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Title: Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation

Abstract

Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast of the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. Inmore » the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage.« less

Authors:
; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
National Science Foundation (NSF)
OSTI Identifier:
1342503
Report Number(s):
NREL/JA-5D00-67113
Journal ID: ISSN 1949-3029
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Sustainable Energy; Journal Volume: 8; Journal Issue: 1
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; batteries; energy storage; optimal scheduling; power system planning; wind energy

Citation Formats

Baker, Kyri, Hug, Gabriela, and Li, Xin. Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation. United States: N. p., 2017. Web. doi:10.1109/TSTE.2016.2599074.
Baker, Kyri, Hug, Gabriela, & Li, Xin. Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation. United States. doi:10.1109/TSTE.2016.2599074.
Baker, Kyri, Hug, Gabriela, and Li, Xin. Sun . "Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation". United States. doi:10.1109/TSTE.2016.2599074.
@article{osti_1342503,
title = {Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation},
author = {Baker, Kyri and Hug, Gabriela and Li, Xin},
abstractNote = {Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast of the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. In the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage.},
doi = {10.1109/TSTE.2016.2599074},
journal = {IEEE Transactions on Sustainable Energy},
number = 1,
volume = 8,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2017},
month = {Sun Jan 01 00:00:00 EST 2017}
}