DP based multi-stage ARO for coordinated scheduling of CSP and wind energy with tractable storage scheme: Tight formulation and solution technique. (1st March 2023)

• Record Type: Journal Article
• Authors: Xiong, Houbo; Yan, Mingyu; Guo, Chuangxin; Ding, Yi; Zhou, Yue
• Is Part Of: Applied energy. Volume 333(2023)
• Publication Date: 2023-03-01
• Subjects: Concentrated solar power plants -- Wind energy -- Power scheduling -- Dynamic programming -- Multi-stage robust optimization -- Thermal energy storage -- Robust dual dynamic programming; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120578
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: The concentrating solar power plants (CSP) have well potential in coordinating with the ever-increasing wind energy during power scheduling. However, the existing studies individually design the day-ahead or intra-day optimization of coordinated scheduling between CSP and wind power, which makes the scheduling decisions not optimal in terms of economic and environmental benefits. Additionally, the non-anticipativity of scheduling decisions are not considered in most of them. This paper proposes a novel dynamic programming (DP) formulated multi-stage robust reserve scheduling (DPMRS) model, which is the first attempt to realize the day-ahead and intra-day joint optimization for coordinated scheduling of CSP and wind power. Under the framework of multi-stage adaptive robust optimization (ARO), DPMRS model enforces the non-anticipativity of scheduling. Besides, a convex modelling technique for thermal energy storage (TES) is presented to ensure the tractability of DPMRS model, whose effectiveness is proved mathematically. Moreover, to efficient solve the DPMRS model, a robust dual dynamic programming with accelerated upper approximation (RDDP-AU) solution methodology is developed, and the mathematical proof for its convergence is provided. Numerical studies on the modified IEEE RTS-79 system and a real-world system in Northwest China validate the effectiveness of the proposed scheduling model and solution methodology. The simulation results demonstrate the DPMRS model brings a 17.22% reduction in scheduling cost, and reduces 57.39% curtailment of renewable energy. Compared with the conventional algorithm, the RDDP-AU significantly reduces the computational consumption by 87.56%, and with the error less than 0.074%. Highlights: DP formulated multi-stage ARO model is proposed for the coordinated scheduling of CSP and wind energy. Novel day-ahead and intra-day joint optimization for power scheduling is realized via solving DPMRS model. Convex modelling technique for energy storage systems is presented. Efficient RDDP-AU solution methodology is developed.