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Demand response (DR) has been demonstrated to be an effective method for reducing peak load and mitigating uncertainties on both the supply and demand sides of the electricity market. One critical question for DR research is how to…
We explore how Demand Response (DR) can effectively provide electricity system services such as for the management of bi-directional power flows and the control of voltage deviations in active distribution networks, without compromising…
Demand response management has become one of the key enabling technologies for smart grids. Motivated by the increasing demand response incentives offered by service operators, more customers are subscribing to various demand response…
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning…
Demand response for residential users is essential to the realization of modern smart grids. This paper proposes a multiobjective approach to designing a demand response program that considers the energy costs of residential users and the…
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life…
Privacy is an essential issue in data trading markets. This work uses a mechanism design approach to study the optimal market model to economize the value of privacy of personal data, using differential privacy. The buyer uses a finite…
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction? Three research questions are key: (1) measuring user satisfaction, (2) combatting sparsity…
Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a…
The expansion of residential demand response programs and increased deployment of controllable loads will require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe…
Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the…
Thermostatically controlled loads and electric vehicles offer flexibility to reduce power peaks in low-voltage distribution networks. This flexibility can be maximized if the devices are coordinated centrally, given some level of…
We study the idea of information design for inducing prosocial behavior in the context of electricity consumption. We consider a continuum of agents. Each agent has a different intrinsic motivation to reduce her power consumption. Each…
Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers' electricity bills by determining the optimal appliance…
Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort. Data-driven approaches based on neural networks have been proposed to facilitate system modelling.…
It can be observed that the purchasing decision of an individual consumer in an electronic marketplace is determined by a set of factors, such as personal characteristics of the consumer, product pricing, minimum price-quantity combination…
We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest. We formulate this question as a Bayesian-optimal mechanism design problem, in which an individual can share…
This paper presents an analysis of competition between generators when incentive-based demand response is employed in an electricity market. Thermal and hydropower generation are considered in the model. A smooth inverse demand function is…
In an electric power system, demand fluctuations may result in significant ancillary cost to suppliers. Furthermore, in the near future, deep penetration of volatile renewable electricity generation is expected to exacerbate the variability…
We study operations of a battery energy storage system under a baseline-based demand response (DR) program with an uncertain schedule of DR events. Baseline-based DR programs may provide undesired incentives to inflate baseline consumption…