Robust optimization of regional biomass supply chain system design and operation with data-driven uncertainties

Abstract

To address climate change and ensure energy security, biomass energy has become a popular renewable energy alternative to traditional fossil fuels. This study focuses on the strategic planning and tactical management problem of a regional biomass supply chain system with multiple feedstock and final products. A novel SVC-based data-driven robust optimization model is developed to provide the tradeoff solutions under uncertainties. Compared with the traditional robust optimization model, it can better depict the uncertain sets, reduce data redundancy, and provide less conservative strategies for decision makers according to their risk preferences. The proposed model is validated through a case study, Meizhou city in China. The results suggested the optimal investment scale and site for biorefineries and biomass power plants at the strategic planning level, as well as the feedstock supply network, inventory management, and production arrangement at the tactic management level. The optimal robust solutions can be derived by varying the envelope level of the uncertain sets. Moreover, sensitivity analysis is performed to investigate the influences of the variations of key parameters. It is found that the produced bioethanol and electricity lack cost advantages in the current situation, even with a 30 % reduction in main cost parameters. Insufficient biomass yield may require extra capital investment to satisfy bioenergy demand but with a higher supply cost. Thus, a high and long-term subsidy is necessary to facilitate the sustainable development of biomass and the low-carbon transition of the energy sector.