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Deep Demand Response: The Case Study of the CITRIS Building at the University of California-Berkeley

  • Author(s): Peffer, Therese, PhD
  • Auslander, David
  • Caramagno, Domenico
  • Culler, David, PhD
  • Jones, Tyler
  • Krioukov, Andrew
  • Sankur, Michael
  • Taneja, Jay
  • Trager, Jason
  • Kiliccote, Sila
  • Yin, Rongxin
  • Lu, Yan
  • Mukka, Prasad
  • et al.
Abstract

The goal of the Distributed Intelligent Automated Demand Response (DIADR) project at UC Berkeley is to reduce peak load of Sutardja Dai Hall by 30% while maintaining a healthy, comfortable, and productive environment for the occupants. The team consists of members fromSiemens, UC Berkeley and Lawrence Berkeley National Laboratory.

This paper describes the work in progress of achieving this goal in the office portion ofthe building given an overcooled building not fully commissioned, grossly oversized chillers, and problems isolating the nanofabrication laboratory. Most of the reduction comes from HVAC: increasing supply air and zone temperatures and reducing ventilation rates. Other reductionsinvolve dimming lights and turning off the tankless water heaters. Engaging the occupants of the building should provide further reduction. The web interface for the distributed load controller allows the prioritization of curtailable appliances and visualization of energy consumption. Aweb-based lighting controller allows the workers to easily control overhead lighting and encourages conservation.

Several new tools have facilitated this work, such as the grid-to-building gateway and the web-based lighting controller. In addition, an innovative data aggregator called sMAP allowed detailed analysis of the building systems operation. A smart phone application (RAP) facilitated an inventory of the building’s plug loads. Occupants were initially administered a survey on indoor environmental quality; short surveys during interventions assessed satisfaction withindoor temperature, light levels, and air quality.

We have made progress towards achieving deep demand response of 30% reduction ofpeak loads, and are establishing a new energy efficient baseline.

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