The Hidden Data Governance Issues an ADMS Project Brings to Energy Delivery Business SystemsPosted to UDC
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- Jan 9, 2020 10:45 pm GMT
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For those planning an advanced distribution management system (ADMS) implementation or just starting the ADMS implementation journey, this article discusses the data governance issues that need to be addressed within your Energy Delivery Business Systems to enable the ADMS suite of advanced applications. For most utilities it will not be enough do what you did to enable your outage management system by integrating with the GIS power system connectivity model and integrating with the customer information system (CIS) customer to power system connectivity to support turning on some of the high-value ADMS applications such as unbalanced load flow (UBLF), suggested switching, switch order management (SOM) and the distribution training simulator (DTS).
Enabling High-Value ADMS Applications
The set of Energy Delivery Business Systems that form the ADMS systems of record needs to grow to include typically your enterprise asset management system (EAM) for asset power system characteristics that are typically collected as part of receiving the assets into stores within the supply chain module of your EAM. This data includes nameplate data and potentially, test results that define the asset’s power system characteristics.
Asset nameplate data is collected and stored in the utility’s EAM along with asset power system characteristics needed to enable high-value ADMS applications.
These power system characteristics are required for UBLF to return results based on the model of your power system and for SOM and suggested switching to only generate valid possible switching plans and to validate the final switching orders created by SOM.
Another system of record that needs to be included when designing what systems of record need to feed the ADMS is your utility’s protection and controls system or database (P&C). This system contains the relay settings for all of the devices and intelligent electric devices defined and configured on your power system. This data is used by the ADMS distribution training simulator application to allow training instructors to place a fault anywhere on your power system and have the DTS simulate the behavior of the power system for the ADMS operators to learn to use the ADMS suite of applications to troubleshoot and remediate faults on the power system.
This P&C data can also be used by SOM to validate that the switching order doesn’t invalidate the protection schemes or allows the ADMS to pick the correct relay group setting to support the required relay reach of the switching order as part of executing the switching order.
Importance of Maintaining Zero Data Latency
This means most utilities will have at least four systems of record that need to be kept current because the ADMS also levies a data latency requirement which can be referred to as ‘zero data latency’. The ADMS architecture supports zero data latency by allowing both designed and the as-built (nominal) versions of the power system model to be loaded into them. By supporting the two versions, the ADMS can support the utility’s business process to commission new sections of the power system without needing to load the power system model at the same time as commissioning it. Zero data latency is required since the ADMS is issuing supervisory control and data acquisition (SCADA) commands to control field devices based on its as-operating model of the power system. If zero data latency can’t be achieved, then a human in the loop will typically be required between the ADMS advanced applications running and the issuing of commands to the field devices via SCADA.
Zero data latency introduces two sets of data governance into the utilities systems of record: one at design time and one at as-builting time. The four (or more) systems of record feeding the ADMS its connected power system model must be synchronized at the time of the ADMS model build process.
Other systems of record that are needed to be included in the data governance service level agreements could be your advanced metering infrastructure (AMI) system, if this is used to create new outages within the ADMS, and if the ADMS uses AMI to validate new outages and uses AMI to verify outages are complete.
Building a Strong Data Governance Foundation
According to industry analysts like Gartner, low data integrity is the leading cause of ADMS implementation failures within the industry. In order to ensure that the key ‘governed processes’ defined through Dx Change Control continue to get refined, communicated, and audited over time, data governance processes and roles need to be put in place.
ADMS data governance needs to provide the overall operational framework that helps to ensure the continued management of distribution data change control, thus, facilitating the high level of data integrity required for a successful ADMS implementation. Data governance needs to address the following business systems: GIS, mobile GIS, CIS, EAM, P&C and AMI when adding/removing assets or customers.
The ADMS data management strategy needs to provide the overall governance processes required to support communication, proactive compliance, audit, exceptions and appeals, and vitality of data governance principles, guidance, and standards.
Data Governance Primary Functions
Data governance deploys and manages the decision rights and accountability framework (roles, processes, etc.) required to achieve the organization's Information Management goals. Data governance includes ownership, data custodianship, data policies, and data standards.
The major data governance processes are:
- Proactive Compliance;
- Reactive Compliance;
- Exceptions and Appeals; and,
Key Benefits of a Good Foundation
A strong data governance foundation for ADMS will help to provide new capabilities required by an ADMS program:
- Governs data as a critical corporate asset throughout its lifecycle;
- Provides change control and management related to data and information across the enterprise;
- Defines and evolves data principles and policies;
- Facilitates proactive compliance of data principles, policies, guidance, and methods;
- Conducts reactive compliance processes to ensure adherence to the above;
- Communicates principles, policies, assets, templates, decision-making tools, guidance, and advice;
- Helps to ensure data quality (timely, consistent, accurate, complete, etc.);
- Supports regulatory compliance (e.g. information privacy, data retention, consistent/timely/accurate regulatory reporting);
- Helps to maximize the reuse of Data Services in the organization;
- Supports the Enterprise Information Architecture;
- Reviews, prioritizes, and changes data along with its associated processes, as required; and,
- Drives continuous improvement of data and information.
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