System Loss Reduction Research Articles

Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods.

A Review of Analytical and Intelligent Methods for Optimal Distributed Generation Placement in Modern Distribution Networks: Techniques, Challenges, and Future Directions

This comprehensive review examines the state-ofthe-art methodologies for optimal Distributed Generation (DG) placement in modern distribution networks, addressing the critical challenges of power quality enhancement and system reliability. The study systematically analyses various approaches including analytical methods, optimization techniques, artificial intelligence algorithms, graph theory applications, simulation-based methods, and hybrid solutions. Recent research indicates that optimized DG placement can achieve up to 60% reduction in system losses and 15% improvement in voltage profiles. The review highlights that while traditional analytical methods provide mathematically robust solutions, they often face scalability challenges in large networks. Artificial intelligence methods, particularly hybrid approaches combining neural networks with conventional techniques, have demonstrated superior performance with 25% better solution quality and 35% reduced computational time. The paper also explores emerging trends, including real-time optimization and blockchain integration, while addressing critical challenges such as renewable energy intermittency and regulatory frameworks. This review contributes to the field by providing a comprehensive evaluation framework for DG placement methods and identifying promising directions for future research in the context of evolving smart grid technologies.

Improved state transition algorithm for hybrid power system reactive power optimization using filter techniques

Abstract Reactive power optimization in power systems is crucial for their normal and efficient operation. However, with the increasing prevalence of active current and direct current (AC-DC) hybrid power grids, existing optimization methods face issues such as poor adaptability, low efficiency, insufficient accuracy, and inadequate convergence. To address these problems, this paper proposes a reactive power optimization model based on an improved state transition algorithm (STA) utilizing filter techniques. The optimization model considers operational costs associated with adjusting transformer tap positions, switching reactive power compensation capacitors, regulating generator terminal voltages, and adjusting active power in DC transmission. Simulation results demonstrate that this method can reduce system losses and voltage control costs while ensuring stable system operation, thus improving the reactive power distribution in AC-DC hybrid transmission systems.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling

Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

Efficient distribution of active and reactive power in distribution networks is crucial for ensuring that consumer demand is met and that electrical power quality is maintained. This requires careful planning, particularly in terms of optimally placing and sizing distributed generator (DG) and capacitor bank (CB) units. These units play a key role in minimizing power losses and voltage fluctuations within the network. However, implementing DG and CB units in unbalanced distribution networks presents unique challenges due to inherent system unbalances. To address these challenges, this article proposes a method for allocating and sizing DG and CB units in unbalanced distribution networks. The approach accounts for the network's unbalance and targets multiple objectives, such as reducing power losses, improving multi-phase voltage stability, and minimizing phase-to-phase voltage unbalance. The fast and flexible radial power flow (FFRPF) technique is employed to model complex interactions and constraints within the network, leading to a multi-objective optimization problem. This optimization problem is solved using the weight aggregated particle swarm optimization (WA-PSO) method, a variant of particle swarm optimization tailored for multi-objective functions. WA-PSO simplifies the process by combining multiple objectives into a single function using weighted aggregation. The efficacy of this approach is validated on 19, 34, and 123-node unbalanced radial distribution networks (URDNs). The results show significant improvements in power delivery efficiency across all tested networks, especially when DG and CB units are operated simultaneously. Specifically, DG and CB integration led to a reduction in system losses by 89.90 %, 88.42 %, and 86.87 %, and a decrease in three-phase voltage unbalance indices by 88.75 %, 81.68 %, and 39.05 %, for the 19, 34, and 123-node systems, respectively, while maintaining voltage stability within acceptable limits. Additionally, CO2 emissions were reduced by 52 %, 62 %, and 53 % when microturbines were utilized instead of coal-based thermal power plants, further highlighting the environmental benefits of the proposed approach.

