Improve Distribution System Research Articles

Improving Distribution System State Estimation by Including Volt-Var Control Information

Improving Distribution System State Estimation by Including Volt-Var Control Information

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Performance evaluation of solar-PV integrated hybrid fuzzy-logic controlled multi-functional UPQC for enhancing PQ features

To improve distribution system voltage and current quality, a newly built solar-PV system connected multi-functional universal power quality compensator (MFUPQC) has been extensively used. The proposed MFUPQC mitigates both load and source-side concerns in a three-phase distribution system. Furthermore, as part of the distributed generation scheme, active power from solar PV is injected into the grid or source when solar PV is available. In this context, the proposed MFUPQC was tested in both PQ enhancement and DG integration modes using a feasible control scheme. The proportional-integral controller is used for shunt- voltage-source inverter (VSI) DC-link control, which is not suitable for regulating DC-link voltage at the desired level due to incorrect gain value selection. In this work, an intelligent hybrid-fuzzy-logic DC-link control of MFUPQC evidences the intelligent knowledge base for better regulation of power-quality issues. The suggested hybrid fuzzy-logic controlled MFUPQC device's performance for both power quality (PQ) improvement and DG integration is validated using the MATLAB/Simulink software tool, and simulation results are provided with an appealing comparison analysis.

Resilience-oriented planning for active distribution systems: A probabilistic approach considering regional weather profiles

Recent experiences with increasing climatic variability highlighted the importance of enhanced power distribution system resilience. Conventional methods of power distribution system planning usually focus on persistentinvestments associated with predicted faulty events, aiming to enhance the overall system reliability. Improving distribution system resilience in response to extreme weather events involves reduction of impact of the events having varying probability of occurrence. The probability of an extreme event varies significantly by location due to a combination of geographical, climatological, and meteorological factors. Thus, instead of requiring persistent investments, the solutions for resilience-oriented system upgrades should be guided by the potential effects and the probability of occurrence of extreme weather events in a specific area. To achieve this objective, the present paper proposes a two-stage stochastic mixed-integer linear programming (MILP) model based on regional weather profiles for the long-term resilience planning of distribution systems against extreme weather events. In the planning stage, investment decisions are made regarding overhead distribution line hardening and the distributed generation (DG) installation. During restoration process in the operational stage, dynamic microgrid formation is carried out for advanced distribution grid operations to minimize the cost corresponding to loss of load. The inclusion of regional weather profiles into planning objectives provide added flexibility to the network operators for analysing the trade-off between investment cost and load loss cost in resilient distribution system planning strategies.

A Dual Fundamental Current Extraction Controller for DSTATCOM in the Distribution System for Power Quality Improvement Under Non-Ideal Source Voltage Condition

Most of the controllers often fail to work properly when encountered with power quality (PQ) issues like unbalanced and distorted grid voltage conditions in three-phase distribution system (TPDS). To address this problem, usually a dynamic static compensator (DSTATCOM) is utilized in TPDS. For enhanced performance, a novel dual fundamental component extraction (DFCE) technique is proposed for generating reference currents (RC) in a TPDS exhausting DSTATCOM. The proposed controller is highly effective for reactive power compensation and source current harmonic suppression under source unbalanced voltages condition with nonlinear load conditions. For computation of RC and phase synchronization, the controller extracts the required fundamental voltage and current at the same time. Consequently, the controller is able to control DSTATCOM operation in TPDS without any phase locked loop (PLL) and low pass filter (LPF). For generation of fundamental components of voltage and current, projected controller utilizes the self-tuning filter (STF). The efficacy of the controller is established in comparison to existing controllers like synchronous reference frame theory, p-q theory and STF-p-q under different grid voltage conditions. To enhance PQ, the suggested controller works efficiently for fundamental current component extraction without using PLL and low/high pass filter in case of grid distorted voltage condition.

