A novel framework for optimal design of solar-powered integrated energy system considering long timescale characteristics

Solar-powered integrated energy systems (IES) play a pivotal role in the global transition towards cleaner energy structures. Optimal design of such systems remains a significant research challenge, particularly in the context of diverse energy sources, multi-objectives, and the impact of uncertainties. In this paper, a novel framework for optimal design of IES is proposed with consideration of multiple objectives, stochastic variation of solar radiation, and the long timescale operational characteristics. A multi-stage hierarchical contraction algorithm is proposed to decouple the optimal scheduling from optimal sizing problem. The setting of multi-stage and hierarchical contraction ensure the optimization performance while mitigate the computational burden. Additionally, a stochastic radiation generation model is proposed based on the clearness index and daily clearness states. The model generates radiation data that considers both the stochastic variability in intra-day radiation intensity and the evolution characteristic of daily radiation intensity. The performance of optimal-sized systems obtained under different methods are compared through full lifespan simulation analysis. The result of the proposed long-timescale-based optimization framework has lower cost of energy over system lifecycle. Meanwhile, it improves the reliability of cooling and hydrogen energy supply by 5.7 % and 0.23 %, respectively, while maintaining robust grid interaction performance.