Integrated energy scheduling under uncertainty for sustainable ports

Renewable energy generation has attracted increasing attention in port energy systems due to the urgent need for sustainable development. This study focuses on an integrated energy system that involves wind energy, photovoltaic energy, hydrogen energy and energy storage in the sustainable port. The multiple energy sources are used to generate electricity to support container loading and unloading in vessels. The realistic container loads are unknown to the port because of the uncertain arrival information, which affect the specific integrated energy scheduling. A two-stage stochastic programming model is proposed to incorporate uncertain demand, multi-energy supply, electricity storage and sales. The vessel delay costs and the related energy costs that are generated from electricity consumption, storage and sales are minimized when allocating the integrated energy to serve berthing vessels. A metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) framework is proposed for solving the model. The proposed metaheuristic algorithm fixes the decision variable values of the first-stage problem and allows transfers to solve sub-problems under all uncertain scenarios. The effectiveness of the proposed algorithm is demonstrated through small-scale, medium-scale, and large-scale numerical experiments in terms of solution quality and computation time. Some experiments are further conducted to analyze the impact of renewable energy generation, renewable energy sources, berthing vessel types, and vessel delay tolerances. Managerial insights can be obtained for optimizing the integrated energy scheduling schemes in sustainable ports. The findings can also provide implications for ports with different scales when optimizing the configurations of renewable energy supply.