A new hybrid CPSO-TLBO optimization algorithm for distribution network reconfiguration

The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

In accordance with the characteristic of radial running an algorithm for distribution network reconfiguration based on Hopfield neural network is put forward. The in-degree of each node is determined by Hopfield neural network, it is determined whether the lines run according to the in-degree of the nodes, and the state of each loop switch is determined according to whether the lines run, and thus the distribution network reconfiguration scheme is determined finally. The energy function of the neural network and its solution method are presented. In the energy function are considered the radial running of distribution network, the lowest distribution network loss and no loop switch in some lines. The IEEE distribution network structure with three power sources obtained by the algorithm is basically consistent to that obtained by genetic algorithm, but the time spent using the former is shorter than that the latter.

This paper describes a computational procedure to establish the optimal distribution of network reconfiguration by means of a novel gray wolf optimization (GWO) algorithm. The procedure aimed to diminish the system’s power loss and produce a better voltage profile while fulfilling the operating constraints described by different operating conditions. Under practical restrictions, the distribution network reconfiguration (DNR) problem is classified as multimodal and highly nonlinear. Constraint breaches were appropriately handled to produce stable convergence characteristics, and high-quality solutions were obtained in a shorter execution time. The 33-bus and 69-bus systems were used to obtain the optimal reconfiguration by incorporating the method developed in this work. The simulation results obtained were collated and compared with the outcomes of other well-known optimization techniques, confirming the efficacy of the GWO algorithm in solving the DNR problem.

This paper presents a runner-root algorithm (RRA) for electric distribution network reconfiguration (NR) problem. The considered NR problem in this paper is to minimize real power loss, load balancing among the branches, load balancing among the feeders as well as number of switching operations and node voltage deviation using max-min method for selection of the final compromised solution. RRA is equipped with two explorative tools, which are random jumps with large steps and re-initialization strategy to escape from local optimal. Moreover, RRA is also equipped with an exploitative tool to search around the current best solution with large and small steps to ensure the obtained result of global optimization. The effectiveness of the applied RRA in both single- and multi-objective has been tested on 33-node and 70-node distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the applied RRA can be an efficient method for network reconfiguration problems with single- and multi-objective.

Stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations

Electricity distribution networks in densely populated urban areas are, in order to increase the reliability of supply, usually designed as meshed networks, though they always operate in radial configuration. Reconfiguration of distribution networks is achieved through switching operations on switching devices of distribution network branches (switching on/off of a branch). It is often the case that the existing configuration is not the configuration with minimum power losses. With the new computer program, developed by the authors, it is possible to find the network configuration that enables operation with minimum losses. The program proposes the best network configuration to the dispatcher, and the latter makes his own decision whether to change the network configuration or not. The mathematical algorithm for the network reconfiguration is presented in the paper, as well as results of the reconfiguration of the electricity distribution network of the city of Maribor, Slovenia.

This paper presents optimal reconfiguration of radial distribution network with integration of distributed energy resources (DER) using improved particle swarm optimization (PSO) algorithm. The benefits of DER integration in distribution system are reducing power losses, improving voltage profiles and load factors, eliminating system upgrades, and reducing environmental impacts. However, the presence of DER could also cause technical problems in voltage quality and system protection. Reconfiguration of distribution network is aimed to minimize power loss and to improve voltage quality in order to enhance the distribution system performance. In this study, reconfiguration method is based on an improved PSO. The method has been tested in a 60-bus Bantul distribution network of Yogyakarta Special Region province, Indonesia. The simulation results show the importance of reconfiguring the network for enhancing the distribution system performance in the presence of DER.

Network reconfiguration is a process to redesign the topology of the network by selective switching ON or OFF of the sectionalizing as well as tie switches in order to alter power flow from distribution substation to consumers. The primary goal of reconfiguration is load balancing, though it also a way to reduce the system’s active power loss. Reconfiguration may also lead to the improvement of system reliability. In this paper, the Teaching-Learning Based Optimization (TLBO) method is applied to reconfigure a radial network having minimal power loss with adequate system reliability. 33-bus IEEE RDS is considered as the test system. The power loss occurred in the network is calculated using the Gauss-Seidel load flow method. Reliability assessment of the reconfigured system is done by computing the performance indices, namely SAIFI, CAIDI, SAIDI, and AENS, and results are compared with that of the original test system.