A CVaR-based stochastic framework for storm-resilient grid, including bus charging stations

This paper proposes a two-stage stochastic framework for improving distribution system resilience against storms, in which the uncertainties associated with load demands, solar irradiance after a storm event, and maximum gust wind speed are considered. In the first stage, the available idle electric buses (EBs) are optimally allocated to the charging stations prior to storm arrival. In the second stage, critical loads are restored through island formation after the storm. In this framework, the solar-powered charging stations of EBs and the distributed generators (DGs) are part of the electric energy resources. The solar charging stations contribute to supplying the critical loads in two ways: 1- The electricity generation through rooftop-installed photovoltaic modules 2- By discharging the batteries of the pre-allocated EBs. The objective of this optimization model is to minimize the expected priority-weighted curtailed energy. At the same time, the risk imposed by the uncertain parameters is taken into account through the conditional value-at-risk (CVaR) index. The proposed framework is expressed in terms of a stochastic mixed-integer linear programming (MILP) optimization problem. This framework is implemented on the 33-node distribution test system, and its superior resilient performance is demonstrated through four case studies.

Optimizing Distributed Generation Placement and Sizing in Distribution Systems: A Multi-Objective Analysis of Power Losses, Reliability, and Operational Constraints

The integration of distributed generation (DG) into distribution networks introduces uncertainties that can substantially affect network reliability. It is crucial to implement appropriate measures to maintain reliability parameters within acceptable limits and ensure a stable power supply for consumers. This paper aims to optimize the location, size, and number of DG units to minimize active power losses and improve distribution System (DS) reliability while considering system operational constraints. To achieve this objective, multiple tests are conducted, and the particle swarm optimization (PSO) technique is implemented. The simulation studies are performed using the ETAP software 19.0.1 version, while the PSO algorithm is implemented in MATLAB R2018a. ETAP enables a comprehensive evaluation of the DG system’s performance, providing valuable insights into its effectiveness in reducing power losses and enhancing system reliability. The PSO algorithm in MATLAB ensures accurate optimization, facilitating the identification of the optimal DG unit location and size. This study uses a modified IEEE-13 bus unbalanced radial DS as the test system, assessing the effects of photovoltaic (PV) and wind DG units under various scenarios and penetration levels. The results demonstrate that the optimal DG unit location and size of either a single PV or wind DG unit significantly reduce power losses, improve DS reliability, and enable effective load sharing with the substation. Moreover, this study analyzes the impact of DG unit uncertainty on system performance. The findings underscore the potential of optimized DG integration to enhance DS efficiency and reliability in the presence of renewable energy sources.

Optimal dynamic multi-microgrid structuring for improving distribution system resiliency considering time-varying voltage-dependent load models

This paper presents an innovative approach for enhancing distribution network resiliency against severe weather-related faults. Given the importance of loads in the resiliency concept, an exact and realistic load model is developed along with the consumption pattern and voltage-dependency of each load type. By defining two technical and economic objective functions, a unique method is used to combine and optimize the objective functions through Imperialist Competitive Algorithm (ICA). Two main strategies, namely the dynamic arrangement of microgrid regions via hourly network reconfiguration as well as grid voltage control through Automatic Voltage Regulation (AVR) of distributed generation facilities, are utilized for increased load restoration. The effect of each of these strategies on grid resiliency is evaluated along with the overall approach efficiency based on simulations on a sample 33-bus network comprising renewable and non-renewable resources and commercial, residential and industrial loads. Using this approach, the strategy to encounter each particular type of failure is obtained.

Reliability Enhancement on Distribution System Using Modified Multi-Objective Particle Swarm Optimization Technique

This research paper introduces an innovative approach for optimizing the placement of fault indicators (FIs) within electric distribution systems. The primary focus is on addressing how the presence of existing protection and control devices affects the time it takes to restore service to customers in the event of a fault. Unlike conventional FI placement methods, this extended approach considers the uncertainties associated with automatic switching and introduces a novel technical objective function known as the Customers' Average Restoration Time Index (CARTI). To tackle the complex task of multi-objective optimization, a solution approach has been devised, with the goal of simultaneously minimizing both economic and technical objectives. This methodology harnesses a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and incorporates a Modified Particle Swarm Optimization technique to select the most suitable solution from the set of Pareto optimal solutions generated. The proposed method's effectiveness is demonstrated through its application in two scenarios: firstly, in the context of bus number four within the Roy Billinton test system (RBTS4), and secondly, in a practical real-life distribution network. The study assesses technical objectives, specifically the System Average Interruption Duration Index (SAIDI) and CARTI, providing insights into the method's practical utility and its capacity to enhance FI placement for improved distribution system performance.

Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach

Distribution network operators and planners face a significant challenge in optimizing planning and scheduling strategies to enhance distribution network efficiency. Using improved particle swarm optimization (IPSO), this paper presents an effective method for improving distribution system performance by concurrently deploying remote-controlled sectionalized switches, distributed generation (DG), and optimal network reconfiguration. The proposed optimization problem’s main objectives are to reduce switch costs, maximize reliability, reduce power losses, and enhance voltage profiles. An analytical reliability evaluation is proposed for DG-enhanced reconfigurable distribution systems, considering both switching-only and repairs and switching interruptions. The problem is formulated in the form of a mixed integer nonlinear programming problem, which is known as an NP-hard problem. To solve the problem effectively while improving conventional particle swarm optimization (PSO) exploration and exploitation capabilities, a novel chaotic inertia weight and crossover operation mechanism is developed here. It is demonstrated that IPSO can be applied to both single- and multi-objective optimization problems, where distribution systems’ optimization strategies are considered sequentially and simultaneously. Furthermore, IPSO’s effectiveness is validated and evaluated against well-known state-of-the-art metaheuristic techniques for optimizing IEEE 69-node distribution systems.

Preventive allocation and post-disaster cooperative dispatch of emergency mobile resources for improved distribution system resilience

Repair crews (RCs) and mobile power sources (MPSs) are emergency mobile resources (EMRs) for distribution system repair and restoration. In this paper, an outage management strategy (OMS) is proposed to improve the distribution system resilience against extreme weather events in both preventive and post-disaster stages. In the preventive stage, considering the uncertainty of fault location, the resource allocation problem is formulated as a two-stage stochastic programming to minimize the expected load shedding cost shortly after the disaster. The post-disaster stage is a cooperative dispatch of EMRs, accompanied by dynamic microgrid formation. With the progress of line repair, the MPSs are dispatched to different locations to support microgrids. The optimal repair sequence of faulted lines and scheduling of MPSs are determined in a multi-timeslot dispatch model. The proposed algorithm is validated by IEEE 123-bus test system and a 118-bus real-world system. The simulation results demonstrate that OMS can efficiently restore critical loads via an optimal utilization of mobile resources.

An MILP Model for Switch, DG, and Tie Line Placement to Improve Distribution Grid Reliability

Remote controlled switches (RCSs) have the ability to isolate the faulted area from other parts of the distribution system. On the other hand, the dispatchable distributed generators (DDGs) and tie lines can supply the interrupted loads after fault occurrence trough microgrids and reduce the outage time. In this regard, this article proposes a planning model for simultaneous placement of RCSs, DDGs, and tie lines to improve distribution system reliability. The presence of renewable distributed generations (RDGs) and energy storage systems, which have an increasing penetration in today's distribution networks are also considered. Moreover, two different practical load shedding methods are considered to balance the total generation and consumption in microgrids. The predetermined expansion plan of RDGs is also considered. To simplify the implementation of the proposed optimization model and ensure the global optimal solution, the planning model is formulated as a mixed integer linear programming model which can be solved by various commercial solvers. Finally, the effectiveness of the proposed model is illustrated by implementation on bus 4 of Roy Billinton test system through various case studies and sensitivity analyses. The results demonstrate the significant impact of the proposed planning model on improving the distribution system reliability.

A New Restoration Strategy in Microgrids after a Blackout with Priority in Critical Loads

The danger of a total blackout in a wide area or, even worse, in a country is always present. The restoration methods after a blackout mainly focus on the strategy that the dispatchers in the control centers of the Transmission System Operator will follow than the abilities that the distribution’s microgrids have. This study suggests a restoration technique to improve distribution system resilience following a blackout, using distributed generation for the restoration of important loads. The goal of the restoration problem is to maximize the number of critical loads that are restored following the catastrophic incident. Under the restrictions of the DGs and the network, the DGs with good black start capability are restored first. Load weight and node importance degree are suggested during the recovery path selection procedure, while taking node topological importance and load importance into account. A mixed-integer linear program (MILP) is used to simulate the issue, and the modified IEEE 39-bus test system is used to verify the efficacy of the suggested restoration approach.

