Search

In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to off-peak time. In contrast to most other mechanisms proposed in the literature, the key feature of our mechanism is its modest requirements on user preferences, i.e., it does not require exact knowledge of user responsiveness to rewards for shifting their demand from the peak to the off-peak time. Specifically, our mechanism utilizes a probabilistic reward structure for users who shift their demand to the off-peak time, and is robust to incomplete information about user demand and/or risk preferences. We approach the problem from the public good perspective, and demonstrate that the mechanism can be implemented via lottery-like schemes. Our mechanism permits to reduce the distribution losses, and thus improve efficiency of electricity distribution. Finally, the mechanism can be readily incorporated into the emerging demand response schemes (e.g., the time-of-day pricing, and critical peak pricing schemes), and has security and privacy-preserving properties., National Science Foundation (U.S.) (Grant CNS-1239166), National Science Foundation (U.S.) (Grant CNS-0910711), National Science Foundation (U.S.) (CCF-0424422)

We propose a raffle-based scheme for the decongestion of a shared resource. Our scheme builds on ideas from the economic literature on incentivizing contributions to a public good. We formulate a game-theoretic model for the decongestion problem in a setup with a finite number of users, as well as in a setup with an infinite number of non-atomic users. We analyze both setups, and show that the former converges toward the latter when the number of users becomes large. We compare our results to existing results for the public good provision problem. Overall, our results establish that raffle-based schemes are useful in addressing congestion problems., National Science Foundation (U.S.) (Grant CNS-0910711), National Science Foundation (U.S.) (Grant CCF-0424422), United States. Air Force Office of Scientific Research (FA9550-06-1-0244)

International audience

Infrastructure inspection and maintenance decision making is a stochastic and partially observable problem. This thesis presents a learning and decision-making approach for developing optimal joint inspection and maintenance policies for civil infrastructure facilities under performance model uncertainty and partially observable condition state. Joint inspection and maintenance decision making refers to the cost-effective policies that capture the interactions between the inspections, and the maintenance and rehabilitation decisions. Performance model uncertainty is the uncertainty about the actual facility deterioration model. Partially observation condition states imply that the true or latent condition of the facility is not captured by the measurement technology used for inspecting the facility due to measurement errors. The proposed approach models single facility inspection and maintenance decision-making problem as a partially observable Markov decision process. The data collected during the agency-facility interaction is used to learn the maximum likelihood estimate of performance model using the Baum-Welch algorithm. Both offline and online versions of the learning algorithm are presented. The probing-optimizing dichotomy, also known as exploration-exploitation dilemma, in choosing between the best strategy based on the past knowledge of deterioration and the strategy that provides information leading to better performance model is also illustrated. Once a set of performance models is learned, each of the models is quantitatively compared with the corresponding true model using an information theoretic measure known as asymmetric Kullback-Leibler divergence to determine its goodness. It is also shown that given a set of new observation sequences gathered after the model learning, each of the sequences or the entire set of sequences can be assigned the model that gives the least log-likelihood on that sequence or the set. This can help in classifying the facility deterioration into different deterioration modes like slow, medium, or fast. Finally, an exact algorithm for arriving at optimal inspection and maintenance decision-making policies is described. It is argued that the proposed approach scores over other recently developed adaptive control approaches in that it directly learns the best model from historical data instead of maintaining and updating beliefs over a set of pre-selected models. Furthermore, since the proposed learning approach is model-based, it makes more efficient use of costly data as compared to the model-less learning approaches. Keywords: Infrastructure Inspection and Maintenance, Baum-Welch Algorithm, Kullback-Liebler Divergence, Sequence Classification, Partially Observable Markov Decision Process, Reinforcement Learning.