Robust coalitional game theoretic optimisation for cooperative energy hubs with correlated wind power. Issue 13 (19th July 2019)

• Record Type: Journal Article
• Authors: Cong, Hao; Wang, Xu; Jiang, Chuanwen
• Is Part Of: IET renewable power generation. Volume 13:Issue 13(2019)
• Page Start: 2391
• Page End: 2399
• Publication Date: 2019-07-19
• Subjects: game theory -- optimisation -- wind power plants -- power generation economics -- electricity supply industry; energy resources -- future multiple energy systems -- robust coalitional game theoretic optimisation model -- multiple EHs -- correlated wind power -- EH model -- wind power generation -- coalitional game theory -- distributed coalition formation algorithm -- robust optimisation method -- correlated historical data -- energy industry -- Khachiyan first‐order algorithm -- cooperative energy hubs; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2018.6232
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : As the development of the energy industry, the integration and complementary of various energy resources will be trends of future multiple energy systems. Energy hub (EH) plays an important role in allocating energy resources efficiently. This study proposes a robust coalitional game theoretic optimisation model to execute the cooperative operation of multiple EHs with correlated wind power. First, an EH model with wind power generation is established. It considers both uncertainties and correlations of wind power. Then, coalitional game theory is introduced to solve the cooperation problem of multiple EHs. Distributed coalition formation algorithm called merge–split rule is developed to form coalitions of EHs. Furthermore, the robust optimisation method is presented to cope with the uncertainties of wind power. Scenarios of correlated historical data are covered by minimum volume enclosing ellipsoid and solved by Khachiyan's first‐order algorithm. Numerical results are given to verify the feasibility and efficiency of our proposed models and methods.