- Integration Capacity Analysis
Our team worked with Southern California Edison (SCE) to evaluate its existing distribution system models in support of requirements set forth in the California Distribution Resource Plan. The new planning process combines operational data with distribution data in a geospatial environment to provide accurate capacity analysis based on historical data. We worked with SCE distribution engineers to collect data, update distribution circuit models and analyze the results of the integration capacity analysis (ICA). We supported the project through data architecture planning, resource planning, business process planning and project implementation.
We continued on for Phase 2 of the project, which included these enhancements: smart inverter integration, technology specific generation profiles, temperature corrections, model validation with AMI, and allowable reverse power flow. As the ICA initiative matures, SCE will be able to use it to derive the optimal location for EV chargers.
Examples of tasks related to data integrity enhancements and improved data processing logic:
- Verifying system conditions with mapping, DMS, OMS, EMS and other systems
- Modeling distribution circuits in CYME
- Creating substation load, substation voltage 576 profiles
- Integrating NEM and Smart-meter data into planning process
- Batch circuit model updates using scripting & assumptions
- Improved data models for generation
- Identifying pre-existing conditions which may cause abnormal ICA results
Throughout the project, the combined team faced numerous obstacles in implementing this complex, multifunction organization. The project team proactively addressed challenges including:
- Data integrity, which required investigation into any and all discrepancies between databases
- Interfacing with multiple business units to merge and correct data
- Maintaining updated circuit models and ensuring data was properly processed
In order to efficiently solve these problems, the team developed a process for analyzing each circuit to a minimum level of data quality. Some tasks had to be manually verified, while other verifications could be written in code such as Python to automate processes and provide standardized assumptions for certain pieces of equipment. One of the most important aspects of any project of this size is an efficient way to view the data and implement filtering and other logic to only view the required or desired information. The team implemented an Oracle APEX web-based environment to track project progress and data integrity verifications. The final analysis results were posted publicly on a new Distributed Energy Resource Interconnection Map (DERiM).
- Data analytics
- Yearly load profile creation
- Circuit model updates
- Business process creation