Experience with time-of-use pricing programs shows that a large majority of low-income customers will benefit from dynamic prices. In fact, not making such prices available to these customers...
Demanding More from DR
Customer-specific demand-response strategies become more sophisticated.
desired load reduction.
Early tests were performed with a very basic level of intelligence built in to the units—microprocessors were in their infancy, at least in these applications—and as a result forecasts were very crude. The end result was that those original units tended to occasionally over control because they couldn’t adapt in real time to intermittent variations in appliance usage. In one case, the customer had turned the unit off, and, when control started, the device was trying to reduce the already 100-percent off-duty cycle even further. When the customer set the unit to run, the control device kept the unit off completely.
Newer versions of this solution are becoming better at predicting the next hour’s natural run time and, as a result, now are being deployed and operated at many utilities. In every case, the utilities are reporting improved load reduction because they are capturing the oversized HVAC systems, as in the example above, and forcing them into duty cycles that actually deliver load reductions. There’s still the risk of over control, and each vendor’s solution offers its own method of mitigating that risk. Some of these smart-control algorithms are moving to the point where the utility simply can send a command to the control device and direct it to determine exactly what the control strategy should be for a specified load-reduction request. From a control-optimization perspective, that is the Holy Grail of direct load control. It maximizes the average load reduction per participant, while moving closer to equalizing each customer’s load-reduction contribution.
Generally speaking, each solution attempts to predict how much the customer’s HVAC would have run during the control period if it were uncontrolled. The controller then implements a control strategy ( i.e., cycle strategy) in a manner that delivers the desired demand reduction. Without this technique, many over-sized central air conditioning systems would not yield significant or appropriate load reductions.
Some solutions make this control strategy adjustment based on compressor run-time data collected the hour prior to the start of the control period. The previous hour’s duty cycle is used to forecast the next hour’s duty cycle. From that forecast, a control-shed percentage based on that predicted duty cycle is implemented. For example, if the previous hour’s natural duty cycle was 50 percent and the new shed cycle was 50 percent, the resulting effective control strategy would be off 75 percent of the time ( i.e., 50-percent reduction of the 50-percent natural duty cycle). If the unit’s connected load was rated at 4 kW, the load reduction would be 1 kW—assuming the duty cycle forecast for the next hour ( e.g., 50 percent) was accurate in the first place.
It’s obvious that there are a lot more variables than just the previous hour’s duty cycle that can be used to predict the next hour’s natural duty cycle. Site conditions ( e.g., sun angle, shading, time of day, humidity, outside and inside temperature, etc. ) as well as a longer observation period all can be influence. As these algorithms become more sophisticated, they’ll become