Reading List

The papers below are organized by broad topic area. This is currently an unordered and unprohibited list! There are more papers listed than we will have time to read or discuss in class. Specific reading assignments will be given before each class. Papers listed below but not covered in class may provide background or source material for class projects.

Introduction to the Power Grid

We will read selected sections from:

• Alexandra von Meier, Electric Power Systems: a Conceptual Introduction, John Wiley and Sons,
  

Introduction to the Smart Grid

• Farhangi, H., “The path of the smart grid,” IEEE Power and Energy Magazine,  8(1), 2010.
• A. Bose, “Smart Transmission Grid Applications and Their Supporting Infrastructure,” IEEE Transactions on Smart Grid, 1(1).
• US Department of Energy, The Smart Grid: An Introduction,

Smart buildings, including appliance scheduling, sensors

• ZonePAC: Zonal Power Estimation and Control via HVAC Metering and Occupant Feedback Bharathan Balaji, Hidetoshi Teraokay, Rajesh Guptay, Yuvraj Agarwal
• Sentinel: Occupancy Based HVAC Actuation using Existing WiFi Infrastructure within Commercial Buildings Bharathan Balajiy, Jian Xuy, Anthony Nwokafory, Rajesh Guptay, Yuvraj Agarwalyz
• Hot Water DJ: Saving Energy by Pre-mixing Hot Water Md. Anindya Prodhan, Kamin Whitehouse
• Thermovote: Participatory Sensing for Efficient Building HVAC Conditioning Varick L. Erickson, Alberto E. Cerpa
• Accurate Real-Time Occupant Energy-Footprinting in Commercial Buildings Yun Cheng , Kaifei Chen , Ben Zhang , Chieh-Jan Mike Liang , Xiaofan Jiang , Feng Zhao
• Towards an Understanding of Campus-Scale Power Consumption Gowtham Bellala, Manish Marwah, Martin Arlitt, Geoff Lyon, Cullen E. Bash

