Bi-Level Coordinated Planning of Port Multi-Energy Systems Considering Source-Load Uncertainty Based on WGAN-GP and SBOA

The high-penetration integration of renewable energy into port power systems is challenged by the stochastic volatility of wind–solar generation and dynamic load demands. To address this, this study proposes a data-driven bi-level coordinated planning framework for port wind–solar-storage systems, integrating a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and hybrid secretary bird optimization algorithm (SBOA) for solution seeking. The WGAN-GP-K-Means++ framework is adopted to capture the high-dimensional spatiotemporal correlations under the uncertainty of source ports and loads, and to generate the wind and solar resource scenarios for typical day. Subsequently, a bi-level planning model is constructed: the upper layer optimizes the siting and sizing of distributed generation and energy storage to minimize the life-cycle net present value, while the lower layer minimizes annual operating costs through multi-scenario dispatch. To resolve the resulting complex mixed-integer programming problem, a nested SBOA-Gurobi algorithm is developed. Case study of a Guangxi port demonstrates that the proposed approach reduces life-cycle cost by 44.94% relative to the baseline grid-connected scheme and exhibits superior convergence stability compared with GA, GRSO, and WOA. Additionally, sensitivity analysis quantifies the impact of electricity pricing policies, shore power utilization rates, and discount rate on the system’s economic benefits. This study provides a decision-support tool for the low-carbon transition and economic planning of port energy systems.