May 19, 2023
Advanced Distribution Management Systems (ADMS) are an important component of any OT 2.0 distribution grid, providing utilities with a range of tools and capabilities to monitor and control the distribution of power on the grid. ADMS systems combine real-time data from sensors, switches, and other grid devices with analytics and machine learning to enable utilities to optimize grid performance, detect and respond to outages, and manage distributed energy resources.
One of the key applications of ADMS is Fault Location, Isolation, and Service Restoration (FLISR). FLISR is a technique used to automatically detect faults on the grid, isolate the affected areas, and restore service to customers as quickly as possible. ADMS systems can use real-time data to quickly detect the location of a fault, and automatically isolate the affected area by opening switches and rerouting power. Once the affected area has been isolated, ADMS systems can quickly restore service to customers by rerouting power from adjacent circuits or dispatching repair crews to the affected area. By automating these processes, FLISR can help reduce the duration and frequency of outages, improving the reliability and resiliency of the grid.
Another important application of ADMS is Volt-VAR Optimization (VVO). VVO is a technique used to optimize the voltage levels on the grid, ensuring that power is delivered to customers at the appropriate levels. ADMS systems can use real-time data from sensors and other devices to monitor voltage levels and can automatically adjust the voltage by controlling voltage regulating devices and VAR control devices. By optimizing voltage levels in this way, VVO can help reduce energy consumption, lower costs, and improve the reliability of the grid.
Closely related to VVO is Conservation Voltage Reduction (CVR). CVR is a technique used to reduce energy consumption by optimizing voltage levels on the grid. ADMS systems can use real-time data from the grid to identify areas where voltage levels can be lowered without affecting quality of service. By reducing voltage levels in this way, CVR helps reduce energy consumption and lower costs, without compromising the reliability or quality of service provided to customers.
Finally, ADMS can be used to automatically adjust energy usage in response to changes in grid conditions or energy prices, helping to manage demand and reduce overall energy consumption.
Distributed Energy Resources (DERs) are becoming increasingly popular among residential and commercial customers. These DERs can be connected to the grid and used to generate, store, and consume energy locally, which can help reduce energy costs and carbon emissions. However, integrating DERs into the grid can be challenging, as they can be intermittent and unpredictable, and can create complex bi-directional flows of power on the grid.
To address these challenges, utilities are deploying Distributed Energy Resource Management Systems (DERMS). DERMS are software platforms that enable utilities to manage and optimize the integration of DERs into the grid. DERMS can be used to monitor and control DERs in real-time, and can be integrated with other grid management systems like ADMS to enable utilities to manage the distribution of power on the grid at a more granular level.
DERMS can perform a wide range of functions to optimize the integration of DERs into the grid. For example, DERMS can be used to forecast the output of renewable energy sources, enabling utilities to better predict and manage the generation and distribution of power. DERMS can also be used to manage the charging and discharging of energy storage systems, allowing utilities to optimize the use of these resources. By optimizing the use of DERs in this way, utilities can reduce energy costs, improve gridresiliency, and reduce carbon emissions, while also meeting the evolving needs of customers and the broader energy system.
An Electric Vehicle Management System (EVMS) is a software platform that enables utilities, charging station operators, and fleet managers to manage the charging and discharging of electric vehicles (EVs). EVMS can be used to monitor the status of EV batteries, manage the charging and discharging schedule of EVs, and optimize the use of renewable energy sources to charge EVs.
The need for EVMS in the OT 2.0 distribution grid is driven by the increasing adoption of EVs and the integration of these vehicles into the grid (Vehicle to Grid (V2G)). EVs can create significant new demands on the grid, particularly during periods of peak demand. EVMS can help utilities manage the distribution of power by controlling the charging and discharging of EVs, enabling them to optimize the use of renewable energy sources and reduce the strain on the grid during times of peak demand.
EVMS can also help utilities manage the costs associated with EV charging. By optimizing charge/discharge time of EVs, utilities can reduce the need for expensive infrastructure investments like new transmission and distribution lines, etc. EVMS can also be used to incentivize customers to charge their EVs during off-peak hours, reducing the overall demand for energy during peak periods and lowering energy costs.
A grid connectivity model is a computer-based representation of the physical infrastructure of the electric grid, including transmission and distribution lines, substations, transformers, and other devices. Grid connectivity models enable utilities to simulate and analyze the behavior of the grid under different conditions, and to plan and optimize the distributionof power.
An up-to-date grid connectivity model is necessary to ensure that utilities have an accurate and complete representation of the grid,enabling them to make informed decisions about grid management, investment, and optimization. In addition, the grid connectivity model is necessary to support the integration of Distributed Energy Resources (DERs) and other new technologies into the grid. As discussed previously, DERs can create complex bi-directional flows of power on the grid and can create new stresses on existing infrastructure. An up-to-date grid connectivity model can help utilities identify and mitigate these stresses, enabling them to optimize the integration of DERs and improve the efficiency and reliability of the grid.
