DNR Optimization for Loss Reduction and Voltage Stability Considering EV Charging Load

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.

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.

Maximum power point tracking (MPPT) control is one of the essential requirements in harnessing wind power of wind energy conversion systems (WECS). The more precise the maximum power point (MPP) is determined, the more optimal the WECS is operated. Amongst MPPT algorithms, a hill-climb search (HCS) algorithm is preferred because of its simplicity however it also has few drawbacks such as the difficulty of selecting an appropriate step size, the premature convergence phenomenon and the speed-efficiency trade-off. A cuckoo search (CS) algorithm is proposed in this paper for finding out a MPP of a WECS driven by a doubly-fed induction generator (DFIG) under various wind speeds which mostly overcomes the above disadvantages. Then, the DFIG-WECS is controlled to track the MPP. This ensures that the DFIG-WECS is always operated at MPPs regardless of various wind speeds. It is realized that the proposed CS algorithm is also a population-based algorithm inspired from the breeding behavior of cuckoos but it is quite simple, powerful and especially requires less parameters to define optimal solutions than a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. Though numerical results, the CS algorithm shows its effectiveness in finding out MPPs in the DFIG-WECS. Furthermore, the obtained results using the CS algorithm are also compared with those using the HCS and PSO algorithms. The comparison demonstrates that the convergence value and speed of the CS algorithm are always better than those of the HCS and PSO algorithms in the MPPT application in the DFIG-WECS.

The analysis of the conducted studies of the operation of high-voltage power lines (LEP) showed that the maximum power losses per corona are characteristic of increased voltages on high-voltage power lines with their low load, as well as in bad weather. Thus, a 5% increase in voltage at low line load leads to an increase in power losses per corona up to 30%. When it rains, power losses per corona for a 330 kV transmission line increase by 10 times compared to losses in good weather, for a 500 kV transmission line, the increase in losses is 12 times, and for a 750 kV transmission line - 13 times. During frost, the maximum power losses per crown are observed, which for a 330 kV transmission line increase by 23 times compared to losses in good weather, for a 500 kV transmission line, the increase in losses is 25 times, and for a 750 kV transmission line - 21 times. It should be noted that deterioration of weather conditions, in which there is an increase in corona power losses, is observed for more than 20% of the entire period of operation of the transmission lines during the year. Under such conditions, more than 80% of the total power losses per corona in the transmission line occur. The development and implementation of organizational and technical measures aimed at reducing power losses at the crown of power lines is an important practical task, the solution of which will contribute to increasing the efficiency of the electrical networks of Ukraine. Studying the possibility of influencing the amount of power losses due to the use of modern wire designs is an urgent task at the stage of designing power lines. The use of innovative brands of wires can lead to an increase in power losses per corona, due to their design features. Heat-shrinking films can be used to apply an insulating layer to sections of power lines, which will prevent corona and reduce losses associated with it. It is necessary to study the distribution of electric field strength along the span of the transmission line to determine the quantitative indicators of power losses per corona

This paper presents a new methodology based on cuckoo search algorithm (CSA) for enhancing the performance of interline power flow controller (IPFC) under multiline transmission. In this approach we perform priority process through the contingency analysis for finding the severity of the line outage. For determining the severity, bus voltage limit violation, overloading of lines and power loss are taken into account. We are applying CSA to determine the optimal location and transmission congestion management of IPFC. An adaptive proportional integral derivative with filter (PIDF) controller is developed to control the deviation and power flow. The simulation is performed using MATLAB for two case studies using an IEEE 118-bus and IEEE 300-bus test system. Simulation results indicate that the location and parameters optimized by using CSA, minimizing the bus voltage abuse, congestion management, and overloaded lines. This method is designed in such a way that it improves the security of the power system. For further improvement of the system, the IPFC is placed in tie-line. The performance of PIDF controller has been studied under different conditions and the simulation result shows that PIDF performs better than PID controller. The performance of CSA is compared with other two optimization techniques such as particle swarm optimization (PSO) and genetic algorithm (GA) under both the IEEE 118-bus and IEEE 300-bus test systems. The result shows that the proposed CSA outperforms the other two models and it best suits the power system security.

The cuckoo search (CS) algorithm, which is a new meta-heuristic optimization algorithm, is an efficient approach to solve continuous function optimization problems. Normally, the parameters of the CS are fixed constants. This may result in affecting the algorithm efficiency. To cope with this issue, we properly tune the parameters of the CS and propose an adaptive cuckoo search (ACS) algorithm to enhance the convergence rate and accuracy of the CS. Then, we validate the proposed ACS against test functions and compare its performance with those of particle swarm optimization (PSO), improved cuckoo search algorithm (ICS) and CS by simulation. The simulation results demonstrate that the ACS obtains some solutions better than those obtained by PSO, ICS and CS.

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%.

<p>Meta Heuristic methods have made a deep impact in the area of optimization in different streams of engineering. The performance of these algorithms is of importance because the hardware implementation of these algorithms is to be carried out for different engineering applications. As an important application in High Voltage DC (HVDC) transmission and Industrial Drives the multilevel inverter fault diagnosis is carried out using the different meta-heuristic methods with Neural Network as the decision making algorithm. The optimization of the weight and the bias values in the neural network diagnosis system is carried out in order to analyze the performance by means of comparing the Mean Square Error (MSE) while the Neural Network is getting trained for different fault conditions in the multilevel inverter. Matlab based implementation is carried out and the results are tabulated and inferred for a Multilevel Inverter fed from the Photovoltaic power generation system. In order to increase the robustness of the fault detection, with renewable energy based power generation system as the source for the Multilevel Inverter, the feature extracted from the multilevel inverter are positive, negative and zero sequence voltage along with the THD of the output voltage. The optimization algorithm used is Particle Swarm Optimization (PSO), Cuckoo Search Algorithm(CSA), Genetic Algorithm(GA) and Tabu Search Algorithm (TSA).</p>

Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank Evolutionary Particle Swarm Optimization (REPSO). The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional Evolutionary Programming (EP) algorithm with a rejuvenation of the additional of ranking element. The main objective of this paper is to reduce the power losses while improving the convergence time. The proposed method will be implemented and the real power losses in the IEEE 33-bus test system will be investigated and analyzed accordingly. The results are compared to the conventional PSO and hybridization EPSO method and it is hoped to help the power system engineer in securing the network with the less power loss in the future.

