Distribution System Load Research Articles

Hi‐Z fault location identification on low voltage distribution systems using IoT‐based digital twin model computation

AbstractDistribution feeders carry and supply power to industrial, commercial and residential loads from the point where sub‐transmission (33 kV level) ends after stepping down to suitable voltages, such as 11 kV and further down to 400/230 V. In recent times, high impedance (Hi‐Z) faults on distribution systems are creating unique challenges to utilities both from operational and safety perspectives. Most of these Hi‐Z faults occur at distribution voltages of 15 kV or below, with the problem being worse at lower voltages. Hi‐Z fault detection technologies emerged and were developed and incorporated on embedded platforms, such as relays, reclosers and sensors, which protect and monitor distribution systems. Although these technologies can detect Hi‐Z fault on feeders, most of them cannot identify the exact location of the fault. Specifically, there is no solution available in literature for detecting Hi‐Z fault location on low voltage (LV) circuits like 3‐phase 4‐wire 400 V distribution network. In this paper, we introduce a novel and a unique algorithm to identify the location of Hi‐Z faults using proposed smart metres IoT data‐based distribution system load flow and digital twin model representation of the network. Furthermore, a case study on the standard 33 bus LV system clearly depicts the functionality of the proposed algorithm.

A novel order characteristic load shifting policy for load factor improvement, peak reduction, and economical operation of distribution systems

Demand side management (DSM) and demand response programs (DRPs) are widely used economic strategies that insist customers shift or curtail loads to economize the generation cost of the distribution system. These policies often involve tedious calculation processes or optimization tools to calculate the apt amount of load to be shredded or shifted to get a new modified load profile. This paper proposes an order characterized load-shifting policy (OCLSP), which is very easy to calculate and yields a considerable decrease in the peak demand, eventually improving the distribution system's load factor, also known as the peak-to-average ratio (PAR). Unlike other DSM and DR strategies, the proposed approach requires no parameters except the forecasted load demand. The proposed OCLSP is implemented on ten distribution systems from the literature which consists of a 3-, 6-, 7-, 10-, 15- units system, one islanded microgrid (MG) system and four grid-connected MG systems. Initially, the load demand of these test systems is restructured using OCLSP, and thereafter, dynamic economic load dispatch (DELD) is performed to minimize the generation cost of the system. A latest circle search algorithm (CSA) is utilized as the optimization tool. Results show that the generation cost of all ten test systems has been decreased by a considerable amount when their load demand is restructured using the proposed OCLSP. The minimum percentage improvement in the PAR of the ten systems was 2 % and maximum improvement was 60 %. Likewise, the percentage decrement in the peak demand was within 2 % to 37.5 % for the ten test systems and the decrement in generation cost rose up to 16 %. All these parameters were in comparison with the base load demand. Numerical results also corroborate to the efficiency and robustness of proposed CSA.

Distribution System Survey and Analysis: A Case Study of Kathmandu University

Load flow analysis helps to understand a power system's voltage profile and power flow distribution, enabling the identification of potential issues such as overloading and under-voltage conditions. Fault analysis ensures reliability and safety by understanding and mitigating abnormal conditions in electrical distribution systems. Together, they contribute to efficient operation, equipment protection, and overall system reliability. This paper presents the Kathmandu University distribution system's load flow and fault analysis. Starting from the careful study of the power usage of the Kathmandu University distribution system then followed by the load flow analysis employed in the Dig Silent Power factory software, critical insights into the system’s performance were unveiled. Additionally, fault analysis within the simulated distribution system provides valuable data to enhance the understanding of the different patterns of currents for the various types of faults that occur at different locations of the Kathmandu University distribution system. This study provides a comprehensive understanding of the Kathmandu University distribution system. It identifies potential issues that can help to make the Kathmandu University power system more reliable and efficient in the future.

