Joint planning of economy and reliability for integrated community energy systems: A similarity-based massive scenario optimization approach

Qu, Jiawei, Liu, Zeyu, Hou, Kai, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Zhu, Lewei, Dong, Xiaohong, Mu, Yunfei and Jia, Hongjie 2025. Joint planning of economy and reliability for integrated community energy systems: A similarity-based massive scenario optimization approach. Applied Energy 381 , 125054. 10.1016/j.apenergy.2024.125054 Item availability restricted.

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Abstract

The uncertainties of renewable energy, multi-energy flexible loads, and equipment failure conditions pose significant challenges in incorporating reliability into the planning of Integrated Community Energy Systems (ICES). To address this, a joint planning model of economy and reliability (JPER) for ICES is proposed. This model is structured as a bi-level framework using the L-shaped method, considering three types of uncertainties: renewable energy fluctuations, multi-energy load variations, and equipment failures. These uncertainties are quantified through normal and N-k contingency operational scenarios. A state similarity (SS) analysis method is employed to identify similar characteristics in operational subproblems, forming the state similarity sets (SS-Sets). For each SS set, the optimization needs to be conducted for only one scenario, and the optimal solutions for the other scenarios can then be directly derived by solving linear equations, significantly reducing computational time. Additionally, the impacts of equipment failures, renewable energy variations, and integrated flexible loads (IFL) are analyzed in the operational strategies of ICES. The results demonstrate that the L-shaped with state similarity (LSS) method dramatically enhances overall computational efficiency by more than tenfold. The planning framework, based on N-k scenarios and integrated with flexible loads, decreases energy storage requirements and improves system reliability. As a result, investment, operational, and overall costs are reduced by 16.53 %, 10.7 %, and 14.71 %, respectively.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of First Compliant Deposit:	6 December 2024
Date of Acceptance:	30 November 2024
Last Modified:	22 Jan 2025 15:30
URI:	https://orca.cardiff.ac.uk/id/eprint/174556

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