An adaptive importance sampling method for probabilistic optimal power flow

Author

Huang, Jie ; Xue, Yusheng ; Dong, Z.Y. ; Wong, K.P.

Author_Institution

State Grid Electr. Power Res. Inst., Nanjing, China

fYear

2011

Firstpage

1

Lastpage

6

Abstract

A new probabilistic optimal power flow method is developed to manage electricity market price risk. The proposed method is an adaptive importance sampling method which based on the principle of importance sampling and reinforcement learning. The estimation result of conventional Monte Carlo simulation is taken as benchmark. Case study is conducted on IEEE 39-Bus system to compare the proposed method with point estimate method, which shows the feasibility and efficiency of the method.

Keywords

Monte Carlo methods; learning (artificial intelligence); load flow; power engineering computing; power markets; IEEE 39-bus system; Monte Carlo simulation; adaptive importance sampling; electricity market price risk; probabilistic optimal power flow; reinforcement learning; Accuracy; Benchmark testing; Computational modeling; Electricity supply industry; Estimation; Monte Carlo methods; Uncertainty; Importance Sampling; Point Estimate Method; Probabilistic Optimal Power Flow; Reinforcement Learning; Risk Management;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Society General Meeting, 2011 IEEE

Conference_Location

San Diego, CA

ISSN

1944-9925

Print_ISBN

978-1-4577-1000-1

Electronic_ISBN

1944-9925

Type

conf

DOI

10.1109/PES.2011.6039167

Filename

6039167