PREDICTING FLEXIBILITY FROM LV NETWORKS BY USING GEOSPATIAL FORECASTING AND SYNTHETIC NETWORKS

PREDICTING FLEXIBILITY FROM LV NETWORKS BY USING GEOSPATIAL FORECASTING AND SYNTHETIC NETWORKS | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 December 2025 V1 Latest version Share on PREDICTING FLEXIBILITY FROM LV NETWORKS BY USING GEOSPATIAL FORECASTING AND SYNTHETIC NETWORKS Authors : Giuditta Pisano 0000-0001-8231-8570 [email protected] , Simona Ruggeri , and Fabrizio Pilo Authors Info & Affiliations https://doi.org/10.22541/au.176722209.98153476/v1 Published IET Conference Proceedings Version of record Peer review timeline 157 views 104 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The flexibility provided by Distributed Energy Resources (DER) and Demand Response (DR) can enhance the ability of distribution networks to accommodate renewable generation and new high-peak/coincident loads. However, flexibility is not infinite, and its use in providing local services to distribution system operators (DSOs) can limit its availability for system ancillary services, required by transmission system operators (TSOs) to ensure security and adequacy. As the energy transition progresses, competition for flexibility between TSOs and DSOs will increase. Similar interaction appears between Medium Voltage (MV) and Low Voltage (LV) grids, for instance, when smaller DSOs are supplied by MV distribution networks operated by a different DSO. The Secondary Substation (SS), the interface between the MV and the LV, can provide ancillary services by changing its working point to keep the MV network operation within technical boundaries. Forecasting the SS behaviour is challenging due to the limited observability of LV networks and the uncertainty of generation/consumption patterns. This paper proposes a methodology that identifies and forecasts the amount of flexibility LV can offer without compromising its operation. To address data scarcity, a suitable methodology for developing and using synthetic networks is also proposed. Case studies demonstrate the validity of the proposed approach. Supplementary Material File (cired-2025-id1266_estensione_fp_finale_clean.docx) Download 10.30 MB File (cired-2025-id1266_estensione_fp_finale_clean.pdf) Download 1018.08 KB Information & Authors Information Version history V1 Version 1 31 December 2025 Peer review timeline Published IET Conference Proceedings Version of Record 1 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords distribution networks distribution planning and operation energy resources monte carlo methods power distribution planning Authors Affiliations Giuditta Pisano 0000-0001-8231-8570 [email protected] University of Cagliari View all articles by this author Simona Ruggeri Università di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica View all articles by this author Fabrizio Pilo University of Cagliari View all articles by this author Metrics & Citations Metrics Article Usage 157 views 104 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Giuditta Pisano, Simona Ruggeri, Fabrizio Pilo. PREDICTING FLEXIBILITY FROM LV NETWORKS BY USING GEOSPATIAL FORECASTING AND SYNTHETIC NETWORKS. Authorea . 31 December 2025. DOI: https://doi.org/10.22541/au.176722209.98153476/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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