Utilities worldwide face smart grid challenges that can impact their mission to deliver reliable, safe, and efficient power. A real-time model of the distribution network infrastructure is an essential component of implementing successful smart grid strategies. Creating a reliable model requires complete, correct, and current data. A recent survey indicates that less than 5% of utilities have confidence in the quality of their data.
Implementation of the following best practices can assist in creating a smart grid network model:
Best Practice: Adopt a highly focused evaluation of data requirements based on identified business drivers
Besides managing demand and optimizing renewable energy resources, utilities must improve fault and outage management, minimize network losses, enhance asset management, and meet stringent regulatory requirements. Finding ways to address these objectives requires big data and a way to manage it.
A high-performance Advanced Distribution Management System (ADMS) unifies data from the Distribution Management System (DMS), SCADA system, and Outage Management System (OMS) to maintain a real-time network model. Using this model, the ADMS performs advanced analytics and controls network devices to address smart grid challenges.
Best Practice: Commit the resources and time to required improve the quality of GIS and other source data
Utilities generate an ever-increasing amount of data that may reside in more than one software system. An enterprise geographic information system (GIS) can deliver a “single version of the truth” to provide consolidated and harmonized data that may serve many departments. A GIS supports asset management and reduces data duplication and associated transposition errors; improves data quality; and simplifies database maintenance.
To create a robust network model, an ADMS requires accurate GIS data along with other data: detailed equipment data; load and critical customer data; SCADA monitoring and control points associated with devices; substation internals; to-be-constructed conductors or devices; and weather forecasts.
Verifying and correcting data from all sources is critical – the more accurate the data is, the more accurate the model and resulting analyses will be.
Best Practice: Prepare the data sources needed for ADMS deployment, using adequate quality control and error correction of source data
Preparing GIS data can mean adding details to the GIS or pulling necessary data from other sources. If the data quality is lacking, it may cause problems for ADMS operations. Validation rules can help keep the GIS up-to-date by flagging missing data, ensuring connectivity, and checking phase and voltage consistency.
Aligning ADMS data with field information is critical, but it can occur in phases to accommodate business priorities: Ensure that power conductors and devices are in the model and include required equipment data, load data, SCADA points, and substation internals. Make quality control and corrections to the data for basic applications like load flow and state estimation, and then do the same for advanced applications like fault location and network reconfiguration.
Updating ADMS data keeps the network model current. A utility should make changes within the GIS and import new data into the ADMS as frequently as needed, depending on the significance of any network infrastructure changes.
Best Practice: Implement the business processes and change management necessary to create and validate the ADMS model, including user roles and training programs An ADMS relies on data that’s as similar to the changing real-world conditions as possible to maintain an accurate network model for smart grid performance. Ensuring that complete, correct, and current data continues to feed into the model requires companywide acceptance and participation. To maintain a dedication to data integrity, a utility will need to adjust its business processes, implement an effective change management initiative, define user roles, and provide training.
Best Practice: Dedicate resources required to update and maintain the model
Maintaining a real-time network model requires a significant commitment, but a properly and fully implemented distribution network model can provide substantial smart grid performance returns.