| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | June 26, 2017 |
| Latest Amendment Date: | May 25, 2022 |
| Award Number: | 1710923 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Anthony Kuh
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | August 1, 2017 |
| End Date: | July 31, 2023 (Estimated) |
| Total Intended Award Amount: | $315,727.00 |
| Total Awarded Amount to Date: | $315,727.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
6425 BOAZ ST RM 130 DALLAS TX US 75205-1902 (214)768-4708 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
6425 BOAZ TX US 75275-0302 |
| 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): | EPCN-Energy-Power-Ctrl-Netwrks |
| 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): | 47.041 |
ABSTRACT
The objective of this research is to develop a novel data-driven decision support system (DSS) to determine efficient short-term operation strategies for accommodating large-scale PV generation and mitigating its adverse effects on distribution network reliability and security. The proposed DSS will 1) improve the spatiotemporal variability and uncertainty quantification for PV generation in distribution networks; 2) determine the accommodated variability and uncertainty boundaries of PV generation to ensure the economic efficiency and security of the distribution networks; 3) propose cost effective dynamic solutions that incorporate the temporal and spatial variability and uncertainty of demand and supply in the distribution networks; 4) capture the interactions among autonomous entities such as microgrids, distributed energy resources (DERs), and controllable demands; with distribution system operator (DSO). This research plan facilitates rapid dissemination of the generated knowledge to the research and education community. Specifically, it promotes innovative collaboration among graduate and undergraduate students to provide effective solutions for the current challenges in the distribution network operation. This project ensures the highest quality of integrated research and education to meet the emerging workforce and educational needs of the U.S. energy sector by introducing new curriculum for undergraduate and graduate programs, promoting interdisciplinary collaboration, recruiting underrepresented minorities and female students, and developing K-12 outreach activities.
The specific objectives of this research are as follows. a) develop a scalable data-driven approach that leverages a multi-task deep learning framework to provide improved spatiotemporal uncertainty measures for the large-scale PV generation in the distribution network. b) quantify the flexibility measures as tertiary regulation services and form distributionally adaptive robust optimization problems to quantify the accommodated spatiotemporal variability and uncertainty. c) provide a tight convex relaxation for the non-convex risk-averse short-term operation problem for the unbalanced distribution networks. The non-convexity in feasibility set is as a result of the introduced integer variables for switching and commitment decisions as well as the unbalanced AC power flow constraints. d) develop decentralized optimization framework to capture the spatial interdependence among the dynamic temporal decisions made by the autonomous entities and the DSO.
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.
Increasing the installed capacity of solar photovoltaic (PV) power generation in power distribution networks offers several potential benefits. These include reduced greenhouse gas emissions, lower transmission and distribution power losses, and decreased electricity costs. Furthermore, these resources, when combined with other controllable generation technologies, could improve the reliability and resilience of the distribution networks. However, the intermittency and variability of solar generation pose challenges to operating distribution networks, particularly with significant penetration of this energy resource. Fluctuations in solar photovoltaic power generation can cause bidirectional power flows, leading to potential voltage fluctuations and violations in distribution networks. Additionally, the uncertainty in solar PV generation profiles poses reliability and security risks to balance the demand and supply in the distribution networks and increases flexibility requirements in the bulk power network to support the power distribution networks.
To address these challenges, this project has developed several frameworks with the following goals:
- Improve predictions of solar PV power generation in the power distribution network.
- Establish limits for how much solar PV power can be integrated into the distribution networks, taking into account the variability in demand and PV energy sources.
- Operate power distribution networks with solar PV generation to improve technical and economic metrics while incorporating the network constraints.
The detailed research activities include:
a. Creating a solar irradiance prediction model using machine learning. This model analyzes historical data from neighboring PV sites to estimate the probability density of future solar irradiance patterns.
b. Developing optimization frameworks to determine the limits of the solar PV power that can be supported by the distribution network, considering various demand types and the inherent uncertainty in solar PV generation.
c. Formulating mathematical frameworks for operating distribution networks with solar PV generation. These frameworks are designed to improve economic efficiency and voltage profile while ensuring energy supply security in the network. They also account for the interactions between microgrids and distribution network operators, improving the overall economic performance of the system while incorporating the network constraints.
The outcomes of this project have been disseminated via journal publications, conference proceedings, dissertations, and presentations at conferences and panels. Several graduate students participated in the research and educational activities associated with this project. Some of the research outcomes were incorporated into the course materials offered at Southern Methodist University. The research and education activities during this project could increase public awareness of the benefits of solar PV generation and the imposed challenges to handle the uncertainty and variability of this clean resource in the network.
Last Modified: 12/13/2023
Modified by: Mohammad E Khodayar
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