Jichen Zhang, Xuan Wei, Yinliang Xu. Bi-Level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(5):2367-2379.
Jichen Zhang, Xuan Wei, Yinliang Xu. Bi-Level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty[J]. CSEE Journal of Power and Energy Systems, 2026, 12(5): 2367-2379.
Jichen Zhang, Xuan Wei, Yinliang Xu. Bi-Level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(5):2367-2379.DOI: 10.17775/CSEEJPES.2022.07520.
Jichen Zhang, Xuan Wei, Yinliang Xu. Bi-Level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty[J]. CSEE Journal of Power and Energy Systems, 2026, 12(5): 2367-2379.DOI: 10.17775/CSEEJPES.2022.07520.
Bi-Level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty
摘要
Abstract
To realize the safe
economical
and low-carbon operation of the pelagic island microgrid group
this paper develops a bi-level energy management framework in a joint energy-reserve market where the microgrid group (MG) operator and renewable and storage aggregators (RSA) are independent stakeholders with their interests. At the upper level
the MG operator determines the optimal transaction prices with aggregators to minimize MG operation costs while ensuring all safety constraints are satisfied under uncertainty. In the lower level
aggregators utilize vessels for battery swapping and transmission among islands in addition to energy arbitrage by participating in energy and reserve markets to maximize their revenue. An upper bound tightening iterative algorithm is proposed for the formulated problem with nonlinear terms and integer variables at the lower level to improve efficiency and reduce the gap between the upper bound and lower bound compared with the existing reformulation and decomposition algorithm. Case studies validate the effectiveness of the proposed approach and demonstrate its advantage in terms of optimality and computation efficiency compared with other methods.