IBPSA-USA

IBPSA-USA Seattle Chapter: Building Grid Integration

IBPSA-USA Seattle Chapter: Building Grid Integration

The IBPSA-USA Seattle Chapter met in April at Arup’s Seattle Office for the chapter’s quarterly meet-up and to discuss all things related to building grid integration.  James McNeill with Edo shared a brief presentation on how the integration of buildings into the electrical grid is a crucial step towards a sustainable and resilient energy future. 

It is always good to see our Seattle peers in-person!
Thank you to James for an informative presentation.

Key Takeaways

  1. There is an increase in interest in the utility sector for demand flexibility provided from grid-interactive buildings, including cooling and heating-related control measures.
  2. An estimate of demand flexibility impact can be simulated by applying DF controls to pre-existing building stock models (e.g., NREL EULP) using EnergyPlus, which can be aggregated to assess utility-scale impact.
  3. Implementation of demand flexibility requires forecasting the change in electric demand associated with control measures. This requires a combination of building physics and machine learning knowledge.
  4. A separate counterfactual demand reduction baseline is needed in addition to the energy reduction baseline. Day matching is commonly used by utilities, but more sophisticated techniques also exist.
  5. Building performance professionals will need a strong understanding of building demand profiles and underlying influences for assessing demand flexibility.
  6. Modeling the control of Virtual Power Plants (VPPs) requires aggregation of individual building models at various spatial scales (e.g., feeder, substation).

Industry Needs

  • Improved tool capabilities for modeling realistic building response at higher temporal resolution (about 5 – 15 minute), including improved control modeling for building stock models.
  • Ability to model building electrical systems (e.g., reactive power) and integration with electric grid models for modeling impact of grid services.
  • Improved data-driven modeling methods for existing buildings that integrate machine learning, gray-box modeling, and data-driven modeling methods.
  • Modeling methods that are scalable to large number of buildings in utility service territories (i.e., thousands of buildings).

A special thank you to Arup Seattle for hosting this event!

Interested in joining an IBPSA-USA chapter near you? Find a local chapter here or contact chapters@ibpsa.us for more information.