P5. Aggregated Load Shedding > Approach

• Identify opportunities to better control electrical loads in groups of buildings by aggregating load shapes and by coordinating control actions,

• Evaluate the potential impact of aggregated load control,

• Identify needed developments in control and communication systems.

Approach:

• Aggregated building load management issues were investigated that included the engineering aspects, implementation issues, customer motivation and legal issues.

• Simulations and field monitored data were used to assess the theoretical limits to aggregated load shedding as well the practical aspects of actual implementing a load shedding strategy. To accomplish the research objectives, the research team focused on:

  • Emphasizing short-term manual or semi-automatic set point adjustments;
  • Using centralized load monitors for operator feedback; and
  • Considering small-scale aggregates of buildings under a single revenue meter.

• Simulations were used to define the limits of load shedding, and optimum combinations of strategies. The research team considered the following:
  
  • Define hypothetical cases to quantify load aggregation benefits (building function, building load shapes, building equipment, climate)
  • Select control strategies (internal loads, thermal mass, secondary equipment, primary equipment)

• The demonstration partner selected for the research was the County Government of Los Angeles. The County was participating in Southern California Edison's load curtailment program. It was interested in learning what curtailment measures to implement to accomplish demand reduction without seriously affecting comfort levels in its facilities. The County operates a 200 acre building complex, including social services, courts, prisons, and administrative buildings. Two buildings were selected for monitoring and load control experiments, including the Edmund Edelman Children's Court (ECC) and the Internal Services Division (ISD) building.
  
• Data were collected for analysis to support building a thermal response model of each building to predict electric demand due to cooling loads. MIT installed power and environmental (zone and HVAC system temperatures) instrumentation in the monitored buildings. Weather data were collected from an on-site weather logging station (ambient temperature, humidity, solar radiation, wind speed and direct, etc).
  
• Load curtailment (shutting off the chillers and upward adjustment of setpoints) and night pre-cooling experiments were conducted for several days, including a winter period and a summer period. Models were calibrated with the measured data and then run with a number of different pre-cooling schemes to predict annual changes in energy costs.

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