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Found 12 papers, 0 papers with code
When are Lossy Energy Storage Optimization Models Convex?
We consider a class of optimization problems involving the optimal operation of a single lossy energy storage system that incurs energy loss when charging or discharging.
A Multi-Battery Model for the Aggregate Flexibility of Heterogeneous Electric Vehicles
The increasing prevalence of electric vehicles (EVs) in the transportation sector will introduce a large number of highly flexible electric loads that EV aggregators can pool and control to provide energy and ancillary services to the wholesale electricity market.
A Distributionally Robust Approach to Regret Optimal Control using the Wasserstein Distance
We compare the minimax regret optimal control design method with the distributionally robust optimal control approach using an illustrative example and numerical experiments.
An Efficient Method for Quantifying the Aggregate Flexibility of Plug-in Electric Vehicle Populations
Plug-in electric vehicles (EVs) are widely recognized as being highly flexible electric loads that can be pooled and controlled via aggregators to provide low-cost energy and ancillary services to wholesale electricity markets.
Data-Driven Approximations of Chance Constrained Programs in Nonstationary Environments
We study sample average approximations (SAA) of chance constrained programs.
Achieving Reliable Coordination of Residential Plug-in Electric Vehicle Charging: A Pilot Study
Wide-scale electrification of the transportation sector will require careful planning and coordination with the power grid.
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
Yet, there have been limited studies on the adversarial robustness of multi-modal models that fuse LiDAR features with image features.
Nonparametric Estimation of Uncertainty Sets for Robust Optimization
We investigate a data-driven approach to constructing uncertainty sets for robust optimization problems, where the uncertain problem parameters are modeled as random variables whose joint probability distribution is not known.
Safe Linear Stochastic Bandits
We introduce the safe linear stochastic bandit framework---a generalization of linear stochastic bandits---where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability.
Transparency and Control in Platforms for Networked Markets
In this paper, we analyze the worst case efficiency loss of online platform designs under a networked Cournot competition model.
Computer Science and Game Theory
An Online Learning Approach to Buying and Selling Demand Response
Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the aggregator, we investigate the extent to which the aggregator might dynamically adapt its offered prices and forward contracts to maximize its expected profit over a time window of $T$ days.
Risk-Sensitive Learning and Pricing for Demand Response
Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of $T$ days.
