Optimizing integrated energy systems: A multi-scenario scheduling approach with stepped carbon trading and two-stage hydrogen production under uncertainty

The uncertainty of building loads and renewable energy sources poses significant challenges in optimizing Integrated Energy Systems (IES). This paper introduces an operational framework for a multi-energy IES that integrates cold, heat, power, and hydrogen, balancing environmental and economic considerations. This approach refines traditional electricity-to-gas conversion with a two-stage system incorporating hydrogen production, aiming to lower environmental impact and boost sustainability by replacing carbon emissions with hydrogen, and enhancing renewable energy utilization. For managing diverse building load data, we apply an innovative kernel-based iterative self-organizing data analysis algorithm (K-l-ISODATA) to analyze load profiles, offering precise daily load data for IES scheduling optimization. Through a case study in a Chinese industrial park, the results show that K-l-ISODATA efficiently clusters high-dimensional load curves sampled every five minutes. In the six typical daily scenarios it generates, hydrogen substitution notably reduces carbon emissions and operational costs, validating the effectiveness of the tiered carbon trading mechanism in curbing emissions.