TY - GEN
T1 - A Consensus ADMM-Based Distributed Volt-VAr Optimization for Unbalanced Distribution Networks
AU - Inaolaji, Adedoyin
AU - Savasci, Alper
AU - Paudyal, Sumit
AU - Kamalasadan, Sukumar
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Volt-VAr optimization (VVO) is usually performed by a central coordinator which provides the optimal setpoints of the control devices in the network for efficient voltage regulation in active distribution networks. However, such a central control scheme is prone to a single-point failure and results in privacy concerns. Conversely, distributed optimization methods decompose the entire network into subsystems such that local controllers compute a local optimization problem and have limited communication with their neighboring control agents, thereby enhancing data dignity. This work, therefore, adopts a distributed VVO approach which is based on the alternating direction method of multipliers (ADMM). The linearized Dist3Flow model (LinDist3Flow) is used as a convex grid model and the distributed VVO minimizes voltage deviation in an unbalanced active distribution system subject to limits on voltage magnitudes and inverter active and reactive power capabilities. Case studies implemented on the IEEE-123 node system and a 2522-node system demonstrate that ADMM-based VVO converges to the same solution as that obtained from the centralized VVO, but the performance of the ADMM algorithm is sensitive to the choice of the penalty parameter. Moreover, if the centralized formulation is convex and already tractable for even large-scale feeders, then solving the distributed counterpart might not necessarily provide any computational advantage but is still desirable for reasons such as data privacy and robustness to processor failure.
AB - Volt-VAr optimization (VVO) is usually performed by a central coordinator which provides the optimal setpoints of the control devices in the network for efficient voltage regulation in active distribution networks. However, such a central control scheme is prone to a single-point failure and results in privacy concerns. Conversely, distributed optimization methods decompose the entire network into subsystems such that local controllers compute a local optimization problem and have limited communication with their neighboring control agents, thereby enhancing data dignity. This work, therefore, adopts a distributed VVO approach which is based on the alternating direction method of multipliers (ADMM). The linearized Dist3Flow model (LinDist3Flow) is used as a convex grid model and the distributed VVO minimizes voltage deviation in an unbalanced active distribution system subject to limits on voltage magnitudes and inverter active and reactive power capabilities. Case studies implemented on the IEEE-123 node system and a 2522-node system demonstrate that ADMM-based VVO converges to the same solution as that obtained from the centralized VVO, but the performance of the ADMM algorithm is sensitive to the choice of the penalty parameter. Moreover, if the centralized formulation is convex and already tractable for even large-scale feeders, then solving the distributed counterpart might not necessarily provide any computational advantage but is still desirable for reasons such as data privacy and robustness to processor failure.
KW - alternating direction method of multipliers (ADMM)
KW - distributed algorithms
KW - distribution systems
KW - optimal power flow
KW - Volt-VAr optimization
UR - https://www.scopus.com/pages/publications/85142774284
U2 - 10.1109/IAS54023.2022.9939862
DO - 10.1109/IAS54023.2022.9939862
M3 - Conference contribution
AN - SCOPUS:85142774284
T3 - Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)
BT - 2022 IEEE Industry Applications Society Annual Meeting, IAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Industry Applications Society Annual Meeting, IAS 2022
Y2 - 9 October 2022 through 14 October 2022
ER -