Unbalanced Radial Distribution Systems Research Articles

Comparison of Crow Search and Practice Swarm Algorithm for Minimization of Losses in Unbalanced Radial Distribution System

Comparison of Crow Search and Practice Swarm Algorithm for Minimization of Losses in Unbalanced Radial Distribution System

Simultaneous feeder reconfiguration, DSTATCOM allocation, and sizing using seagull optimization algorithm in unbalanced radial distribution systems

Simultaneous feeder reconfiguration, DSTATCOM allocation, and sizing using seagull optimization algorithm in unbalanced radial distribution systems

A co-optimized approach for state estimation based micro phasor measurement unit allocation in power distribution infrastructure

Due to the highly complex and dynamic electrical characteristics of the 3-phase unbalanced power delivery networks (PDNs), it is challenging for utilities to observe the network entirely. This impediment necessitates the PDNs to become intelligent nowadays in the presence of various smart devices. Micro-phasor measurement units (µPMUs) are expected to play a crucial role in observing the smart power distribution system due to their micro-seconds resolution and millidegree precision accuracy. The placement of µPMUs in PDNs is also very challenging due to their associated costs. So, allocating a minimal number of µPMUs in PDN with fulfilling the topological observability can elucidate the issue of real-time visibility of the entire system. This paper presents a novel co-optimized placement strategy of µPMUs (CPMP) in PDNs that minimizes the µPMU numbers and the estimation error in the buses other than the µPMU assigned nodes. This work also displays a novel dynamic heuristic distribution system state estimation (DDSSE) to estimate the system's unknown states by using only the information of bus voltage phasors at the µPMU specified buses. The DDSSE is used to estimate the root mean square error (RMSE) in estimated voltage magnitudes and angles. Besides this, the efficacy of the grey-wolf optimizer (GWO) is presented in this work to solve the newly formulated placement problem. The results are validated on an Indian 19-bus and 34-bus unbalanced radial distribution system (URDS), and a detailed comparative study of different heuristic algorithms is also analyzed in this work.

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device’s impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method’s efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network.

Optimal renewable resource for enhanced performance of unbalanced radial distribution systems

Optimal renewable resource for enhanced performance of unbalanced radial distribution systems

Optimal allocation of DGs for non-linear objective function modeling in a three-phase unbalanced distribution system using crow search optimization algorithm

The rapid growth of consumers, industrial development and global economic expansion inspired a lot of improvement in reliable power supplies. The integration of distributed generations (DGs) in unbalanced radial distribution systems (RDS) is one of the solutions to handle all above issues. This paper proposes an optimal planning for unbalanced RDS integrated with DGs. The objective function is designed as a non-linear function, which is framed as the combination of active power losses, voltage deviation and load balancing index of RDS. The Crow Search Optimization Algorithm (CSOA) is used for identifying the optimal size, location, power factor for DG and distribution system phase in order to reduce the proposed objective function by satisfying all system constraints. The proposed model is tested on 33-bus unbalanced RDS. There is reduction of annual cost of energy loss (ACEL) due to proper integration of DG. The obtained results prove that the proposed CSOA can effectively solve the non-linear objective function and can help to obtain optimized results for unbalanced RDS.

Multi-Objective Optimal planning of Renewable Energy Sources & Electric Vehicle Charging Stations in Unbalanced Radial Distribution Systems using Harris Hawk Optimization Algorithm

Multi-Objective Optimal planning of Renewable Energy Sources & Electric Vehicle Charging Stations in Unbalanced Radial Distribution Systems using Harris Hawk Optimization Algorithm

Analysis of Unbalanced Radial Distribution System with SVR and Impact of Phase Shifter Angle considering Time Varying Loads

ABSTRACT Distribution network experiences unbalancing due to random switching of loads, low voltage profile, high R/X ratio, and unequal loading in the different phases, which inturn causes more losses compared to transmission line. The voltage profiles, active, and reactive power in each phase are distinct due to unbalancing. In the present work, the authors analyzed unbalanced distribution network in presence of Series Voltage Regulator (SVR) considering the reduction in the losses and improvement in the voltage profile. The main highlights of the paper are: (i) placement and analysis of unbalance radial network with SVR at various phase angles; (ii) reduction in active, reactive power loss of the network and improvement in voltage profile; (iii) saving in cost of energy loss at different phase angle of SVR; and (iv) impact analysis of seasonal time varying load model. The placement of SVR at candidate node by considering real, reactive power loss reduction, and percentage rate of under voltage mitigated problem. Before and after SVR placements, the forward and backward sweep algorithm is used for the load flow calculation for analysis. The analysis is carried out on IEEE 25-bus radial distribution network with SVR phase shifter considering time varying seasonal load variations.

