A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid. (1st April 2020)

• Record Type: Journal Article
• Authors: Guo, Li; Hou, Ruosong; Liu, Yixin; Wang, Chengshan; Lu, Hai
• Is Part Of: Applied energy. Volume 263(2020)
• Publication Date: 2020-04-01
• Subjects: Typical day selection -- Evaluation index system -- Mixed integer multi-objective linear programming -- Two-stage fuzzy programming; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.114606
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel typical day selection method is proposed. The typical day selection process is formulated as a MIMLP problem. Better performance on key information extraction of the original data is realized. A robust planning scheme can be obtained based on the MIMLP method. Abstract: Focusing on the problem of capacity planning for a stand-alone wind-photovoltaic-diesel-battery microgrid, this paper constructs a novel evaluation index system of typical day selection with comprehensive consideration of statistical information, typicality, and extreme scenarios information of the original data. The novel typical day selection method is formulated as mixed integer multi-objective linear programming (MIMLP) problem and the extreme indicators are considered as flexibility constrains in the MIMLP model to address the robustness of the planning scheme. The proposed MIMLP problem is converted into a series of single objective problems and solved via the two-stage fuzzy programming method to balance the performance of each objective. Based on the proposed method, a bilevel programming model is established to achieve the optimal capacity design of stand-alone wind-photovoltaic-diesel-battery microgrid. The advantages of the proposed MIMLP model are verified via comprehensive comparison with traditional k-means clustering method and a linear programming method of typical day selection based on actual data of northwestern China.