Power Factor Optimization with Cos Phi Mode Trainee

The power factor cos phi measures the efficiency of using electrical power in a system. A low power factor indicates the presence of high reactive power consumption, which can lead to increased energy losses and operational costs. To overcome this problem, an effective power factor regulation system is required. This research designed and developed the Cos Phi Trainer, which operates in two modes, manual and automatic, to improve the power factor at an electrical load. The system is designed to give users a practical understanding of measuring and improving power factors by setting up compensation capacitors. In manual mode, the user directly sets the value of the capacitor used to fix the power factor. While in automatic mode, the system uses current and voltage sensors to measure the power factor value in real-time and automatically adjusts the capacitor to achieve the optimal power factor. The trainer has an easy-to-use interface and a measurement panel that displays important parameters such as current, voltage, active power, reactive power, and power factor. The test results show that the system can significantly increase the power factor in manual and automatic modes. Thus, this Cos Phi Trainer is an effective tool for training in the laboratory of Indorama Engineering Polytechnic and teaching about electrical power management and energy loss reduction in the electrical system. Keywords: Power factor, manual, active power, reactive power, trainer

Estimation of abnormal states in shunt capacitor banks using transient disturbance feature extraction

Shunt capacitor banks are essential for reactive power compensation, ensuring voltage stability, and reducing system losses. These banks consist of multiple units with components in series and parallel. A few component failures do not immediately affect the safe operation of the capacitor bank, but component breakdown can lead to voltage redistribution. Under combined factors such as system overvoltage and equipment aging, and others can trigger an avalanche effect causing capacitor breakdown, resulting in significant safety accident risks. Practical operation experience shows that partial component breakdown generates many transient disturbance signals. Quantitative analysis of these signals can detect capacitor bank anomalies early. This paper proposes the quantitative extraction of transient disturbance characteristics using the Prony algorithm and estimates the phase and number of capacitors that break down to judge capacitor anomalies. The simulation part verifies the theoretical analysis and detection algorithm’s correctness through numerical simulations and PSCAD (Power Systems Computer Aided Design) electromagnetic transient simulations. The numerical simulations consider different signal lengths, noise levels, attenuation coefficients, and oscillation frequencies. In the PSCAD simulation environment, verification models are built under varying sampling frequencies, numbers of breakdown components, signal lengths, and signal-to-noise ratios. These simulation results verify the accuracy of the detection algorithm under different conditions.

Natural logarithm particle swarm optimization for loss reduction in an island power system

In an island power system, optimizing energy management is fundamental since there are renewable sources with their limitations. This management includes the allocation and capacity of energy sources to supply the loads. In this context, optimizing losses in the system contributes to improve the efficiency of this management. This paper proposes the losses optimization and energy management in the island power system. The authors propose the Natural Logarithm Particle Swarm Optimization to solve the problem and compare it with the Attractor Point Algorithm and Evolutionary Particle Swarm Optimization. And with that, we also propose a particle initialization for the studied particle-based algorithms to guarantee convergence in radial power systems. This is because the system configuration influences the response of the algorithm convergence. These techniques were applied to the IEEE-34 unbalanced radial island system.•Natural Logarithm Particle Swarm Optimization differs from classical PSO in that it does not calculate the velocity of the particles. Therefore, the method considers a cloud of particles with a natural logarithmic trajectory to solve the reduction of losses in a power system with a radial topology.•Natural Logarithmic Particle Swarm Optimization uses an initialization equation to minimize the initial estimation process, which is relevant to the convergence process.

Optimizing electric vehicle scheduling strategies for advanced distribution system planning and operation with comprehensive cost-benefit analysis

Electric vehicles (EVs) are a compelling way to advance environmentally sustainable transportation systems, thanks to their potential to significantly reduce carbon emissions. Nevertheless, the seamless integration of EVs into distribution systems is fraught with challenges that require thorough investigation. With the ultimate goal of creating an ideal planning plan, the primary objective of this research is to examine electric vehicle planning strategies (EVPSs) utilizing a thorough cost and benefit analysis technique. Because EVs may operate as both a dispersed energy source and a load, they present the idea of Vehicle-to-Grid (V2G) technology. An EV with V2G technology installed can actively support the distribution system's power needs, particularly in the area of reactive power support. In order to fully benefit from integration of electric vehicles (EVs) into distribution systems, EV charging activities must be coordinated with the concurrent availability of V2G technology. This study involves the implementation of two separate PSs, each of which focuses on specific aspects of distribution system improvement. Active power distribution (APD) and reactive power distribution (RPD) are the two categories into which the strategies are separated. Their shared objective is to reduce system losses by strategically utilizing the V2G technology that is built into electric vehicles. The APD technique is predicated on the idea of minimizing losses by optimizing EV charging and discharging (CDC). On the contrary, strategy based on RPD revolves around minimizing losses through optimal charging of EVs and smart injection of reactive power into the distribution system. In addition, this research presents an in-depth cost-benefit analysis of the developed EVPSs, which provides valuable insights from a planning perspective. Finally, this study assesses how the distribution system's reorganization has improved the system's planning and operation. The effectiveness and resilience of the suggested strategy are convincingly illustrated by simulations conducted on a 33-bus distribution system, which validate these crucial observations.

