TY - GEN
T1 - Uncertainty Analysis in Distribution Networks Integrated with Renewables by Probabilistic Collocation Method
AU - Maharjan, Manisha
AU - Banerjee, Abhishek
AU - Kavasseri, Rajesh G.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/11
Y1 - 2021/4/11
N2 - Increased penetration of distributed generation (DG) driven by Variable Renewable Energy (VRE) sources and integration of modern loads constituted by Electric Vehicles (EV) and behind-the-meter smart appliances pose operational challenges for traditional distribution systems. This paper introduces a framework based on probabilistic collocation method (PCM) to model and analyzes the effects of inherent uncertainties, both in generation, and load, on distribution systems. First, the uncertainties are modeled by statistical distributions that closely mimic their physical behavior and studied through Monte-Carlo (MC) simulations. Later, an analytical PCM based approach is formulated and designed on the modified IEEE 13-node test feeder including VRE. A comparative study demonstrates the effectiveness of the proposed PCM based uncertainty modeling in distribution feeders with lesser computational burden and improved accuracy.
AB - Increased penetration of distributed generation (DG) driven by Variable Renewable Energy (VRE) sources and integration of modern loads constituted by Electric Vehicles (EV) and behind-the-meter smart appliances pose operational challenges for traditional distribution systems. This paper introduces a framework based on probabilistic collocation method (PCM) to model and analyzes the effects of inherent uncertainties, both in generation, and load, on distribution systems. First, the uncertainties are modeled by statistical distributions that closely mimic their physical behavior and studied through Monte-Carlo (MC) simulations. Later, an analytical PCM based approach is formulated and designed on the modified IEEE 13-node test feeder including VRE. A comparative study demonstrates the effectiveness of the proposed PCM based uncertainty modeling in distribution feeders with lesser computational burden and improved accuracy.
KW - Probabilistic collocation method
KW - distribution feeder
KW - monte-carlo
KW - uncertainties
KW - variable renewable energy
UR - http://www.scopus.com/inward/record.url?scp=85113384151&partnerID=8YFLogxK
U2 - 10.1109/NAPS50074.2021.9449669
DO - 10.1109/NAPS50074.2021.9449669
M3 - Conference contribution
AN - SCOPUS:85113384151
T3 - 2020 52nd North American Power Symposium, NAPS 2020
BT - 2020 52nd North American Power Symposium, NAPS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd North American Power Symposium, NAPS 2020
Y2 - 11 April 2021 through 13 April 2021
ER -