Investment analysis for optimal planning of electric vehicle charging station on a reconfigured unbalanced radial distribution system

Investment analysis for optimal planning of electric vehicle charging station on a reconfigured unbalanced radial distribution system

Performance analysis of unbalanced radial feeder for integrating energy storage system with wind generator using inherited competitive swarm optimization algorithm

In recent years, the battery energy storage system (BESS) has been considered as a promising solution for mitigating wind turbine generator intermittencies. In this paper, optimal location and capacity of the BESS with wind power penetrations, and charging/discharging dispatches of BESS are determined using inherited competitive swarm optimization (ICSO) algorithm while improving the performance of the unbalanced distribution network subject to technical constraints. The strategy uses the average load of the feeder as the deciding criterion for charging or discharging the battery. The sustainable average load (SAL) charging method is utilized to charge the battery. The free-running and conservative discharging methods of the battery are studied. The proposed methodology is investigated on IEEE 37 bus unbalanced radial distribution system. The application results show that the proposed methodology enhances various performance objectives of distribution system by optimally coordinating the dispatches of BESS.

An Effective Bi-Stage Method for Renewable Energy Sources Integration into Unbalanced Distribution Systems Considering Uncertainty

The output generations of renewable energy sources (RES) depend basically on climatic conditions, which are the main reason for their uncertain nature. As a result, the performance and security of distribution systems can be significantly worsened with high RES penetration. To address these issues, an analytical study was carried out by considering different penetration strategies for RES in the radial distribution system. Moreover, a bi-stage procedure was proposed for optimal planning of RES penetration. The first stage was concerned with calculating the optimal RES locations and sites. This stage aimed to minimize the voltage variations in the distribution system. In turn, the second stage was concerned with obtaining the optimal setting of the voltage control devices to improve the voltage profile. The multi-objective cat swarm optimization (MO-CSO) algorithm was proposed to solve the bi-stages optimization problems for enhancing the distribution system performance. Furthermore, the impact of the RES penetration level and their uncertainty on a distribution system voltage were studied. The proposed method was tested on the IEEE 34-bus unbalanced distribution test system, which was analyzed using backward/forward sweep power flow for unbalanced radial distribution systems. The proposed method provided satisfactory results for increasing the penetration level of RES in unbalanced distribution networks.

Optimal allocation of energy storage system and its benefit analysis for unbalanced distribution network with wind generation

Battery energy storage system (BESS) can play a major role to overcome the challenges with renewable energy sources, especially in the context of Smart Grid (SG). In this manuscript, BESS optimal location and capacitance of wind power penetrations, and charging/discharging dispatches of BESS are determined using inherited competitive swarm optimization (ICSO) algorithm while improving the performance of the unbalanced distribution network subject to technical constraints. This approach utilizes the average feeder load from decisive criterion for charging or discharging battery. The wind turbine generator (WTG) charging and sustainable average load (SAL) charging methods for BESS are compared considering conservative mode of discharging. The proposed methodology is investigated on IEEE 37 bus unbalanced radial distribution system. The application outcomes displays that proposed methodology enhances various performance objectives of distribution system by optimally coordinating the dispatches of BESS.

Applied machine learning in wind speed prediction and loss minimization in unbalanced radial distribution system

ABSTRACT Environmental parameter consideration has always prompted wind power to be used as renewable energy. However, the biggest challenge lies in wind energy integration to the power grid due to wind intermittency. Wind speed or wind power forecasting is one of the approach to manage this intermittency. Numerous prediction methods have been reported in previous literatures over few years. In this work, multivariate wind speed forecasting using Machine Learning framework in a python environment is executed. Several statistical models and neural network models are examined to best predict the wind speed of Surat, India [22.2587° N, 71.1924° E]. The model efficiency is tested in terms of measurements of correlation factors and Mean Absolute Error values. The predicted wind speed value is further considered for the power generation from the wind farm and integrated to the distribution system. Load Impedance Matrix method is implemented for Distribution System Load Flow analysis for being robust and simple with single-step computation. IEEE-19 bus and IEEE-25 bus-unbalanced radial distribution systems are considered for finding the power losses in the branches with wind power as Distributed Generation. An efficient and effective optimization technique, Teaching Learning-Based Optimization, is used to obtain the optimal location and capacity of Distributed Generation to minimize the power loss in the distribution lines.

