Contingency‐constrained operation optimization of microgrid with wind and solar generations: A decision‐driven stochastic adaptive‐robust approach

• Main Authors: Mohammad Reza Ebrahimi, Nima Amjady
• Format: Article
• Language: English
• Published: Wiley 2021-02-01
• Series: IET Renewable Power Generation
• Online Access: https://doi.org/10.1049/rpg2.12026
• ISSN: 1752-1416; 1752-1424

Abstract This paper presents a decision‐driven stochastic adaptive‐robust microgrid operation optimization model considering the uncertainties of wind and solar generations, electricity price, and demand as well as the availability uncertainties of microgrid's components. Unlike previous works, this paper utilizes stochastic adaptive‐robust optimization approach to model both continuous and binary uncertainties simultaneously. To do so, adaptive‐robust optimization is used to model the continuous uncertainties, while the binary uncertainties are modelled by means of stochastic programming. An operating dispatchable unit usually exhibits a higher forced outage rate than a de‐committed one. Hence, due to the effect of the optimization decisions on the availability uncertainties, this research work proposes an intrinsic scenario production technique to model these binary uncertainties. In addition, a tri‐level decomposition method is introduced to solve the proposed microgrid operation optimization problem. In this decomposition method, the worst‐case realization of continuous uncertain parameters and unit commitment decisions are determined at each iteration considering the produced scenarios in the previous iteration. Case studies on the IEEE 69‐bus test system exhibit the effectiveness of the proposed decision‐driven stochastic adaptive‐robust model and the proposed solution method.