DP based multi-stage ARO for coordinated scheduling of CSP and wind energy with tractable storage scheme: Tight formulation and solution technique

Xiong, Houbo, Yan, Mingyu, Guo, Chuangxin, Ding, Yi and Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714 2023. DP based multi-stage ARO for coordinated scheduling of CSP and wind energy with tractable storage scheme: Tight formulation and solution technique. Applied Energy 333 , 120578. 10.1016/j.apenergy.2022.120578

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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%.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of Acceptance:	23 December 2022
Last Modified:	13 Feb 2023 16:30
URI:	https://orca.cardiff.ac.uk/id/eprint/156879

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