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

Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations.  To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively. &nbsp

A DNR Using Evolutionary PSO for Power Loss Reduction

The total power losses in distribution network system can be minimized by network configuration. In this area of research, most of the researchers have used multiple types of optimization technique to determine the optimal problem solving. In this paper, an efficient hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for feeder network reconfiguration. The main objective is to reduce the power losses in the distribution network system and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster to find the optimal solution. The proposed method is applied and its impact on the network reconfiguration for real power loss and voltage profiles is investigated. In network reconfiguration, the network topologies change through On/Off of the sectionalizing and tie switches in order to optimize network operation parameters. The aim is to find the best configuration which consists of switches that will contribute to a lower loss in the distribution network system. The method was tested on a IEEE 33-bus system to show the effectiveness of the EPSO method over the traditional PSO and EP method

A 33kV Distribution Network Feeder Reconfiguration by Using REPSO for Voltage Profile Improvement

The complexity of modern power system has contributed to the high power losses and over load in the distribution network. Due to that reason, Feeder Reconfiguration (FR) is required to identify the best topology network in order to fulfill the power demand with reduced power losses while stabilizing the magnitude of voltage. This paper addresses a new optimization method which is called as Rank Evolutionary Particle Swarm Optimization (REPSO). It has been produced by a hybridization of the conventional Particle Swarm Optimization (PSO) and the traditional Evolutionary Programming (EP) algorithm. The main objective of this paper is to improve the voltage profile while solves the overload problem by reducing the power losses respectively. The proposed method has been implemented and the real power losses in the 33kVdistribution system has been investigated and analyzed accordingly. The results are compared to the conventional Genetic Algorithm (GA), EP and PSO techniques and it is hoped to help the power system engineer in securing the network in the future

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

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future

Mitigating unbalance using distributed network reconfiguration techniques in distributed power generation grids with services for electric vehicles: A review

© 2019 Elsevier Ltd With rapid movement to combat climate change by reducing greenhouse gases, there is an increasing trend to use more electric vehicles (EVs) and renewable energy sources (RES). With more EVs integration into electricity grid, this raises many challenges for the distribution service operators (DSOs) to integrate such RES-based, distributed generation (DG) and EV-like distributed loads into distribution grids. Effective management of distribution network imbalance is one of the challenges. The distribution network reconfiguration (DNR) techniques are promising to address the issue of imbalance along with other techniques such as the optimal distributed generation placement and allocation (OPDGA) method. This paper presents a systematic and thorough review of DNR techniques for mitigating unbalance of distribution networks, based on papers published in peer-reviewed journals in the last three decades. It puts more focus on how the DNR techniques have been used to manage network imbalance due to distributed loads and DG units. To the best of our knowledge, this is the first attempt to review the research works in the field using DNR techniques to mitigate unbalanced distribution networks. Therefore, this paper will serve as a prime source of the guidance for mitigating network imbalance using the DNR techniques to the new researchers in this field

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

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