• Mohsenian-Rad, A.-H.; Leon-Garcia, A., ‘Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments,” IEEE Transactions on Smart Grid, 1(2), 2010.
• Chen, Y.-W.; Chen, X.; Maxemchuk, N, “The Fair Allocation of Power to Air Conditioners on a Smart Grid,” IEEE Transactions on Smart Grid, 3(4), 2012.
• P. Srikantha, C. Rosenberg, and S. Keshav, An Analysis of Peak Demand Reductions due to Elasticity of Domestic Appliances, Proc. e-Energy, May 2012.
• S. Barker, A. Mishra, D. Irwin, P. Shenoy, and J. Albrecht. Smartcap: Flattening peak electricity demand in
  smart homes. In Pervasive Computing and Communications (PerCom), 2012 IEEE International Conference
  on, pages 67–75. IEEE, 2012.
• Gopinath Karmakar, Ashutosh Kabra, and Krithi Ramamritham. Coordinated scheduling of thermostatically controlled real-time systems under peak power constraint. In Real-Time and Embedded Technology and Appli- cations Symposium (RTAS), April 2013.
• T.W. Hnat, V. Srinivasan, J. Lu, T.I. Sookoor, R. Dawson, J. Stankovic, and K. Whitehouse. The hitchhiker’s
  guide to successful residential sensing deployments. In Proceedings of the 9th ACM Conference on Embedded
  Networked Sensor Systems, pages 232–245. ACM, 2011.
• D. Snoonian. Smart buildings. Spectrum, IEEE, 40(8):18–23, 2003.
• Mathieu, J.L.; Price, P.N.; Kiliccote, S.; Piette, M.A., “Quantifying Changes in Building Electricity Use, With Application to Demand Response,” IEEE Transactions on Smart Grid, 2(3), 2011.
• Georgievski, I.; Degeler, V.; Pagani, G. A.; Nguyen, T. A.; Lazovik, A.; Aiello, M., “Optimizing Energy Costs for Offices Connected to the Smart Grid,” IEEE Transactions on Smart Grid, 3(4), 2012.
• additional reading:
  • G. Kardaras, A. Rossello-Busquet, V.B. Iversen, J. Soler, and L. Dittmann. Regulating electricity demand
    peaks for home appliances using reversible fair scheduling. In Internet Multimedia Services Architecture and
    Application (IMSAA), 2010 IEEE 4th International Conference on, pages 1–6. IEEE, 2010.
  • A. Rossell´o-Busquet, G. Kardaras, V.B.I.J. Soler, and L. Dittmann. Reducing electricity demand peaks by
    scheduling home appliances usage.
  • S.K. Lee. On-line multiprocessor scheduling algorithms for real-time tasks. In TENCON’94. IEEE Region 10’s
    Ninth Annual International Conference. Theme: Frontiers of Computer Technology. Proceedings of 1994, pages
    607–611. IEEE, 1994.
  • D. Irwin, A. Wu, S. Barker, A. Mishra, P. Shenoy, and J. Albrecht. Exploiting home automation protocols for
    load monitoring in smart buildings. BuildSys, November, 2011.
  • Y. Agarwal, B. Balaji, S. Dutta, R.K. Gupta, and T. Weng. Duty-cycling buildings aggressively: The next fron- tier in hvac control. In Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on, pages 246–257. IEEE, 2011.
  • G. Kardaras, A. Rossello-Busquet, V.B. Iversen, J. Soler, and L. Dittmann. Regulating electricity demand peaks for home appliances using reversible fair scheduling. In Internet Multimedia Services Architecture and Application (IMSAA), 2010 IEEE 4th International Conference on, pages 1–6. IEEE, 2010.
  • A. Rossello-Busquet, G. Kardaras, V.B.I.J. Soler, and L. Dittmann. Reducing electricity demand peaks by scheduling home appliances usage.
  • S.K. Lee. On-line multiprocessor scheduling algorithms for real-time tasks. In TENCON’94. IEEE Region 10’s Ninth Annual International Conference. Theme: Frontiers of Computer Technology. Proceedings of 1994, pages 607–611. IEEE, 1994.
  • T. Weng, B. Balaji, S. Dutta, R. Gupta, and Y. Agarwal. Managing plug-loads for demand response within buildings. In ACM Workshop on Embedded Sensing Systems For Energy-Efficiency In Buildings, 2011.
  • A. Molina-Markham, G. Danezis, K. Fu, P. Shenoy, and D. Irwin. Designing privacy-preserving smart meters with low-cost microcontrollers. Financial Cryptography and Data Security, pages 239–253, 2012.
  • M. LeMay, R. Nelli, G. Gross, and C.A. Gunter. An integrated architecture for demand response communications and control. In Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, pages 174– 174. IEEE, 2008.
  • L.V. Thanayankizil, S.K. Ghai, D. Chakraborty, and D.P. Seetharam. Softgreen: Towards energy management of green office buildings with soft sensors. In Communication Systems and Networks (COMSNETS), 2012 Fourth International Conference on, pages 1–6. IEEE, 2012.

Demand Response

• Conejo, A.J.; Morales, J.M.; Baringo, L., “Real-Time Demand Response Model,” IEEE Transactions on Smart Grid, 1(2), 2010.
• Rahimi, F.; Ipakchi, A., “Demand Response as a Market Resource Under the Smart Grid Paradigm,” IEEE Transactions on Smart Grid, 1(1), 2010.
• Medina, J.; Muller, N.; Roytelman, I., “Demand Response and Distribution Grid Operations: Opportunities and Challenges,” IEEE Transactions on Smart Grid, 1(2), 2010.
• Saele, H.; Grande, O.S, “Demand Response From Household Customers: Experiences From a Pilot Study in Norway” IEEE Transactions on Smart Grid, 2(1), 2011.
• additional reading:
• Y. Agarwal, B. Balaji, S. Dutta, R.K. Gupta, and T. Weng. Duty-cycling buildings aggressively: The next fron- tier in hvac control. In Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on, pages 246–257. IEEE, 2011.
• M.H. Albadi and EF El-Saadany. Demand response in electricity markets: An overview. In Power Engineering Society General Meeting, 2007. IEEE, pages 1–5. IEEE, 2007.
• Y. Simmhan, S. Aman, B. Cao, M. Giakkoupis, A. Kumbhare, Q. Zhou, D. Paul, C. Fern, A. Sharma, and V. Prasanna. An informatics approach to demand response optimization in smart grids. NATURAL GAS, 31:60, 2011.