A complete grid connectivity model is critical for OT 2.0, enabling utilities to simulate and analyze the behavior of the grid under different conditions, optimize the distribution of power, and improve the efficiency and reliability of the grid. By maintaining an accurate and complete representation of the grid, utilities can make informed decisions about grid management, investment, and optimization, and meet the evolving needs of customers and the broader energy system.
Geographic Information Systems (GIS) are an important too lfor grid modernization and OT 2.0 systems, enabling utilities to map, visualize, and analyze data on the physical infrastructure of the grid, including transmission and distribution lines, substations, transformers, and other grid devices. By integrating GIS with grid management systems like ADMS and DERMS, utilities can better manage and optimize the distribution of power, reduce the duration and frequency of outages, and improve the reliability and resiliency of the electric grid.
GIS can also be used to support emergency management and disaster response. By determining locationally within the grid where there maybe a major issue, utilities can quickly identify and respond to outages and other grid disturbances, reducing the duration and impact of these events. GIS helps utilities model and predict the impact of severe weather events or other natural disasters, enabling them to pre-position resources and respond more effectively to these events.
Asset management is an important aspect of OT 2.0, enabling the performance optimization of their physical infrastructure. Asset management involves the identification, monitoring, maintenance, and replacement of physical assets, including transmission and distribution lines, substations, transformers, and other devices.
The need for asset management is driven by several factors. First, aging infrastructure is a significant challenge for utilities. Many transmission and distribution lines, substations, transformers and other devices were built several decades ago and are reaching the end of their useful life, and often used beyond their rated life. Asset management can help utilities identify aging infrastructure and prioritize investments in maintenance, repair, and replacement. Additionally, asset management can help utilities monitor the state and optimize the performance of DERs, ensuring that they are integrated into the grid in a safe and effective manner.
The increasing demand for renewable energy and other clean technologies is creating new opportunities and challenges for asset management. Renewable energy technologies often require new infrastructure investments like new transmission and/or distribution lines. Asset management helps utilities prioritize these investments and ensure that they are cost-effective and aligned with the overall goals of the OT 2.0 distribution grid.
Device management is an important aspect of OT 2.0 distribution grids, enabling utilities to remotely monitor and manage the large number of devices that are connected to the grid, including smart meters, distribution automation equipment, distributed energy resources (DERs) and new IIoT sensors that may be installed. Device management systems are software platforms that enable utilities to monitor the status and performance of these devices, and to remotely update or configure them as needed.
By monitoring and managing devices in real-time, utilities can quickly detect and respond to issues, reducing the duration and frequency of outages, and improving the reliability and resiliency of the grid. Device management can also help utilities optimize the configuration of connected DERs. Device management is also needed to address the growing cybersecurity risks associated with modern grid systems. As more devices become connected to thegrid, the risk of cyberattacks and other security threats increases. Device management systems can help utilities identify and mitigate these risks byproviding security updates, and enabling utilities to remotely manage andupdate the software and firmware on these devices.
At present, device management visibility within (OT) systems is limited, yet it is a fundamental component of OT2.0. This technology allows utility companies to track and control a vastnumber of devices connected to the grid, surpassing the capabilities of OT1.0/1.5 systems. This monitoring and management serve to enhance the grid's efficiency, reliability, and security.
By integrating device management on a larger scale within a thoroughly instrumented OT 2.0 system, the marginal cost of the device's continual usage and operation is minimized. When we spread out physical activity costs such as installation and inspection over the lifespan of the device, which remains installed and operational with minimal onsite visits, the economics begin to resemble that of software. This is especially true as the grid is equipped with an increasing number of control devices and sensors. However, failing to implement device management could result in rising material expenses as the count of devices and sensors escalates.
Short and long-term forecasting are critical components of modern grid management and OT 2.0 operations, particularly in systems with high penetration of Distributed Energy Resources (DERs) and bi-directional power flow. Short-term forecasting refers to the prediction of energy demand, supply, and storage needs over the next few hours or days, while long-term forecasting allows for predictions over the next few weeks or months.
Short-term forecasting is particularly important in systems with high levels of DERs, as these resources can be intermittent and difficult to predict. For example, solar panels generate more energy during day lighthours, while wind turbines generate more energy during periods of high wind. Short-term forecasting helps utilities predict the output of these resources in real-time, enabling them to manage the distribution of power more effectively. Short-term forecasting can also be used to manage the charging and discharging of energy storage systems, enabling utilities to optimize the use of these resources and reduce energy costs. As can be easily seen, integrationof forecasting with ADMS, DERMS, EVMS, etc. is a critical component of managingan OT 2.0 distribution grid.
Long-term forecasting is also important in systems with high levels of DERs, as these resources can be subject to changes in weather patterns, technological advances, and government policies. Long-term forecasting can help utilities predict the future demand for energy and the availability of renewable resources like solar and wind power, as well as Electric Vehicle penetration, enabling them to plan for the construction of new infrastructure,the deployment of new technologies, and the development of new policies and regulations.
