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Title: Optimizing wind farm siting to reduce power system impacts of wind variability

Abstract

We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics—forward and backward greedy algorithms—to find approximate solutions. The results are compared with the exact optimization results for a well-selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. Here, we find an approximate efficient frontier to compare trade-offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm ismore » more effective at reducing the wind power variability than the forward greedy algorithm. Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [3]
  1. Georgia Inst. of Technology, Atlanta, GA (United States). School of Industrial & Systems Engineering; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Georgia Inst. of Technology, Atlanta, GA (United States). School of Industrial & Systems Engineering, School of Public Policy
  3. Georgia Inst. of Technology, Atlanta, GA (United States). School of Industrial & Systems Engineering
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1525745
Alternate Identifier(s):
OSTI ID: 1501952
Report Number(s):
LLNL-JRNL-759581
Journal ID: ISSN 1095-4244; 947567
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Wind Energy
Additional Journal Information:
Journal Volume: 22; Journal Issue: 7; Journal ID: ISSN 1095-4244
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 97 MATHEMATICS AND COMPUTING; multiobjective optimization; siting variability reduction

Citation Formats

Musselman, Amelia, Thomas, Valerie M., Boland, Natashia, and Nazzal, Dima. Optimizing wind farm siting to reduce power system impacts of wind variability. United States: N. p., 2019. Web. doi:10.1002/we.2328.
Musselman, Amelia, Thomas, Valerie M., Boland, Natashia, & Nazzal, Dima. Optimizing wind farm siting to reduce power system impacts of wind variability. United States. doi:10.1002/we.2328.
Musselman, Amelia, Thomas, Valerie M., Boland, Natashia, and Nazzal, Dima. Tue . "Optimizing wind farm siting to reduce power system impacts of wind variability". United States. doi:10.1002/we.2328.
@article{osti_1525745,
title = {Optimizing wind farm siting to reduce power system impacts of wind variability},
author = {Musselman, Amelia and Thomas, Valerie M. and Boland, Natashia and Nazzal, Dima},
abstractNote = {We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics—forward and backward greedy algorithms—to find approximate solutions. The results are compared with the exact optimization results for a well-selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. Here, we find an approximate efficient frontier to compare trade-offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm is more effective at reducing the wind power variability than the forward greedy algorithm. Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset.},
doi = {10.1002/we.2328},
journal = {Wind Energy},
issn = {1095-4244},
number = 7,
volume = 22,
place = {United States},
year = {2019},
month = {3}
}

Journal Article:
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