Load/Price Forecasting

• Shu Fan and R.J. Hyndman. Short-term load forecasting based on a semi-parametric additive model. Power
  Systems, IEEE Transactions on, 27(1):134 –141, feb. 2012.
• Angel Pardo, Vicente Meneu, and Enric Valor. Temperature and seasonality influences on spanish electricity
  load. Energy Economics, 24(1):55 – 70, 2002.
• additional reading:
  • M.O. Oliveira, D.P. Marzec, G. Bordin, A.S. Bretas, and D. Bernardon. Climate change effect on very short-term electric load forecasting. In PowerTech, 2011 IEEE Trondheim, pages 1 –7, june 2011.
  • S. Mirasgedis, Y. Sarafidis, E. Georgopoulou, D.P. Lalas, M. Moschovits, F. Karagiannis, and D. Papakonstanti-
    nou. Models for mid-term electricity demand forecasting incorporating weather influences. Energy, 31(23):208
    – 227, 2006.
  • R.C. Garcia, J. Contreras, M. Van Akkeren, and J.B.C. Garcia. A garch forecasting model to predict day-ahead electricity prices. Power Systems, IEEE Transactions on, 20(2):867–874, 2005.
  • S. Aman, Y. Simmhan, and V.K. Prasanna. Improving energy use forecast for campus micro-grids using indirect indicators. In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, pages 389–397. IEEE, 2011.
  • J. Contreras, R. Espinola, F.J. Nogales, and A.J. Conejo. Arima models to predict next-day electricity prices. Power Systems, IEEE Transactions on, 18(3):1014–1020, 2003.
  • T. Niimura, H.S. Ko, and K. Ozawa. A day-ahead electricity price prediction based on a fuzzy-neuro autoregres- sive model in a deregulated electricity market. In Neural Networks, 2002. IJCNN’02. Proceedings of the 2002 International Joint Conference on, volume 2, pages 1362–1366. IEEE, 2002.
  • F.J. Nogales, J. Contreras, A.J. Conejo, and R. Espinola. Forecasting next-day electricity prices by time series models. Power Systems, IEEE Transactions on, 17(2):342–348, 2002.

Modeling

• SPOT: A Smart Personalized Office Thermal Control System Peter Xiang Gao, S. Keshav

• Zhifang Wang; Scaglione, A.; Thomas, R.J., “Generating Statistically Correct Random Topologies for Testing Smart Grid Communication and Control Networks,” IEEE Transactions on Smart Grid, 1(1), 2010.
• Pagani, G.A.; Aiello, M., “Towards Decentralization: A Topological Investigation of the Medium and Low Voltage Grids,” IEEE Transactions on Smart Grid, 2(3), 2011.
• Y. Deng, H. Lin, A.G. Phadke, S. Shukla, J.S. Thorp, and L. Miil, "Communication network modeling and
  simulation for wide area measurement application," In Innovative Smart Grid Technologies (ISGT), 2012 IEEE
  PES, pages 1–6. IEEE, 2012.

Storage, including EV

• Hidden Costs of Power Cuts and Battery Backups Deva P. Seetharam, Ankit Agrawal, Tanuja Ganu, Jagabondhu Hazra, Venkat Rajaraman, Rajesh Kunnath
• Scaling Distributed Energy Storage for Grid Peak Reduction Aditya Mishra, David Irwin, Prashant Shenoy, Ting Zhu

