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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded


Month Degree Awarded


First Advisor

Prashant Shenoy

Subject Categories

Computer Engineering


Modern buildings are heavy power consumers. For instance, of the total electricity consumed in the US, 75% is consumed in the residential and commercial buildings. This consumption is not evenly distributed over time. Typical consumption profile exhibits several peaks and troughs. The peakiness, in turn, dictates the electric grid's generation, transmission and distribution costs, and also the associated carbon emissions.

This thesis discusses challenges involved in achieving the sustainability goals in buildings and electric grids. It investigates building and grid energy footprint optimization techniques to achieve the following goals: 1) making buildings energy efficient, 2) cutting building's electricity bills, 3) cutting utility's costs in electricity generation and distribution, 4) reducing carbon footprints, and 5) making the aggregate electricity consumption profile grid-friendly.

In this thesis, we first design SmartCap, a system to enable homes flatten their consumption/demand by scheduling background loads (such as A/Cs, refrigerator), without causing user discomfort and without direct user involvement. Demand flattening facilitates aggregate peak reduction, which in turn enables grids to 1) reduce carbon emissions, and 2) cut installation and operational costs. Our results demonstrate that SmartCap can decrease the average deviation from mean power by over 20% across all periods with high deviation, thereby flattening the peaky demand. Next, we present SmartCharge, an intelligent battery charging system that shifts a building's electricity consumption to off-peak periods by storing low-cost energy for use during high-cost periods, without active user involvement. We show that SmartCharge can typically save 10-15% in bills and can reduce the grid-wide peak demand by up to 20%. We then extend SmartCharge to GreenCharge, which integrates on-site renewables in a building's electricity consumption. Our experiments show that GreenCharge can cut user electricity bills up to 20%. After GreenCharge, we investigate the use of large-scale distributed energy storage at buildings throughout the grid to flatten grid demand, while 1) maintaining end-user incentives for storage adoption at grid-scale, and 2) ensuring grid stability. We design PeakCharge, an online peak-aware charging algorithm to optimize the use of energy storage in the presence of a peak demand surcharge. Empirical evaluations show that total storage capacity required by PeakCharge to flatten grid demand is within 18% of the capacity required by a centralized system. Finally, we examine the efficacy of employing different combinations of energy storage technologies at different levels of the grid’s distribution hierarchy to cut electric utility's daily operational costs. We present an optimization framework for modeling the primary characteristics of various energy storage technologies and important tradeoffs in placing different storage technologies at different levels of the distribution hierarchy. We show that by employing hybrid storage technologies at multiple levels of the distribution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%.