Online Algorithms for Optimal Energy Distribution in Microgrids

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Reducing the cost of operational water on military bases through modeling, optimization, and control. Efficient Selection of Consumers for Automated Demand Response Programs Found under: "Efficient integration of smart appliances for demand response programs". Estimating the benefits of cooperation in a residential microgrid: A data-driven approach. Understanding the potential for electricity savings and assessing feasibility of a transition towards DC powered buildings.

How much electricity can we save by using direct current circuits in homes? Comparison of two residential Smart Grid pilots in the Netherlands and in the USA, focusing on energy performance and user experiences.

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Using BEopt EnergyPlus with energy audits and surveys to predict actual residential energy usage. Thermostat metrics derived from HVAC cycling data for targeted utility efficiency programs.

Transaction Paper Abstracts - September 2018 (IEEE Transactions on Smart Grid)

An empirically-validated methodology to simulate electricity demand for electric vehicle charging. A unit commitment study of the application of energy storage toward the integration of renewable generation. A temporal assessment of vehicle use patterns and their impact on the provision of vehicle-to-grid services. Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level. EV, forecasting model, probabilistic evaluation, scenario generation, smart grid, smart homes.

The sensitivity of vehicle-to-grid revenues to plug-in electric vehicle battery size and EVSE power rating. Electricity forecasting on the individual household level enhanced based on activity patterns.

Non-intrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data. A multi-objective assessment of the effect of solar PV array orientation and tilt on energy production and system economics. Modeling peak load reduction and energy consumption enabled by an integrated thermal energy and water storage system for residential air conditioning systems in Austin, Texas.

Combining a dynamic battery model with high-resolution smart grid data to assess microgrid islanding lifetime.


Climate control : smart thermostats, demand response, and energy efficiency in Austin, Texas. A non-intrusive approach for classifying residential water events using coincident electricity data. A cooperative multi-agent deep reinforcement learning framework for real-time residential load scheduling. A behavior-centered framework for real-time control and load-shedding using aggregated residential energy resources in distribution microgrids. The two-way energy and information flows in the SG, together with the smart devices, bring new perspectives to energy management and demand response.

Algorithms for Optimal Energy Management in the Smart Grid

Meanwhile, innovative grid components, such as microgrid MG and electric vehicle, are emerging as new applications which bring many benefits as well as more chanllegens in SG. Therefore, we explore possible solutions to these chanllegening but interesting problems. In this dissertation, we first present an introduction of the SG, and the research involved in different areas of SG. We then investigate an online algorithm for energy distribution in a SG environment.

The proposed online algorithm are quite general, suitable for a wide range of utility, cost and pricing functions.

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  • A distributed online algorithm for optimal real-time energy distribution in smart grid.

And it is asymptotically optimal without any future information. Following this, we then propose a distributed online algorithm.

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Comparing to the previous one, it solves the online problem in a distributed manner and mitigates the user privacy issue by not sharing user utility functions. Both algorithms are evaluated with trace-driven simulations and shown to outperform a benchmark scheme. We then propose a hierarchical power scheduling approach to optimally manage power trading, storage and distribution in a smart power grid with a Macrogrid and cooperative MGs.