• Sortomme, E.; El-Sharkawi, M.A , “Optimal Charging Strategies for Unidirectional Vehicle-to-Grid, ” IEEE Transactions on Smart Grid, 2(1), 2011.
• Dallinger, D.; Krampe, D.; Wietschel, M., “Vehicle-to-Grid Regulation Reserves Based on a Dynamic Simulation of Mobility Behavior,” IEEE Transactions on Smart Grid, 2(2), 2011
• Shengnan Shao; Pipattanasomporn, M.; Rahman, S., “Demand Response as a Load Shaping Tool in an Intelligent Grid With Electric Vehicles,” IEEE Transactions on Smart Grid, 2(4), 2011.
• Sundstrom, O.; Binding, C., “Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints,” IEEE Transactions on Smart Grid, 3(1), 2012.
• Grillo, S.; Marinelli, M.; Massucco, S.; Silvestro, F. , “Optimal Management Strategy of a Battery-Based Storage System to Improve Renewable Energy Integration in Distribution Networks,” IEEE Transactions on Smart Grid, 3(2), 2012. (Note: specifically in a distribution network)
• O. Ardakanian, C. Rosenberg, and S. Keshav, On the Impact of Storage in Residential Power Distribution Systems, Proc. Greenmetrics Workshop, June 2012.
• A. Bar-Noy, M. Johnson, and O. Liu. Peak shaving through resource buffering. Approximation and Online Algorithms, pages 147–159, 2009.
• A. Mishra, D. Irwin, P. Shenoy, J. Kurose, and T. Zhu. Smartcharge: cutting the electricity bill in smart homes with energy storage. In Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet, page 29. ACM, 2012.
• R. Urgaonkar, B. Urgaonkar, M.J. Neely, and A. Sivasubramaniam. Optimal power cost management using stored energy in data centers. In Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, pages 221–232. ACM, 2011.
• additional reading:
  • P. Van de Ven, N. Hegde, L. Massoulie, and T. Salonidis. Optimal control of residential energy storage under price fluctuations. In ENERGY 2011, The First International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies, pages 159–162, 2011.
  • S. Govindan, A. Sivasubramaniam, and B. Urgaonkar. Benefits and limitations of tapping into stored energy for datacenters. In Computer Architecture (ISCA), 2011 38th Annual International Symposium on, pages 341–351. IEEE, 2011.
  • N. Sharma, S. Barker, D. Irwin, and P. Shenoy. Blink: managing server clusters on intermittent power. ACM
    SIGPLAN Notices, 46(3):185–198, 2011.
  • I. Koutsopoulos, V. Hatzi, and L. Tassiulas. Optimal energy storage control policies for the smart power grid. In Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on, pages 475–480. IEEE, 2011.
  • A. Oudalov, R. Cherkaoui, and A. Beguin. Sizing and optimal operation of battery energy storage system for peak shaving application. In Power Tech, 2007 IEEE Lausanne, pages 621–625. IEEE, 2007.

Intermittent/Renewable Energy Generation

• Complete Automation of Future Grid for Optimal Real-Time Distribution of Renewables Kiyoshi Nakayama, Kyle E. Benson, Lubomir F. Bic, Michael B. Dillencourt

• Vijay Arya, Partha Dutta, and Shivkumar Kalyanaraman, "On Mitigating Wind Energy Variability with Storage," IEEE COMSNETS 2013.
• N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, "Predicting solar generation from weather forecasts using
  machine learning," In Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference
  on, pages 528–533. IEEE, 2011.
• Ortega-Vazquez, M.A.; Kirschen, D.S., “Assessing the Impact of Wind Power Generation on Operating Costs,” IEEE Transactions on Smart Grid, 1(3), 2010.
• Hill, C.A.; Such, M.C.; Dongmei Chen; Gonzalez, J.; Grady, W.M. , “Battery Energy Storage for Enabling Integration of Distributed Solar Power Generation,” IEEE Transactions on Smart Grid, 3(2), 2012.

