TY - GEN
T1 - Advancement of automatic generation control in power systems with large share of variable energy resources
AU - Tsydenov, Evgeny A.
AU - Prokhorov, Anton V.
AU - Wang, Li
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper proposes the practical implementation of an approach for improvement of automatic generation control performance and discusses its particular importance for power systems with large share of variable energy resources. The approach allows advancement of the functional block responsible for estimation of plant participation factors, which increases flexibility and selectivity of power flow control. Real time optimization model was established to reach different control goals. To meet the performance requirements, the artificial neural network was developed for power flow estimation. To improve performance and reduce computational burden, the Lasso regression method was proposed and tested for selection of the model features relevant for the considered control task. Finally, the software tool was developed to implement the algorithm, based on the proposed approach, and tested on a model of real 60 GW interconnection containing 464 nodes and 742 branches. The results of the software testing confirm its feasibility and easy integration into existing automatic generation control systems.
AB - This paper proposes the practical implementation of an approach for improvement of automatic generation control performance and discusses its particular importance for power systems with large share of variable energy resources. The approach allows advancement of the functional block responsible for estimation of plant participation factors, which increases flexibility and selectivity of power flow control. Real time optimization model was established to reach different control goals. To meet the performance requirements, the artificial neural network was developed for power flow estimation. To improve performance and reduce computational burden, the Lasso regression method was proposed and tested for selection of the model features relevant for the considered control task. Finally, the software tool was developed to implement the algorithm, based on the proposed approach, and tested on a model of real 60 GW interconnection containing 464 nodes and 742 branches. The results of the software testing confirm its feasibility and easy integration into existing automatic generation control systems.
UR - http://www.scopus.com/inward/record.url?scp=85124690347&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124690347&partnerID=8YFLogxK
U2 - 10.1109/IAS48185.2021.9677237
DO - 10.1109/IAS48185.2021.9677237
M3 - Conference contribution
AN - SCOPUS:85124690347
T3 - Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)
BT - 2021 IEEE Industry Applications Society Annual Meeting, IAS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Industry Applications Society Annual Meeting, IAS 2021
Y2 - 10 October 2021 through 14 October 2021
ER -