Advanced Distribution Management System Research Articles

Smart Grid Technologies: Advancements and Applications in Nigeria

This study explores the impact of smart grid technologies on the modernization of power grids in response to evolving energy demands and the integration of renewable energy sources in Nigeria. It aims to empirically assess advancements in smart grid technologies, focusing on four key objectives: eval_uating the impact of smart meters on energy consumption and peak demand reduction, analyzing the effectiveness of Advanced Distribution Management Systems (ADMS) in enhancing grid reliability and efficiency, assessing the role of demand response programs in balancing supply and demand, and examining the integration and management of Distributed Energy Resources (DERs) within smart grids. The research employs a quantitative methodology, collecting and analyzing data from utility companies that have implemented smart grid technologies. Key findings reveal that smart meters significantly reduce energy consumption and peak demand by providing real-time monitoring, while ADMS improves grid reliability and operational efficiency through enhanced fault detection and automated control. Demand response programs effectively balance energy supply and demand, reducing peak loads and energy costs. The integration of DERs increases renewable energy utilization and grid stability but also presents challenges related to variability in power output and management complexity. The study concludes that smart grid technologies play a crucial role in achieving a more resilient and efficient power grid, though addressing challenges related to DER integration and system management is essential for maximizing their benefits.

Power Hardware-in-the-Loop Smart Inverter Testing with Distributed Energy Resource Management Systems

The increasing integration of grid-connected photovoltaic (PV) inverters and inverter-based resource (IBR) systems into the power grid emphasizes the critical need for standardized procedures to ensure their reliability and effective grid support functions. This research is driven by the gap in standardized testing methodologies for smart inverters, which are pivotal for the stability and quality of power in distributed energy systems. We used a Power Hardware-in-the-Loop (PHIL) laboratory setup to conduct a comprehensive analysis of smart inverters within a simulated real-world grid environment. Our approach integrates a Distributed Energy Resource Management System (DERMS) with PHIL testing to evaluate the smart inverter’s performance across various operational modes. The detailed test protocols mimic real-world grid conditions, enabling the examination of the inverter’s dynamic response to grid disturbances, control strategies, and communication protocols. The primary aim of this study is to rigorously test and validate the primary functions of smart inverters, focusing on their impact on overall grid stability and power quality management. This includes advanced features like Volt–VAR, Volt–Watt, dynamic power factor control, and the seamless integration of smart inverters into DERMSs and Advanced Distribution Management Systems (ADMSs). Furthermore, we aim to bridge the current gap in standardized testing procedures, contributing to the establishment of robust standards and operating protocols for smart inverter integration into the grid.

Integrating degradation forecasting into distribution grids’ advanced distribution management systems

Advanced distribution management systems (ADMSs) are considered tools for deploying control and management strategies to address challenges in future distribution grids populated with distributed energy resources (DERs). This work presents a framework to integrate a degradation forecasting (DF) layer into ADMSs. Based on observations of considered DERs’ degradation behaviors, a load-dependent degradation model is constructed and integrated into a dynamical Markov chain-based degradation prediction model. Multiple sources of data can be used to train the Markov prediction model, and then Evidence Theory (ET) is used to fuse predicted results from the prediction model to enhance the reliability of the prediction model. Then, the predicted results are used to adjust energy management (EM), which is a component of the ADMS concept, to abate the degradation of components with higher degradation costs. The proposed strategy is verified on the IEEE 33 bus system by numerical simulations. In addition, controller-hardware-in-the-loop (CHIL) experimentation for the proposed scheme is implemented. Both the numerical simulations and CHIL experimentation show operation cost savings for the studied system.

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. In this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

Integrating Advanced Applications in Industrial ADMS for Estimating PV Resources and Topology

In contrast to traditional utility monitoring and operational tools, advanced distribution management systems (ADMS) provide the advanced operational features to monitor, secure and operate the distribution system in an integrated man- ner. Large-scale adoption of ADMS at utilities is in the early stage since advanced applications within ADMS environments are still evolving. The topology estimation module is one of the important and challenging ADMS application with enhanced automation. For accurate topology estimation, the topology estimator should capture both uncertainties due to load and PV injection in node measurement data. Load/PV estimation module can provide individual/disaggregated load and PV estimates and supports accurate network estimates. This paper provides a proof-of- the concept for the integration of advanced applications (Load/PV estimation and topology estimation) within an industrial ADMS environment using utility feeder data.

