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In previous posts, I’ve highlighted the benefits of creating a distribution network model using an ADMS (Advanced Distribution Management System). I also discussed how ADMS loves big data. Now let’s examine the best practices for creating and maintaining your smart grid network model.
Where should you start?
While it makes sense that a real-time model of network infrastructure can help address smart grid challenges (managing demand, optimizing renewable energy resources, and more), many utilities find that building and maintaining an accurate network model – with complete, correct, and current data – is the most difficult hurdle.
To further complicate matters, a utility’s data often resides in multiple software systems. A solid solution is to employ an enterprise geographic information system, or GIS, which can provide a “single version of the truth” to consolidate and harmonize the data that supports users across a utility’s organization.
Accurate data is the foundation
To create a robust network model, an ADMS requires accurate GIS data combined with detailed equipment, load, and critical customer data, and more. Verifying and correcting data from all sources is critical – the more accurate the data is, the more accurate the model and resulting analyses and optimizations will be.
Conquering big data may seem like a daunting task, but following these best practices can help ensure a utility has the accurate data it needs to create a reliable smart grid network model:
- Adopt a highly focused evaluation of data requirements based on identified business drivers
- Commit the resources and time required to improve the quality of GIS and other source data
- Prepare the data sources needed for ADMS deployment, including adequate quality control and error correction of source data
- Implement the business processes and change management necessary to create and validate the ADMS model, including user roles and training programs
- 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.