Optimal distributed generation allocation in distribution system for power loss minimization and voltage stability improvement

Voltage instability and power loss are significant problems in distribution Systems (DS). However, these problems are usually mitigated by the optimal integration of distributed generation (DG) units in the DN. In this regard, the optimal location and size of the DGs are crucial. Otherwise, System performance will deteriorate. This study is conducted to place the DGs in the radial DS. Mayfly Algorithm (MA) is used to determine the optimal placement and size of the DGs to minimize power loss, increase voltage stability in radial DS. The simulation results showed a reduction in the percentage of power loss is 69.14% for three PV-type DG unit integration. The corresponding percentage of power loss reduction is 98.09 % for three WT-type DG units by installing DG units to the test System. Similarly, the minimum bus voltage stability improves to 0.959 per unit for three PV type DG unit integration. The VSI after DG allocation increases to 0.989 per unit for three WT type DG units by optimal installing DG units. Comparative studies have been conducted, and the results have shown the effectiveness of the proposed method in reducing the power loss and improving the voltage stability of the DS. The proposed algorithm is evaluated in the IEEE-69 bus radial DS using MATLAB.

Improving distribution system resilience by undergrounding lines and deploying mobile generators

To improve the resilience of electric distribution systems, this paper proposes a stochastic multi-period mixed-integer linear programming model that determines where to underground new distribution lines and how to coordinate mobile generators in order to serve critical loads during extreme events. The proposed model represents the service restoration process using the linearized DistFlow approximation of the AC power flow equations as well as binary variables for the undergrounding decisions of the lines, the configurations of switches, and the locations of mobile generators during each time period. The model also enforces a radial configuration of the distribution network and considers the transportation times needed to deploy the mobile generators. It is shown that long-term line undergrounding decisions which are cognizant of short-term mobile generator deployments yield superior results relative to undergrounding decisions made without considering mobile generators. Using an extended version of the IEEE 123-bus test system, numerical simulations show that combining the ability to underground distribution lines with the deployment of mobile generators can significantly improve the resilience of the power supply to critical loads. • Resilience can be improved via mobile generators and underground lines. • Stochastic optimization for siting underground lines and operating mobile generators. • Show benefits from jointly considering undergrounding and mobile generator decisions.

Implementation of an ADALINE-Based Adaptive Control Strategy for an LCLC-PV-DSTATCOM in Distribution System for Power Quality Improvement

This study investigated the problem of controlling a three-phase three-wire photovoltaic (PV)-type distribution static compensator (DSTATCOM). In order to model, simulate, and control the system, the MATLAB/SIMULINK tool was used. Different controllers were applied to create switching pulses for the IGBT-based voltage source converter (VSC) for the mitigation of various power quality issues in the PV-DSTATCOM. Traditional control algorithms guarantee faultless execution or outcomes only for a restricted range of operating situations due to their present design. Alternative regulators depend on more resilient neural network and fuzzy logic algorithms that may be programmed to operate in a variety of settings. In this study, an adaptive linear neural network (ADALINE) was proposed to solve the control problem more efficiently than the existing methods. The ADALINE method was simulated and the results were compared with the results of the synchronous reference frame theory (SRFT), improved linear sinusoidal tracer (ILST), and backpropagation (BP) algorithms. The simulation results showed that the proposed ADALINE method outperformed the compared algorithms. In addition, the total harmonic distortions (THDs) of the source current were estimated under ideal grid voltage conditions based on IEEE-929 and IEEE-519 guidelines.

Behavior-Aware Aggregation of Distributed Energy Resources for Risk-Aware Operational Scheduling of Distribution Systems

Recently there has been a considerable increase in the penetration level of distributed energy resources (DERs) due to various factors, such as the increasing affordability of these resources, the global movement towards sustainable energy, and the energy democracy movement. However, the uncertainty and variability of DERs introduce new challenges for power system operations. Advanced techniques that account for the characteristics of DERs, i.e., their intermittency and human-in-the-loop factors, are essential to improving distribution system operations. This paper proposes a behavior-aware approach to analyze and aggregate prosumers’ participation in demand response (DR) programs. A convexified AC optimal power flow (ACOPF) via a second-order cone programming (SOCP) technique is used for system scheduling with DERs. A chance-constrained framework for the system operation is constructed as an iterative two-stage algorithm that can integrate loads, DERs’ uncertainty, and SOCP-based ACOPF into one framework to manage the violation probability of the distribution system’s security limits. The benefits of the analyzed prosumers’ behaviors are shown in this paper by comparing the optimal system scheduling with socially aware and non-socially aware approaches. The case study illustrates that the socially aware approach within the chance-constrained framework can utilize up to 43% more PV generation and improve the reliability and operation of distribution systems.

