Networked Microgrid Security and Privacy Enhancement By the Blockchain-enabled Internet of Things Approach

This paper proposes a novel framework for privacy and security enhancement of power trading in the networked microgrids (MGs) based on the blockchain-enabled Internet of Things (IoT) approach. Utilizing the blockchain-enabled IoT technology in the power trading of the network MGs can potentially lead to some significant advantages such as fewer system risks, mitigate financial fraud, and less the operational cost. A newly stochastic framework based on the unscented transform (UT) is employed to model the uncertainties of renewable energy resources and hourly load demand. Consequently, the proposed framework is tested on the network MG containing residential MG (as a non-crucial load), commercial MG (as an intermediate level load), and hospital MG (as a crucial load), to validate the effectiveness and high performance of the proposed technique.

[1]  Amin Khodaei,et al.  Microgrid Optimal Scheduling With Multi-Period Islanding Constraints , 2014, IEEE Transactions on Power Systems.

[2]  Nikos D. Hatziargyriou,et al.  Leader-Follower Strategies for Energy Management of Multi-Microgrids , 2013, IEEE Transactions on Smart Grid.

[3]  Suryanarayana Doolla,et al.  Demand Response in Smart Distribution System With Multiple Microgrids , 2012, IEEE Transactions on Smart Grid.

[4]  Morteza Dabbaghjamanesh,et al.  An Optimization Technique Based on Profit of Investment and Market Clearing in Wind Power Systems , 2016 .

[5]  Hyoungshick Kim,et al.  Security and Privacy Challenges in the Internet of Things [Security and Privacy Matters] , 2017, IEEE Consumer Electronics Magazine.

[6]  Yu Zhang,et al.  Robust Energy Management for Microgrids With High-Penetration Renewables , 2012, IEEE Transactions on Sustainable Energy.

[7]  Jianhui Wang,et al.  Decentralized Energy Management System for Networked Microgrids in Grid-Connected and Islanded Modes , 2016, IEEE Transactions on Smart Grid.

[8]  Sajad Najafi Ravadanegh,et al.  Optimal Power Dispatch of Multi-Microgrids at Future Smart Distribution Grids , 2015, IEEE Transactions on Smart Grid.

[9]  Jianhui Wang,et al.  Energy Management Systems in Microgrid Operations , 2012 .

[10]  H. Bevrani,et al.  Adaptive Energy Consumption Scheduling for Connected Microgrids Under Demand Uncertainty , 2013, IEEE Transactions on Power Delivery.

[11]  Shahab Mehraeen,et al.  Effective Scheduling of Reconfigurable Microgrids With Dynamic Thermal Line Rating , 2019, IEEE Transactions on Industrial Electronics.

[12]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[13]  Wencong Su,et al.  Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources , 2014, IEEE Transactions on Smart Grid.

[14]  Suryanarayana Doolla,et al.  Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids , 2013, IEEE Transactions on Industrial Electronics.

[15]  J.A.P. Lopes,et al.  Using Low Voltage MicroGrids for Service Restoration , 2007, IEEE Transactions on Power Systems.

[16]  Shahab Mehraeen,et al.  A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[17]  Abdollah Kavousi-Fard,et al.  Effective scheduling operation of coordinated and uncoordinated wind-hydro and pumped-storage in generation units with modified JAYA algorithm , 2017, 2017 IEEE Industry Applications Society Annual Meeting.