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1. Analyzing the Impact of Decarbonizing Residential Heating on the Electric Distribution Grid

   https://doi.org/10.1145/3607114.3607119  

   Wamburu, John; Bashir, Noman; Irwin, David; Shenoy, Prashant (June 2023, ACM SIGEnergy Energy Informatics Review)

   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. 

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2. Heating with justice: Barriers and solutions to a just energy transition in cold climates

   https://doi.org/10.1016/j.resconrec.2024.107699  

   McKenna, Claire; Gronlund, Carina; Vaishnav, Parth (September 2024, Resources, Conservation and Recycling)

   Residential heat pumps could reduce greenhouse gas emissions, but increase energy burdens, the proportion of income households spend on utility bills. We analyze utility bills, thermostat settings and energy burdens for a sample of 51 households in Michigan, half below median income. We recruit a contractor to conduct energy assessments of these households and provide them with energy retrofit recommendations, including estimated costs and savings. We find that low-income households choose similar temperature setpoints to higher-income households, but live in less efficient homes. Below-median income households, which today experience a median energy burden of 6%, would see it rise to 10% if they shifted to electric heat pumps from natural gas. Weatherization could offset this increase, bringing burdens down to pre-electrification levels. However, median payback is 24 years, making retrofits infeasible for the poorest. Our results are indicative of an energy poverty trap that could hinder an equitable energy transition. 

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3. Equitable Network-Aware Decarbonization of Residential Heating at City Scale

   https://doi.org/10.1145/3575813.3576870  

   Lechowicz, Adam; Bashir, Noman; Wamburu, John; Hajiesmaili, Mohammad; Shenoy, Prashant (June 2023, CM International Conference on Future Energy Systems (e-Energy))

   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. 

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4. Emission-aware Energy Storage Scheduling for a Greener Grid

   https://doi.org/10.1145/3396851.3397755  

   Jha, Rishikesh; Lee, Stephen; Iyengar, Srinivasan; Hajiesmaili, Mohammad H.; Irwin, David; Shenoy, Prashant (June 2020, Eleventh ACM International Conference on Future Energy Systems)

   Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions --- equivalent to a drop of 23.3% in our electric grid emissions. 

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5. Measuring social equity in urban energy use and interventions using fine-scale data

   https://doi.org/10.1073/pnas.2023554118  

   Tong, Kangkang; Ramaswami, Anu; Xu, Corey; Feiock, Richard; Schmitz, Patrick; Ohlsen, Michael (June 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities. 

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