Confidentiality preservation in user-side integrated energy system management for cloud computing

Abstract Under the development of information and communication technologies, this paper discusses a cloud-based user-side integrated energy management to improve the energy utilization efficiency in a smart community, for which a two-level model is proposed that relies on an energy hub and load aggregators. Furthermore, to address the issue of preserving confidentiality for the cloud service, an information-masking mechanism is designed based on linear mapping functions, whose information-masking requirements and processes are specified accordingly. Numerical results demonstrate the effectiveness of the proposed model and of the method for cloud computing.

[1]  Tao Jiang,et al.  Optimal dispatch strategy for integrated energy systems with CCHP and wind power , 2017 .

[2]  Hongbin Sun,et al.  Impacts of optimization interval on home energy scheduling for thermostatically controlled appliances , 2015 .

[3]  Hongbin Sun,et al.  Integrated Energy Management System: Concept, Design, and Demonstration in China , 2018, IEEE Electrification Magazine.

[4]  Zheng Gui-lin Design and Implementation of Energy Management and Control System Based on ZigBee Network , 2012 .

[5]  Ali Mohammad Ranjbar,et al.  Demand side management for a residential customer in multi-energy systems , 2016 .

[6]  Kankar Bhattacharya,et al.  Optimal Operation of Residential Energy Hubs in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[7]  Alberto Sangiovanni-Vincentelli,et al.  Smart Buildings in the Smart Grid: Contract-Based Design of an Integrated Energy Management System , 2015 .

[8]  Brian Ó Gallachóir,et al.  An integrated gas and electricity model of the EU energy system to examine supply interruptions , 2017 .

[9]  W. Fichtner,et al.  The future role of Power-to-Gas in the energy transition: Regional and local techno-economic analyses in Baden-Württemberg , 2018 .

[10]  Matti Lehtonen,et al.  Home load management in a residential energy hub , 2015 .

[11]  Hongbin Sun,et al.  Integrated energy systems , 2016 .

[12]  Hongjie Jia,et al.  Optimal day-ahead scheduling of integrated urban energy systems , 2016 .

[13]  Yunfei Mu,et al.  Energy-Internet-oriented microgrid energy management system architecture and its application in China , 2018, Applied Energy.

[14]  V. Geros,et al.  Implementation of an integrated indoor environment and energy management system , 2005 .

[15]  Yong Fu,et al.  Multi-stage Stochastic Optimal Operation of Energy-efficient Building with Combined Heat and Power System , 2014 .

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

[17]  Evgueniy Entchev,et al.  Energy and cost analyses of a hybrid renewable microgeneration system serving multiple residential and small office buildings , 2014 .

[18]  Xiao-Ping Zhang,et al.  Real-Time Scheduling of Residential Appliances via Conditional Risk-at-Value , 2014, IEEE Transactions on Smart Grid.

[19]  Shahram Jadid,et al.  Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system , 2015 .