Voltage Optimization on Low Voltage Distribution Transformer Zones Using Batteries in Uganda

In the context of Uganda's rapidly growing energy demands and the need for sustainable solutions, this study explores the implementation of voltage optimization techniques in Low Voltage (LV) distribution transformer zones. The research focuses on the innovative integration of batteries to optimize voltage levels, thereby enhancing the efficiency and reliability of the electrical distribution system. By analyzing real-time data from various LV transformer zones in Uganda, this study investigates the impact of voltage fluctuations on the overall power distribution network. The research methodology involves the design and deployment of battery energy storage systems (BESS) strategically placed within LV distribution transformer zones. These BESS units are utilized to store excess energy during periods of low demand and release it during peak hours, ensuring consistent voltage levels and minimizing losses in the distribution network. The study evaluates the effectiveness of this approach through extensive simulations and on-site experiments, considering factors such as battery capacity, charging/discharging rates, and load variations. A comprehensive cost-benefit analysis is conducted to evaluate the potential financial savings and environmental impact associated with this sustainable energy solution. The findings of this research indicate significant improvements in voltage regulation, reduced system losses, and enhanced reliability in LV distribution transformer zones. Additionally, the study demonstrates the feasibility of integrating batteries into the existing infrastructure, thereby contributing to the optimization of the energy distribution system in Uganda. The outcomes of this research provide valuable insights for policymakers, utility companies, and researchers, emphasizing the importance of embracing innovative technologies to address the energy challenges faced by developing nations like Uganda.

Optimal Planning of Battery Swapping Stations Incorporating Dynamic Network Reconfiguration Considering Technical Aspects of the Power Grid

In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and safe operation. Therefore, this study proposes an optimal planning method for battery swapping stations that integrates dynamic power distribution network reconfiguration while addressing technical aspects of the grid. The proposed method aims to concurrently optimize the placement and capacity of battery swapping stations, along with power distribution network reconfiguration, to enhance grid reliability and efficiency. The optimization model accounts for various factors including power quality, technical considerations, grid limitations, and operational expenses. A multi-objective optimization framework is devised to simultaneously reduce system losses, improve voltage stability, and mitigate environmental impacts of the power distribution network incorporating DG units. Case studies are conducted to illustrate the efficacy of the proposed approach in enhancing overall grid performance while accommodating the integration of battery swapping stations. The findings underscore the significance of considering technical factors and grid reconfiguration in battery swapping station planning to achieve optimal system operation and maximize benefits for electric vehicle users and grid operators alike.

Reduction of Power Distribution System Losses by Using Novel Heuristic Algorithm

The Distribution Networks are the most widespread part of electrical power systems. Researcher has been trying to in the solutions to reduce high energy losses associated with them due to the low voltage level of the supply framework. Capacitors based techniques have been proposed to reduce these losses. These techniques minimize power losses, improve stability, and enhance the voltage profile of the distribution system under normal operating conditions. Finding optimal place and proper size of the capacitor is still a very hot area of research. In this research work, an innovative procedure based on the Archimedes optimization Algorithm (AOA) is presented to solve the optimal capacitor placement as well as sizing problem to improve the voltage profile and reduce the energy losses in the network. The suggested AOA based technique, is implemented on IEEE 33 bus network to find the optimal locations to place the capcitors in the distribution network. The performance of AOA is also compared with a standard model to verify the effectiveness of the scheme. By using AOA active power losses reduced upto 23.62% and reacrive losess by 21.86% respectively. The simulation results reveal that AOA based technique offer better results regarding the capacitor placement for voltage profile improvement of buses and significant reduction in active power losses as comapred to previous studies.

