As energy demand continues to grow, utilities are finding efficient and reliable ways to distribute power while maintaining or even reducing current generation levels. To help achieve these objectives, many utilities are incorporating automation processes into their operations and business workflows. Esri provides three network implementation patterns that support this progressive utility automation, regardless of where a utility is at in its digital journey.
To incorporate more automation, some utilities are adopting demand response programs to manage peak loads; implementing strategies, such as critical peak pricing and time-of-use billing; and investing in modern technologies, including advanced metering infrastructure (AMI), Digital Grid devices, and Advanced Distribution Management System (ADMS) applications. While the data from these technologies can help optimize grid operations, the volume of data generated can be challenging for utilities to manage and make sense of, significantly diminishing the value of these technologies.
Geospatial technology is paving the way for utilities to maximize their digital investments in advanced meters and Digital Grid devices, empowering them to operate more efficiently, reliably, and profitably. The once-humble GIS has evolved, and is still evolving, within the utility enterprise as a system for automating map production into a core-enabling enterprise technology. Today, GIS serves as the backbone for various utility operations, including design engineering, work and outage management, mobile workforce management, ADMS advanced distribution applications, and automated vehicle location (AVL). When properly integrated, it compiles the data from various utility business systems, such as graphic work design (GWD) and digital as-builting systems, providing the means to transform these large data sets into actionable information for the utility.
GIS plays an increasingly critical role in helping utilities successfully implement new automation technologies and advance through each phase of adoption. This article examines how Esri’s deployment patterns correspond with the four key phases of system automation identified by UDC for the utility’s transition to becoming a Digital Utility®:
- Facilities Management and Workforce Automation
- Integration of Energy Delivery Systems for Workforce Optimization
- Integration of Energy Delivery Data Repositories for Asset Optimization
- Integration of Near Real-Time Field Automation
Content Overview
Phase 1: Facilities Management and Workforce Automation
In this initial phase of the utility automation lifecycle, a set of manual processes are automated around a specific business area, or often, one department within the organization. This stage focuses on improving facilities’ health and wealth, workforce automation, task automation, and departmental-specific systems by using standalone applications that operate independently of one another. For instance, this might involve implementing a CAD tool for engineering or a Work Management System (WMS). Other standalone applications utilities may implement for further process improvements include Supervisory Control and Data Acquisition (SCADA), preventative maintenance systems, and distribution planning systems.
The GIS plays a less critical role during this phase due to limited integrations with these systems and applications. As such, a WMS may lack native geospatial functionality. While the GIS can be used to keep track of where utility assets are located, it may not necessarily show how the assets are interconnected. In later phases of the automation lifecycle, these standalone business systems and applications get enhanced through integrations with the GIS and other geospatial technologies.
Option 1 of Esri’s strategy provides a deployment option suited to utilities with a disconnected, or non-traceable, electric network that want to enhance their business processes using standalone applications.
Phase 2: Integration of Energy Delivery Systems for Workforce Automation
Many utilities are currently in this second phase of adoption within the automation lifecycle. In this phase, the standalone applications that were implemented in phase one start to get integrated with other energy delivery systems. Historically, these integrations were achieved using an enterprise application integration (EAI) architecture, where individual systems were integrated with other standalone systems. In recent years, however, a service-oriented architecture (SOA) has emerged, which has helped with facilitating integration points amongst multiple systems.
A high-value integration for utilities at this phase is positioning the GIS to drive operational systems, such as an Outage Management System (OMS) and Facility Inspection and Maintenance programs; engineering systems, including Distribution Planning Systems (DPS); and non-spatial systems. To drive these systems and area-based survey and location-based inspections, the GIS now not only manages the locations of assets but also the electric network connectivity at enough detail for the outage prediction engines to perform well. However, it’s important to note that during this phase, most planning engineers still need to update their distribution planning tools after they are loaded from the GIS networks, as the GIS does not have the data to automate this update. Each time the planning tools get reloaded from the GIS, the engineers must align the GIS information with what was actually built in the field as well as manually input the SCADA information into the simulations.
Utilities can additionally integrate their GWD tool with their WMS to streamline the entry of compatible units (CUs). While a WMS excels at workforce automation and crew scheduling, the manual entry of materials can often be time-consuming, tedious, and prone to errors. By integrating with the GWD tool, the process of inputting design details becomes automated, eliminating redundant data entry and human error.
Similarly, integration between the OMS and Mobile Workforce Management (MWM) can effectively streamline the coordination of restoration activities during storm events, compared to using these systems as standalone applications.
Phase 2 Use Case – Outage Restoration Automation
Consider the following scenario. When an outage is reported, a crew is dispatched to the event. As the outage progresses, the OMS rolls up additional reported outages and determines an upstream device is the cause. It then redirects the crew to the probable outage location. As the crew restores service at the source of the outage, the MWM is updated with the repair results. This information is then sent back to the OMS and used to update the current status of the outage event. The OMS dynamically reallocates crew assignments to the remaining outage locations as service is restored.
In phase two of automation, the GIS plays an important role by spatially enabling the above-mentioned business processes. However, there are still many untapped opportunities that utilities can derive from the GIS through increased automation in the subsequent phases.
Option 2 of Esri’s strategy supports network management for utilities using a connected Utility Network model. Integrations with the GIS and trace functionality further improve business processes and workforce optimization.
