Distributionally robust multi‐period energy management for CCHP‐based microgrids. Issue 19 (10th July 2020)

• Record Type: Journal Article
• Authors: Shi, Zhichao; Liang, Hao; Dinavahi, Venkata
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 19(2020)
• Page Start: 4097
• Page End: 4107
• Publication Date: 2020-07-10
• Subjects: optimisation -- linear programming -- power generation scheduling -- energy management systems -- distributed power generation -- cogeneration -- integer programming -- power generation dispatch; power‐based microgrids -- uncertain renewable generation -- wind power -- CCHP‐based microgrids energy management -- multiperiod distributionally robust energy management model -- second‐order conic representable ambiguity set -- second‐order conic programme problem -- robust optimisation method -- distributionally robust multiperiod energy management -- energy utilisation efficiency; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2020.0469
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : To improve the overall energy utilisation efficiency, the research of combined cooling, heat, and power (CCHP)‐based microgrids has become prevalent recently. However, the increasing penetration of uncertain renewable generation such as wind power brings new challenges to CCHP‐based microgrids energy management. In this study, the authors propose a two‐stage multi‐period distributionally robust energy management model for CCHP‐based microgrids, and this model considers the non‐anticipativity of uncertainty in dispatch process. A second‐order conic representable ambiguity set is designed to capture the uncertainty of wind power. Based on linear decision rule approximation, the proposed problem is transformed into a tractable mixed‐integer second‐order conic programme problem. Case studies and comparison experiments are conducted in the Matlab environment with real‐world data to validate the performance of the proposed approach. Particularly, the proposed method achieves a less conservative solution and smaller cost compared with a robust optimisation method with the same reliability guarantee. In addition, it is more reliable than the deterministic method which does not consider uncertainty.