Energy Profile Clustering with Balancing Mechanism towards more Reliable Distributed Virtual Nodes for Demand Response Ioannis Koskinas * , Apostolos C. Tsolakis * , Venizelos Venizelou † , Dimosthenis Ioannidis * , George E. Georghiou † , and Dimitrios Tzovaras * * Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece {jkosk, tsolakis, djoannid, dimitrios.tzovaras}@iti.gr † Department of Electrical and Computer Engineering, University of Cyprus, 1678 Nicosia, Cyprus, {venizelou.venizelos, georgiou}@ucy.ac.cy Abstract—As the energy markets become more dynamic, cus- tomers’ segmentation has become a major concern, especially for Aggregators that contain Distributed Energy Resources in their portfolio. Furthermore, the management complexity in the direction of insightful Demand Response (DR) actions that will yield high profit margins and will hedge against economical risks has been increased since the incorporation of low and medium customers in DR programs. Grouping customers, as independent accumulated virtual nodes (VNs) to the grid, based on their energy profile and their contractual characteristics, facilitates Aggregators overcoming markets’ and network’s constraints, as well as the designing of collective price policies and purposeful DR strategies. This paper proposes a fully featured methodology that encompasses a soft clustering approach, based on the Gaus- sian Mixture Model with Expectation Maximization Algorithm, presenting a Temporal Data Dynamic Segmentation (TDDS) algorithm that not only allocates low and medium customers in VNs that share common energy profiles, but also preserves an internal balance in the VNs’ resources, in terms of their ability to satisfy reliably DR requests, exploiting the clusters’ intersection points to balance the VNs without disrupting their energy profile purity. Experimental results demonstrate an increase in the reliability of each cluster by up to 17.6% without disrupting the clustering coherence. Index Terms—Soft Clustering, Gaussian Mixture Model, Ex- pectation Maximization, Demand Response, Reliability I. I NTRODUCTION The development towards smart distribution grids and the decentralization of the power systems requires the technology, modern buildings and other individual assets (e.g., appliances, HVAC systems, etc.) to be energy efficient, as well as en- ergy flexible. The existence of flexibility in power systems is extremely crucial in order to facilitate integration of the highly volatile Renewable Energy Sources (RES) and cover their intermittency with Demand Response (DR) strategies. The achievement of the aforementioned integration requires efficient data monitoring, which has been achieved through advanced metering infrastructures such as smart meters [1]. However, the wide variety of event information and the large volume of data pose high risks in operation and power distribution between electricity customers, which affects the reliability and the profitability of the power network [2], [3]. In addition, current DR markets, require quite significant amounts of available flexibility per customer (e.g. 1-3 MW), making it extremely difficult for small and medium customers to partic- ipate in them. For this reason, clustering electricity customers based on their energy characteristics (consumption, generation, storage, etc.) is necessary, and an upcoming promising solution for risk elimination and introduction of new revenue streams. Clustering is a data mining technique where electric cus- tomers are selected and categorized in various groups (clus- ters) based on their energy profiles. In addition, this method expedites the specification of intrinsic patterns in the big data sets that have emerged. Essentially, given that all smart meters generate large volumes of data, and in most cases without detailed information, their management can significantly be facilitated by grouping data and customers into smaller groups, offering the extraction of higher level of information and the provision of intuitive understanding of their behaviour. For the energy sector, clustering advantages are mainly identified for those who have access to large amounts of energy-related data such as Transmission System Operators (TSOs), Distribu- tion System Operators (DSOs), retailers, utilities, Aggregators and other decision support systems which are responsible for instant operations and fast decision making. As clusters introduce aggregated information to the distribution nodes, and grouped customers can be handled collectively and not individually, the concept of Virtual Nodes (VNs) is used in the presented work for the created clusters. The VNs are easier to handle entities for both Aggregators and systems operators, especially in the context of DR requests. Going over the literature on the field, the first surveys were done by utilities, system operators and researchers, using the monthly usage and some fixed information (e.g. voltage levels, demand), categorizing households and load profiles based on the following classes: demographics and socio-economic fac- tors, dwelling characteristics, habits (e.g. consumption timing), energy conservation, energy efficiency goals, knowledge about electricity consumption and the attitude of use. Presently, data and detailed measurements for more than tens of thousands 978-1-7281-7660-4/21/$31.00 © 2021 IEEE 2021 International Conference on Smart Energy Systems and Technologies (SEST) | 978-1-7281-7660-4/21/$31.00 ©2021 IEEE | DOI: 10.1109/SEST50973.2021.9543218 Authorized licensed use limited to: Centre for Research and Technology (C.E.R.T.H.). 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