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001051589 020__ $$a978-3-032-03097-9 (print)
001051589 020__ $$a978-3-032-03098-6 (electronic)
001051589 0247_ $$2doi$$a10.1007/978-3-032-03098-6_16
001051589 0247_ $$2ISSN$$a0302-9743
001051589 0247_ $$2ISSN$$a1611-3349
001051589 0247_ $$2datacite_doi$$a10.34734/FZJ-2026-00516
001051589 037__ $$aFZJ-2026-00516
001051589 1001_ $$0P:(DE-Juel1)184782$$aTitz, Maurizio$$b0
001051589 1112_ $$aNordic Energy Informatics Academy Conference$$cStockholm$$d2025-08-20 - 2025-08-22$$wSweden
001051589 245__ $$aA Data-Driven Analysis of Unscheduled Flows in the European Power System
001051589 260__ $$aCham$$bSpringer Nature Switzerland$$c2025
001051589 29510 $$aEnergy Informatics. EIA Nordic 2025
001051589 300__ $$a237 - 253
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001051589 4900_ $$aLecture Notes in Computer Science$$v16096
001051589 520__ $$aCross-border electricity trading is becoming increasingly important in the European power system. Unscheduled power flows induce additional costs and may lead to congestion and impair power grid operation. In this contribution we provide a data-centric analysis of unscheduled flows in the Central European power grid. Using methods from explainable machine learning, we identify the main driving factors for unscheduled flows and quantify their impact. Unscheduled flows in the meshed part of the grid can be attributed to transit or loop flows primarily and are well described by a linear model. The performance is substantially worse for unscheduled flows on bridges, with forecast errors being the most important drivers. This performance gap is probably due to data quality issues.
001051589 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x0
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001051589 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b1$$eCorresponding author
001051589 773__ $$a10.1007/978-3-032-03098-6_16$$p237–253$$v16096$$y2025
001051589 8564_ $$uhttps://juser.fz-juelich.de/record/1051589/files/unscheduled_flows.pdf$$yOpenAccess
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