Making Distribution Automation Work: The Need for Smart Data
The unique data challenges for the electric utility industry
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The unique data challenges for the electric utility industryElectric utilities have a unique operating environment in that they must serve all willing customers, they are one of the most capital-intensive industries, the product that they produce is consumed immediately, and yet the planning and pricing for that product might have leads times greater than a year. Against this backdrop, utilities are also challenged with the same rapidly evolving technology (increased computing power, smaller footprints, increased connectivity, etc.) that affects many other industries. In particular for utilities, as assets that are deployed get “smarter,” there are a greater amounts of data that needs to be managed. Data from renewable generation, sensors, smart meters, load management, and the need to act on this data for internal and external use all contribute to the issue. At some point, the sheer volume of the data becomes a problem. For example, interval data from a few million smart meters seemed so daunting—adding dozens of terabytes to the storage requirements—that pundits labeled the resulting flood of ones and zeros a “data tsunami.” To brace themselves against the data tsunami, utilities needed to ramp up the capacity of their storage area networks (SAN). No portion of the electric power grid is changing more significantly and rapidly than the electric distribution system. The traditional philosophy of those who design and manage distribution systems has been to maintain acceptable electrical conditions at the lowest possible cost for all customers. Recent distribution operating practice seeks to improve the efficiency and reliability of the distribution system, accommodate a high penetration of distributed energy resources, and maximize utilization of existing distribution assets without compromising safety and established operating constraints. Significant changes to the distribution design and operating practices—often referred to as grid modernization—are needed to accommodate these requirements. These changes are impacting utility systems and creating new sources of data, often at unprecedented volumes. Fortunately, the concern about the sheer volume has been mitigated by the decreasing cost and increasing capacity of storage. A terabyte of data was considered a spectacular volume but is now considered “volume as usual”, and this capacity curve for storage will continue its own Moore’s Law-like trend.
CIM for Analytics: Realizing the value in dataNow that data acquisition and storage are no longer challenging, the question of what to do with the data is now the central challenge of grid modernization. The value of data is realized through the application of analytics across the various domains shown in Figure 2. As shown in this figure, asset management is a domain that spans the utility enterprise, stretching from the customer premise meters to substation assets to lines and poles and involving disparate functional areas of planning and procurement, maintenance, and operation. Consequently, the asset-management decisions that play out on a daily basis are varied and complex and are therefore best made on the basis of a dispassionate analysis of relevant data. This domain provides a good example of how utilities are realizing the value in data and the enabling role of CIM in this endeavor. High-value assets such as power transformers are routinely kept in service beyond their nominal lifetime. From a planning and procurement perspective, the analysis for the repair vs. replace decisions for such assets is driven by risk-management calculations. From a maintenance perspective, right-sizing the maintenance program requires an accurate assessment of the asset condition, which is made on the basis of asset health analytics. From an operational perspective, smart tradeoff of use-related deterioration against revenue is a key aspect of maximizing efficiency of the distribution system. There is a growing realization of the value of a program to manage utility assets that is analytic-centric. Relevant standards for these are the British standard PAS-55 and the international standard ISO 55000, which enable the institutionalization of best practices in asset management and consistent outcomes. 
Figure 2 Examples of some of the grid data sources and challenges Directory: Projects -> IEEEmagCIM -> Shared%20Documents -> Article%204%20-%20Making%20distribution%20automation%20work%20-%20the%20need%20for%20smart%20data -> Drafts Projects -> Terminal Decision Support Tool Systems Engineering Graduate Capstone Course Aiman Al Gingihy Danielle Murray Sara Ataya Projects -> Rajinder Sachar Committee Projects -> Cape Lookout National Seashore Historic Resource Study By Projects -> Cape Lookout National Seashore Historic Resource Study By Projects -> Revolutionizing Climate Modeling – Project Athena: a multi-Institutional, International Collaboration Projects -> What is a Hurricane? Projects -> General Information Projects -> Press Information Projects -> 1 Introduction 3 2 Designing an Embedded System 4 Drafts -> Making Distribution Automation Work: The Need for Smart Data Download 1.36 Mb. Share with your friends:
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