Smart Grid Communications: Architecture and Protocols

• Adapting Data Quality with Multihop Routing for Energy Harvesting Wireless Sensor Networks Nalini et al.
• Improving Sensor Data Delivery During Disaster Scenarios with Resilient Overlay Networks Nalini Venkatasubramanian et al.
• Assessing the impact of geographically correlated failures on overlay-based data dissemination Kyungbaek Kim and Nalini Venkatasubramanian

• Power Systems Engineering Research Center, Communication Requirements and Integration Options for Smart Grid Deployment, April 2012.
• Kansal, P.; Bose, A., “Bandwidth and Latency Requirements for Smart Transmission Grid Applications,” IEEE Transactions on Smart Grid, 3(3), 2012.
• Bakken, D.E.; Bose, A.; Hauser, C.H.; Whitehead, D.E.; Zweigle, G.C., “Smart Generation and Transmission With Coherent, Real-Time Data,”  Proceedings of the IEEE, 99(6), 2011
• Gungor, V.C.; Sahin, D.; Kocak, T.; Ergut, S.; Buccella, C.; Cecati, C.; Hancke, G.P., “Smart Grid Technologies: Communication Technologies and Standards,” IEEE Transactions on Industrial Informatics, 7(4), 2011.
• T.M. Overman and R.W. Sackman, "High Assurance Smart Grid: Smart Grid Control Systems Communications Architecture," 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), Oct. 4-6, 2010.
• Kulkarni, P.; Gormus, S.; Zhong Fan; Ramos, F., “AMI Mesh Networks—A Practical Solution and Its Performance Evaluation,” IEEE Transactions on Smart Grid, 3(3), 2012.
• Birman, K.P.; Chen, J.; Hopkinson, E.M.; Thomas, R.J.; Thorp, J.S.; van Renesse, R.; Vogels, W., “Overcoming Communications Challenges in Software for Monitoring and Controlling Power Systems,” Proceedings of the IEEE, 93(5), 2005.
• S.  Keshav, C. Rosenberg, “How internet concepts and technologies can help green and smarten the electrical grid,”  2010 ACM SIGCOMM Workshop on Green Networking.
• H. Gjermundrod, D.E. Bakken, C.H. Hauser, and A. Bose, "Gridstat: A flexible qos-managed data dissemination
  framework for the power grid," IEEE Transactions on Power Delivery, 24(1):136–143, 2009.
• Y.J. Kim, M. Thottan, V. Kolesnikov, andW. Lee, "A secure decentralized data-centric information infrastructure
  for smart grid," IEEE Communications Magazine, 48(11):58–65, 2010.
• additional background reading:
  • V. Terzija, G. Valverde, D. Cai, P. Regulski, V. Madani, J. Fitch, S. Skok, M.M. Begovic, and A. Phadke. Wide- area monitoring, protection, and control of future electric power networks. Proceedings of the IEEE, 99(1):80–93, 2011.
  • Y. Deng, H. Lin, A.G. Phadke, S. Shukla, J.S. Thorp, and L. Mili. Communication network modeling and simulation for wide area measurement applications. In Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pages 1–6. IEEE, 2012.
  • K. Narendra and T. Weekes. Phasor measurement unit (pmu) communication experience in a utility environment. In 2008 Cigre Canada Conference-Technology and Innovation for the Canadian Power Grids of the Future (Winnipeg, Manitoba, Canada) XI. BIOGRAPHIES, 2008.
  • M. Shahraeini, M.H. Javidi, and M.S. Ghazizadeh. Comparison between communication infrastructures of centralized and decentralized wide area measurement systems. Smart Grid, IEEE Transactions on, 2(1):206– 211, 2011.

PMU placement methodologies

• On the Impact of PMU Placement on Observability and Cross-Validation Daniel Gyllstrom, Elisha Rosensweig and Jim Kurose

• D. Dua, S. Dambhare, R.K. Gajbhiye, and SA Soman. Optimal multistage scheduling of pmu placement: An ilp approach. Power Delivery, IEEE Transactions on, 23(4):1812–1820, 2008.
• D. Gyllstrom, E. Rosensweig, and J. Kurose. On the impact of pmu placement on observability and cross- validation. In Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet, page 20. ACM, 2012.
• N.M. Manousakis, G.N. Korres, and P.S. Georgilakis. Taxonomy of pmu placement methodologies. Power Systems, IEEE Transactions on, 27(2):1070–1077, 2012.