Three-phase model of SCIG-based variable speed wind turbine for unbalanced DSLF analysis

Steady state performances of the electric power distribution system are normally assessed or evaluated based on load flow analysis. To properly carry out the analysis, a valid steady state load flow model of each distribution system component, including the wind power plant (WPP), needs to be developed. The present paper proposes a method for modeling and integrating squirrel cage induction generator (SCIG)-based variable speed WPP into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on a single-phase T-circuit model of fixed speed WPP, which has successfully been applied to balanced electric power systems. In the present work, the single-phase T-circuit model is extended and modified to be used in steady state load flow analysis of three-phase unbalanced distribution systems embedded with SCIG-based variable speed WPP. Results of the case studies confirm the validity of the proposed method.

Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.

Design Requirements for a Dynamic Series Compensator for Voltage Sags Mitigation in Low Voltage Distribution System

:Power quality issues have become an increasing concern due to an increase of sensitive loads in distribution system. A dynamic series compensator or Dynamic Voltage Restorer (DVR) is a custom device that can be used to protect sensitive loads from various power quality problems from disturbed incoming supply. The dynamic voltage restorer (DVR) has become popular as a cost effective solution for the protection of sensitive loads from voltage sags. This paper highlights a series of discussion, analysis and studies performed on the active power injection requirements for a DVR under various system and load conditions, as well as for different types of fault in both single phase and three-phase fault. The analysis and experimental results validate the effectiveness of the DVR for mitigation voltage sag in low voltage distribution system.

Power Quality Improvement on 11kV/440V Distribution System using DSTATCOM

At the PCC and source side, current and voltage harmonics are compensated by utilizing a distribution static compensator. In this work, we also compensated for voltage control and reactive power correction. An improved reference current switching signal is suggested using a synchronous reference frame control technique. The analysis is done on an 11kV/440V three-phase four-wire unbalanced nonlinear load distribution system. The DC link voltage and harmonic reduction of the suggested distribution system performance is compared with the PI and fuzzy logic controllers. Harmonic distortion is reduced, and reactive power is successfully compensated with the suggested method. These simulation results are obtained by MATLAB/SIMULINK software.

Optimizing the Load Frequency of a Two-Area Interlinked Power System using Artificial Intelligence Techniques

Power costs are increasing on a daily basis, generating changes in system frequency and causing serious concerns with system stability. It has become a major problem to offer customers with uninterrupted and high-quality power. To mitigate these issues, a linked power system's load distribution and network frequency should be constantly reviewed. Load frequency control adjusts the generator's energy output and tie line power between prescribed limits. As well as regulating generator output power, load frequency control also adjusts tie line power. The disturbance in the frequency due to different load changes is regulated using the proposed scheme. In this article, a two-area load frequency control system is constructed and evaluated using various control approaches, including a proportional integral derivative (PID) controller, a proportional integral (PI) controller, a fuzzy logic-based controller, and an Artificial Neural Network (ANN). The goal is to assess the power system's resilience under different loading conditions with these control schemes. The performance of the controllers is compared based on peak-undershoot, peak-overshoot, and settling time, focusing on tie line power and frequency response. To achieve this, the design is implemented using MATLAB/SIMULINK software.

Planning of fast charging infrastructure for electric vehicles in a distribution system and prediction of dynamic price

The increasing number of electric vehicles (EVs) has led to the need for installing public electric vehicle charging stations (EVCS) to facilitate ease of use and to support users who do not have the option of residential charging. The public electric vehicle charging infrastructures (EVCIs) must be equipped with a good number of EVCSs, with fast charging capability, to accommodate the EV traffic demand, which would otherwise lead to congestion at the charging stations. The location of these fast-charging infrastructures significantly impacts the distribution system (DS). We propose the optimal placement of fast-charging EVCIs at different locations in the distribution system, using multi-objective particle swarm optimization (MOPSO), so that the power loss and voltage deviations are kept at a minimum. Time-series analysis of the DS and EV load variations are performed using MATLAB and OpenDSS. We further analyze the cost benefits of the EVCIs under real-time pricing conditions and employ an autoregressive integrated moving average (ARIMA) model to predict the dynamic price. The simulated test system without any EVCI has a power loss of 164.36 kW and squared voltage deviations of 0.0235 p.u. Using the proposed method, the results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and squared voltage deviations of 0.0182 p.u. in the system. Significant cost benefits for the EVCIs are also achieved, and an R-squared value of dynamic price predictions of 0.9999 is obtained. This would allow the charging station operator to make promotional offers for maximizing utilization and increasing profits.

Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems

Efficient critical load restoration under extreme natural disasters is a promising solution to establish resilient distribution systems. Deep reinforcement learning (DRL) approaches are widely adopted in the load restoration problem to avoid incorporating the accurate distribution system model and improve online decision efficiency. However, the vulnerability of DRL towards adversarial examples may lead to unpracticable decisions and pose potential threats to load restoration. To address this issue, this paper proposes a robustness assessment and enhancement method for DRL-enabled distribution system load restoration. In particular, the distribution system load restoration problem is formulated as a Markov decision process, and a deep Q-network is adopted to learn the optimal decision policy. Then, an adversarial example generation optimization model incorporating the deep Q-network is established to implement the robustness assessment of the DRL-enabled load restoration against adversarial examples. Furthermore, adversarial training with the experience replay of adversarial examples is adopted to retrain the agent and improve the stability of the load restoration decision-making. Finally, the effectiveness of the proposed method is analyzed and verified in the modified IEEE 33-bus system and IEEE 123-bus system. The results show that robustness evaluation and enhancement significantly reduce the application risk of DRL in load restoration with safety-critical requirements.

Structural Performance for the Load Distribution System Applicable to Precast Coping by Scaled Model Test

Structural Performance for the Load Distribution System Applicable to Precast Coping by Scaled Model Test

Optimizing the hybrid PVDG and DSTATCOM integration in electrical distribution systems based on a modified homonuclear molecules optimization algorithm

AbstractThe increasing use of non‐linear loads in electrical distribution systems (EDS) led to a greater need for reactive power compensation, losses minimization, improved voltage and stability. This paper proposes the optimal integration of hybrid photovoltaic distributed generation (PVDG) and distribution static synchronous compensator (DSTATCOM) into IEEE 33 and 69‐bus EDS. A modified version of homonuclear molecules optimization (mHMO) is developed to determine the optimal allocation of the devices, while minimizing a multi‐objective function (MOF) based on total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices (ICPVDGand ICDSTATCOM). The primary objective of the mHMO is to enhance the equilibrium between exploration and exploitation in the original HMO by implementing a fresh exploration stage. The effectiveness of mHMO was assessed using CEC17 benchmark functions. The findings demonstrate that mHMO achieved excellent results, including high‐quality solution and a favourable convergence rate. Additionally, results demonstrate that mHMO outperforms its basic version in reducing TAPL by 94.27% and 97.87% for the two EDS, while improving voltage profiles and reducing the cost of integrated devices. This study shows the potential of hybrid PVDG‐DSTATCOM in improving the performance of EDS and highlights the effectiveness of mHMO in optimizing their integration.

Mobile energy storage systems with spatial–temporal flexibility for post-disaster recovery of power distribution systems: A bilevel optimization approach

In recent years, the damage to power distribution systems caused by the frequent occurrence of extreme disasters in the world cannot be ignored. In the face of the customer’s demand for high power supply reliability and high power quality, it is urgent to establish a resilient distribution network that can not only resist extreme disasters and quickly recover the power distribution system loads, but also ensure a high voltage quality of the distribution system during recovery process. Therefore, mobile energy storage systems with adequate spatial–temporal flexibility are added, and work in coordination with resources in an active distribution network and repair teams to establish a bilevel optimization model. The objective of the upper-level optimization model is minimum the total load curtailment of the distribution system after the disaster. And the objective of the lower-level optimization model is minimum the voltage offset of power supply buses during the recovery stage. A modified IEEE 33-bus distribution test system is used to verify this model. The simulation results show that the bilevel optimization strategy proposed in this research can effectively reduce the voltage offset during the recovery process of distribution systems, and ensure the minimum total load curtailment simultaneously.

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles of PIs’ bounds. It relies on the online learning ability of reinforcement learning (RL) to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve PIs’ quality. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay (PER) strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.

