Self-adaptive Controllers for Renewable Energy Communities Based on Transformer Loading Estimation
Paper i proceeding, 2022

In this paper, self-adaptive controllers for renewable energy communities based on data-driven approach are proposed to mitigate the voltage rise and transformer congestion at the community level. In the proposed approach, the transformer loading percentage is estimated by the trained data-driven model, which uses the extreme gradient boosting regression algorithm based on a measurement set acquired from critical coupling points of the communities. To avoid voltage rise issues, the droop control parameters (i.e., voltage threshold for P - V, Q - V curves) are adaptively tuned based on the solar irradiance availability and estimated transformer loading. The proposed approach has been tested in the IEEE European LV distribution network. Results showed that the control approach could effectively reduce 22.2 % of the total overloaded instances, while still keeping voltage magnitude in the operation range. This method can help DSOs manage voltage violation and congestion without further communication.

transformer loading estimation

Droop control

transformer congestion

voltage rise

Författare

Minh Quan Tran

Technische Universiteit Eindhoven

Trung Thai Tran

Technische Universiteit Eindhoven

Phuong H. Nguyen

Technische Universiteit Eindhoven

Tam T. Mai

Det Norske Veritas (DNV)

Anh Tuan Le

Chalmers, Elektroteknik, Elkraftteknik

2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022


9781665485371 (ISBN)

2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022
Prague, Czech Republic,

Ämneskategorier

Kommunikationssystem

Reglerteknik

Annan elektroteknik och elektronik

DOI

10.1109/EEEIC/ICPSEurope54979.2022.9854677

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Senast uppdaterat

2024-01-03