By Erik Shepard, Enspiria
Today’s utility is faced with greater challenges than ever before. Even as demand for energy grows, utilities must seek to more efficiently and reliably distribute power, while maintaining or reducing present generation levels. Utilities are adopting demand response based programs in order to shave peak loads, as well as utilizing strategies such as critical peak pricing and time-of-use billing. Investments are being made in technologies such as advanced metering infrastructure (AMI) and smart grid devices, which provide load management through intelligent distribution and substation automation. The volume of data being produced by these smart meters and devices is overwhelming; without some way to make sense of it, the value of these technologies is seriously diminished.
The once-lowly GIS, which entered the utility enterprise simply as a system for automating map production, has evolved—is evolving—into a core-enabling technology. Today, in addition to supporting map production, the GIS also serves as the backbone for design engineering, work and outage management, mobile workforce management and automated vehicle location (AVL). As the intelligent utility enterprise continues to evolve into the utility of the future, geospatial technology is providing the means to transform the dizzying volumes of data into actionable information. This allows advanced smart grid applications to make decisions in real-time, based on a spatial framework that incorporates accurate and timely data supplied by various utility business systems such as job design and field redlining.
The adoption of utility automation has a lifecycle with four key phases. Geospatial technology plays an increasingly important role with each incremental phase of adoption.
Facilities Management and Workforce Automation Applications
The first phase in the adoption of utility automation targets facilities health and wealth, workforce automation, task automation or departmental-specific systems. Examples include implementing a graphic work design tool for engineering or a work management system. These applications typically follow the initial GIS deployment, which itself could be considered part of phase one (or maybe phase zero). In this first phase, the key target is to automate a set of manual processes around one business area, or often, one department. In some cases, GIS plays a major role in the system deployed; for example, the design tool is often a value-added application on top of the GIS. In other cases, the GIS may play a less central role—a work management system may have no native geospatial functionality. Other standalone applications include SCADA, preventative maintenance and distribution planning.
Integrating Energy Delivery Systems for Workforce Automation
The second phase targets the integration of energy delivery systems for workforce optimization and business process improvement. In this phase, the isolated systems deployed in the first phase begin to see integration as a way to further improve business processes. A frequently identified, high-benefit, integration involves leveraging the GIS to drive operational and engineering systems such as outage management, work management, facility inspection and maintenance, and distribution planning. Many utilities have realized the value inherent in integrating the graphic work design tool with the work management system to streamline entry of compatible units. The work management system excels at workforce automation and crew scheduling, but inputting material is time-consuming, tedious and error-prone. The graphic work design tool is a major natural medium for entering design details, and leveraging this integration eliminates redundant data entry and a potential source of error. Similarly, integration between the outage management system and mobile workforce management can coordinate restoration activities through the chaos of a storm event. As an outage is reported, a crew is dispatched to the event. As the outage progresses, the outage management system may roll up additional reported outages and determine that an upstream device is actually the culprit in the outage and re-route the crew to the new probable location of the outage. As the crew restores service at the outage, mobile workforce management may be updated with the results of the repair, which is then returned to outage management and used to update the current status of the outage event. As service is restored, outage management can dynamically reallocate crew assignments to remaining outage locations.
Today, many utilities are at this second phase of adoption. Historically, integration has been accomplished following an enterprise application integration (EAI) architecture, with individual systems being integrated to other standalone systems. In recent years, a service oriented architecture (SOA) has helped to facilitate integration points between multiple systems, but even in many of these cases, the underlying business processes are still highly departmentalized and not truly enterprise enabled. GIS plays an increasingly important role by spatially enabling these business processes, but there are still many opportunities for second day benefits to be derived from the GIS.
Integrating Energy Delivery Data Marts for Asset Optimization
The third phase in the adoption of utility automation integrates deeper level data, moving past the applications to integrate historic and forecasted performance, reliability, condition and investment data, current trending and supplementary data such as weather, environment and customer demand. In this phase, these data are brought together to address fundamental questions utilities face, to not just automate reactive work, but to also optimize proactive, performance-based asset management and reliability centered maintenance. The data also closes the loop during the CAPEX planning process by looking at all relevant asset performance indicators. Some examples of data marts that can be brought to bear include outage device cause codes, switching logs, 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 and forecasted load and reliability. Because much of this data is spatial in nature—locations of crews, weather and topological connectivity of the distribution network—this variety of data is truly only meaningful in the context of a geospatial information system. Any analysis of this data requires location to obtain a complete picture.
An example might serve to better illustrate this phase of adoption. The utility distribution planning system that forecasts load and reliability already uses an underlying spatial model from the GIS for its network topology. To analyze the forecasted reliability against historical performance of the network, historical reliability is analyzed together with maintenance and inspection history. For one inspection activity, maintenance and inspection details show a lower productivity, while historical reliability shows lower reliability scores at locations where this PM program task is not being adequately addressed. Running the reliability forecasting model on these suspect circuits shows a lower forecasted set of reliability indices as compared to other parts of the network, and also shows that the forecasted CAPEX required to improve reliability on these circuits is three times the allocated preventative maintenance. Geospatially enabled business intelligence (BI) is the key enabling technology that facilitates this analysis.
Integrating Near Real-time Field Automation
The fourth and final phase of adoption is the integration of near real-time field automation. Data marts provide valuable and useful data in the form of historical records, as well as forecasts, but they do not provide the real-time data needed for decision making in the smart grid. Smart grid devices react and respond to changing conditions in the field. As demand varies spatially across the distribution network, smart grid devices can react by shifting load from one circuit to another. GIS and geospatial technology are critical enabling technologies for the distribution management and advanced applications, because they provide the base power distribution connectivity model, complete with customer meters connected by phase, which is key to supporting real-time power flow based applications such as FLISR, VVO and switching. In addition, spatially enabled intelligence enables decision makers from operations to the boardroom to visualize real-world field conditions; the volume of data produced by smart meters and smart devices is overwhelming and not meaningful without visualization. For this fourth phase, geospatial technology is foundational.
Utility of the Future
Today’s utilities are faced with balancing multiple constraints of reliability, safety and profitability while addressing new mandates for efficiency and a reduced carbon footprint. While technology investments in the first two phases of the utility automation lifecycle go a long way toward addressing these constraints, by automating previously manual processes and streamlining workforce management, the third and fourth phases will define the transition to the utility of the future. Realization of these phases without geospatial technology is infeasible, as the utility enterprise is fundamentally spatial with assets distributed across service territories of varying areas. Geospatial technology provides an increasing contribution to the value chain with successive phases of utility automation adoption.
Geospatial technology is paving the way for the utilities to leverage the investments being made in advanced meters and smart grid devices, enabling them to operate more efficiently, reliably and profitably. With geospatial technology, today’s utilities are building the smart grid and transforming themselves into the utility of the future.
Dr. Shepard is a senior consultant with Enspiria Solutions, with more than 18 years experience helping clients realize a return on their transformational technology investments. He has particular interest in the role that energy efficiency programs and smart grid technologies play in improving both environmental quality and the utility bottom line.