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Residential solar installations are becoming increasingly popular among homeowners. However, renters and homeowners living in shared buildings cannot go solar as they do not own the shared spaces. Community-owned solar arrays and energy storage have emerged as a solution, which enables ownership even when they do not own the property or roof. However, such community-owned systems do not allow individuals to control their share for optimizing a home’s electricity bill. To overcome this limitation, inspired by the concept of virtualization in operating systems, we propose virtual community-owned solar and storage—a logical abstraction to allow individuals to independently control their share of the system. We argue that such individual control can benefit all owners and reduce their reliance on grid power. We present mechanisms and algorithms to provide a virtual solar and battery abstraction to users and understand their cost benefits. In doing so, our comparison with a traditional community-owned system shows that our AutoShare approach can achieve the same global savings of 43% while providing independent control of the virtual system. Further, we show that independent energy sharing through virtualization provides an additional 8% increase in savings to individual owners.

 

Major innovations in computing have been driven by scaling up computing infrastructure, while aggressively optimizing operating costs. The result is a network of worldwide datacenters that consume a large amount of energy, mostly in an energy-efficient manner. Since the electric grid powering these datacenters provided a simple and opaque abstraction of an unlimited and reliable power supply, the computing industry remained largely oblivious to the carbon intensity of the electricity it uses. Much like the rest of the society, it generally treated the carbon intensity of the electricity as constant, which was mostly true for a fossil fuel-driven grid. As a result, the cost-driven objective of increasing energy-efficiency — by doing more work per unit of energy — has generally been viewed as the most carbon-efficient approach. However, as the electric grid is increasingly powered by clean energy and is exposing its time-varying carbon intensity, the most energy-efficient operation is no longer necessarily the most carbon-efficient operation. There has been a recent focus on exploiting the flexibility of computing’s workloads—along temporal, spatial, and resource dimensions—to reduce carbon emissions, which comes at the cost of either performance or energy efficiency. In this paper, we discuss the trade-offs between energy efficiency and carbon efficiency in exploiting computing’s flexibility and show that blindly optimizing for energy efficiency is not always the right approach. 

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. 

Residential heating, primarily powered by natural gas, accounts for a significant portion of residential sector energy use and carbon emissions in many parts of the world. Hence, there is a push towards decarbonizing residential heating by transitioning to energyefficient heat pumps powered by an increasingly greener and less carbon-intensive electric grid. However, such a transition will add additional load to the electric grid triggering infrastructure upgrades, and subsequently erode the customer base using the gas distribution network. Utilities want to guide these transition efforts to ensure a phased decommissioning of the gas network and deferred electric grid infrastructure upgrades while achieving carbon reduction goals. To facilitate such a transition, we present a network-aware optimization framework for decarbonizing residential heating at city scale with an objective to maximize carbon reduction under budgetary constraints. Our approach operates on a graph representation of the gas network topology to compute the cost of transitioning and select neighborhoods for transition. We further extend our approach to explicitly incorporate equity and ensure an equitable distribution of benefits across different socioeconomic groups. We apply our framework to a city in the New England region of the U.S., using real-world gas usage, electric usage, and grid infrastructure data. We show that our networkaware strategy achieves 55% higher carbon reductions than prior network-oblivious work under the same budget. Our equity-aware strategy achieves an equitable outcome while preserving the carbon reduction benefits of the network-aware strategy. 

Heating buildings using fossil fuels such as natural gas, propane and oil makes up a significant proportion of the aggregate carbon emissions every year. Because of this, there is a strong interest in decarbonizing residential heating systems using new technologies such as electric heat pumps. In this paper, we conduct a data-driven optimization study to analyze the potential of replacing gas heating with electric heat pumps to reduce CO 2 emission in a city-wide distribution grid. We conduct an in-depth analysis of gas consumption in the city and the resulting carbon emissions. We then present a flexible multi-objective optimization (MOO) framework that optimizes carbon emission reduction while also maximizing other aspects of the energy transition such as carbon-efficiency, and minimizing energy inefficiency in buildings. Our results show that replacing gas with electric heat pumps has the potential to cut carbon emissions by up to 81%. We also show that optimizing for other aspects such as carbon-efficiency and energy inefficiency introduces tradeoffs with carbon emission reduction that must be considered during transition. Finally, we present a detailed analysis of the implication of proposed transition strategies on the household energy consumption and utility bills, electric grid upgrades, and decarbonization policies. We compute the additional energy demand from electric heat pumps at the household as well as the transformer level and discuss how our results can inform decarbonization policies at city scale. 

Abstract Environmental inequalities are often large and consequential, exacerbating vertical inequalities of income and class and horizontal inequalities along lines of race and ethnicity. Climate policies can widen these inequalities as well as mitigate them, depending on their design. Decarbonization of the US electricity sector illustrates these possibilities. A strategy narrowly focused on carbon reduction alone is likely in some regions to increase disparities in exposure to localized co-pollutants emitted by fossil fuel combustion and, in some cases, to increase exposure in absolute terms. Strategies that in addition explicitly mandate improvements in air quality, both overall and specifically for frontline communities, can couple decarbonization with remediation of environmental inequalities and broad-based gains in public health.