TY - GEN
T1 - Soft Actor Critic Based Volt-VAR Co-optimization in Active Distribution Grids
AU - Hossain, Rakib
AU - Gautam, Mukesh
AU - Mansourlakouraj, Mohammad
AU - Livani, Hanif
AU - Benidris, Mohammed
AU - Baghzouz, Yahia
N1 - Funding Information:
ACKNOWLEDGEMENT This paper is based upon work supported by the U.S. DoE’s Office of EERE under the Solar Energy Technologies Office Award Number DE-EE0009022.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt-VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.
AB - Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt-VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.
KW - deep reinforcement learning
KW - Distribution grids
KW - soft actor critic
KW - Volt-VAR optimization
UR - https://www.scopus.com/pages/publications/85141534923
UR - https://www.mendeley.com/catalogue/f80b5ec3-d7a0-3ae3-80c5-1226aae39340/
U2 - 10.1109/PESGM48719.2022.9916976
DO - 10.1109/PESGM48719.2022.9916976
M3 - Conference contribution
AN - SCOPUS:85141534923
T3 - IEEE Power and Energy Society General Meeting
BT - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
PB - IEEE Computer Society
T2 - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Y2 - 17 July 2022 through 21 July 2022
ER -