Control strategy for flexible interconnection device considering transformer power losses

This paper proposes a flexible interconnection device control strategy. It takes transformer power loss into account to solve the problems of voltage overrun, station load imbalance, and unbalanced transformer capacity allocation that occur after the large-scale connection of distributed power sources into the low-voltage distribution network to realize the mutual aid of active power and load balancing between interconnected stations. Firstly, the VSC control strategy with capacitor current feedback active damping link is introduced to accurately execute the power commands and maintain the DC bus voltage stability. Then, the load regulation strategy with transformer power loss is proposed to generate the power commands of the flexible interconnection device. Finally, the simulation results show that the control strategy of the flexible interconnection device with transformer power loss mentioned in this paper can flexibly control the power transmitted by each port and maintain the bus voltage stability. The simulation results show that the flexible interconnection device control strategy proposed in this paper, which takes into account the transformer power loss, can flexibly control the transmission power at each port to keep the bus voltage stable. Moreover, it can effectively reduce system loss compared with the traditional load regulation strategy.

Reducing two-level systems dissipations in 3D superconducting niobium resonators by atomic layer deposition and high temperature heat treatment

Superconducting qubits have arisen as a leading technology platform for quantum computing, which is on the verge of revolutionizing the world's calculation capacities. Nonetheless, the fabrication of computationally reliable qubit circuits requires increasing the quantum coherence lifetimes, which are predominantly limited by the dissipations of two-level system defects present in the thin superconducting film and the adjacent dielectric regions. In this paper, we demonstrate the reduction of two-level system losses in three-dimensional superconducting radio frequency niobium resonators by atomic layer deposition of a 10 nm aluminum oxide Al2O3 thin films, followed by a high vacuum heat treatment at 650 °C for few hours. By probing the effect of several heat treatments on Al2O3-coated niobium samples by x-ray photoelectron spectroscopy plus scanning and conventional high resolution transmission electron microscopy coupled with electron energy loss spectroscopy and energy dispersive spectroscopy, we witness a dissolution of niobium native oxides and the modification of the Al2O3-Nb interface, which correlates with the enhancement of the quality factor at low fields of two 1.3 GHz niobium cavities coated with 10 nm of Al2O3.

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

AbstractThis paper studies the effect of the number of switching (NOS) per day of capacitor banks on loss reduction in radial distribution systems. To this aim, the daytime (more precisely, 24 h) is divided into different numbers of time segments (equal to the same NOS) for capacitors’ size switching. The resulting non‐linear programming with discontinuous derivatives (called DNLP) model is solved subject to related constraints. The results reveal the impact of hourly switching of capacitor banks on further loss reduction (namely 118.4435, 83.7856, and 101.738 MWh for three IEEE systems) and higher net savings (i.e. k$5.6067, k$4.2772, and k$5.3542 for the same systems) of radial distribution systems compared to daily switching. Then, the hyper‐tuned Random Forest model is trained based on the IEEE 69‐bus network, fine‐tuned by the IEEE 10‐bus network, and fitted by the IEEE 33‐bus network to have an intelligent multi‐classification task with the highest accuracy. Numerical simulation, in both classic and intelligent parts, is presented to demonstrate the performance of DeepOptaCap. For the final step, DeepOptaCast is compared to other intelligent models of Light Gradient Boosting Method (LGBM), Decision Tree, and XGBoost, regarding KPIs of mean absolute percentage error, root mean squared percentage error, mean absolute error, root mean squared error, and coefficient of determination to demonstrate the model's superiority.

