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Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This article highlights the potential conflict between electricity prices and carbon emissions and the resulting tradeoffs in carbon-aware and cost-aware load scheduling. To address this tradeoff, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively. 

The impact of human activity on the climate is a major global challenge that affects human well-being. Buildings are a major source of energy consumption and carbon emissions worldwide, especially in advanced economies such as the United States. As a result, making grids and buildings sustainable by reducing their carbon emissions is emerging as an important step toward societal decarbonization and improving overall human well-being. While prior work on demand response methods in power grids and buildings has targeted peak shaving and price arbitrage in response to price signals, it has not explicitly targeted carbon emission reductions. In this paper, we analyze the flexibility of building loads to quantify the upper limit on their potential to reduce carbon emissions, assuming perfect knowledge of future demand and carbon intensity. Our analysis leverages real-world demand patterns from 1000+ buildings and carbon-intensity traces from multiple regions. It shows that by manipulating the demand patterns of electric vehicles, heating, ventilation, and cooling (HVAC) systems, and battery storage, we can reduce carbon emissions by 26.93% on average and by 54.90% at maximum. Our work advances the understanding of sustainable infrastructure by highlighting the potential for infrastructure design and interventions to significantly reduce carbon footprints, benefiting human well-being. 

Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback of increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion of proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. We also study the benefits of using energy storage when we rate control solar. To do so, we present a decentralized algorithm to charge and discharge batteries for each smart solar. Our evaluation on a city-scale dataset shows that our approach enables 2.6× more solar penetration while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30× faster convergence. Finally, we demonstrate energy storage can help netmeter more solar energy while ensuring fairness and grid constraints are met.