An Approximately Optimal Algorithm for Scheduling Phasor Data Transmissions in Smart Grid Networks
K. G. Nagananda, P. P. Khargonekar

TL;DR
This paper presents an approximation algorithm for scheduling phasor data transmissions in smart grids, optimizing data flow within the grid's communication infrastructure while considering electrical properties and NP-hard constraints.
Contribution
The paper introduces a novel approximation algorithm for scheduling phasor data that accounts for electrical network properties and NP-hard constraints, improving data transmission efficiency.
Findings
The algorithm achieves near-optimal scheduling performance.
Performance evaluated on IEEE test bus systems.
Provides polynomial-time solutions for a complex NP-hard problem.
Abstract
In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the realtime hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPower System Optimization and Stability · Microgrid Control and Optimization · Optimal Power Flow Distribution