A learning-based proactive scheme for improving distribution systems resilience against windstorms

This paper presents a proactive operation scheme for improving distribution system resiliency against natural hazards, specifically windstorms. In this context, important attributes associated with the windstorm consisting the distance from the windstorm route, the wind speed, the distance from tall trees and buildings, and cable type are used in a deep neural network (DNN) engine to identify the vulnerable branches and predict their failure during the windstorm. The DNN predictive model is integrated in the proposed scheme. Afterwards, a power flow-based optimization engine is employed to proactively enhance the grid resiliency. Grid resiliency is measured by the inevitable action of load shedding. For minimum load shedding, the optimization engine reconfigures the network topology, optimizes the droop parameter settings, and allocates mobile energy storage systems (ESSs) before the arrival of the windstorm. This optimization engine is integrated in the proposed scheme. To validate its performance, the proposed proactive scheme is tested on a 33-bus test system with a mix of diesel units (DUs), wind turbines (WTs), and photovoltaic units (PVs). The simulation results demonstrate that without the proposed learning mechanism, the load shedding can reach up to 36% for the system under study, while the learning-based scheme can reduce the load shedding to 13%. The proposed learning-based proactive operation scheme would substantially improve the distribution system resiliency during windstorms.

Potential of Mobile Energy Hubs for Enhancing Resilience of Electricity Distribution Systems

• An operational framework presented to improve distribution system resilience using Mobile Energy Hubs. • This paper provides an effective management to enhance the distribution system resilience. • MEHs installed on trucks are expected to deliver the required power for supplying the islanded critical loads. • The optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time. In this paper, an operational framework is presented to improve electrical distribution network resilience based on the Mobile Energy Hubs (MEHs) concept. In fact, critical loads should be immediately islanded in a post-flood state and then recovered. Accordingly, this paper focuses on providing an effective management solution to enhance the functioning of electricity distribution systems with the objective of maximizing restoration of critical loads and minimizing their restoration time span based on MEH. To this end, MEHs are installed on trucks to deliver the required power for supplying the islanded critical loads in zones affected by a flood. Besides, in order to demonstrate a practical resilient structure, possible damage inflicted on other critical infrastructures is considered. Moreover, obstacles resulting from the destruction of the transportation infrastructure caused by a flood are overcome by using the shortest path algorithm (SPA). In this case, the optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time aiming to improve the network resilience indicators. Finally, the proposed framework is studied in a standard test electricity distribution network. Simulations are carried out to evaluate the network resilience indicators of the proposed framework in obtaining a resilient distribution network during natural disasters.

Reliability Assessment Based on Optimal DGs Planning in the Distribution Systems

Due to increased load demands, distribution systems suffer from high power losses, low voltage levels, high current, and low reliability. To solve these problems, integrate distributed generator units (DG) into the distribution system. DG units are among the most popular methods of improving distribution system reliability, power losses, and bus voltage improvement through the placement and selection of distributed generator units in an optimal location and size. This work proposed Enhanced Particle Swarm Optimization (EPSO) technology to find the optimum location and size of DG units to reduce power losses, improve bus voltage level, and employed the Transient Electricity Analyzer (ETAP) to evaluate the reliability of the distribution system network. ETAP is a programming tool for modeling, analysis, design, optimization, operation, and control of electrical power systems. These findings may be useful in conducting reliability assessments and correctly utilizing dispersed generation sources for future power system growth by power utilities and power producer companies. The proposed method was employed on the Iraqi distribution system (AL-Abasia distribution network (F10 feeder)). After adding three DG units to the distribution system, theer adding three DG units to the distribution system, the obtained simulation results showed a significant reduction in power losses, voltage levels, and reliability enhancement.