Smart Grid Monitoring, Measurement, and Analytics

• Minimizing Intrusiveness in Home Energy Measurement David Lachut, et. al.
• nPlug: A Smart Plug for Alleviating Peak Loads Tanuja Ganu, Deva P. Seetharam, Vijay Arya, Rajesh Kunnath, Jagabondhu Hazra Saiful A Husain, Liyanage Chandratilake De Silva, Shivkumar Kalyanaraman
• An Analysis of Peak Demand Reductions Due to Elasticity of Domestic Appliances P. Srikantha, C. Rosenberg, S. Keshav
• It’s Different: Insights into home energy consumption in India Nipun Batra , Manoj Gulati , Amarjeet Singh , Mani B. Srivastava

• De La Ree, J.; Centeno, V.; Thorp, J.S.; Phadke, A.G., “Synchronized Phasor Measurement Applications in Power Systems,” IEEE Transactions on Smart Grid, 1(1), 2010.
• McBee, K.D.; Simoes, M.G., “Utilizing a Smart Grid Monitoring System to Improve Voltage Quality of Customers,” IEEE Transactions on Smart Grid, 3(2), 2012. seems lile a problem of interest to HG&E
• Junqi Liu; Junjie Tang; Ponci, F.; Monti, A.; Muscas, C.; Pegoraro, P.A., “Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids,” IEEE Transactions on Smart Grid, 3(2), 2012. (distribution, rather than transmission networks)
• Bobba, R.; Heine, E.; Khurana, H.; Yardley, T., “Exploring a tiered architecture for NASPInet,” Proc. Innovative Smart Grid Technologies (ISGT), 2010.
• P. Trachian, "Machine learning and windowed subsecond event detection on pmu data via hadoop and the
  openpdc," In Power and Energy Society General Meeting, 2010 IEEE, pages 1–5. IEEE, 2010.
• Islanding detection in distribution networks
• additional background reading:
  • J. Hazra, K. Das, D.P. Seetharam, and A. Singhee, "Stream computing based synchrophasor application for power grids," In Proceedings of the first international workshop on High performance computing, networking and analytics for the power grid, pages 43–50. ACM, 2011.
  • A. Armenia and J.H. Chow. A flexible phasor data concentrator design leveraging existing software technologies. Smart Grid, IEEE Transactions on, 1(1):73 –81, june 2010.
  • M. Chenine, L. Vanfretti, S. Bengtsson, and L. Nordstroum. Implementation of an experimental wide-area monitoring platform for development of synchronized phasor measurement applications. In Power and Energy Society General Meeting, 2011 IEEE, pages 1 –8, july 2011.
  • N. Dahal, R.L. King, and V. Madani. Online dimension reduction of synchrophasor data. In Transmission and Distribution Conference and Exposition (T D), 2012 IEEE PES, pages 1 –7, may 2012.
  • Matthew Edwards, Aseem Rambani, Yifeng Zhu, and Mohamad Musavi. Design of hadoop-based framework for analytics of large synchrophasor datasets. Procedia Computer Science, 12(0):254 – 258, 2012.
  • I. Herbert and H. Takashi. A real time pmu data and neural network approach to analyze voltage stability. In Advanced Power System Automation and Protection (APAP), 2011 International Conference on, volume 2, pages 1263 –1267, oct. 2011.
  • Yunqi Kan and Zhaoyang Qu. Design data structure for wams datastream mining base on gps time scale. In Mobile Congress (GMC), 2010 Global, pages 1 –3, oct. 2010.
  • Z. Qu and S. Zhang. The wams power data processing based on hadoop.
  • EO Schweitzer, D. Whitehead, G. Zweigle, and K.G. Ravikumar. Synchrophasor-based power system protection and control applications. In Protective Relay Engineers, 2010 63rd Annual Conference for, pages 1–10. IEEE, 2010.