The penetration of distributed generation (DG) units has been steadily increasing in distribution networks (DNs). However, oversizing and improper locating or operating of them can increase the losses or deteriorate the voltage profile. In this regard, distribution network reconfiguration (DNR) can be envisioned as a solution to maximize the DG penetration while improving the voltage profile and minimizing the losses. So, a study on DNR with the presence of DGs is necessary, in which the switching action costs are taken into consideration, since they can impose an extra cost on the daily operations. This study proposes a solution to solve the siting, sizing, and operating of DGs and DNR problems simultaneously considering switching action costs, losses costs, and reactive power generation of DGs. To consider the reactive power limits, their capability curve (P–Q curve) is included in the model. DNR is a combinatorial optimization problem, while the entire problem is modeled as a mixed-integer nonlinear programming problem. To solve that, tabu search algorithm (TSA) as one of the most efficient global solver for combinatorial problems is used, and its results are validated by particle swarm optimization (PSO) algorithm. To enlighten the effectiveness of the proposed approach, 5 scenarios are defined on IEEE 33-bus and IEEE 69-bus test systems. The results are also compared to previous studies. It is shown that, thanks to embedding the reactive power generation and switching costs at the same time, maximum loss reductions can be realized, while the results are more realistic and reliable.

Voltage stability is an important issue in power system operation. Flexible AC transmission systems, so-called FACTS devices, help to improve voltage stability and minimize real power losses. The effectiveness of FACTS devices depend on their proper location and rating. This paper presents a method, based on line flow sensitivity factors such as bus voltage stability index and line voltage stability index, to find suitable locations of multi-type FACTS devices. Also this paper proposes an application of particle swarm optimization (PSO) and harmony search algorithm (HSA) in optimizing the rating of FACTS devices. The proposed approaches are evaluated with three different objective functions namely, minimization of real power loss, improvement of voltage profile and enhancement of voltage stability. The performance of proposed methods is analyzed on IEEE 14 bus system by implementing FACTS devices such as static var compensator (SVC), thyristor controlled series capacitor (TCSC) and unified power flow controller (UPFC).The analysis shows that there is a reduction in real power loss and improvement in voltage stability and voltage profile of the system after employing FACTS devices. It also shows that both real power loss and bus voltage stability index (BVSI) have been reduced more with FACTS ratings obtained from PSO than with that from HSA.

<p><span>Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks.</span></p>

Reconfiguration of distribution networks by a series of switching operations is a simple and inexpensive way to improve reliability and reduce power losses, which without the addition of additional equipment to the network, makes optimal use of distribution systems. In this paper, optimal reconfiguration is proposed to improve reliability and minimize power losses as objective functions. To demonstrate the functionality and applicability of the proposed method, five states are defined based on the trade-off between buyer-seller. Particle swarm optimization (PSO) algorithm has been used to solve this problem. Then, the proposed method has been implemented on the Lorestan Distribution Network.

This paper presents a study that considers reconfiguring a given substation’s distribution network at Dilla, Ethiopia, to minimize losses with the help of the Grasshopper Optimization Algorithm and Particle Swarm Optimization. The main aim is to achieve the best switch combination to minimize power loss, subject to system-imposed constraints. A mathematical model was carried out of the best network configuration at the Dilla feeder, inspired by grasshopper behavior. The proposed method was tested over a 76-bus radial distribution system at the Dilla substation. Compared with PSO and other existing methods, the performance of GOA is far better in reducing active and reactive power losses. The reduction of power loss with the help of GOA comes to be 70.3%, while with PSO, it comes out to be 68.8%. The voltage profile improved reasonably, stabilizing around one pu for all the buses. The results illustrated that GOA could be an effective and practical strategy for optimizing the distribution networks with increased reliability and decreased power losses. The paper concludes with a comparative analysis of the proposed GOA and existing methods for distribution network reconfiguration (DNR), showing that the GOA is more effective in reducing voltage deviation (VD) and power losses in the system.

The optimal allocation of distributed generation at optimal bus so that the performances of power system networks have been improved which is presented using Particle Swarm Optimisation Technique. The voltage stability and power losses are main concern for safe operations of power system networks. Recently due to their stressed operations for increasing electrical power loads, voltage instability and voltage collapse are evitable, which are main problems in the power system networks. By using different type of DG of optimal size at optimal location, the overall power losses are reduced and voltage profile of power system network is improved. The particle swarm optimization (PSO) technique has been used for optimal location of distributed generation (DG type-1) of the Optimal value to reduce overall power losses and maintained voltage profile. The results show that the overall power losses of the power system networks are reduced and at the same time the voltage profile is maintained using optimally locating DG in the power system networks. The proposed methodologies are tested on IEEE-9 bus, IEEE-24 bus and IEEE-30 bus distribution systems using MATLAB tool-box and MATPOWER 6.0 software package.KeywordsDistributed generationOptimal location of DGOptimal size of DGParticle Swarm Optimization