Optimal sizing of renewables and battery systems for hybrid AC/DC microgrids based on variability management. (1st September 2022)

• Record Type: Journal Article
• Authors: Xie, Peng; Jia, Youwei; Lyu, Cheng; Wang, Han; Shi, Mengge; Chen, Hongkun
• Is Part Of: Applied energy. Volume 321(2022)
• Publication Date: 2022-09-01
• Subjects: Hybrid AC/DC microgrid -- System variability -- Optimal sizing -- Mixed-integer nonlinear programming -- Decomposition-coordination algorithm; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119250
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel optimal sizing model for AC/DC microgrid renewable and ESS capacity planning is proposed. Two essential indices, VMI and UMI, are newly proposed to quantify the volatility and uncertainty levels. A novel E-ADMM algorithm is develop to further enhance the computational performance and ensure global convergence. Abstract: This paper proposes an explicit approach to optimal sizing of the integrated battery energy storage systems in hybrid renewable AC/DC islanded microgrids, where the economic impacts of variability management (in concerning operational volatility and uncertainty) are comprehensively investigated. To quantitatively measure the variability levels at different time scales, two indices (i.e. volatility management index and uncertainty management index) are firstly proposed from the perspectives of system planning and operation. Moreover, the probability boundaries of both indices are derived to facilitate the initial system design processes. In particular, the concerned problem is formulated as a bi-level mixed-integer nonlinear programming model to counterbalance the variability and its induced cost at both planning and operation stages. Finally, an original decomposition-coordination algorithm based on the dual decomposition and alternate iterative strategies is proposed to ensure the efficient convergence of the solution process for the constructed mathematical model. Numerical simulations are carried out by analyzing the impacts of various factors associated with optimal sizing solutions and comparing with state-of-the-art approaches. Results show that the proposed sizing technique can facilitate designers finding the optimal capacity combination of renewables and batteries under preset renewables penetration, capital cost, and system variability levels.