Abstract
Increase in the proliferation of distributed energy resources require real-time situational awareness for efficient grid operations. State estimation plays an important role for the real-time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hier-archical spectral clustering-based network partitioning algorithm followed by a decentralized compressive sensing (DCS)-based state estimation. The applicability of the proposed network partitioning algorithm is tested on an IEEE 123-bus network, an IEEE 8,500-node system, and a 6,000+ node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum number of edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6,000+ node distribution network after dividing the network into 18 optimal partitions. Simulation results show that the DCS-based state estimation recovers the system states with high accuracy and low complexity.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - 2023 |
Event | 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) - Washington, D.C. Duration: 16 Jan 2023 → 19 Jan 2023 |
Conference
Conference | 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) |
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City | Washington, D.C. |
Period | 16/01/23 → 19/01/23 |
Bibliographical note
See NREL/CP-5D00-83876 for preprintNREL Publication Number
- NREL/CP-5D00-86223
Keywords
- alternating direction method of multipliers
- compressive sensing
- decentralized state estimation
- network partition
- power distribution network
- spectral clustering