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Infrastructure networks, such as electrical power grids, transportation and water supply systems, support critical societal functions of society. Failures of such networks can have severe consequences, and quantification of the probability of failure of such systems is essential for understanding and managing their reliability. Analytical and simulation methods have been proposed to solve such kinds of problems, among which sampling methods feature prominently. Recently, the authors extended widely used structural reliability algorithms, subset simulation, cross-entropy-based importance sampling as well as uncertainty quantification methods built from particle integration methods and exact confidence, all for efficient reliability analysis in discrete spaces. This paper tests the performance of these algorithms for static network reliability assessment. In particular, we compare these methods for optimal power flow problems in various IEEE benchmark models. Overall, the cross-entropy-based method outperforms the other methods in all benchmark models except the largest IEEE 300, while the adaptive effort subset simulation and particle integration methods are more suitable for handling high-dimensional problems. By building up the benchmark models, we provide unified examples for comparing different emerging methods in static network reliability assessment and also to support improvement or combination of these methods.
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Determining Reserve Requirements for Energy Storage to Manage Demand-Supply Imbalance in Power Grids
Proper integration of energy storage systems (ESS) into existing or future grids will depend on the effectiveness of models which seek optimal placement and sizing at the transmission and distribution levels. Current literature reviews reveal sizing methodologies can be improved to ease infrastructure integration, and those works with models useful for planning focus solely on micro-grids, wind power and forecasting, photovoltaics, or small communities. It is of interest to create an efficient, reliable ESS sizing model for large scale grids that contains interpretable models, has less sensitivity due to low model uncertainty, yet still is dependable due to an imposed reliability criterion. This work determined the minimum feasible size ESS to satisfy reserve requirements for a power grid with a high penetration of renewable sources. Results showed imposing a reliability criterion through loss of load expectation (LOLE) and energy index of reliability (EIR) resulted in more conservative capacity needs.
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- Award ID(s):
- 1646229
- PAR ID:
- 10076823
- Date Published:
- Journal Name:
- 2018 IEEE Electronic Power Grid (eGrid)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
