Abstract

Capturing the dynamic behavior of the power distribution grids, especially under high penetration of renewables, is of high interest for grid operators. The distribution power grids are not fully observable due to lack of sufficient metering infrastructure, especially downstream of medium-voltage substations. Therefore, fusion of data recorded at significantly different reporting rates is proposed to increase the situational awareness of the system with non-negligible effect on the accuracy of the monitoring tool. Higher reporting rates are possible for next generation smart meters (SM), but they raise higher concerns about data privacy, already an issue for SM rollout. This article proposes a framework for knowledge extraction from high reporting-rate SM data. The process takes place at SM level and with low computation and communication costs and preserving user privacy, with the scope to increase the accuracy of the monitoring tools for distribution power grids. The methodology makes use of statistical metrics able to capture system dynamics relevant for network diagnosis. The proposed approach is validated on a three-phase low-voltage power flow model applied to a realistic testbed microgrid and real field measurements synchronized at 1 s.

Highlights

  • THE power distribution grids are facing major structural and operational transformations due to increasing deployment of distributed energy resources (DER), especially renewable energy resources (RES) at low voltage (LV) feeders on top of changes in the typical LV load profiles

  • One can mention voltage and frequency support, fault detection and localization [4], electricity thefts detection [5], or demand side response [6], [5]. While these analytical tools commonly rely on the data coming from the smart meter (SM), the information is filtered information on the energy delivered in – sometimes unspecified – time intervals rather than, for example a much useful, power profile

  • Validate the model framed by these metrics using real field measurements from high reporting rate SM (HRRSM) and a three-phase LV loadflow model applied to a realistic testbed microgrid with more than 50% renewables installed on the LV feeders

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Summary

INTRODUCTION

THE power distribution grids are facing major structural and operational transformations due to increasing deployment of distributed energy resources (DER), especially renewable energy resources (RES) at low voltage (LV) feeders on top of changes in the typical LV load profiles.

HIGH REPORTING RATE SMART METERS
Applications and cyber-security at the SMX layer
PROBLEM AND METHODOLOGY
The problem and related works
USE-CASES AND EVALUATION OF RESULTS
Findings
CONCLUSION

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