Security and Privacy

• Malicious False Data Injection in Hierarchical Electric Power Grid State Estimation Systems Yangyue Feng, et. al

• Metke, A.R.; Ekl, R.L., “Security Technology for Smart Grid Networks,” IEEE Transactions on Smart Grid, 1(1), 2010.
• H. Khurana, M. Hadley, Ning Lu, and D.A. Frincke, "Smart-grid security issues," Security Privacy, IEEE, 8(1):81
  –85, jan.-feb. 2010.
• P. McDaniel and S. McLaughlin. Security and privacy challenges in the smart grid. Security Privacy, IEEE,
  7(3):75 –77, may-june 2009.
• Kim, T.T.; Poor, H.V., “Strategic Protection Against Data Injection Attacks on Power Grids, “ IEEE Transactions on Smart Grid, 2(2), 2011.
• Kalogridis, G.; Cepeda, R.; Denic, S.Z.; Lewis, T.; Efthymiou, C., “ElecPrivacy: Evaluating the Privacy Protection of Electricity Management Algorithms,” IEEE Transactions on Smart Grid, 2(4), 2011.
• Sikdar, B.; Chow, J.H., “Defending Synchrophasor Data Networks Against Traffic Analysis Attacks,” IEEE Transactions on Smart Grid, 2(4), 2011.
• Shuang Wang; Lijuan Cui; Jialan Que; Dae-Hyun Choi; Xiaoqian Jiang; Cheng, S.; Le Xie, “A Randomized Response Model for Privacy Preserving Smart Metering,” IEEE Transactions on Smart Grid, 3(3), 2012.
• additional reading:
  • Dong Wei, Yan Lu, M. Jafari, P. Skare, and K. Rohde. An integrated security system of protecting smart grid
    against cyber attacks. In Innovative Smart Grid Technologies (ISGT), 2010, pages 1 –7, jan. 2010.
  • T.M. Overman and R.W. Sackman. High assurance smart grid: Smart grid control systems communications
    architecture. In Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference
    on, pages 19 –24, oct. 2010.
  • G.N. Ericsson. Cyber security and power system communication;essential parts of a smart grid infrastructure.
    Power Delivery, IEEE Transactions on, 25(3):1501 –1507, july 2010.
  • F. Li, B. Luo, and P. Liu. Secure information aggregation for smart grids using homomorphic encryption. In
    First IEEE International Conference on Smart Grid Communications (SmartGridComm), pages 327–332, 2010.
  • S. Clements and H. Kirkham. Cyber-security considerations for the smart grid. In Power and Energy Society
    General Meeting, 2010 IEEE, pages 1 –5, july 2010.
  • Y. Simmhan, A.G. Kumbhare, B. Cao, and V. Prasanna. An analysis of security and privacy issues in smart grid software architectures on clouds. In Cloud Computing (CLOUD), 2011 IEEE International Conference on, pages 582–589. IEEE, 2011.

Energy Disaggregation and Load Monitoring

• G.W. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870–1891, 1992.
• A.G. Ruzzelli, C. Nicolas, A. Schoofs, and G.M.P. O’Hare. Real-time recognition and profiling of appliances through a single electricity sensor. In Sensor Mesh and Ad Hoc Communications and Networks (SECON), 2010 7th Annual IEEE Communications Society Conference on, pages 1–9. IEEE, 2010.
• J. Froehlich, E. Larson, S. Gupta, G. Cohn, M. Reynolds, and S. Patel. Disaggregated end-use energy sensing for the smart grid. Pervasive Computing, IEEE, 10(1):28–39, 2011.
• L.K. Norford and S.B. Leeb. Non-intrusive electrical load monitoring in commercial buildings based on steady- state and transient load-detection algorithms. Energy and Buildings, 24(1):51–64, 1996.
• D. Rahayu, B. Narayanaswamy, S. Krishnaswamy, C. Labb ́, and D.P. Seetharam. Learning to be energy-wise: discriminative methods for load disaggregation. In Future Energy Systems: Where Energy, Computing and Communication Meet (e-Energy), 2012 Third International Conference on, pages 1–4. IEEE, 2012.
• J.Z. Kolter and M.J. Johnson. Redd: A public data set for energy disaggregation research. In Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, 2011.
• J.Z. Kolter, S. Batra, and A.Y. Ng. Energy disaggregation via discriminative sparse coding. In Neural Infor- mation Processing Systems, 2010.
• D. Irwin, A. Wu, S. Barker, A. Mishra, P. Shenoy, and J. Albrecht. Exploiting home automation protocols for load monitoring in smart buildings. BuildSys, November, 2011.