| NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
| Recipient: |
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| Initial Amendment Date: | February 12, 2018 |
| Latest Amendment Date: | February 12, 2018 |
| Award Number: | 1756006 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Alan Sussman
OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | March 1, 2018 |
| End Date: | February 28, 2021 (Estimated) |
| Total Intended Award Amount: | $167,240.00 |
| Total Awarded Amount to Date: | $167,240.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
201 PRESIDENTS CIR SALT LAKE CITY UT US 84112-9049 (801)581-6903 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
UT US 84112-8930 |
| 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): | CAREER: FACULTY EARLY CAR DEV |
| Primary Program Source: |
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| 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
Energy systems are transitioning from fossil fuels to renewable energy resources, such as solar and wind. Despite their obvious environmental advantages, large-scale integration of renewable energy faces a number of technical difficulties. These challenges are linked to the fact that the availability of renewable energy, i.e., wind and sunlight, depends on nature rather than the controllable process of burning a fuel. This project develops innovative software tools that enable the deployment of flexible transmission, which is a cost-effective solution to address these challenges. As a result of these developments, system operators will be able to benefit from a new resource that was not available to them before. Particularly, this project aims to employ flexible transmission in order to alleviate the uncertainty and intermittency of renewable energy resources, and facilitate higher levels of renewable energy production. With the help of efficient mathematical modeling and high-performance computing, the models developed in this project are fast and appropriate for real-time operation. As renewable energy is economical and emission-free, this project will have a significant positive impact on the national health, prosperity, and welfare. Additionally, this project will integrate computational methods and algorithm development in power engineering education to fill a much-needed gap in power engineering curriculum.
The objective of this project is to enable frequent utilization of flexible transmission for one of the largest and most complex cyber-physical system that exists today: the North American power grid. Co-optimization of flexible transmission and generation dispatch is not possible yet due to the computational burden of the underlying mathematical problem. Specifically, transmission flexibility, in the form of controllable impedance, introduces non-convexities to power system operation that are challenging to handle within the limited available computational time. This project substantially reduces such computational burden through a novel and fast optimization technique, which exploits the mathematical structure of power flows. Consequently, utilization of flexible transmission can become possible which can help reduce the operation cost and improve the system reliability. This project also aims to mitigate the intermittencies and uncertainties associated with renewable generation by utilizing transmission flexibility. It employs stochastic optimization as the mathematical framework to model renewable energy uncertainties, and optimizes flexible transmission and controllable generation as the decision variables. Algorithm decomposition and high-performance computing are employed to reduce the solution time required for stochastic optimization in order to ensure fast and efficient computation. This is an essential component of the project as the computational time available for real-time operation is less than five minutes. The education component of this project enriches the power engineering curriculum, by developing educational modules, on computational methods, algorithm design, and high-performance computing for power engineering students.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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.
Energy systems are transitioning from fossil fuels to renewable energy resources, such as solar and wind. Despite their obvious environmental advantages, large-scale integration of renewable energy faces several technical difficulties. These challenges are linked to the fact that the availability of renewable energy, i.e., wind and sunlight, depends on nature rather than the controllable process of burning a fuel. Additionally, increased share of renewable energy has changed the flow patterns in the transmission system and increased the level of congestion in the network. To further increase the share of renewable energy, the transmission system needs to be upgraded, either through building new transmission lines or enhancing the utilization of the existing grid. The latter is cheaper and can be achieved faster. One way to improve the utilization of the existing transmission grid is employment of power flow control technologies that can reroute power to the paths that are not congested. Although power flow control technology has existed for a few decades, existing power system operation software tools have limited capabilities in co-optimizing the set point of power flow controllers with generation dispatch. Thus, the full benefits of the technology cannot be materialized.
This project has developed efficient models that enable enhanced utilization of power flow controllers within energy management systems. As a result of these developments, system operators can benefit from a new resource that was not available to them before. Specific findings of this project are summarized below:
- Proper modeling of power flow controllers is essential for realizing the benefits of power flow control technology. Tractability of the model should be a central focus of algorithm development. The most advanced technology without a proper operation algorithm would be limited like how the speed of a fast car would be limited on a dirt road.
- Power flow control will always help improve the grid operation objective, which is often cost minimization. This may or may not be aligned with other desirable outcomes, such as renewable curtailment reduction or emission reduction.
- Renewable energy curtailment can be reduced, using power flow control, depending on 1) existing generation mix and specifically the share of coal, 2) congestion patterns in the grid, 3) locations of renewable generation and how they contribute to network congestion, and 4) location of power flow controllers.
- Grid emissions may be reduced, using power flow control, depending on the following factors: 1) existing generation mix and specifically the share of coal, 2) congestion patterns in the grid, 3) locations of renewable generation and how they contribute to network congestion, and 4) location of power flow controllers.
- Power flow control can improve risk management in grid operation if appropriate modeling practices are used.
As renewable energy is economical and emission-free, the developments of this project can positively impact the national health, prosperity, and welfare. Additionally, this project has extensively trained four graduate students, who are well-versed in power engineering, optimization, and computer programming, and thus, contributing to the Nation’s workforce. Finally, educational modules are developed on power flow control and algorithm development in power engineering that are currently being taught in advance electrical engineering courses.
Last Modified: 06/29/2021
Modified by: Mostafa Sahraei Ardakani
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