TY - GEN
T1 - SUncover
T2 - 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
AU - Padullaparthi, Venkata Ramakrishna
AU - Sarangan, Venkatesh
AU - Sivasubramaniam, Anand
PY - 2019/2/1
Y1 - 2019/2/1
N2 - As the technology costs of solar rooftops decline, small scale rooftops continue to become viable and grow without formal subsidies from the utility companies. Many new installations will continue to exist behind-the-meter (remain invisible), which poses challenges to the operating utilities for infrastructure and operations planning. This paper presents an approach to estimate the behind-the-meter solar PV size and the battery capacities. The proposed approach is based on energy balancing in buildings, and relies on data that is commonly available with utilities. The proposed approach is validated using a real-world dataset of 716 residential customers from a developed economy. The approach is compared with two baselines used by the studied Utility company. Results from the case study show that in 85% of the cases, the proposed approach has an accuracy of 98% in estimating the rooftop PV capacity (accuracies of baselines were in range 0%-30%). In estimating the battery capacity, the approach's estimates had less than 20% error in 70% of the cases (versus 35% error for baselines). The approach is also capable of discovering battery (dis)charging schedules, which is an additional useful information for utilities.
AB - As the technology costs of solar rooftops decline, small scale rooftops continue to become viable and grow without formal subsidies from the utility companies. Many new installations will continue to exist behind-the-meter (remain invisible), which poses challenges to the operating utilities for infrastructure and operations planning. This paper presents an approach to estimate the behind-the-meter solar PV size and the battery capacities. The proposed approach is based on energy balancing in buildings, and relies on data that is commonly available with utilities. The proposed approach is validated using a real-world dataset of 716 residential customers from a developed economy. The approach is compared with two baselines used by the studied Utility company. Results from the case study show that in 85% of the cases, the proposed approach has an accuracy of 98% in estimating the rooftop PV capacity (accuracies of baselines were in range 0%-30%). In estimating the battery capacity, the approach's estimates had less than 20% error in 70% of the cases (versus 35% error for baselines). The approach is also capable of discovering battery (dis)charging schedules, which is an additional useful information for utilities.
UR - http://www.scopus.com/inward/record.url?scp=85071496712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071496712&partnerID=8YFLogxK
U2 - 10.1109/ISGT.2019.8791573
DO - 10.1109/ISGT.2019.8791573
M3 - Conference contribution
T3 - 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
BT - 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 February 2019 through 21 February 2019
ER -