Multi-stage risk-based assessment for wind energy accommodation capability: A robust and non-anticipative method

Xiong, Houbo, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Guo, Chuangxin, Ding, Yi and Luo, Fengji 2023. Multi-stage risk-based assessment for wind energy accommodation capability: A robust and non-anticipative method. Applied Energy 350 , 121726. 10.1016/j.apenergy.2023.121726 Item availability restricted.

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Abstract

The volatility and uncertainty of wind energy has brought great challenges to its accommodation in electric power systems. To determine a wind power trading strategy in the real-time power market, the accurate assessment of wind energy accommodation capability (WAC) is of great significance. However, most current studies investigate the wind energy accommodation potential from the perspective of dispatch, but rarely provide quantitative assessment results. Additionally, the non-anticipativity of decisions has not been considered in the assessment yet. This paper proposes a novel quantitative assessment method for WAC based on the multi-stage robust optimization (RO), which addresses the anticipativity issues in the traditional two-stage method while maintaining the decision-making process' robustness. In the assessment method, the operational risk is integrated via a tractable scheme to obtain dynamic admissible WAC boundaries. To obtain the optimal solution of the proposed multi-stage RO problem, a fast robust dual dynamic programming (FRDDP) algorithm is employed, where the adaptive techniques are developed to accelerate the computation. Numerical studies on the modified IEEE 14-Bus system and a real-world system in Zhejiang Province of China validate the effectiveness of the proposed assessment model and adaptive acceleration techniques. The simulation results demonstrate the presented method brings a 18.87% reduction in operational cost, and reduces 29.34% curtailment of wind energy. Compared with the original FRDDP, the adaptive technique significantly reduces the computational consumption by 73.34% on the real-world test system.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of First Compliant Deposit:	1 November 2023
Date of Acceptance:	2 August 2023
Last Modified:	07 Nov 2023 14:37
URI:	https://orca.cardiff.ac.uk/id/eprint/163389

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