Particle swarm optimisation (PSO) is an intelligent optimisation algorithm with different variants that are developed according to the targeted application. These variants are obtained by changing the parameters of the PSO algorithm like inertia weight, constriction factor, and acceleration factor. These variants can be utilised for various power system optimisation requirements such as distribution automation, for a targeted application. Distribution network reconfiguration (DNR) is one such application that involves high computational complexity and requires an intelligent optimisation technique for its solution. In this paper different variants of PSO are applied to DNR problem like binary PSO, crazy PSO, and enhanced inter coded PSO for 16 bus and 33 bus test systems. These variants ensure proper exploration and exploitation of the search space to obtain optimum and quick solution for the DNR problem.

Uniform voltage distribution based constructive algorithm for optimal reconfiguration of electric distribution networks

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Turatbek Omorov, Beishenaly Takyrbashev, Taalaibek Koibagarov, Tilebaldy Zhanybaev; Identification and monitoring of power losses in distribution networks as a composition of AMR. AIP Conf. Proc. 5 January 2023; 2552 (1): 020002. https://doi.org/10.1063/5.0114527 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Distribution network reconfiguration problem has been known as a common issue in distribution network operation. Up to now, distribution network reconfiguration has been used for minimizing the power loss, reliability concerns and emergency problems. According to the increasing installation rate of Distributed Generations (DGs) and the rapidly growing number of Electric Vehicles (EVs), as a large responsive loads, distribution network operators have encountered new problems. These new aspects of network operation problem consist of congestion problem and require to established distribution market to execute the market process. This paper presents a novel methodology to solve this problem with the application of Distribution Locational Marginal Price (DLMP), using Genetic Algorithm (GA). The standard IEEE 33-bus network has been selected as a suitable case study and simulation results demonstrate the compatibility of proposed method. Comparison with other papers have also been addressed in case of not considering DGs and congestion issue. In the following, DGs participation in distribution electricity market, the congestion problem and numerical results has been shown and discussed.

This paper studies the reciprocal impact of distribution network protection and reconfiguration of active Nowadays, with increasing energy consumption in distribution networks and the demand of consumers to buy highly reliable power, it is of high importance to establish adapt between protection and operation of the power system. distribution system. Accordingly, a distribution network reconfiguration is carried out to find the optimal switching operations in an economic way. Since the switching operations will change network topology as well as short circuit level of buses, the protection coordination may be invalid. To address this issue, constraints of the coordination of protection relays and fuses is being formulated and added to the reconfiguration problem. Moreover, the nonlinear equations of the problem are linearized and are transformed the reconfiguration problem into Mixed-Integer Linear Programming (MILP) one to achieve the global optimal solution. The proposed method has been implemented on 33 bus distribution network. The results clearly show effectiveness of the active and reactive power management in an intelligent distribution network considering protection concepts.

Optimal placement of distributed generation in a distribution system is a problem which involves the optimization of both discrete and continuous variables. In radial distribution network, distributed generation (DG) plays a crucial role in satisfying the demands of electrical energy. It is a small generating plant which supports the distribution network by minimizing system power losses, improving the stability and voltage of the network. In this paper, Teaching Learning Based Optimization (TLBO) technique is used for finding optimal DG locations and sizes simultaneously. The main goal is to achieve substantial improvement in the voltage profile and significant reduction in network power losses. The TLBO is inspired by the influence of the teaching by the teacher on the students in a class. In this paper, GUI is developed in Matlab to perform load flow and to also obtain optimal locations and capacities of DG units. The proposed method was tested on IEEE 15 bus, 33 bus and 69 bus systems and the results were discussed.

In the distribution network containing distributed generators (DG), both DG and capacitors can be adjusted for reactive power, and there is a strong complementarity between the two. If the reactive power compensation ability of each grid-connected DG can be fully utilized, it will effectively reduce the voltage fluctuation and the number of equipment actions, which will help to improve the operation level of the distribution network. In this paper reactive power optimization of distribution networks containing distributed power sources has studies. An improved evolutionary programming (EP) method is proposed to solve the optimization goal, using a variable step-size evolution method and optimization strategy with small population and high convergence accuracy. The algorithm improves the random dynamic step method. In different stages of optimization, different step sizes are used to improve the optimization effect. At the same time, considering that some wind farms are equipped with AVC and some wind farms are equipped with capacitor banks, the evolutionary operator is improved. The operator evolves directly on the integer, so it is easier to find the global optimal solution. This paper has used IEEE33 bus system to verification. Results show that the DG can reduce active power loss in distribution network and ameliorate the voltage level. Meanwhile, the improved evolutionary programming method has good optimization effect, fast convergence speed and high search efficiency.