Fault Analysis of Microgrid With Grid-Connected and Islanded mode Using IoT-Wavelet Approach

Now a days microgrid is one of the most widely used method in power network to reduce system losses as well as improve the reliability in the field of electrical systems. Integration of power projects typically involves adding new distributed energy sources with and without compensating devices to an existing power system network. It is essential to design new protection scheme due to changes in the topology and dynamic behavior of the system. Now fast fault detection algorithmic approaches are necessary to integrate different types of generating sources and loads under smart environment. The protection scheme must provide physical monitoring as well as parametrical with the help of new technologies. Internet-of-things(IoT) is one of the source to monitor electrical systems under various environmental conditions of the system. Wavelet (WT) basically investigates the fault transient signals of different frequency and divides the waveform into different approximate and detailed coefficient values, which provides the important knowledge about the classification and location of fault. The detection of faulty-line and the location of fault by implementation wavelet detailed coefficients of Bior1.5 mother wavelet. This proposed method provides fault analysis of IoT based protection of microgrid with Grid-connected and Islanded Mode Using Wavelet Approach under various types of faults.

Reduction of Electrical Losses’ Analysis on Distribution Systems with Distributed Generation and Energy Storage Systems

The increasing use of distributed generation inthe Electric Power Systems has brought these several problems, since the controllers of these systems must take into account one more variable for any decisions. Within this context, there was a special emphasis on the use of electric energy storage systems to reduce losses, with the allocation of them as a load at the over-generation points and to reduce system losses at peak demand points. This article presents an analysis of the distributed generation to compare the electrical losses using electrical energy storage. The technique used to estimate the electric energy generation of a photovoltaic system in one day, according to temperature and irradiation data, was the application of artificial neural networks. Thus, the storage element appropriate to the demand and generation levels was first defined and the OpenDSS tool was used to perform the study of the load flow and to measure the effect of the use of these storage systems in the IEEE 37 bus network. Using energy storage in this application has proved to be advantageous, reducing the losses of active power at the peaks and reducing over-generation during the day, measured as crucial for the future of the distribution.

Comparative Analysis of DG Units with Shunt Capacitive Banks on the Improvement of Voltage Profiles in Distribution Networks.

Electrical distribution systems’ reliability is a critical parameter for electrical networks and their end-users. Among the different parameters associated with these systems’ reliability, voltage profile is most commonly affected in the electrical network due to different internal and external parameters such as lines and feeders’ impedance, system overloads, and increments on end-users. A poor voltage profile can have adverse impacts on the end user, such as long-term harm to their electric and electronic devices. For both consumers and companies that provide electricity, a low voltage profile thus leads to a loss of power. The Distributed Generation (DG) system is a viable option to improve voltage profiles while conserving the environment by unlocking the usage of any renewable energies following recent technological advancements and environmental concerns. This article proposes an alternative method to enhance voltage profiles in a distribution network (Abuja Electricity Distribution Company, AEDC) by comparing the most effective approach for power losses and voltage profile compensation through DGs and capacitor banks. Detailed simulations are conducted to explain and validate the results with the optimum system voltage improvement and loss reduction. This work has found that the voltage profiles are more enhanced with DG units compared to capacitor banks. The power losses were reduced from the initial value of 1196.4 kW to 762.4 kW with a saved energy of 36.3% with DG-unit as compared to FC with a power loss reduction of 1005.2 kW (energy saved of 16.0%).

Performance analysis of power system dynamics with facts controllers: Optimal placement and impact of SSSC and STATCOM

This paper explores the impact of integrating Flexible AC Transmission Systems (FACTS) controllers, specifically the Static Synchronous Series Compensator (SSSC) and the Static Synchronous Compensator (STATCOM), on power system performance. The objective of this study is to evaluate how these controllers can enhance various aspects of power system operation, including voltage regulation, power flow stability, and overall system efficiency. The methodology involves simulating power systems with and without the deployment of SSSC and STATCOM and analyzing their effects on voltage profiles, power flow characteristics, and system losses. The findings reveal that both SSSC and STATCOM significantly improve voltage stability and power flow control, leading to reduced system losses and enhanced operational efficiency. This study introduces a novel approach by comparing the performance enhancements provided by SSSC and STATCOM in different operational scenarios, offering valuable insights into their effectiveness. The results underscore the potential of FACTS technology in advancing power system stability and efficiency, making a substantial contribution to the field of power system optimization.