a critical review of different methods used in siting and sizing distributed generators alongside their results, test systems and gaps in literature

Optimal Feeder Reconfiguration Optimization problem in Power Distribution Networks

Optimal feeder reconfiguration is a method used to determine optimal on/off statuses of tie and sectionalizing switches in order to reconfigure the network and improve certain objective goals. Mathematically, OFR is a mixed-integer nonlinear programsubjected to system constraints consisting of power flow equations, voltage limits, feeder capability limits and requirements for maintaining radial configuration of the network.In this paper, network reconfiguration problem is solved using branch exchange method. Solution involves a search for optimal on/off switch positionby transferring loads from one feeder to another, until no load transfer can further reduce the power losses, violations of voltage limits, and violations of branch capacity limits.Branch exchange method is applied on two feeder network, and results show that this method can be successfully used to decrease losses, improve voltage profile and resolve the overloading problem

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios

Reconfiguration of Distribution Networks with Presence of DGs to Improving the Reliability

In this paper, the network reconfiguration in the presence of distributed generation units with the aim of improving the reliability of the network is studied. For this purpose four reliability parameters in the objective function are considered, which is average energy not supplied system average interruption frequency index, system average interruption duration index and momentary average interruption frequency index. The new method will be normalized objective function. Another suggestion of this paper are considering the different fault rates, locating time of faults type and prioritization of customers based on their importance. This nonlinear problem has optimized by particle swarm optimization (PSO) algorithm

Multi-agent control and operation of electric power distribution systems

This dissertation presents operation and control strategies for electric power distribution systems containing distributed generators. First, models of microturbines and fuel cells are developed. These dynamic models are incorporated in a power system analysis package. Second, operation of these generators in a distribution system is addressed and load following schemes are designed. The penetration of distributed generators (DGs) into the power distribution system stability becomes an issue and so the control of those DGs becomes necessary. A decentralized control structure based on conventional controllers is designed for distributed generators using a new developed optimization technique called Guided Particle Swarm Optimization. However, the limitations of the conventional controllers do not satisfy the stability requirement of a power distribution system that has a high DG penetration level, which imposes the necessity of developing a new control structure able to overcome the limitations imposed by the fixed structure conventional controllers and limit the penetration of DGs in the overall transient stability of the distribution system. Third, a novel multi-agent based control architecture is proposed for transient stability enhancement for distribution systems with microturbines. The proposed control architecture is hierarchical with one supervisory global control agent and a distributed number of local control agents in the lower layer. Specifically, a central control center supervises and optimizes the overall process, while each microturbine is equipped with its own local control agent.;The control of naval shipboard electric power system is another application of distributed control with multi-agent based structure. In this proposal, the focus is to introduce the concept of multi-agent based control architecture to improve the stability of the shipboard power system during faulty conditions. The effectiveness of the proposed methods is illustrated using a 37-bus IEEE benchmark system and an all-electric naval ship

A DNR and DG Sizing Simultaneously by Using EPSO

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an efficient hybridization of both Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) methods which is called the Evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration

Reconfiguration of Distribution Networks with Presence of DGs to improving the Reliability

In this paper, the network reconfiguration in the presence of distributed generation units with the aim of improving the reliability of the network is studied. For this purpose four reliability parameters in the objective function are considered, which is average energy not supplied system average interruption frequency index, system average interruption duration index and momentary average interruption frequency index. The new method will be normalized objective function. Another suggestion of this paper are considering the different fault rates, locating time of faults type and prioritization of customers based on their importance. This nonlinear problem has optimized by particle swarm optimization (PSO) algorithm