Natural disasters has been causing an increasing amount of economic losses in the past two decades. Natural disasters, such as hurricanes, winter storms, and wildfires, can cause severe damages to power systems, significantly impacting industrial, commercial, and residential activities, leading to not only economic losses but also inconveniences to people’s day-today life. Improving the resilience of power systems can lead to a reduced number of power outages during extreme events and is a critical goal in today’s power system operations. This paper presents a model for decentralized decision-making in power systems based on distributed optimization and implemented it on a modified RTS-96 test system, discusses the convergence of the problem, and compares the impact of decision-making
mechanisms on power system resilience. Results show that a decentralized decision-making algorithm can significantly reduce power outages when part of the system is islanded during severe transmission contingencies.
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An Operational Resilience Metric for Modern Power Distribution Systems
The electrical power system is the backbone of our nations critical infrastructure. It has been designed to withstand single component failures based on a set of reliability metrics which have proven acceptable during normal operating conditions. However, in recent years there has been an increasing frequency of extreme weather events. Many have resulted in widespread long-term power outages, proving reliability metrics do not provide adequate energy security. As a result, researchers have focused their efforts resilience metrics to ensure efficient operation of power systems during extreme events. A resilient system has the ability to resist, adapt, and recover from disruptions. Therefore, resilience has demonstrated itself as a promising concept for currently faced challenges in power distribution systems. In this work, we propose an operational resilience metric for modern power distribution systems. The metric is based on the aggregation of system assets adaptive capacity in real and reactive power. This metric gives information to the magnitude and duration of a disturbance the system can withstand. We demonstrate resilience metric in a case study under normal operation and during a power contingency on a microgrid. In the future, this information can be used by operators to make more informed decisions based on system resilience in an effort to prevent power outages.
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- Award ID(s):
- 1846493
- NSF-PAR ID:
- 10222651
- Date Published:
- Journal Name:
- 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)
- Page Range / eLocation ID:
- 334 to 342
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/QRS-C51114.2020.00065
