Uncertainty-driven optimization of photovoltaic-integrated building-level energy Hubs: Advancing SDG 7 targets

This paper develops a bi-level uncertainty-driven optimization model whose fundamental elements are risk management at the upper level and the techno-economic and environmental assessment at the lower level to enhance the energy efficiency of photovoltaic-integrated building-level energy hubs (BEHs), supporting the goals of Sustainable Development Goal 7. The lower level uses the energy hub tool to model buildings as BEHs, efficiently coordinating energy carriers, conversion technologies, and storage systems. It aims to determine optimal operational schedules that meet various constraints while minimizing operational costs. The upper level applies risk-averse decision-making using information-gap decision theory to handle multiple, interdependent uncertainties over the operational horizon. A hybrid solution approach combines the non-dominated sorting genetic algorithm II at the upper level with the DICOPT solver at the lower level, addressing the model's complex, mixed-integer, and nonlinear structure. A hybrid decision-making tool is developed by integrating the fuzzy satisfying method with the distance metric methodology to identify the optimal solution from the Pareto frontier. The model is validated through cases on an industrial building, demonstrating superior performance over traditional approaches in key performance indicators. Overall, the model offers a robust and scalable method for optimizing energy efficiency in the building sector under simultaneous uncertainties.