Evaluation of Data-Enhanced Hierarchical Control for Distribution Feeders With High PV Penetration

Reliable and resilient grid operations with high penetration levels of distributed energy resources (DERs) can be achieved with improved situational awareness and seamless integration of DERs with utility enterprise controls. This paper presents the details of the development of a data-enhanced hierarchical control (DEHC) architecture and the results of its evaluation. The DEHC is a hybrid control framework that enables the efficient, reliable, and secure operation of distribution grids with extremely high penetrations of solar photovoltaic (PV) generation by seamlessly integrating centralized utility controls, distributed controls for DERs, and autonomous grid-edge controls. In the DEHC architecture, the advanced distribution management system (ADMS) controls the legacy devices (such as load tap changers and capacitor banks), the PV smart inverters are dispatched by real-time optimal power flow, and the grid-edge devices regulate local voltages in coordination with each other. The DEHC is demonstrated using a commercial ADMS platform, real utility distribution feeder models, and grid-edge devices. The performance of the DEHC architecture is evaluated using simulations and hardware-in-the-loop experiments with voltage regulation as the control objective. The results show that the DEHC enables high penetration levels of PV in distribution feeders by effectively managing system voltages through the synergistic operation of ADMS, distributed PV smart inverter controls, and secondary-level grid-edge device control.

Admittance Matrix Validation for Power Distribution System Models Using a Networked Equipment Model Framework

The evolving nature of electric power distribution systems is motivating the development of advanced applications for utility planning, operation, management, and control. These model-based applications interact with multiple subsystems within software platforms referred to as advanced distribution management systems. Recently, the Common Information Model has been used to enable data model standardization across several entities. Model fidelity is the key to the development of leading-edge, scalable model-based algorithms and assessment of emerging technologies such as distributed energy resources for grid planning and operation. However, distribution system models are error-prone and model validation is challenging due to: (1) the need for considerable time and effort to maintain the network models, (2) update delays due to the evolving nature of the real-world systems that models are derived from, and (3) inevitable human errors in the process which are difficult to detect. In this paper, we extend a model validation application from our prior work referred to as the Model Validator to derive and validate a system admittance matrix using the Common Information Model. First, we introduce different modules for computing device-level primitive admittance matrices from Common Information Model parameters. These individual primitive admittance matrices are then used to validate corresponding entries in a full system admittance matrix. In addition, we validate the overall system admittance matrix by checking for gaps in coverage or extra elements that are not expected. The proposed approach checks the validity of each piece of network model equipment separately and identifies inconsistencies between Common Information Model parameters and the system admittance matrix at the device level. Furthermore, where devices can be specified using different combinations of Common Information Model parameters, each parameter combination is validated separately. This breakdown can help distribution engineers more efficiently apply root cause analysis for inconsistencies. The Model Validator application is implemented on top of GridAPPS-D, an open-source standards-based platform for advanced distribution management system application development. The effectiveness of the proposed application is demonstrated on six different test cases: four IEEE feeders, the Electric Power Research Institute J1 circuit, and a taxonomy feeder.

Voltage Regulation Performance Evaluation of Distributed Energy Resource Management via Advanced Hardware-in-the-Loop Simulation

This paper evaluates the performance of coordinated control across advanced distribution management systems (ADMS), distributed energy resources (DERs), and distributed energy resource management systems (DERMS) using an advanced hardware-in-the-loop (HIL) platform. This platform provides a realistic laboratory testing environment, including accurate dynamic modeling of a real-world distribution system from a utility partner, real controllers (ADMS and DERMS), physical power hardware (DERs), and standard communications protocols. One grid service—voltage regulation—is evaluated to show the performance of the coordinated grid automation system. The testing results demonstrate that the coordinated DERMS and ADMS system can effectively regulate system voltages within target operation limits using DERs. The realistic laboratory HIL testing results give utilities confidence in adopting the grid automation systems to manage DERs to achieve system-level control and operation objectives (e.g., voltage regulation). This helps utilities mitigate potential risks (e.g., instability) prior to field deployment.

