Optimizing wind farm siting to reduce power system impacts of wind variability
[1];
[2];
[3];
- Georgia Inst. of Technology, Atlanta, GA (United States). School of Industrial & Systems Engineering; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Georgia Inst. of Technology, Atlanta, GA (United States). School of Industrial & Systems Engineering, School of Public Policy
- Georgia Inst. of Technology, Atlanta, GA (United States). School of Industrial & Systems Engineering
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. 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.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC52-07NA27344
- OSTI ID:
- 1525745
- Alternate ID(s):
- OSTI ID: 1501952
- Report Number(s):
- LLNL-JRNL-759581; 947567
- Journal Information:
- Wind Energy, Vol. 22, Issue 7; ISSN 1095-4244
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed
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journal | November 2019 |
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