Abstract
Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - 2021 |
Event | 2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States Duration: 26 Jul 2021 → 29 Jul 2021 |
Conference
Conference | 2021 IEEE Power and Energy Society General Meeting, PESGM 2021 |
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Country/Territory | United States |
City | Washington |
Period | 26/07/21 → 29/07/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
NREL Publication Number
- NREL/CP-5D00-82308
Keywords
- Distribution system analysis
- Gaussian process
- global sensitivity analysis
- PVs
- Sobol indices