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Title: Uncertainty Quantification for Capacity Expansion Planning

Abstract

This report quantifies the uncertainty in output decisions from a Capacity Expansion Planning (CEP) model. The need to understand how uncertainties within CEP models and modeling assumptions affect Quantities of Interest (QoIs) such as expansion and operating costs, as well as expansion decisions remains an ongoing challenge in scientific research and industrial operations. This area of research is particularly important for models which seek to capture how large networks will evolve and operate under increased sources of variable generation, i.e., higher penetration of renewable technologies such as solar and wind generators. Uncertainty quantification (UQ) of CEP models which estimate expansion costs and decisions, and production cost models which estimate operating costs and dispatch decisions, is a key focus of research at NREL. The Regional Energy Deployment System (ReEDS) represents a state-of-the-art CEP model and considers a range of possible grid evolutions in an attempt to identify key drivers, ramifications, and decisions which contribute to better informed investment and policy decisions. However, research to quantify how uncertainties and model assumptions, such as unit commitment (UC), within ReEDS may be affecting its outputs remains challenging due to to size and complexity of the model

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; US Department of Education
OSTI Identifier:
1659787
Report Number(s):
NREL/TP-2C00-76708
MainId:9369;UUID:e04c2fc4-367d-473f-a6e3-29f34c0cc56e;MainAdminID:13356
DOE Contract Number:  
AC36-08GO28308; P200A180014
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
uncertainty quantification; sensitivity analysis; machine learning; data-driven modeling; production cost modeling; capacity expansion planning; polynomial chaos; polynomial expansions; active subspaces; stochastic programming; reserve capacity margin; natural gas price; transmission availability; cost of wind; cost of excess load; unit commitment; expansion cost; operation cost; installed wind capacity; installed gas capacity; loss of load cost

Citation Formats

Diaz, Paul, King, Ryan, Sigler, Devon, Cole, Wesley, and Jones, Wesley. Uncertainty Quantification for Capacity Expansion Planning. United States: N. p., 2020. Web. doi:10.2172/1659787.
Diaz, Paul, King, Ryan, Sigler, Devon, Cole, Wesley, & Jones, Wesley. Uncertainty Quantification for Capacity Expansion Planning. United States. https://doi.org/10.2172/1659787
Diaz, Paul, King, Ryan, Sigler, Devon, Cole, Wesley, and Jones, Wesley. 2020. "Uncertainty Quantification for Capacity Expansion Planning". United States. https://doi.org/10.2172/1659787. https://www.osti.gov/servlets/purl/1659787.
@article{osti_1659787,
title = {Uncertainty Quantification for Capacity Expansion Planning},
author = {Diaz, Paul and King, Ryan and Sigler, Devon and Cole, Wesley and Jones, Wesley},
abstractNote = {This report quantifies the uncertainty in output decisions from a Capacity Expansion Planning (CEP) model. The need to understand how uncertainties within CEP models and modeling assumptions affect Quantities of Interest (QoIs) such as expansion and operating costs, as well as expansion decisions remains an ongoing challenge in scientific research and industrial operations. This area of research is particularly important for models which seek to capture how large networks will evolve and operate under increased sources of variable generation, i.e., higher penetration of renewable technologies such as solar and wind generators. Uncertainty quantification (UQ) of CEP models which estimate expansion costs and decisions, and production cost models which estimate operating costs and dispatch decisions, is a key focus of research at NREL. The Regional Energy Deployment System (ReEDS) represents a state-of-the-art CEP model and considers a range of possible grid evolutions in an attempt to identify key drivers, ramifications, and decisions which contribute to better informed investment and policy decisions. However, research to quantify how uncertainties and model assumptions, such as unit commitment (UC), within ReEDS may be affecting its outputs remains challenging due to to size and complexity of the model},
doi = {10.2172/1659787},
url = {https://www.osti.gov/biblio/1659787}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {6}
}