Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Bi-Level Phase Load Balancing Methodology with Clustering-Based Consumers’ Selection Criterion for Switching Device Placement in Microgrids

Version 1 : Received: 9 December 2020 / Approved: 9 December 2020 / Online: 9 December 2020 (12:26:52 CET)

How to cite: Grigoraș, G.; Neagu, B.; Scarlatache, F.; Noroc, L.; Chelaru, E. Bi-Level Phase Load Balancing Methodology with Clustering-Based Consumers’ Selection Criterion for Switching Device Placement in Microgrids. Preprints 2020, 2020120226 (doi: 10.20944/preprints202012.0226.v1). Grigoraș, G.; Neagu, B.; Scarlatache, F.; Noroc, L.; Chelaru, E. Bi-Level Phase Load Balancing Methodology with Clustering-Based Consumers’ Selection Criterion for Switching Device Placement in Microgrids. Preprints 2020, 2020120226 (doi: 10.20944/preprints202012.0226.v1).

Abstract

In the last years, the Distribution Grid Operators (DGOs) assumed transition strategies of the distribution grids towards an active area associated with the "Smart Grids" concept. They are considering the use of Artificial Intelligence techniques, combined with advanced technologies and real-time remote communication solutions of the enormous data amounts, to develop smart solutions into the small size distribution grids, also called microgrids (μGs). These solutions will provide support for the DGOs to ensure an optimal operation of the technical infrastructure of the μGs. In this context, a bi-level methodology for solving the phase load balancing problem in the μGs with complex topologies and a high number of single-phase consumers, considering a clustering-based selection criterion of the consumers for placement of the switching devices, was proposed in the paper. A real μG from a rural area, with 114 consumers integrated into the Smart Metering System (SMS), belonging to the DGO from Romania, was considered in testing the proposed methodology. An implementation degree of 17.5%, corresponding to the phase load balancing equipment installed to only 20 consumers from the μG, led to a faster computational time with 43% and reducing the number of switching operations by 92% than in the case of a full implementation degree (100%). The performance indicators related to the unbalance factor and energy-saving used in the evaluation of the technical benefits highlighted the efficiency of the proposed methodology.

Subject Areas

microgrids; phase load balancing; consumers’ selection criterion; switching devices; unbalance factor; energy-saving.

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