Multistage robust energy management for microgrids considering uncertainty. Issue 10 (25th April 2019)

• Record Type: Journal Article
• Authors: Shi, Zhichao; Liang, Hao; Huang, Shengjun; Dinavahi, Venkata
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 10(2019)
• Page Start: 1906
• Page End: 1913
• Publication Date: 2019-04-25
• Subjects: power grids -- energy management systems -- distributed power generation -- optimisation -- stochastic programming -- dynamic programming -- stochastic processes; significant problem -- novel multistage robust energy management model -- grid‐connected microgrids -- renewable generation -- multistage energy management problem -- robust version -- common stochastic dual dynamic programming method -- robust optimal solution -- multistage robust model -- modern power systems -- distributed energy resources; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6594
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Microgrids play an important role in modern power systems which can integrate different kinds of distributed energy resources (DERs). To deal with the uncertainty from various factors such as renewable generation, robust energy management for microgrids has become a significant problem. In this work, a novel multistage robust energy management model for grid‐connected microgrids is developed which considers the uncertainty of renewable generation and load demand. The multistage energy management problem is complex and computationally difficult. To solve this problem, a robust version of dual dynamic programming method is proposed which includes a forward pass and a backward pass procedure and has a similar framework with the common stochastic dual dynamic programming (SDDP) method. Based on real datasets, a case study is carried out to validate the effectiveness of the proposed model and solution methodology. Numerical results show that the proposed approach can effectively achieve the robust optimal solution, and the comparison with other methods also testifies the advantage of the proposed multistage robust model.