Privacy-Enhanced Scheduling for Smart Power Grids

Our power grid infrastructure is built to support electrical appliances where the electrical needs are simple. With energy demand getting higher and more complex, smart power grids are the way of the future. With traditional power grids, we only send electricity to homes without any feedback. However, in smart power grids, we can create a new communication channel where we can gather feedback from home users. This allows for better energy efficiency, flexibility during peak hours using smarter load balancing methods, as well as scheduling and automation on the client side. Within the smart power grid infrastructure, there are schedulers to help distribute the energy to different clients. The Federal government had invested $3.4 billion on smart power grids, with utilities receiving grants ranging from $400,000 to $200 million. However, most of the schedulers currently focus on energy savings only. Per a recent audit, out of 99 grant recipients, 36 did not take the steps to ensure customer's privacy [1]. In this work, the capabilities of smart grid schedulers are extended with an emphasis on privacy. Privacy is important because of the two-way communication involved in smart power grids, which will contain information such as their energy demand and schedules. Scheduling techniques with information dispersal, All Or Nothing transforms improved with hybrid key encryption, and monitoring using Markov chains are integrated to improve the privacy of home users in the grid. Tradeoffs between privacy and performance are also explored.

[1]  Mariana Hentea,et al.  Smart power grid security: A unified risk management approach , 2010, 44th Annual 2010 IEEE International Carnahan Conference on Security Technology.

[2]  Jong-Keun Park,et al.  Northeast Asia power system interconnection and smart grid operation strategies in South Korea , 2013, 2013 IEEE Power & Energy Society General Meeting.

[3]  Giacomo Verticale,et al.  Privacy-friendly appliance load scheduling in smart grids , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[4]  Leandros Tassiulas,et al.  Control and optimization meet the smart power grid: scheduling of power demands for optimal energy management , 2010, e-Energy.

[5]  Pravin Chopade,et al.  Modeling and visualization of Smart Power Grid: Real time contingency and security aspects , 2012, 2012 Proceedings of IEEE Southeastcon.

[6]  Arobinda Gupta,et al.  A mobility aware scheduler for low cost charging of electric vehicles in smart grid , 2014, 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS).

[7]  J. O. Petinrin,et al.  Smart power grid: Technologies and applications , 2012, 2012 IEEE International Conference on Power and Energy (PECon).

[8]  Mohd Amran Mohd Radzi,et al.  Application of smart power grid in developing countries , 2013, 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO).

[9]  Parv Venkitasubramaniam,et al.  Scheduling with privacy constraints , 2012, 2012 IEEE Information Theory Workshop.

[10]  Eike Kiltz,et al.  Secure Hybrid Encryption from Weakened Key Encapsulation , 2007, CRYPTO.

[11]  Xun Gong,et al.  Designing privacy preserving router scheduling policies , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[12]  Xi Fang,et al.  Managing smart grid information in the cloud: opportunities, model, and applications , 2012, IEEE Network.

[13]  S. R. Thilaga,et al.  Advanced Cloud Computing in Smart Power Grid , 2012 .

[14]  Ronald L. Rivest,et al.  All-or-Nothing Encryption and the Package Transform , 1997, FSE.

[15]  Mina Sajjadi,et al.  SMART POWER GRID SECURITY SERVICES: RISK MANAGEMENT APPROACH CONSIDERING BOTH OT AND IT DOMAINS CASE STUDY: SHIRAZ POWER DISTRIBUTION COMPANY , 2013 .