Interaction Strategy of User Side Storage Devices for the Day-Ahead Dispatch of Distributed Integrated Energy Systems

Multi-energy complementarity is beneficial to reduce the operating cost and improve the reliability of energy systems. This paper presents an optimization framework for the day-ahead dispatch of distributed integrated energy system (DIES), to explore the interaction strategy of user side storage devices participating in the economic dispatch of DIES. Firstly, the model of DIES is established based on the concept of energy hub. Then, on this basis, the optimization model for the day-ahead dispatch of a DIES is built to achieve the lowest operation cost. Furthermore, the feasibility of the proposed method is validated by extensive cases studies. Results show that multi-energy complementary and energy storage devices can reduce the operating cost of energy systems effectively.

[1]  Shahab Bahrami,et al.  Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets , 2016 .

[2]  Pierluigi Mancarella,et al.  Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[3]  J. McCalley,et al.  A Multiperiod Generalized Network Flow Model of the U.S. Integrated Energy System: Part II—Simulation Results , 2007, IEEE Transactions on Power Systems.

[4]  Ali Mohammad Ranjbar,et al.  Integrated Demand Side Management Game in Smart Energy Hubs , 2015, IEEE Transactions on Smart Grid.

[5]  Yusheng XUE,et al.  Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices , 2018 .

[6]  Zuomin Dong,et al.  Bi-level planning for integrated energy systems incorporating demand response and energy storage under uncertain environments using novel metamodel , 2018, CSEE Journal of Power and Energy Systems.

[7]  Samaneh Pazouki,et al.  Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty , 2016 .

[8]  Mohammad Reza Mohammadi,et al.  Optimal planning of renewable energy resource for a residential house considering economic and reliability criteria , 2018 .

[9]  Zhaohong Bie,et al.  Reliability evaluation of integrated energy systems based on smart agent communication , 2016 .

[10]  Tao Yu,et al.  Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market , 2018 .

[11]  Ali Mohammad Ranjbar,et al.  A cloud computing framework on demand side management game in smart energy hubs , 2015 .

[12]  J. McCalley,et al.  A Multiperiod Generalized Network Flow Model of the U.S. Integrated Energy System: Part I—Model Description , 2007, IEEE Transactions on Power Systems.

[13]  G. Andersson,et al.  Optimal Power Flow of Multiple Energy Carriers , 2007, IEEE Transactions on Power Systems.