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Distributed multi-scenario optimal sizing of integrated electricity and gas system based on ADMM

Chen, Jian, Lin, Ziliang, Ren, Junzhi, Zhang, Weitong, Zhou, Yue ORCID: and Zhang, Yicheng 2020. Distributed multi-scenario optimal sizing of integrated electricity and gas system based on ADMM. International Journal of Electrical Power and Energy Systems 117 , 105675. 10.1016/j.ijepes.2019.105675

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The evolution and application of energy conversion equipment such as gas turbines and power to gas (P2G) has greatly improved the coupling characteristics of integrated electricity and gas system (IEGS). Therefore, it is necessary to take natural gas system into consideration when coping with the optimal sizing problems for electricity system. However, it is difficult to solve the problem based on a single typical scenario during the long-term planning period. This paper proposes a distributed multi-scenario optimization framework based on alternating direction multiplier method (ADMM), decoupling the original optimal sizing problem into an investment sub-problem and multiple operation sub-problems considering multiple scenarios. In addition, this paper establishes a bidirectional coupling IEGS model which includes the dynamic characteristic of the gas system and uncertainties of renewable energy and load. In order to evaluate the feasibility, the proposed framework and model are applied to a modified IEEE 33 nodes system combined with 7 nodes gas system. Case studies are presented to further study the impact of operation flexibility, lifetime loss and cost reduction potential of battery energy storage system (BESS). The results indicate that the proposed framework can effectively deal with the multi-scenario optimal sizing problem of IEGS. Moreover, this method also have a good performance in analyzing the influence of flexibility, battery lifetime and multi-stage investment strategy.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0142-0615
Date of Acceptance: 30 October 2019
Last Modified: 07 Nov 2022 11:15

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