Cooperative optimization scheduling of the electricity-gas coupled system considering wind power uncertainty via a decomposition-coordination framework

Yachao Zhang, Zhanghao Huang, Feng Zheng, Rongyu Zhou, Jian Le and Xueli An

Energy, 2020, vol. 194, issue C

Abstract: The extensive installation of gas-fired units and the integration of large-scale wind power connected to power grid have strengthened the interdependency between power system and natural gas network. Consequently, wind power uncertainty accompanying with the coupling interaction by gas-fired units has brought new challenges to the safe and economic operation of electricity-gas coupled system. A decomposition-coordination framework is developed to study the cooperative optimization operation of the integrated electricity-gas coupled system. In this framework, a data-driven distributionally robust optimization (DDRO) model is proposed to solve the power system scheduling problem with wind power uncertainty. Aiming to minimize the expectation of the operation cost under the worst-case distribution, DDRO combines the advantages of the traditional robust optimization (RO) and stochastic optimization. Case studies are implemented on two electricity-gas coupled systems of different scales to verify the effectiveness of the proposed decomposition-coordination framework with DDRO. Specifically, compared with the distributionally robust optimization (DRO) model based on moment information, the solution obtained by DDRO can save 1765.01 $ for the 6-bus power system and 2331.18 $ for the IEEE 24-bus power system, respectively. It is demonstrated that DDRO can achieve less conservative and more economic scheduling solutions compared to DRO.

Keywords: Data-driven distributionally robust optimization; Wind power uncertainty; Electricity-gas coupled system; Decomposition-coordination framework (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:194:y:2020:i:c:s0360544219325228

DOI: 10.1016/j.energy.2019.116827

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