Risk-constrained optimal scheduling with combining heat and power for concentrating solar power plants. (15th September 2020)

• Record Type: Journal Article
• Authors: Fang, Yuchen; Zhao, Shuqiang
• Is Part Of: Solar energy. Volume 208(2020)
• Page Start: 937
• Page End: 948
• Publication Date: 2020-09-15
• Subjects: Concentrating solar power -- District heating -- Combined heat and power -- Optimal scheduling -- Information gap decision theory; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2020.08.043
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: The CSP plant is employed to participate in heating. The IGDT is used to formulate a scheduling strategy for uncertainty risk-constrained. The proposed method could provide two different strategies to hedge the risks. Abstract: Concentrating Solar Power (CSP), a schedulable renewable energy technology, realizes the conversion of "solar-heat-electric". The benefits from the thermal energy storage (TES) and its potential for heating need to be explored. In desert areas with low population density and relatively concentrated industrial loads, a CSP plant equipped with back-pressure turbines (BT) as generators can use waste heat from power generation to supply heat, which effectively improves the energy production efficiency of CSP plants. Thus, a combined heat and power scheduling model for CSP plants to participate in heating is established in this paper. In order to cope with the uncertain parameters of the model, the information gap decision theory (IGDT) is introduced to describe them. IGDT can draw on the risk-seeker strategy and risk-averse strategy to deal with the uncertainty, and thus provide a reasonable decision basis for the scheduling operation. In addition, we ensure that the storage cycling of CSP plants is energy neutral, which could allow the plant to optimize its output for multiple consecutive days to meet demands. Finally, the model is verified to ensure that the predefined profit level could be achieved and the corresponding scheduling strategy under different operational risk is obtained.