Identification of distributed energy resources in low voltage distribution networks

Abstract

International policies and targets to globally reduce carbon dioxide emissions have contributed to the increasing penetration of distributed energy resources (DER) in low-voltage distribution networks. The growth of technologies such as rooftop PV systems and EV has, to date, not been rigorously monitored and record-keeping is deficient. This has brought new technical challenges related to the operation and planning in low-voltage distribution networks requiring innovative techniques to increase flexibility, reliability, and security of supply on this side of the electrical systems. In this regard, this thesis explores techniques to actively monitor these systems in low voltage distribution systems.

Non-intrusive load monitoring (NILM) method, commonly used for energy management systems, contribute to the effective integration of clean technologies within existing distribution networks. In this thesis, NILM methods are developed for the classification and disaggregation of DER electrical signatures from aggregated measurements at customer and distribution levels. Electrical profiles of EV and PV systems are allocated within aggregated measurements including conventional electrical appliances. Publicly available data and an experimental dataset including either one or several households are used to train and test classification and regression models. The NILM methods proposed here are based on the usage of conventional machine learning techniques such as kNN, RF, SVM, and MLP. This provides the proposed algorithms with realistic processing times, a key factor needed to differentiate highly variable DER power profiles from other loads and to update real-time conditions of the electrical system to distribution network operators.

The results achieved confirm the effectiveness of the methodologies proposed to individually identify DER with outstanding performance metrics for both EV and PV electrical profiles. This demonstrates the potential of the methods proposed to be implemented as an embedded function of smart meters in customer and low voltage distribution sides to increase observability in distribution networks.

Thesis embargoed until 31 July 2025.

Date of Award	Jul 2022
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	This project is part of the Collaborative REsearch of Decentralization, ElectrificatioN, Communications and Economics (CREDENCE) project, which is funded by a US-Ireland Department for the Economy (DfE), Science Foundation Ireland (SFI) and a US National Science Foundation (NSF) award under the Research and Development Partnership Program (Centre to Centre) award (grant number USI 110)
Supervisor	David Laverty (Supervisor), Aoife Foley (Supervisor) & D John Morrow (Supervisor)