An adaptive method for tuning process noise covariance matrix in EKF-based three-phase distribution system state estimation

This paper proposes a new adaptive method for online tuning of process noise covariance matrix in the Extended Kalman Filter based three-phase distribution system state estimator. Specifically, a new form of exponential function is proposed for tuning the process noise covariance matrix, adapting it to the level of state changes. A new indicator derived from normalized innovations of the available real-time and virtual measurements is developed for tracking the level of state changes. The proposed method relies on measurements of the existing distribution networks, and therefore can be easily implemented in the advanced distribution management system. The method efficiently adapts process noise to system state variations and can deal with both quasi steady-state and unexpected sudden state changes caused by changes in the system topology, generation or demand. Comparison results with other state-of-the-art adaptive methods on the modified IEEE 37-bus and IEEE 123-bus distribution systems show that the proposed method achieves better accuracy under quasi steady-state condition while being more robust to unexpected sudden state changes.

Restoration switching analysis in the integrated architecture for distribution network operation

Distribution network restoration is a recurrent issue of interest to power industry because electric distribution grids remain vulnerable against natural disasters and customers' expectations toward the continuity of electricity service are increasing in modern society. The increasing requirement for operation efficiency has encouraged the modernization of power distribution system by implementing new technologies of control and automation and data conditioning and processing. The integrated architecture of advanced distribution and outage management systems provides situational awareness of distribution networks. Fully automated restoration takes a central role with data from the distribution state estimation for achieving the feeder loading and switching plan able to reestablish the interrupted service. In active distribution networks, restoration switching analysis moves from a multi-objective model, where operator chooses the most suitable switching plan, to a single-objective optimization model for the fully automated restoration. The numerical analysis under a real-world substation is performed in three stages taking into account, firstly, the multi-objective solution and, then, the compromise solution under load forecast and, at last, the benefits of the automatic switching operation are verified.

Event-Driven Predictive Approach for Real-Time Volt/VAR Control With CVR in Solar PV Rich Active Distribution Network

The focus of this paper is on analyzing the impact of conservation voltage reduction in the presence of active devices such as solar photovoltaic (PV) and developing controls that leverage these distributed energy resources. An event-driven predictive approach for real-time volt/volt-ampere reactive (VAR) optimization, along with a local two-level adaptive volt/VAR droop-based control algorithm for advanced distribution management systems, is introduced. The methodology covers aggregated and autonomous controls under different timescale operations, including the impact and effect of unpredicted events such as cloud transients on PV power production. Besides, the control schemes include the uncertainties in PV power generation and load power demand. The proposed methodology is validated in a real-time framework using the real-time digital simulator platform through co-simulation with models based on Python and OpenDSS (Open Distribution System Simulator). The developed methodology is tested on the modified IEEE 123-feeder test system. The results reveal that the proposed methodology works well in the presence of high penetrations of PV power, produces significant energy savings, and mitigates over-/undervoltage problems.

A Multi-Site Networked Hardware-in-the-Loop Platform for Evaluation of Interoperability and Distributed Intelligence at Grid-Edge

Electric power systems have experienced large increases in the number of intelligent, connected and controllable devices being deployed, leading to a high degree of distributed intelligence at the grid-edge. These devices, both utility-owned and consumer-owned, include renewable generation, energy storage, remote switches, voltage regulators, and smart controllable loads. These new devices provide significant potential for increased operational flexibility that can be leveraged to achieve system reconfiguration, resiliency improvements, power quality improvements, and distribution system automation. However, two significant challenges must be addressed before these assets can be leveraged for operations: interoperability and system level validation prior to deployment. Because of the complexity of distributed control systems, and their interactions with legacy centralized controls, a purely simulations-based approach for pre-deployment validation is not sufficient. It requires hardware-in-the-loop (HIL) testing to emulate hardware devices and evaluate their performance. Additionally, securely integrating multiple test facilities at utility operators and vendors can enable the rapid scale-up of evaluation platforms and remove the need for multiple expensive standalone installations. Presented in this paper, is the development of a multi-site evaluation platform that employs Advanced Distribution Management Systems (ADMS), distributed control devices, real-time HIL assets, secure communication links, and protocol adapters. The testbed has been developed at three sites, with one site hosting the ADMS and the other two hosting HIL capabilities. This platform uses standards-based approaches and open-source tools, and hence can serve as a template for other researchers and institutions to implement their multi-site evaluation frameworks for pre-deployment testing.

Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions

Advancedd distribution management systems (ADMSs) integrate distributed generation (DG) units and battery energy storage systems (BESSs), and they are a promising solution to reduce the greenhouse gas emissions in the energy sector. This study addresses the sustainable ADMS (SADMS) design problem by determining the power flow optimal economic dispatches from traditional power plants, DG units, and BESSs to residential areas while maximizing the total profit. The designed SADMS provides a demand response programs with various energy pricing schemes that correspond to different customers and energy consumption loads. Three carbon emission policies (carbon tax, carbon cap, and carbon trade) are considered in the model. An advancedd two-phase (ATP) approach is proposed to solve the described problem. In the first phase, an artificial neuro-fuzzy system (ANFS) is developed based on self-learning and self-adjusting processes to determine the customer demand response loads and DG unit output energies in uncertain environments. A combined ANFS and optimization solver is proposed in the second phase to determine the optimal SADMS economic dispatch. The application of the proposed approach is examined using an empirical case study in Taiwan. The results demonstrate that the proposed ATP approach can determine the optimal economic dispatch with an extremely small deviation in demand response load of 0.92%. In addition, our approach increases the total profit (improving total profit by approximately 1.1%, 0.8%, and 1.9% for the three different carbon emission policy objective functions) and reduces the computational time (by 3.0–6.0 times) compared to those of the genetic algorithm. Finally, the proposed model illustrates that carbon trade is the best policy for improving the total SADMS profit while satisfying the given environmental constraints.

Effective Control and Management Scheme for Isolated and Grid Connected DC Microgrid

This article addresses a voltage control and energy management strategy of active distribution systems with a grid-connected dc microgrid as well as for an islanded dc microgrid with hybrid energy resources. In the islanded mode, a control and management strategy using a backup diesel generator (DG), a renewable energy source (RES), and an energy storage system plays a vital role in maintaining the microgrid bus voltages within the limits. However, operating backup diesel generator (DG) has its own challenges including startup delay, frequent switching, and uneven loading when operated along with a RES. Additionally, fuel efficiency and emission characteristics vary with loading since most of DGs are driven by constant-speed diesel engines. Hence, an exhaustive power management scheme (PMS) is proposed by utilizing the hybrid energy storage system. Real-time simulation and experimental validation of the proposed scheme are provided using a real-time digital simulator (RTDS) and a laboratory-scale prototype, respectively. Extreme scenarios including DG failure/scheduled maintenance, low power generation, and battery charge are analyzed in the islanded mode. Furthermore, a dc microgrid is connected to an IEEE active distribution system feeder to analyze control and management challenges for the grid connected mode with contribution from a microgrid and with no contribution from a microgrid. These scenarios resemble more realistic unbalanced utility grid conditions. A centralized optimization problem is formulated at an advanced distribution management system level to maintain all the node voltages within limits in the IEEE test system. RTDS is used to simulate dc microgrid connected with the IEEE test system and an optimization algorithm is implemented in MATLAB. Superior performance of the developed algorithms are demonstrated and validated for coordination between centralized optimization at ADMS and the microgrid energy management system.

Machine Learning-Enabled Distribution Network Phase Identification

The distribution network has typically been the least observable and most dynamic and locally controlled element in the power grid. Complete information about the network topology is continuously changing and is not always readily available when needed. This makes the phase identification and network rebalancing a hard, costly, and time-consuming task for electric utilities, however, it is of great importance to future grid planning and advanced distribution management system (ADMS) type operation. Phase identification traditionally is executed manually, although there are existing voltage measurement based methods that are not always reliable. This paper develops a machine learning based data mining method for an accurate and efficient phase identification of residential customers in a distribution network by leveraging power consumption data collected through the advanced metering infrastructure (AMI). The proposed method uses a high-pass filter to remove the redundant and irrelevant parts of the power consumption time series, then identifies the residential customers' phase connectivity by proposing a modified clustering algorithm. Simulation results show the effectiveness of the proposed method in phase identification in both small and large networks and under complete and incomplete data scenarios.

