My research under the guidance of Prof. Vassilis Kekatos focuses on investigating ways to improve situational awareness in power distribution grids under communication constraints to facilitate grid control tasks. In broad terms, we are looking at exploiting passively (every 15 mins or 1 hour) and actively (say every 5 seconds over 1 minute) collected smart meter and phasor-measurement (PMU) data for various distribution grid learning tasks (such as recovering non-metered loads and identifying grid topology). While the passive scheme complies with the throughput rate of smart meters, the active scheme exploits the controllability of smart inverters found in solar panels, energy storage units, and electric vehicles to purposefully probe the grid and subsequently record the response of the underlying physical system.
Using results linking linear algebra with graph theory, we were able to show that the locations of the (non)-metered buses on a distribution grid graph were sufficient in determining the recoverability of non-metered loads in the passive and active schemes. In addition to studying conditions for system identifiability, we have also developed semi-definite programming (SDP)- and second-order conic programming (SOCP)- based solvers for power flow and power system state estimation under the active scheme.
My current research efforts are focused on design of optimal probing sequences, grid probing for load inference and topology processing using PMU data as well as extensions of our system identifiability results under the passive scheme to multi-phase and meshed networks.
- S. Bhela, V. Kekatos, and S. Veeramachaneni, “Enhancing Observability in Distribution Grids using Smart Meter Data,” IEEE Trans. on Smart Grid, 2017 (early access). [online] [preprint]
- S. Bhela, V. Kekatos, and S. Veeramachaneni, “Smart Inverter Grid Probing for Learning Loads: Part I – Identifiability Analysis,” IEEE Trans. on Power Systems, 2018 (submitted) [preprint]
- S. Bhela, V. Kekatos, and S. Veeramachaneni, “Smart Inverter Grid Probing for Learning Loads: Part II – Probing Injection Design,” IEEE Trans. on Power Systems, 2018 (submitted). [preprint]
- S. Bhela, V. Kekatos, and H. Veeramachaneni, “Power Grid Probing for Load Learning: Identifiability over Multiple Time Instances,” IEEE Workshop on Comp. Adv. in Multi-Sensor Adaptive Proc. (CAMSAP), Curacao, Dec. 2017. [online][preprint] [slides]
- S. Bhela, V. Kekatos, and S. Veeramachaneni, “Power Distribution System Observability with Smart Meter Data,” IEEE Global Conf. on Signal and Inf. Process. (GlobalSIP), Symposium on Smart Grids, Montreal, Canada, Nov. 2017. (GSA Travel Fund Program Award). [online] [preprint] [slides]
- S. Bhela, V. Kekatos, L. Zhang, and S. Veeramachaneni, “Enhancing Observability in Power Distribution Grids,” IEEE Intl. Conf. on Acoustics, Speech, and Signal Process. (ICASSP), New Orleans, LA, Mar. 2017. [online] [preprint] [poster]
- S. Bhela, “A Game-theoretic Framework to Investigate Conditions for Cooperation between Wind Power Producers and Energy Storage Operators,” M.S. Thesis. [online]