Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation

09/20/2019
by   Zhixiong Hu, et al.
0

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Loève expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/13/2020

Probabilistic Load-Margin Assessment using Vine Copula and Gaussian Process Emulation

The increasing penetration of renewable energy along with the variations...
research
03/30/2020

A Blackbox Yield Estimation Workflow with Gaussian Process Regression for Industrial Problems

In this paper an efficient and reliable method for stochastic yield esti...
research
05/18/2023

Dynamic Term Structure Models with Nonlinearities using Gaussian Processes

The importance of unspanned macroeconomic variables for Dynamic Term Str...
research
11/11/2021

Space-time reduced-order modeling for uncertainty quantification

This work focuses on the space-time reduced-order modeling (ROM) method ...
research
02/28/2021

On Surrogate Learning for Linear Stability Assessment of Navier-Stokes Equations with Stochastic Viscosity

We study linear stability of solutions to the Navier–Stokes equations wi...
research
04/19/2023

Constructing a simulation surrogate with partially observed output

Gaussian process surrogates are a popular alternative to directly using ...
research
01/10/2022

Efficient forecasting and uncertainty quantification for large scale account level Monte Carlo models of debt recovery

We consider the problem of forecasting debt recovery from large portfoli...

Please sign up or login with your details

Forgot password? Click here to reset