| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | August 6, 2014 |
| Latest Amendment Date: | March 2, 2015 |
| Award Number: | 1442495 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sankar Basu
sabasu@nsf.gov (703)292-7843 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2014 |
| End Date: | August 31, 2017 (Estimated) |
| Total Intended Award Amount: | $399,980.00 |
| Total Awarded Amount to Date: | $415,980.00 |
| Funds Obligated to Date: |
FY 2015 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1109 GEDDES AVE STE 3300 ANN ARBOR MI US 48109-1015 (734)763-6438 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1301 Beal Avenue AnnArbor MI US 48109-2122 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Special Projects - CNS, CyberSEES |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Integrating high penetrations of renewable energy resources into electric power systems requires additional back-up capacity (reserves) to manage real-time power imbalance. Load control can provide reserves, possibly at lower cost and/or with less environmental impact than most power plants. However, scheduling load-based reserves is challenging because of uncertainty -- the availability of reserves is a function of stochastic factors including weather and load usage patterns. This research project is therefore investigating data-driven, distribution-free approaches to managing load control uncertainty in power system scheduling problems. By generalizing distributionally robust optimization algorithms, chance-constrained optimal power flow solution techniques are being developed to manage the time-varying, correlated, and complex uncertainty associated with load control. Furthermore, these new techniques can be used to determine conditions under which load control becomes a competitive option. Specifically, trade-offs between load control uncertainty and profitability are being quantified in order to assess the impact of uncertainty on environmental sustainability.
The methods being developed through this work form a basis for quantifying the net environmental impact of using uncertain load control for reserves. In particular, they enable more informed utilization of load control for supporting the integration of renewable energy resources. The results will guide load control program design, power system market design, and, more broadly, energy policy that seeks to balance cost and environmental impact. Furthermore, the optimization approaches being developed in this work are applicable to other power system problems, and to stochastic problems in other fields.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project brought together three researchers from electrical engineering and operations research with a long term vision of developing methodologies to manage uncertainty in future electric power systems and quantifying how uncertainty affects power system sustainability. Specifically, this project developed data-driven and distribution-free optimization methods suited to dispatching power systems with both fluctuating renewable energy sources and flexible loads contributing to balancing reserves via load control. We investigated load control uncertainty; formulated optimal power flow problems include load-based reserves; applied emerging distributionally robust optimization (DRO) techniques to the problem; developed new DRO techniques; compared the performance of these emerging/new techniques to standard approaches in terms of solution reliability, cost, computational requirements; and investigated the impact of uncertainty on costs and sustainbility (e.g., greenhouse gas emissions) via case studies. We found that realistic load control uncertainty is substantial; formulations must include mechanisms for managing uncertainty in ways that do not allow it to propagate. We also found that DRO techniques generally compare favorably against conventional methods; though solutions can be conservative and sometimes require significant computational power. Our novel DRO techniques reduce the size of the uncertainty set leading to reduced conservatism/cost without a significant decrease in performance or increase in computational power. Using loads to balance renewable power intermittency enables higher penetrations of renewables and less curtailment, reducing greenhouse gas emissions. The project contributed to the support of 4 PhD students and 2 undergraduate students. It resulted in 17 papers and 4 abstracts. We also developed an interactive game for high school students to learn power system optimization and the role of uncertainty and reserves; to date, it has been deployed 3 times including at UM Summer Camps and the materials are available to the public online.
Last Modified: 11/09/2017
Modified by: Johanna Mathieu
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