Game theoretic analysis of Advanced Metering Infrastructure adoption Dipayan Ghosh Cornell University with Stephen Wicker, Dawn Schrader, William Schulze and Lawrence Blume 11/2/2011

Electricity market crisis 800% increase in electricity prices over 6 months

Daily Load Profile Data: ISO-NE 5% decrease in load would have led to 50% lower prices

Advanced Metering Infrastructure (AMI)  Consumer-end metering system Two-way communications between AMI modules and other management devices Detailed information for utility  Fast response to demand and supply signals  Real-time prices Demand response System load levelization Cost reductions

Privacy concerns of AMI Temporally precise, fine granularity consumer data Data collection Utility Household

Identifiable consumer behavior [4]: (a) aggregate power consumption data; (b) derived switch events; (c) load events; (d) reference and estimated presence intervals Privacy concerns of AMI

Privacy-aware design principles 1.Provide full disclosure of data collection 2.Require consent to data collection 3.Minimize collection of personal data 4.Minimize association and identification of data with individuals 5.Minimize and secure data retention Fair Information Practices Department of Health, Education and Welfare

Privacy-aware architecture for AMI [5]

Issues with implementation  Energy industry resistant to change Welfare assessments  PA-AMI has limited benefits to utilities Denying utility access to consumer information eliminates avenues for profit  Consumers unaware of privacy risks Financial value of personal data How to analyze issues analytically?

AMI adoption game v : value of consumer privacy of consumption data e : cost of opting in (or out) of DR program s : savings to consumer associated with AMI adoption g : profit to utility from sale of consumption data l : savings to utility from DR program c : AMI installation cost n : risk to utility of DR program termination r : expected penalty for sale of consumption data [2]

AMI adoption game Game theoretic analysis of AMI game between a representative individual consumer and the utility [2]. The desired Nash equilibrium for implementation of privacy-aware AMI is {AM, PA-AMI} [2]

AMI adoption game  Requirements for PA-AMI adoption Risk of selling data (r) must be greater than the difference between the profit from collecting data (g) and the risk of public outcry against NPA-AMI (n) r > g - n Consumer savings must be greater than the consumer’s cost of effort of adopting AMI s > e

Regulatory regimes for AMI introduction Requirements for PA-AMI adoption Regime 1: (1) v + s -2xv – xs – xe –yv – ys > 0 ; (2) v + s – xv – xs – yv – 2ys – yd > 0 Regime 2: v + s – zv – 2zs – zd > 0 Regime 1: standard power meter (SM) retention permitted Regime 2: advanced metering upgrade requirement [1]

Conclusions and future work  PA-AMI adoption rates  Regression model for privacy valuation  Willingness-to-Pay v. Willingness-to-Accept  PHEV and V2G privacy risks

Questions

References 1.D. Ghosh, D. Schrader, W. Schulze, and S. Wicker, “Economic analysis of Advanced Metering Infrastructure adoption,” ISGT USA ‘12. 2.D. Ghosh, S. Wicker and L. Blume, “Game theoretic analysis of Advanced Metering Infrastructure,” ISGT Europe ‘11. 3.D. Ghosh and S. Wicker, “Designing a privacy-aware framework for vehicle-to-grid implementation,” working M. Lisovich, D. Mulligan, and S. Wicker, “Inferring personal information from demand response systems,” IEEE Security and Privacy, Feb S. Wicker and R. Thomas, “A privacy-aware architecture for demand response systems,” HICSS ‘10. 6.S. Wicker and D. Schrader, “Privacy-aware design principles for information networks,” Proceedings of the IEEE, 2011.