TY - JOUR
T1 - A Hierarchically Coordinated Operation and Control Scheme for DC Microgrid Clusters under Uncertainty
AU - Xu, Qianwen
AU - Xu, Yan
AU - Xu, Zhao
AU - Xie, Lihua
AU - Blaabjerg, Frede
N1 - Funding Information:
Manuscript received June 23, 2019; revised November 20, 2019 and March 1, 2020; accepted April 15, 2020. Date of publication April 28, 2020; date of current version December 16, 2020. This work was supported in part by the Ministry of Education (MOE), Republic of Singapore, under Grant AcRF TIER 1 2019-T1-001-069 (RG75/19), in part by the National Research Foundation (NRF) of Singapore under Project NRF2018-SR2001-018, and in part by Wallenberg-NTU Presidential Postdoc Fellowship in Nanyang Technological University, Singapore. Y. Xu’s work is supported by Nanyang Assistant Professorship from Nanyang Technological University, Singapore. Paper no. TSTE-00686-2019. (Corresponding author: Yan Xu.) Qianwen Xu, Yan Xu, and Lihua Xie are with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2010-2012 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - In the existing works of microgrid clusters, operation and real-time control are normally designed separately in a hierarchical architecture, with the real-time control in the primary and secondary levels, and operation in the tertiary level. This article proposes a hierarchically coordinated control scheme for DC MG clusters under uncertainty. In each MG, the tertiary level controller optimizes the operating cost in the MG by taking into account the real-time uncertainties of renewable generations and loads deviated from the forecasting data; and the primary controller responds to the real-time power fluctuations through an optimised droop curve. The hierarchically coordinated optimization problem is formulated to optimize the power set points and droop curve coefficients simultaneously under uncertainties using an adjustable robust optimization model. For the MG cluster, the energy sharing of each MG in the cluster is optimized to minimize the total operating cost and the transmission loss. The overall optimization problem is solved in a distributed manner by alternating direction method of multipliers (ADMM) where each MG entity only exchanges boundary information (i.e. the power exchange of MG entity with the MG cluster), thus information privacy and plug-and-play feature of each MG are guaranteed. The proposed approach optimally coordinates the operation and real-time control layers of a DC MG cluster with uncertainties; it achieves decentralized power sharing at the real-time control layer and distributed optimization at the operation layer, featuring high scalability, reliability and economy. Case studies of a DC MG cluster are conducted in Matlab/Simulink in order to demonstrate the effectiveness of the proposed approach.
AB - In the existing works of microgrid clusters, operation and real-time control are normally designed separately in a hierarchical architecture, with the real-time control in the primary and secondary levels, and operation in the tertiary level. This article proposes a hierarchically coordinated control scheme for DC MG clusters under uncertainty. In each MG, the tertiary level controller optimizes the operating cost in the MG by taking into account the real-time uncertainties of renewable generations and loads deviated from the forecasting data; and the primary controller responds to the real-time power fluctuations through an optimised droop curve. The hierarchically coordinated optimization problem is formulated to optimize the power set points and droop curve coefficients simultaneously under uncertainties using an adjustable robust optimization model. For the MG cluster, the energy sharing of each MG in the cluster is optimized to minimize the total operating cost and the transmission loss. The overall optimization problem is solved in a distributed manner by alternating direction method of multipliers (ADMM) where each MG entity only exchanges boundary information (i.e. the power exchange of MG entity with the MG cluster), thus information privacy and plug-and-play feature of each MG are guaranteed. The proposed approach optimally coordinates the operation and real-time control layers of a DC MG cluster with uncertainties; it achieves decentralized power sharing at the real-time control layer and distributed optimization at the operation layer, featuring high scalability, reliability and economy. Case studies of a DC MG cluster are conducted in Matlab/Simulink in order to demonstrate the effectiveness of the proposed approach.
KW - coordination
KW - DC microgrid cluster
KW - distributed optimization
KW - operation
KW - real-time control
UR - http://www.scopus.com/inward/record.url?scp=85091691451&partnerID=8YFLogxK
U2 - 10.1109/TSTE.2020.2991096
DO - 10.1109/TSTE.2020.2991096
M3 - Journal article
AN - SCOPUS:85091691451
SN - 1949-3029
VL - 12
SP - 273
EP - 283
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
IS - 1
M1 - 9080125
ER -