Making Distribution Automation Work: The Need for Smart Data

Making Distribution Automation Work: The Need for Smart Data

Introduction

Grid modernization is a transformative phenomenon that is expanding (1) the number and variety of active participants and devices, (2) the types of energy sources, and (3) the kinds of active business processes. The result is a significant increase in operational complexity at many utilities—complexity that is not supported by current corporate governance policies and business practices. The reality is that this complexity is only handled well with the implementation extensive computer-based automation, which depends on multiple systems at multiple locations owned and operated by multiple parties cooperating within defined business scenarios. This complex interconnectivity requires information transfers between systems that were designed to operate independently. The challenge is to ensure that the hundreds of transfers involved in these complex processes are all informationally compatible with one another in order to achieve the desired end result. This is complicated in that each time that a new system is acquired without using standards, the system will generate data in a form unique to that system. This data will often need to be transformed either before or after it is sent to other systems for consumption. When this data has different and possibly conflicting properties as described by its metadata, complexity is introduced into the data transfer and into the “care and feeding” of the systems at both ends of the integration. For instance, consider the different possible data definitions of “customer” that may be used by many different transacting systems: the Customer Information System (CIS) might have it defined as a string datatype with a length of 12, the Meter Asset Management (MAM) might have it defined as a long integer datatype and the Customer Relationship Management (CRM) system might have it defined as an alphanumeric datatype with a length of 14. These are random examples but these are exactly the types of differences that may exist between systems that share the same semantic of “customer” that then have to be resolved when integration occurs.

The solution to this complexity needs to start with an awareness at the corporate level that data-governance policies are needed to ensure data compatibility and consistency across all computer systems. Further, projects undertaken to procure distribution systems should also be compliant with the data-governance policy, starting with the requirements phase. It is time for electric utilities to commit to taking the time to identify all the key computer system interactions within a utility and redefine or harmonize the supporting business processes. While the governance review and the process of data transformation represent a significant investment, they pay significant dividend in system agility as opposed to the brittle interfaces that typically reflect organizations that lack mature data-governance policies.

A key foundational element to satisfy these policies and requirements—as well as to minimize the impact of adding or replacing major systems—is a common language for all data systems—a common semantic model. Various evolving power system standards represent power systems using a CIM at different levels in the power system sub-domains. For the Common Information Model (CIM) these are, IEC 61970/61968/62325 [1], also of interest are IEC 61850 - supervisory control and data acquisition (SCADA), and IEEE C37.118 phasor measurement unit (PMU). The CIM standard is an ontology that defines the semantics of the terms used to describe a power system in a unified way. This reduces the difficulty of interoperation between distinct proprietary applications used to manage power systems.

The CIM approach to solving the problems described in this article is for most, if not all, system interactions involving information exchange or database access to be based on the CIM semantic model and supporting standards. The scope of this article includes systems for advanced distribution management, geographic information, meter data management, outage management, work management, asset management, facilities management, customer information, and many others. However, the overall scope of the CIM includes models for generation, transmission, and markets, which are described in the other articles in this special publication on the CIM.