Print Email Facebook Twitter Cooperative Economic Scheduling for Multiple Energy Hubs Title Cooperative Economic Scheduling for Multiple Energy Hubs: A Bargaining Game Theoretic Perspective Author Fan, Songli (Shanghai Jiao Tong University) Li, Zhengshuo (Tsinghua University) Wang, Jianhui (Southern Methodist University) Piao, L. (TU Delft Algorithmics) Ai, Qian (Shanghai Jiao Tong University) Date 2018 Abstract Under the background of global energy conservation, the energy hub (EH)-based integrated energy system is becoming the transition direction of future energy structure. In this paper, we study the cooperative economic scheduling problem for multiple neighboring integrated energy systems on the basis of EH. Different with the traditional non-cooperative mode where each EH operates individually, these EHs constitute a cooperative community and can share energy among them. Considering the autonomy and selfinterest of different EHs, the coordinated management problem is modeled as a bargaining cooperative game, where involved EHs will bargain with each other about the exchanged energy and the associated payments. The bargaining solution can achieve a fair and Pareto-optimal balance among the objective functions of different EHs. A distributed optimization is applied to find the bargaining solution of the cooperative system, to guarantee the autonomous scheduling and information privacy of EHs. Numerical studies demonstrate the effectiveness of the bargaining-based cooperative economic scheduling framework, and also show the improvement of benefits of the community system. Subject Cooperative gamedistributed approachenergy hubenergy tradingmultiple energy systemNash bargaining To reference this document use: http://resolver.tudelft.nl/uuid:9ecbacfc-1675-4e72-8d59-9049668b016f DOI https://doi.org/10.1109/ACCESS.2018.2839108 ISSN 2169-3536 Source IEEE Access, 6, 27777-27789 Part of collection Institutional Repository Document type journal article Rights © 2018 Songli Fan, Zhengshuo Li, Jianhui Wang, L. Piao, Qian Ai Files PDF 08361850.pdf 4.38 MB Close viewer /islandora/object/uuid:9ecbacfc-1675-4e72-8d59-9049668b016f/datastream/OBJ/view