Day-ahead dispatch optimization with dynamic thermal rating of transformers and overhead lines

• Several studies have demonstrated how Dynamic Line Rating (DLR) could be an effective solution for increasing transmission capacity of existing overhead lines. As opposed to Static Line Ratings (SLR), DLR allows for higher power flows depending on real time thermal state of conductors, which highly depend on actual weather conditions. Similarly, recent advances in transformer thermal modelling revealed the feasibility of Dynamic Transformer Rating (DTR) based on the temporal evolution of top oil and winding hot spot temperatures. However, the joint dynamic thermal rating of both overhead lines and transformers in transmission networks has not been thoroughly addressed yet in the literature. This paper proposes a day-ahead dispatch optimization problem based on DC-Optimal Power Flow, where transformer top oil and hot spot dynamics are directly accounted for together with dynamic line ratings of selected transmission lines. Simulated weather data from an actual power system are mapped to the IEEE RTS 24 bus system thus allowing for the estimation of DLR on several lines and the influence of ambient temperature on transformer rating. Results indicate the potential benefits that using DLR in conjunction with DTR could provide for the optimal power system dispatch. The proposed approach does not only indicate advantages compared to standard rating scenarios, but also shows a positive impact that dynamic line rating has on unlocking transformer constraints and vice versa.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Keyword

Dynamic line rating

Dynamic transformer rating

Optimal power dispatch

Power system optimization

Wind power integration

Publication and Content Type

ref (subject category)

art (subject category)