TY - GEN
T1 - Learn Dynamic Hosting Capacity Based on Voltage Sensitivity Analysis
AU - Wu, Jiaqi
AU - Yuan, Jingyi
AU - Weng, Yang
AU - Ayyanar, Raja
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The extensive use of distributed energy sources (DERs) presents the substantial design, planning, and operational issues for distribution systems, thus prompting the broad adaption of methodologies for photovoltaics (PV) hosting capacity analysis (HCA). Traditional HCA methods require running power flow analysis iteratively, typically in the time-series scenario, to consider the dynamic pattern. However, the time-consuming HCA techniques fail to offer online prediction in large distribution networks because of the computational burden. To tackle the computation challenge, we first provide a deep learning-based problem formulation for HCA, which performs offline training and calculates hosting capacity in real time. The applicable learning model, long short-term memory (LSTM), uses historical time-series data to identify the underlying periodic patterns in distribution systems. However, the accuracy of HC estimation is low in the LSTM without considering system spatial information correlated with HC. To capture such spatial correlation from system measurements, we design dual forget gates in the LSTM and propose a novel Spatial-Temporal LSTM. Moreover, as voltage violations are observed to be one of the most critical constraints of HCA, we construct a voltage sensitivity gate to increase the weight on voltage variation and reduce the mismatch in HC determination. The simulation results on different feeders, such as IEEE 123-bus and utility feeders, validate our designs.
AB - The extensive use of distributed energy sources (DERs) presents the substantial design, planning, and operational issues for distribution systems, thus prompting the broad adaption of methodologies for photovoltaics (PV) hosting capacity analysis (HCA). Traditional HCA methods require running power flow analysis iteratively, typically in the time-series scenario, to consider the dynamic pattern. However, the time-consuming HCA techniques fail to offer online prediction in large distribution networks because of the computational burden. To tackle the computation challenge, we first provide a deep learning-based problem formulation for HCA, which performs offline training and calculates hosting capacity in real time. The applicable learning model, long short-term memory (LSTM), uses historical time-series data to identify the underlying periodic patterns in distribution systems. However, the accuracy of HC estimation is low in the LSTM without considering system spatial information correlated with HC. To capture such spatial correlation from system measurements, we design dual forget gates in the LSTM and propose a novel Spatial-Temporal LSTM. Moreover, as voltage violations are observed to be one of the most critical constraints of HCA, we construct a voltage sensitivity gate to increase the weight on voltage variation and reduce the mismatch in HC determination. The simulation results on different feeders, such as IEEE 123-bus and utility feeders, validate our designs.
KW - data-driven method
KW - deep learning
KW - distributed energy resource
KW - hosting capacity
KW - long short-term memory (LSTM)
KW - spatial-temporal correlation
KW - voltage sensitivity
UR - http://www.scopus.com/inward/record.url?scp=85174675125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174675125&partnerID=8YFLogxK
U2 - 10.1109/PESGM52003.2023.10252543
DO - 10.1109/PESGM52003.2023.10252543
M3 - Conference contribution
AN - SCOPUS:85174675125
T3 - IEEE Power and Energy Society General Meeting
BT - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023
PB - IEEE Computer Society
T2 - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Y2 - 16 July 2023 through 20 July 2023
ER -