Energy analytics applications to improve network planning of electricity distribution network operators
Creators: name: AL KHAFAF, Nameer
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400805
subject: Electrical energy transmission, networks and systems
classificationCode: 461103
subject: Deep learning

description: The energy consumption data from smart meters have been widely recognised as a key enabler for delivering a range of benefits to the electricity industry. An in-depth analysis of this data can drive the development and implementation of various intelligence models that can support Distribution Network Operators (DNO) in network planning and meeting legal obligations. This thesis explores some of the state-of-the-art applications of machine learning and data mining in analysing energy data and delivering benefits to DNO. The thesis covers cluster analysis of smart meter data for demand response programs, deep learning approach to detect illegal grid connect of residential rooftop Photovoltaics (PV) generation, benefits of energy storage systems for residential customers and correction of residential customers cross-referencing in Low Voltage (LV) network.

First, a novel clustering validity index to find the optimal size of clusters is proposed. The method is an entropy measure based on eigenvalues of the correlation matrix of clusters' time series. The intuition behind the proposed metric, inspired by synchronization of time series signals, is that the size of clusters is optimal when distinguishability between them is maximal. The proposed metric, Entropy of Eigenvalue (EoE), is tested on synthetic data with known cluster size, and show that it outperforms existing methods. EoE is then applied on two real-world energy consumption datasets it to find the optimal number of clusters. As a result, a time series dataset can be partitioned into optimal size. This allows DNO to identify which customers to target for demand side management and how best to target each cluster based on the clusters' consumption profile.
 
Second, two deep learning models are proposed for two purposes; to identify customers with illegal connection of rooftop PV generation and to find the exact date or range of dates of when PV came online. This will form as part of the DNOs tools to identify illegal grid connect PV generation. The contribution is twofold; there is no work in the literature that applies deep learning in this research area which creates a gap on a suitable and effective classifier and the research on deep learning is mature and well-studied which makes it difficult for DNOs to adopt a model suitable for the application. Hence a novel deep learning model is not proposed rather how to use existing ones and adapt it to the research problem. The main finding is that DNOs can use deep learning to detect illegal rooftop installation with 98% accuracy using smart meter consumption data only.
 
Third, an Australian case study is presented on how residential battery installations lead to behavioural changes in the way energy is consumed. The case study provides insights to both distribution network operators and policymakers based on analysis of real-world dataset. The data comprises more than 5000 energy consumers with either distributed generation systems, such as PV and Energy Storage Systems (ESS), or without. The methodology focuses on the analysis of energy consumption of consumers with PV and ESS energy consumers and compare them against consumers without ESS. In addition, an economic analysis on the benefit of installing ESS is presented using payback period and internal rate of return. The main finding is that residential ESS provide a range of benefits to DNOs which can be used in applications such as demand side management. However, given that the cost and the payback period for the ESS technology is still high, governments should continue supporting the rollout of such systems by subsidy programs and implementing necessary policy directives until the technology becomes cheaper.

Finally, a metric is proposed to detect cross-referencing of energy consumers to distribution transformers at the low voltage distribution level using smart meter voltage magnitude measurements only. The method is tested on three real-world datasets each having 450+ consumers and 3-5 transformers. The proposed measure can detect the cross-referencing error with 100% accuracy. This allows utilities to proactively identify all customers that will be impacted by planned outages so they can be notified in accordance with relevant laws.