Chance‐constrained maintenance scheduling for interdependent power and natural gas grids considering wind power uncertainty. Issue 5 (28th February 2019)

• Record Type: Journal Article
• Authors: Wang, Chong; Wang, Zhaoyu; Wang, Jianhui; Hou, Yunhe
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 5(2019)
• Page Start: 686
• Page End: 694
• Publication Date: 2019-02-28
• Subjects: wind power -- integer programming -- approximation theory -- maintenance engineering -- stochastic programming -- power generation dispatch -- power grids -- linear programming -- natural gas technology -- power generation scheduling; four‐node natural gas system -- six‐bus power system -- 20‐node natural gas system -- modified IEEE 118‐bus power system -- interdependent power -- natural gas grids -- wind power uncertainty -- power grids -- chance‐constrained maintenance scheduling model -- integrated gas‐electric grids -- wind energy integration -- wind power probability -- nonconvex models -- natural gas systems -- piecewise linear approximation method -- nonlinear models -- mixed integer linear models -- big‐M formulation method -- chance‐constrained stochastic programming model -- deterministic programming model -- unit commitment; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.5887
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Considering the increased interactions between power grids and natural gas grids, this study presents a chance‐constrained maintenance scheduling model for integrated gas‐electric grids with wind energy integration. Given the uncertainties of wind power, the loss of wind power probability is modelled as a chance constraint, ensuring the high utilisation of wind power. To overcome the adversities caused by the non‐linear and non‐convex models of natural gas systems, a piecewise linear approximation method is employed to transform the non‐linear models into a group of mixed integer linear models. A big‐M formulation method is used to construct inequality constraints for lines/pipelines to be under maintenance. In addition, unit commitment is also coordinated to achieve the best maintenance strategies. The proposed chance‐constrained stochastic programming model is converted into an equivalent deterministic programming model via a strong extended formulation for the sample average approximation by leveraging the star‐inequalities. Several tests on a four‐node natural gas system with a six‐bus power system and a 20‐node natural gas system with a modified IEEE 118‐bus power system demonstrate the effectiveness of the proposed model.