We present analysis and results of a social game encouraging energy efficient behavior in occupants by distributing points which determine the likelihood of winning in a lottery. We estimate occupants utilities and formulate the interaction between the building manager and the occupants as a reversed Stackelberg game in which there are multiple followers that play in a non-cooperative game. The estimated utilities are used for determining the occupant behavior in the non-cooperative game. Due to nonconvexities and complexity of the problem, in particular the size of the joint distribution across the states of the occupants, we solve the resulting the bilevel optimization problem using a particle swarm optimization method. Drawing from the distribution across player states, we compute the Nash equilibrium of the game using the resulting leader choice. We show that the behavior of the agents under the leader choice results in greater utility for the leader.

The Internet of Things (IoT) promises many advantages in the control and monitoring of physical systems from both efficacy and efficiency perspectives. However, in the wrong hands, the data might pose a privacy threat. In this article, we consider the tradeoff between the operational value of data collected in the IoT and the privacy of consumers. We present a general framework for quantifying this tradeoff in the IoT, and focus on a smart grid application for a proof of concept. In particular, we analyze the tradeoff between smart grid operations and how often data are collected by considering a realistic direct-load control example using thermostatically controlled loads, and we give simulation results to show how its performance degrades as the sampling frequency decreases. Additionally, we introduce a new privacy metric, which we call inferential privacy. This privacy metric assumes a strong adversary model and provides an upper bound on the adversary’s ability to infer a private parameter, independent of the algorithm he uses. Combining these two results allows us to directly consider the tradeoff between better operational performance and consumer privacy.

We describe a social game that we designed for encouraging energy efficient behavior amongst building occupants with the aim of reducing overall energy consumption in the building. Occupants vote for their desired lighting level and win points which are used in a lottery based on how far their vote is from the maximum setting. We assume that the occupants are utility maximizers and that their utility functions capture the tradeoff between winning points and their comfort level. We model the occupants as non-cooperative agents in a continuous game and we characterize their play using the Nash equilibrium concept. Using occupant voting data, we parameterize their utility functions and use a convex optimization problem to estimate the parameters. We simulate the game defined by the estimated utility functions and show that the estimated model for occupant behavior is a good predictor of their actual behavior. In addition, we show that due to the social game, there is a significant reduction in energy consumption.

In the electricity grid, networked sensors which record and transmit increasingly high-granularity data are being deployed. In such a setting, privacy concerns are a natural consideration. In order to obtain the consumer's valuation of privacy, we design a screening mechanism consisting of a menu of contracts offered to the energy consumer with varying guarantees of privacy. The screening process is a means to segment customers. Finally, we design insurance contracts using the probability of a privacy breach to be offered by third-party insurance companies.