A Two-Layer Framework for Quantifying Demand Response Flexibility at Bulk Supply Points

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.

[1]  A. Rabl,et al.  Energy signature models for commercial buildings: test with measured data and interpretation , 1992 .

[2]  Michael Zeifman,et al.  Disaggregation of home energy display data using probabilistic approach , 2012, IEEE Transactions on Consumer Electronics.

[3]  Sila Kiliccote,et al.  Taxonomy for Modeling Demand Response Resources , 2014 .

[4]  Rongxin Yin Study on Auto-DR and Pre-Cooling of Commercial Buildings with Thermal Mass in California , 2010 .

[5]  Jing Zhou,et al.  A bottom-up assessment method of demand response potential , 2014, 2014 International Conference on Power System Technology.

[6]  David E. Claridge,et al.  A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model , 1993 .

[7]  Philip Haves,et al.  Demand Shifting With Thermal Mass in Large Commercial Buildings: Field Tests, Simulation and Audits , 2005 .

[8]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[9]  Kody M. Powell,et al.  Reduced-order residential home modeling for model predictive control , 2014 .

[10]  David S. Watson,et al.  Fast Automated Demand Response to Enable the Integration of Renewable Resources , 2013 .

[11]  Sila Kiliccote,et al.  Predictability and Persistance of Demand Response Load Shed in Buildings , 2015 .

[12]  Yi Yang,et al.  Feature Extraction for Load Identification Using Long-Term Operating Waveforms , 2015, IEEE Transactions on Smart Grid.

[13]  Jian Liang,et al.  Load Signature Study—Part I: Basic Concept, Structure, and Methodology , 2010, IEEE Transactions on Power Delivery.

[14]  Jovica V. Milanovic,et al.  Artificial-Intelligence-Based Methodology for Load Disaggregation at Bulk Supply Point , 2015, IEEE Transactions on Power Systems.

[15]  Sila Kiliccote,et al.  Statistical analysis of baseline load models for non-residential buildings , 2009 .

[16]  Ned Djilali,et al.  Renewable resources portfolio optimization in the presence of demand response , 2016 .

[17]  Peng Xu Demand Shifting with Thermal Mass in Large Commercial Buildings in a California Hot Climate Zone , 2010 .

[18]  Gabriel H. Tucci,et al.  Analysis and Methodology to Segregate Residential Electricity Consumption in Different Taxonomies , 2012, IEEE Transactions on Smart Grid.

[19]  Sila Kiliccote,et al.  Linking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation , 2016 .

[20]  Johanna L. Mathieu,et al.  Using Whole-Building Electric Load Data in Continuous or Retro-Commissioning , 2011 .

[21]  Yongli Zhu,et al.  Load profile disaggregation by Blind source separation: A wavelets-assisted independent component analysis approach , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[22]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .