The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With large-scale DER penetration in distribution grids, traditional outage detection methods, which rely on customers report and smart meters' “last gasp” signals, will have poor performance, because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages. To address these challenges, we propose a data-driven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee. Specifically, we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages. However, existing change point detection methods require post-outage voltage distribution, which are unknown in distribution systems. Therefore, we design a maximum likelihood estimator to directly learn distribution pa-rameters from voltage data. We prove the estimated parameters-based detection also achieves optimal performance, making it extremely useful for fast distribution grid outage identifications. Furthermore, since smart meters have been widely installed in distribution grids and advanced infrastructure (e.g., PMU) has not widely been available, our approach only requires voltage magnitude for quick outage identification. Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.
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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
- NSF-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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