Phase 3: Integration of Energy Delivery Data Repositories for Asset Optimization
The third phase of utility automation focuses on achieving a deeper level of integration, or interoperability, between systems for enhanced insights and analytics into the data. Integrations extend beyond the standalone applications to include historical information related to grid performance, grid reliability, asset conditions, and investments. They also include current trending information for the grid and forecasted information for investments, grid reliability, and grid performance, along with other accompanying factors such as weather, environment, and customer demand.
During this phase, historical data from various sources is compiled to address fundamental questions faced by the utility. The goal is not only to automate reactive work tasks but also to optimize grid operations, implement proactive performance-based asset and linear asset management, and apply reliability-centered and forecasted failure-centered maintenance. These measures help close the loop during the capital expenditure (CAPEX) planning process by automating the analysis of all relevant asset performance indicators.
The historical data collected gets stored and managed in a data repository, which can include the following:
- Outage device cause codes
- Switching logs
- Duration and percentage the grid has operated at above thermal thresholds
- Historical investments and work orders
- Historical customer load and customer profiles
- Time-of-use demand
- Weather
- Asset book values
- Historical and forecasted budgets and revenues
- Inspection and maintenance records
- Forecasted load and reliability
The integrated technology in this phase combines previously disparate data sets to support asset investment and optimization. Because much of this data is spatial – locations of crews, weather, and topological connectivity of the distribution network – it is only meaningful in the context of a geospatial information system, such as GIS. Practical analysis of this data requires location and knowledge of what is connected to gain a complete picture of the operational distribution grid.
Phase 3 Use Cases – Reliability Analysis and Increased Customer Satisfaction
To better illustrate this phase of adoption, consider the following examples. To assess the impact on a system’s reliability indices, the reliability engineer must correlate information using historical system performance alongside forecasted system performance from various systems
The dashboard shown in Figure 5 utilizes information from the GIS, OMS, Distributed Energy Resource Management System (DERMS), SCADA Historian, AMI, and DPS business systems, all from the same performance period. Geospatially-enabled business intelligence (BI) is the key technology that facilitates this analysis. Using the dashboard, the reliability engineer can quickly determine how much a specific feeder or subsection of a feeder has impacted the system’s reliability indices. The engineer can additionally identify where all the momentary outages have occurred and any power quality issues that may have been caused by new DERs connections, empowering them to recommend improvements to provide better levels of customer service.
Similarly, the Transformer Load Management dashboard in Figure 6 enables engineers and operators to proactively manage utility assets for better customer service. The dashboard identifies transformers that have been overloaded during specific time periods or temperature thresholds. Using historical information from AMI, the dashboard can pinpoint transformers that may be at risk of future overloads during peak demand days in high or low temperatures. Based on these insights, engineers can take proactive measures to prevent overload outages by upgrading at risk transformers.
Option 3 of Esri’s strategy supports network management for an advanced, high-fidelity Utility Network model. The utility can now drive advanced ADMS applications with the GIS, using historical data to conduct proactive asset management for optimized grid operations.
Phase 4: Integration of Near Real-Time Field Automation
The fourth and final phase of adoption involves the integration of near real-time field automation. The spatially-enabled intelligence allows decision makers – from operations to the boardroom – to visualize real-world field conditions. The data repositories established in phase 3 provide valuable and useful data in the form of historical records and forecasts but lack the real-time data necessary for effective decision-making in the Digital Grid. GIS-enabled, advanced Digital Grid devices respond to changing conditions in the field, making the near real-time automation in this phase key for their successful operation. As demand fluctuates across the distribution network, these devices intelligently react by shifting load from one circuit to another.
GIS and geospatial technologies are critical enabling technologies for distribution management and advanced applications in this phase. They provide the base power distribution connectivity model complete with customer meters connected by phase, which is essential for supporting real-time power flow-based applications such as Fault Location, Isolation, and Service Restoration (FLISR), Volt/Var Optimization (VVO), and Optimal Switching.
In addition to these applications, substation automation (SA) and distribution automation (DA) enable the grid to respond to events and self-heal as quickly as possible while minimizing customer disruptions. Integration with non-operational asset health information from Intelligent Electronic Devices (IEDs) can help operators identify equipment issues before they lead to outages. This historical information can be further analyzed to develop preventive maintenance programs.
Phase 4 Use Case – Wildfire Mitigation PSPS
During this phase, wildfire mitigation is optimized as Public Safety Power Shutoff (PSPS) events become automated and targeted. The grid receives near real-time data from the field, allowing it to intelligently shut off and redirect power based on weather conditions, such as high winds.
Option 3 of Esri’s strategy further supports the utility’s goal of managing grid operations as efficiently as possible with near real-time data and analytics, as it now transforms into a full-fledged Digital Utility® in the final phase of utility automation.
Digital Utility® of the Future
Today’s utilities are faced with balancing multiple constraints of reliability, safety, and profitability, while also addressing new mandates for efficiency and a reduced carbon footprint. Technology investments in the first two phases of the utility automation lifecycle go a long way towards addressing these constraints by automating manual processes and streamlining workforce management. However, it is the intelligent automation introduced in the third and fourth phases that has the most impact on transforming utility operations.
Geospatial technology plays an increasingly significant role with each successive phase of utility automation adoption. Esri’s Network Information Management solution provides a comprehensive GIS deployment pattern that supports network information management at each stage of the utility automation lifecycle, as depicted in the following diagram and table. UDC can assist your utility in identifying which phase you are currently in and determining the steps needed to advance to the next phase of automation.
As the intelligent utility enterprise continues to evolve, GIS provides the means for the utility to build a Digital Grid and transform itself into the Digital Utility® of the future.
Connect with UDC to learn how we can help you in your digital transformation journey.