A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources. (1st February 2021)

• Record Type: Journal Article
• Authors: Zhang, Yachao; Liu, Yan; Shu, Shengwen; Zheng, Feng; Huang, Zhanghao
• Is Part Of: Energy. Volume 216(2021)
• Publication Date: 2021-02-01
• Subjects: Multi-energy coupled system -- Data-driven distributionally robust optimization -- Temporal-spatial correlation -- Distribution uncertainty -- Coordination scheduling; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.119171
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The increasing expansion of wind and gas turbine installation has intensified the interdependency between power system and natural gas network, which poses great challenges to the coordination scheduling of the multi-energy coupled system (MECS). The data-driven robust optimization (DDRO) model is proposed for the energy coupled system. In this model, the temporal-spatial correlation of wind power can be considered based on the minimum volume enclosing convex hull uncertainty set, and the confidence set about the probability distribution for wind power scenarios with the form of the norm-1 and norm- inf constraints is constructed to handle wind power uncertainty. Moreover, to describe natural gas transient characteristics, the hydrodynamic model for gas flow represented as a series of partial differential equations is transformed by the Wendroff difference scheme and linearization technique. And then the master-subproblem framework and tri-level duality-free decomposition method is developed to solve the above model. Finally, the proposed model and solving method are carried out on two test systems with different scale, and the robust optimization models and distributionally optimization models in the existing literatures are implemented for comparison. Simulation results demonstrate the effectiveness and superiority of the proposed model for solving the coordination scheduling problem of MECS. Highlights: Temporal-spatial correlation and distribution uncertainty are taken into account. A data-driven distributionally robust optimization scheduling model is set up. The framework combining duality-free decomposition and C&CG is developed. The operation time-scale differences of multi-energy coupled system are considered.