Harmonic injection of domestic nonlinear loads in Ghana's power distribution system: analysis and mitigation using a low-cost notch filter

Due to global efforts to save energy, digital light processing (DLP) and direct view televisions (DVTVs) are being replaced by energy-efficient liquid crystal display (LCD) and light emitting diode (LED) televisions. However, these energy-efficient appliances cause harmonics to be injected into the power distribution system, posing a threat to power quality. This study investigates the harmonics generated by common domestic appliances, particularly LCD and LED TVs used in homes in Ghana. Field harmonic measurements were taken using a C.A. 8335 Power Quality Analyzer at a selected facility and were then replicated in a simulation using MATLAB/SIMULINK to model the facility's area network capacity of 100-kVA, 11kV/433V. The study proposes a notch filter as a harmonic mitigation technique, which is integrated into the simulation design and found to be effective at reducing total harmonic distortion current to 0.05 % when applied in parallel to nonlinear loads. The study also compares the harmonic distortion generated by LED TVs and LCD TVs, finding that both types generate high levels of distortion.

Electric Vehicles for Distribution System Load Pickup Under Stressed Conditions: A Network Equilibrium Approach

We investigate the potential effects of electric vehicles (EVs) on reducing the load shedding in heavily loaded distribution systems (DSs). Given proper incentives, a considerable number of EVs could provide vital support to holding the load and improving operating flexibility. We propose a network equilibrium model that integrates market clearing in DSs and traffic flow balance in transportation systems. This model captures the decentralized interactions between key stakeholders in transportation and distribution networks as well as the spatial distribution of EV traffic in response to endogenously determined incentive signals. To mitigate the computational challenges brought by the non-convex network equilibrium model, we develop an equivalent convex reformulation with guaranteed global convergence. Numerical studies are conducted to demonstrate the effectiveness of the proposed model and computational performance. The proposed method could provide a fundamental tool for analyzing interdependent transportation and distribution networks coupled by charging facilities and EVs.

An improved hybrid approach for the simultaneous allocation of distributed generators and time varying loads in distribution systems

Distributed Generation (DG) studies are generally conducted considering a single type of DG units integrated with the Distribution System (DS). However, these studies may not evaluate optimum benefits offered by the DG integration. This paper presents a novel framework for individual and simultaneous allocation of different types of DG units in DS while considering varying load demand and probabilistic generation of DG units. A Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) based new hybrid method that utilizes a single controlling parameter is proposed for simultaneous allocation of DG units, aiming to minimize a multi-objective index. The strength of the proposed SSA-PSO hybrid approach is validated on seventeen benchmark functions and the performance of the novel framework for optimal allocation of DG units is validated through implementation on the 69-bus system. The results demonstrate that the proposed hybrid approach performs better as compared to other meta-heuristics techniques. Moreover, simultaneous allocation significantly reduces the multi-objective index as compared to individual DG units’ integration.

Integrated T&D Voltage Stability Assessment Considering Impact of DERs and Distribution Network Topology

Electric distribution systems are going through a major upgrade with increasing penetration of distributed energy resources (DERs) and impacting transmission system operational analysis. Traditionally, the transmission system’s voltage stability margin is computed by performing a continuation power flow (CPF) analysis. In transmission-CPF, loads in distribution systems are aggregated at the transmission-distribution (T&D) interface buses, and the distribution system’s electrical characteristics, like feeder length, increasing penetration of DERs, and associated control, are not considered. The traditional CPF thus considers the distribution network as a purely passive network without active network management and distributed energy resources. In this paper, two integrated T&D CPF methods are implemented using a co-simulation framework that uses individual transmission and distribution solvers. The first method is iteratively-coupled T&D CPF (IC-TDCPF), where the iterative coupling concept forces convergence of boundary variables at T&D PCC (Point of common coupling) at every CPF step. The second method, Loosely Coupled T&D CPF (LC-TDCPF), relaxes the boundary convergence constraints at PCC to achieve faster calculation speed. Moreover, a reduced distribution network equivalent is also developed to enable faster computation of VSM by reducing the number of integrated T&D buses. The developed T&D CPF co-simulation framework uses an open-source platform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MATPOWER</i> for solving transmission system and open-source software <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OPENDSS</i> for solving distribution system. Three large integrated T&D systems with 4196, 10587, and 12295 buses are developed to demonstrate the impact of DERs, voltage-dependent load models, and distribution network topology on integrated voltage stability margin using the proposed T&D CPF and co-simulation.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.