Optimal DG Allocation in Unbalanced Radial Distribution Systems Using Improved Grey Wolf Optimizer

Optimal DG Allocation in Unbalanced Radial Distribution Systems Using Improved Grey Wolf Optimizer

A New Affine Arithmetic-Based Optimal Network Reconfiguration to Minimize Losses in a Distribution System Considering Uncertainty Using Binary Particle Swarm Optimization

In the present work, Binary Particle Swarm Optimization (BPSO) based optimal re-configuration for balanced and unbalanced radial distribution networks using Affine Arithmetic (AA), with uncertainty in generation and load, is proposed to minimize the system losses. An expression for three phase real affine power loss is derived with partial deviations of real power loss in lines with respect to power injections in other buses and also with respect to power injections in other phases in case of unbalanced distribution systems. The major contribution of the present work is the application of AA based optimal network reconfiguration, to both balanced and unbalanced radial distribution networks with uncertainty. The proposed method is tested on IEEE 16, 33, 85 and 119 bus balanced distribution systems and an unbalanced 123 bus system with Distributed Generation (DG) connected at some buses. The optimal loss intervals obtained by the proposed method are compared with that obtained by Interval Arithmetic (IA) and Monte Carlo (MC) simulations based methods. The simulation results show that proposed AA based analysis gives an optimal reconfiguration, for both balanced and unbalanced radial distribution systems with uncertainty as compared to existing IA based method.

Analysis of Unbalanced Radial Distribution System with Summer Time Varying Load and Growth

Analysis of Unbalanced Radial Distribution System with Summer Time Varying Load and Growth

Cyber–physical attacks on power distribution systems

This study investigates the impacts of stealthy false data injection (FDI) attacks that corrupt the state estimation operation of power distribution systems (PDS). In particular, the authors analyse FDI attacks that target the integrity of distribution systems optimal power flow (DSOPF) in order to maximise the system operator losses. The branch current state estimation method is implemented to accurately model the PDS, and convex relaxations are applied to the DSOPF model. The effects of the FDI attacks are analysed on the IEEE 34-bus unbalanced radial distribution system, with distributed energy resources (DERs) along the feeder. A 24 h DSPOF is performed, and the results depict the changes in the voltage profile and the additional power injection from the DERs, which consequently lead to the increase of the DSOPF cost.

Optimal placement of electric vehicle charging station for unbalanced radial distribution systems

ABSTRACTTransportation is one of the dominant applications where abundant fossil fuel usage can be experienced across the world. To save the earth from carbon emission and depletion of conventional energy resources, alternate energy sources are to be introduced in transportation which will lead to Electric Vehicles (EV) and Hybrid Electric Vehicles (HEV). The infrastructure of the power system is also adaptable to the changes of electrified transportation. In this research work, a methodology is proposed to find the optimal placement of Electric Vehicle Charging Station (EVCS) along with the optimal capacity of charging stations for Unbalanced Radial Distribution System (URDS) by using Particle Swarm Optimization (PSO). This paper also explains the effect of EVCS placement in RDS w.r.t voltage profile and real power losses. Also, the insertion of EVCS to the power system draws additional power from the electrical grid. To make the system more self-sustainable and reliable, this research work proposes additional distributed generators (DGs) at optimal locations to reduce the impact of EVCS on the system by incorporating it. The proposed methodology is validated with two standard unbalanced IEEE test cases such as IEEE 19 bus and IEEE 25 bus URDS.

Optimal Reconfiguration With Reliability in Unbalanced Radial Distribution System Using Particle Swarm Optimization

This paper presents an optimal reconfiguration by using particle swarm optimization (PSO) with reliability in unbalanced radial distribution system. The proposed algorithm is done with two schemes namely minimization of total loss and reliability for subsequent operation of sectionalize and tie line switches. However, a population-based approach is more concerned about reliability computation with contingency load loss index value. In this paper, IEEE 25-bus having three tie lines have been proposed in radial distribution system tested on MATLAB.