Master of Science

thesisPower Grids across the world are getting "smarter" that need smart meters installed at each home to monitor power flow. These smart meters record power consumption data at every minute or even every second. These fine-grained data expose private information about the residents of the house at many levels. Knowledge about the number of occupants, times of occupancy, appliance information, and much more can be inferred from analyzing patterns in the electricity usage. A solution to obscure these data is to add a battery to each home and use it strategically to manipulate the readings observed at the smart meter. To minimize the correlation between the power used by the home appliances and the reported value, the battery usage should be independent of the real demand. Deploying such a solution at a large scale can result in sudden peaks in the energy usage. This is an alarming concern for the electric utility companies as they may cause outages, making such solutions a threat to the stability of the grid. This thesis is the first to expose this shortcoming and propose a system to mitigate the problem while maintaining the privacy of the residents. We also investigate the trade-off between the possible extent of data exposed and electric load reduced