Time series aggregation-based design and operation optimization of a solar-driven organic Rankine cycle incorporating variation of environmental temperature and solar radiation

Abstract

The efficient utilization of renewable energy, e.g., solar energy, has become a major requirement to build a clean and efficient energy system and achieve the goal of carbon neutrality. Organic Rankine cycle (ORC) is one of the most promising technologies for converting solar energy into power. However, life-span low operation performance is still a barrier to the wide-spread application of ORC. Design and off-design operation optimization is significant in improving the life-span performance of ORC. In the routine methods, the ORC system is usually designed under a specific rated condition. However, the ORC mostly operates under off-design conditions. The conventional method separates the off-design operation process from the design process and typically loses solution optimality. In the present study, the off-design operation conditions are incorporated into the design process. A time series aggregation-based method for design and operation optimization of a solar driven ORC is proposed. K-means algorithm is applied to achieve the optimal aggregation operating points for the environmental temperature and solar radiation. The design and operation optimization model incorporating the off-design component performance is developed. The optimal design and operating schemes are achieved based on the aggregated operating points, and the effectiveness of the proposed method is validated. The results indicate that the ORC obtained by the proposed method features a 37.37% lower electricity production cost compared with the conventional method.