Energy-sharing initiatives among offshore microgrids (OMGs) and electric vessel (EV) fleets are anticipated to pave the way toward a highly cost-effective and stable offshore power-transport system. To this end, this study proposes a collaborative framework for stability-aware expansion planning of alternating current/direct current (AC/DC) OMGs and the electrification of maritime passenger transport (MPT). The OMG planning is structured as a three-stage optimization model that enhances economic efficiency and stability by co-optimizing AC/DC retrofit investments, normal operation strategies, and transient responses under N-1 contingency conditions. The MPT electrification model coordinates EV deployment and the retirement of fossil-fueled vessels to determine the optimal composition of hybrid fleets, which provide energy-sharing and flexibility services for OMGs while meeting MPT demands. The two sub-models are further synthesized into a collaborative planning model for OMGs and hybrid vessel fleets through coordinated constraints. By embedding the proposed planning model into the Shapley value approach, a decentralized collaborative framework based on a multi-loop iterative procedure is developed. This framework ensures privacy protection and decision-making independence while capturing the coalition behaviors of all entities. Numerical studies verify the effectiveness of the proposed approach.

This paper proposes a collaborative planning model for active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on physical characteristics of the problem, breaking down the holistic problem into two sub-problems for solution. Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. Subproblem II uses a mixed-integer second-order cone programming (MISOCP) model to plan ADN and retrofit or construct V2G charging stations (V2GCS), as well as multiple distributed generation resources (DGRs). The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning. The presented model is tested in the 47-node ADN in Longgang District, Shenzhen, China, and the IEEE 33-node ADN, demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.