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Abstract Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO₂emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption. 

Abstract As regional grids increase penetrations of variable renewable electricity (VRE) sources, demand-side management (DSM) presents an opportunity to reduce electricity-related emissions by shifting consumption patterns in a way that leverages the large diurnal fluctuations in the emissions intensity of the electricity fleet. Here we explore residential precooling, a type of DSM designed to shift the timing of air-conditioning (AC) loads from high-demand periods to periods earlier in the day, as a strategy to reduce peak period demand, CO₂emissions, and residential electricity costs in the grid operated by the California Independent System Operator (CAISO). CAISO provides an interesting case study because it generally has high solar generation during the day that is replaced by fast-ramping natural gas generators when it drops off suddenly in the early evening. Hence, CAISO moves from a fleet of generators that are primarily clean and cheap to a generation fleet that is disproportionately emissions-intensive and expensive over a short period of time, creating an attractive opportunity for precooling. We use EnergyPlus to simulate 480 distinct precooling schedules for four single-family homes across California’s 16 building climate zones. We find that precooling a house during summer months in the climate zone characterizing Downtown Los Angeles can reduce peak period electricity consumption by 1–4 kWh d⁻¹and cooling-related CO₂emissions by as much as 0.3 kg CO₂ d⁻¹depending on single-family home design. We report results across climate zone and single-family home design and show that precooling can be used to achieve simultaneous reductions in emissions, residential electricity costs, and peak period electricity consumption for a variety of single-family homes and locations across California. 

Abstract High fractions of variable renewable electricity generation have challenged grid management within the balancing authority overseen by the California’s Independent System Operator (CAISO). In the early evening, solar resources tend to diminish as the system approaches peak demand, putting pressure on fast-responding, emissions-intensive natural gas generators. While residential precooling, a strategy intended to shift the timing of air-conditioning usage from peak-demand periods to cheaper off-peak periods, has been touted in the literature as being effective for reducing peak electricity usage and costs, we explore its impact on CO₂emissions in regional grids like CAISO that have large disparities in their daytime versus nighttime emissions intensities. Here we use EnergyPlus to simulate precooling in a typical U.S. single-family home in California climate zone 9 to quantify the impact of precooling on peak electricity usage, CO₂emissions, and residential utility costs. We find that replacing a constant-setpoint cooling schedule with a precooling schedule can reduce peak period electricity consumption by 57% and residential electricity costs by nearly 13%, while also reducing CO₂emissions by 3.5%. These results suggest the traditional benefits of precooling can be achieved with an additional benefit of reducing CO₂emissions in grids with high daytime renewable energy penetrations.