Demand response (DR) currently plays a significant role in the operation of the electric grid. As a result, quantification of DR flexibility is an important aspect in the utilization of various DR resources. Generally, the evaluation of DR flexibility at bulk supply points (BSPs) is a challenging problem, especially without the monitoring of downstream customers' load profiles in some areas. To solve this problem, we develop a two-layer DR flexibility estimation framework. In the top layer, a top-down optimization approach is proposed to disaggregate the BSP load into different building categories based on a suite of prototype building (PB) load profiles. In the bottom layer, simplified DR estimation models are deployed to quantify the theoretical DR flexibility of each PB type. Key advantages of this framework include: 1) quantifying DR flexibility at BSPs without relying on smart meter data or detailed customer surveys and 2) providing day-ahead, hour-ahead, and near real-time prediction of DR resources based on weather forecasts and other data. Case studies demonstrate the effectiveness of load disaggregation and DR flexibility quantification at a BSP. The prediction is compared with detailed physical models, and the mean relative errors for upper/lower DR capacity at the BSP are 1.5% and 3.1%, respectively.
BT - IEEE Transactions on Smart Grid DA - 12/2016 DO - 10.1109/TSG.2016.2636873 IS - 4 LA - eng N2 -Demand response (DR) currently plays a significant role in the operation of the electric grid. As a result, quantification of DR flexibility is an important aspect in the utilization of various DR resources. Generally, the evaluation of DR flexibility at bulk supply points (BSPs) is a challenging problem, especially without the monitoring of downstream customers' load profiles in some areas. To solve this problem, we develop a two-layer DR flexibility estimation framework. In the top layer, a top-down optimization approach is proposed to disaggregate the BSP load into different building categories based on a suite of prototype building (PB) load profiles. In the bottom layer, simplified DR estimation models are deployed to quantify the theoretical DR flexibility of each PB type. Key advantages of this framework include: 1) quantifying DR flexibility at BSPs without relying on smart meter data or detailed customer surveys and 2) providing day-ahead, hour-ahead, and near real-time prediction of DR resources based on weather forecasts and other data. Case studies demonstrate the effectiveness of load disaggregation and DR flexibility quantification at a BSP. The prediction is compared with detailed physical models, and the mean relative errors for upper/lower DR capacity at the BSP are 1.5% and 3.1%, respectively.
PY - 2018 SP - 3616 EP - 3627 ST - IEEE Trans. Smart Grid T2 - IEEE Transactions on Smart Grid TI - A Two-Layer Framework for Quantifying Demand Response Flexibility at Bulk Supply Points VL - 9 SN - 1949-3053 ER -