Lillian J. Ratliff

Energy management via pricing in LQ dynamic games

This paper investigates the use of pricing mechanisms as a means to achieve a desired feedback control strategy among selfish agents in the context of HVAC resource allocation in buildings. We pose the problem of resource allocation as a linear-quadratic game with many dynamically coupled zone occupants(agents) and an uncoupled social planner. The social planner influences the game by choosing the quadratic dependence on control actions for each agents cost function. We propose a neighborhood-based simplification of the dynamic game that results in a more realistic and scalable framework than is considered in standard dynamic game theory. In addition, we construct the pricing design problem as a convex feasibility problem and apply our method to an eight zone building model.

Fundamental limits of nonintrusive load monitoring

Provided an arbitrary nonintrusive load monitoring (NILM) algorithm, we seek bounds on the probability of distinguishing between scenarios, given an aggregate power consumption signal. We introduce a framework for studying a general NILM algorithm, and analyze the theory in the general case. Then, we specialize to the case where the error is Gaussian. In both cases, we are able to derive upper bounds on the probability of distinguishing scenarios. Finally, we apply the results to real data to derive bounds on the probability of distinguishing between scenarios as a function of the measurement noise, the sampling rate, and the device usage.

A dynamical systems approach to energy disaggregation

Energy disaggregation, also known as non-intrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances. Studies have shown that simply providing disaggregated data to the consumer improves energy consumption behavior. However, placing individual sensors on every device in a home is not presently a practical solution. Disaggregation provides a feasible method for providing energy usage behavior data to the consumer which utilizes currently existing infrastructure. In this paper, we present a novel framework to perform the energy disaggregation task. We model each individual device as a single-input, single-output system, where the output is the power consumed by the device and the input is the device usage. In this framework, the task of disaggregation translates into finding inputs for each device that generates our observed power consumption. We describe an implementation of this framework, and show its results on simulated data as well as data from a small-scale experiment.

Characterization and computation of local Nash equilibria in continuous games

We present derivative-based necessary and sufficient conditions ensuring player strategies constitute local Nash equilibria in non-cooperative continuous games. Our results can be interpreted as generalizations of analogous second-order conditions for local optimality from nonlinear programming and optimal control theory. Drawing on this analogy, we propose an iterative steepest descent algorithm for numerical approximation of local Nash equilibria and provide a sufficient condition ensuring local convergence of the algorithm. We demonstrate our analytical and computational techniques by computing local Nash equilibria in games played on a finite-dimensional differentiable manifold or an infinite-dimensional Hilbert space.

Incentive Design and Utility Learning via Energy Disaggregation

The utility company has many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company--consumer interaction as a principal--agent problem. We present an iterative algorithm for designing incentives while estimating the consumers utility function. Incentives are designed using the aggregated as well as the disaggregated (device level) consumption data. We simulate the iterative control (incentive design) and estimation (utility learning and disaggregation) process for examples including the design of incentives based on the aggregate consumption data as well as the disaggregated consumption data.

Energy disaggregation via adaptive filtering

The energy disaggregation problem is recovering device level power consumption signals from the aggregate power consumption signal for a building. We show in this paper how the disaggregation problem can be reformulated as an adaptive filtering problem. This gives both a novel disaggregation algorithm and a better theoretical understanding for disaggregation. In particular, we show how the disaggregation problem can be solved online using a filter bank and discuss its optimality.

On the Characterization of Local Nash Equilibria in Continuous Games

We present a unified framework for characterizing local Nash equilibria in continuous games on either infinite-dimensional or finite-dimensional non-convex strategy spaces. We provide intrinsic necessary and sufficient first- and second-order conditions ensuring strategies constitute local Nash equilibria. We term points satisfying the sufficient conditions differential Nash equilibria. Further, we provide a sufficient condition (non-degeneracy) guaranteeing differential Nash equilibria are isolated and show that such equilibria are structurally stable. We present tutorial examples to illustrate our results and highlight degeneracies that can arise in continuous games.

Social game for building energy efficiency: Incentive design

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.

Genericity and structural stability of non-degenerate differential Nash equilibria

We show that non-degenerate differential Nash equilibria are generic among local Nash equilibria in games with smooth costs and continuous strategy spaces, and demonstrate that such equilibria are structurally stable with respect to smooth perturbations in player costs. This implies that second-order conditions suffice to characterize local Nash equilibria in an open-dense set of games where player costs are smooth functions. Furthermore, equilibria that are computable using decoupled myopic approximate best-response persist under perturbations to the cost functions of individual players.

Blind Identification Via Lifting

Abstract Blind system identification is known to be an ill-posed problem and without further assumptions, no unique solution is at hand. In this contribution, we are concerned with the task of identifying an ARX model from only output measurements. We phrase this as a constrained rank minimization problem and present a relaxed convex formulation to approximate its solution. To make the problem well posed we assume that the sought input lies in some known linear subspace.