Many pre-existing air conditioner load control programs can provide valuable operational flexibility but have not been incorporated into electricity ancillary service markets or grid operations. Multiple demonstrations have shown that residential air conditioner (AC) response can deliver resources quickly and can provide contingency reserves. A key policy hurdle to be overcome before AC load control can be fully incorporated into markets is how to balance the accuracy, cost, and complexity of methods available for the settlement of load curtailment. Overcoming this hurdle requires a means for assessing the accuracy of shorter-term AC load control demand reduction estimation approaches in an unbiased manner. This paper applies such a method to compare the accuracy of approaches varying in cost and complexity – including regression analysis, load matching and control group approaches – using feeder data, household data and AC end-use data. We recommend a practical approach for settlement, relying on an annually updated set of tables, with pre-calculated reduction estimates. These tables allow users to look up the demand reduction per device based on daily maximum temperature, geographic region and hour of day, simplifying settlement and providing a solution to the policy problem presented in this paper.
BT - Energy Policy DA - 05/2013 DO - 10.1016/j.enpol.2012.12.024 N2 -Many pre-existing air conditioner load control programs can provide valuable operational flexibility but have not been incorporated into electricity ancillary service markets or grid operations. Multiple demonstrations have shown that residential air conditioner (AC) response can deliver resources quickly and can provide contingency reserves. A key policy hurdle to be overcome before AC load control can be fully incorporated into markets is how to balance the accuracy, cost, and complexity of methods available for the settlement of load curtailment. Overcoming this hurdle requires a means for assessing the accuracy of shorter-term AC load control demand reduction estimation approaches in an unbiased manner. This paper applies such a method to compare the accuracy of approaches varying in cost and complexity – including regression analysis, load matching and control group approaches – using feeder data, household data and AC end-use data. We recommend a practical approach for settlement, relying on an annually updated set of tables, with pre-calculated reduction estimates. These tables allow users to look up the demand reduction per device based on daily maximum temperature, geographic region and hour of day, simplifying settlement and providing a solution to the policy problem presented in this paper.
PB - Elsevier PY - 2013 SP - 175 EP - 185 ST - Energy Policy T2 - Energy Policy TI - Incorporating residential AC load control into ancillary service markets: Measurement and settlement VL - 56 SN - 03014215 ER -