Optimal Energy Dispatch of Distributed PVs for the Next Generation of Distribution Management Systems

Advanced Distribution Management Systems (ADMS) are being widely adopted by electric utilities for managing and optimizing the operations of their distribution systems. Distributed photovoltaic (DPV) systems with smart inverters can be controlled to adjust active power and reactive power outputs, and they are envisioned to become a part of (centrally or distributed) controllable assets managed by the ADMS for optimizing grid operations. This paper proposes an optimal energy dispatch strategy controlling DPV systems for regulating distribution voltages and achieving conservation voltage reduction. A convex optimization model is proposed with the use of linearized power flow, and the gradient projection algorithm is used to solve the optimal active power and reactive power outputs of smart inverters. The proposed optimal energy dispatch is implemented using an open-source ADMS platform, and simulation results have demonstrated the effectiveness of the proposed approach on improving distribution grid operations.

Advanced Applications in an Advanced Distribution Management System: Essentials for Implementation and Integration

For more than a decade, the distribution utility industry has recognized and increasingly accepted the concept of an advanced distribution management system (ADMS). In a nutshell, an ADMS combines the functional domains of distribution supervisory control and data acquisition (SCADA) system, the outage management system, switching management, and distribution network analysis and optimization into a single seamless system. The distribution network analysis and optimization functions are referred to as advanced applications. However, surprisingly few distribution utilities have successfully implemented an ADMS with advanced applications. Even fewer have system operators making full use of such applications. Numerous small-scale pilot projects have been executed and completed where the value of such advanced applications as fault location isolation and service restoration (FLISR) and integrated volt/volt-ampere reactive optimization (IVVO) have been positively confirmed. Successful systemwide operational deployments of advanced applications, however, are much less common. There are multiple reasons for this, including insufficient numerical robustness of advanced applications to provide solutions under a wide variety of system conditions. Other reasons include model-magnitude, complexity, and data-quality issues.

Distribution Operations: The Evolution of Distributed Energy Resources [Guest Editorial

Utilities around the world are experiencing change with the increasing digitalization of their systems and those of their customers. Efforts to decarbonize the electric power system have led to growth in renewable energy resources, particularly distributed energy resources (DERs), such as generation, storage, and flexible load, both in front of and behind the meter. As this change occurs in any given utility service area, new tools, such as advanced distribution management systems (ADMSs), are needed to manage utility operations, planning, and business issues, some of which are discussed here.

Paving the Way for Advanced Distribution Management Systems Applications: Making the Most of Models and Data

The development and deployment of advanced distribution management systems (ADMSs) is imperative to address the increasingly complex operational challenges faced by aging electric power distribution systems, while ensuring reliable and resilient operations. Along with integrating the processes across multiple systems that are typically isolated, an ADMS allows for the development of applications that can readily access information from various subsystems, apply advanced analytics, and effectively control and coordinate traditionally segmented systems. This article presents an overview of ADMS applications for emerging electric power distribution systems and identifies future research directions.

Advanced FLISR With Intentional Islanding Operations in an ADMS Environment Using GridAPPS-D

An advanced distribution management system (ADMS) is a software platform that supports grid management and decision support applications to address the growing operational challenges faced by power distribution systems to ensure reliable, resilient, and economic operations. In this paper, a fault location, isolation, and restoration (FLISR) application is developed and integrated with an open-source standards-based platform for ADMS application development viz. GridAPPS-D, developed by Pacific Northwest National Laboratory (PNNL). Also, a new distribution system restoration (DSR) algorithm, an integral part of the FLISR application, is proposed that utilizes backup feeders and distributed generators (along with intentional islanding features) to restore critical loads. The proposed application is developed in python and is tested on a modified IEEE 8500-node test system using the GridAPPS-D platform. The successful deployment of the FLISR application on the GridAPPS-D platform provides a proof-of-concept for the adoption of new and advanced ADMS applications to support future power distribution systems.