Reliability Parameterised Distribution Grid Flexibility Aggregation Considering Renewable Uncertainties

verfasst von: Neelotpal Majumdar, Prapatsara Kengkat, Rauan Yermekbayev, Lutz Hofmann
Abstract: Active distribution networks (ADNs) are increasingly assuming an important role in future power system operations. Due to incremental phasing out of thermal power plants, a shift of ancillary services provision from the renewables is underway. Therefore, increased focus on the renewable rich distribution grid level is of prime importance. Active and reactive power flexibility (PQ-flexibility) quantification from the underlying distribution grid at the vertical interconnection to the overlaying grid is a topic of current research. A two dimensional PQ-flexibility map at the vertical interconnection serves as a basis for flexibility provision between grid operators. A terminology adapted in current research is the Feasible Operating Region (FOR) of the underlying distribution grid. The task of flexibility aggregation is further complicated when renewable power injection uncertainties are considered. The two dimensional PQ-flexibility map or FOR requires adjustments considering the probable generation scenarios. Therefore, a reliability parameterized flexibility aggregation segregated into confidence intervals is practical. The undertaken study adapts a method for generating spatially correlated renewable generation uncertainties from wind power plants (WPP) and photovoltaic generation. A corresponding statistical analysis is performed for a reliability parameterisation of the PQ-fexibility seggregated into confidence intervals. Subsequently, a FOR determination adhering to the determined confidence intervals is proposed. Results present multiple reliability parameterized two dimensional PQ-flexibility maps, classified according to the confidence intervals.
Organisationseinheit(en): Institut für Elektrische Energiesysteme
Typ: Aufsatz in Konferenzband
Publikationsdatum: 2023
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Artificial intelligence, Energieanlagenbau und Kraftwerkstechnik, Erneuerbare Energien, Nachhaltigkeit und Umwelt, Elektrotechnik und Elektronik, Modellierung und Simulation
Ziele für nachhaltige Entwicklung: SDG 7 – Erschwingliche und saubere Energie
Elektronische Version(en): https://doi.org/10.1109/UPEC57427.2023.10294524 (Zugang: Geschlossen)