Multi-energy Demand Response Management in Energy Internet: A Stackelberg Game Approach
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摘要
A multi-energy Demand response management(DRM) approach in Energy Internet is proposed by employing Stackelberg game theory. The multi-energy trading problem in DRM is formulated as a Stackelberg game, where an energy provider, as a leader, adjusts energy prices, and residential smart energy hubs, as followers, dynamically schedule multiple energy flows according to the prices. The existence and uniqueness of the equilibrium of the proposed game model are analyzed.A multi-energy DRM algorithm for reaching the game equilibrium is developed. Simulation results show that the proposed game-based approach can effectively reduce residential energy costs and improve revenues of the energy provider.
A multi-energy Demand response management(DRM) approach in Energy Internet is proposed by employing Stackelberg game theory. The multi-energy trading problem in DRM is formulated as a Stackelberg game, where an energy provider, as a leader, adjusts energy prices, and residential smart energy hubs, as followers, dynamically schedule multiple energy flows according to the prices. The existence and uniqueness of the equilibrium of the proposed game model are analyzed.A multi-energy DRM algorithm for reaching the game equilibrium is developed. Simulation results show that the proposed game-based approach can effectively reduce residential energy costs and improve revenues of the energy provider.
A multi-energy Demand response management(DRM) approach in Energy Internet is proposed by employing Stackelberg game theory. The multi-energy trading problem in DRM is formulated as a Stackelberg game, where an energy provider, as a leader, adjusts energy prices, and residential smart energy hubs, as followers, dynamically schedule multiple energy flows according to the prices. The existence and uniqueness of the equilibrium of the proposed game model are analyzed.A multi-energy DRM algorithm for reaching the game equilibrium is developed. Simulation results show that the proposed game-based approach can effectively reduce residential energy costs and improve revenues of the energy provider.
引文
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[10]J.Wu,W.Zhou,W.Zhong,et al.,“Dual energy scheduling for microgrids in energy internet:A non-cooperative game approach”,Proc.of IEEE International Conference on Energy Internet,Beijing,China,pp.48-52,2017.
[11]P.Samadi,A.H.M.Rad,R.Schober,et al.,“Advanced demand side management for the future smart grid using mechanism design”,IEEE Transactions on Smart Grid,Vol.3,No.3,pp.1170-1180,2012.
[12]W.Zhou,J.Wu,W.Zhong,et al.,“Electricity consumption scheduling with consumers’comfort and preference in smart grid”,Chinese Journal of Electronics,Vol.25,No.6,pp.1151-1158,2016.
[13]C.Yang,W.Lou,J.Yao,et al.,“On charging scheduling optimization for a wirelessly charged electric bus system”,IEEE Transactions on Intelligent Transportation Systems,2017,Vol.19,No.6,pp.1814-1826,2018.
[14]Y.Liu,C.Yuen,N.U.Hassan,et al.,“Electricity cost minimization for a microgrid with distributed energy resource under different information availability”,IEEE Transactions on Industrial Informatics,Vol.62,No.4,pp.2571-2583,2015.
[15]Y.Liu,C.Yuen,R.Yu,et al.,“Queuing-based energy consumption management for heterogeneous residential demands in smart grid”,IEEE Transactions on Smart Grid,Vol.7,No.3,pp.1650-1659,2016.
[16]D.Monderer and L.S.Shapley,“Potential games”,Games and Economic Behavior,Vol.14,No.1,pp.124-143,1996.
[17]S.Boyd,L.Vandenberghe,“Convex Optimization”,Cambridge University Press,New York,USA,pp.654-656,2004.
[2]M.Geidl,G.Koeppel,P.Favre-Perrod,et al.,“Energy hubs for the future”,IEEE power and energy magazine,Vol.5,No.1,pp.24-30,2007.
[3]W.Zhong,K.Xie,C.Yang,et al.,“ADMM-based distributed auction mechanism for energy hub scheduling in smart buildings”,IEEE Access,Vol.6,pp.45635-45645,2018.
[4]P.Palensky and D.Dietrich,“Demand side management:Demand response,intelligent energy systems,and smart loads”,IEEE transactions on industrial informatics,Vol.7,No.3,pp.381-388,2011.
[5]S.Maharjan,Q.Zhu,Y.Zhang,et al.,“Dependable demand response management in the smart grid:a Stackelberg game approach”,IEEE Transactions on Smart Grid,Vol.4,No.1,pp.120-132,2013.
[6]B.Chai,J.Chen,Z.Yang and Y.Zhang,“Demand response management with multiple utility companies:a two-level game approach”,IEEE Transactions on Smart Grid,Vol.5,No.2,pp.722-731,2014.
[7]Y.Dai,Y.Gao,H.Gao and H.Zhu,“Real-time pricing scheme based on Stackelberg game in smart grid with multiple power retailers”,Neurocomputing,Vol.260,pp.149-156,2017.
[8]A.Sheikhi,S.Bahrami and A.M.Ranjbar,“An autonomous demand response program for electricity and natural gas networks in smart energy hubs”,Energy,Vol.89,pp.490-499,2015.
[9]A.Sheikhi,M.Rayati,S.Bahrami,et al.,“A cloud computing framework on demand side management game in smart energy hubs”,Energy,Vol.64,pp.1007-1016,2015.
[10]J.Wu,W.Zhou,W.Zhong,et al.,“Dual energy scheduling for microgrids in energy internet:A non-cooperative game approach”,Proc.of IEEE International Conference on Energy Internet,Beijing,China,pp.48-52,2017.
[11]P.Samadi,A.H.M.Rad,R.Schober,et al.,“Advanced demand side management for the future smart grid using mechanism design”,IEEE Transactions on Smart Grid,Vol.3,No.3,pp.1170-1180,2012.
[12]W.Zhou,J.Wu,W.Zhong,et al.,“Electricity consumption scheduling with consumers’comfort and preference in smart grid”,Chinese Journal of Electronics,Vol.25,No.6,pp.1151-1158,2016.
[13]C.Yang,W.Lou,J.Yao,et al.,“On charging scheduling optimization for a wirelessly charged electric bus system”,IEEE Transactions on Intelligent Transportation Systems,2017,Vol.19,No.6,pp.1814-1826,2018.
[14]Y.Liu,C.Yuen,N.U.Hassan,et al.,“Electricity cost minimization for a microgrid with distributed energy resource under different information availability”,IEEE Transactions on Industrial Informatics,Vol.62,No.4,pp.2571-2583,2015.
[15]Y.Liu,C.Yuen,R.Yu,et al.,“Queuing-based energy consumption management for heterogeneous residential demands in smart grid”,IEEE Transactions on Smart Grid,Vol.7,No.3,pp.1650-1659,2016.
[16]D.Monderer and L.S.Shapley,“Potential games”,Games and Economic Behavior,Vol.14,No.1,pp.124-143,1996.
[17]S.Boyd,L.Vandenberghe,“Convex Optimization”,Cambridge University Press,New York,USA,pp.654-656,2004.