Kelly Twomey Sanders

We employed smart-meter data from 140,000 households to examine how residential AC operation in Southern California was associated with meteorological conditions, socioeconomic characteristics, and housing-related factors across different climate zones. We characterized AC operation patterns through two metrics that quantify the daily duration and intensity of cooling, enabling an assessment of adaptive capacity to heat beyond what traditional measures of AC ownership capture. Our results quantify how socioeconomic status and dwelling characteristics shape cooling behavior, including the likelihood, duration, and electricity consumption of AC operation, both during daytime and nighttime. After normalizing cooling metrics to account for heat exposure and dwelling size, we examined potential cooling gaps across our study region, relating these to income disparities and to the presence of vulnerable populations. Our work highlights the need for equity-oriented adaptation strategies that reduce the cost burden of cooling in order to mitigate heat-related risks.

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 CO₂ emissions 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.

To demonstrate how a mega city can lead in decarbonizing beyond legal mandates, the city of Los Angeles (LA) developed science-based, feasible pathways towards utilizing 100% renewable energy for its municipally-owned electric utility. Aside from decarbonization, renewable energy adoption can lead to co-benefits such as improving urban air quality from reductions in combustion-related emissions of oxides of nitrogen (NO_x), primary fine particulate matter (PM_2.5) and others. Herein, we quantify changes to air pollutant concentrations and public health from scenarios of 100% renewable electricity adoption in LA in 2045, alongside aggressive electrification of end-use sectors. Our analysis suggests that while ensuring reliable electricity supply, reductions in emissions of air pollutants associated with the 100% renewable electricity scenarios can lead to 8% citywide reductions of PM_2.5 concentration while increasing ozone concentration by 5% relative to a 2012 baseline year, given identical meteorology conditions. The combination of these concentration changes could result in net monetized public health benefits (driven by avoided deaths) of up to $1.4 billion in year 2045 in LA, results potentially replicable for other city-scale decarbonization scenarios.

The California Independent System Operator (CAISO) utilizes a system-wide, voluntary demand response (DR) tool, called the Flex Alert program, designed to reduce energy usage during peak hours, particularly on hot summer afternoons when surges in electricity demand threaten to exceed available generation resources. However, the few analyses on the efficacy of CAISO Flex Alerts have produced inconsistent results and do not investigate how participation varies across sectors, regions, population demographics, or time. Evaluating the efficacy of DR tools is difficult as there is no ground truth in terms of what demand would have been in the absence of the DR event. Thus, we first define two metrics that to evaluate how responsive customers were to Flex Alerts, including the Flex Period Response, which estimates how much demand was shifted away from the Flex Alert period, and the Ramping Response, which estimates changes in demand during the first hour of the Flex Alert period. We then analyze the hourly load response of the residential sector, based on ∼200 000 unique homes, on 17 Flex Alert days during the period spanning 2015–2020 across the Southern California Edison (SCE) utility’s territory and compare it to total SCE load. We find that the Flex Period Response varied across Flex Alert days for both the residential (−18% to +3%) and total SCE load (−7% to +4%) and is more dependent on but less correlated with temperature for the residential load than total SCE load. We also find that responsiveness varied across subpopulations (e.g. high-income, high-demand customers are more responsive) and census tracts, implying that some households have more load flexibility during Flex Alerts than others. The variability in customer engagement suggests that customer participation in this type of program is not reliable, particularly on extreme heat days, highlighting a shortcoming in unincentivized, voluntary DR programs.

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, CO₂ emissions, 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⁻¹ and cooling-related CO₂ emissions by as much as 0.3 kg CO₂ d⁻¹ depending 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.

Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels intensify the heat stress that populations experience. Although air conditioning (AC) is a key adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding of patterns of AC ownership. Developing high resolution estimates of AC ownership is critical for identifying communities vulnerable to extreme heat and for informing future electricity system investments as increases in cooling demand will exacerbate strain placed on aging power systems. In this study, we utilize a segmented linear regression model to identify AC ownership across Southern California by investigating the relationship between daily household electricity usage and a variety of humid heat metrics (HHMs) for ~160000 homes. We hypothesize that AC penetration rate estimates, i.e. the percentage of homes in a defined area that have AC, can be improved by considering indices that incorporate humidity as well as temperature. We run the model for each household with each unique heat metric for the years 2015 and 2016 and compare differences in AC ownership estimates at the census tract level. In total, 81% of the households were identified as having AC by at least one heat metric while 69% of the homes were determined to have AC with a consensus across all five of the heat metrics. Regression results also showed that the r ² values for the dry bulb temperature (DBT) (0.39) regression were either comparable to or higher than the r ² values for HHMs (0.15–0.40). Our results suggest that using a combination of heat metrics can increase confidence in AC penetration rate estimates, but using DBT alone produces similar estimates to other HHMs, which are often more difficult to access, individually. Future work should investigate these results in regions with high humidity.

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 CO₂ emissions 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, CO₂ emissions, 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 CO₂ emissions by 3.5%. These results suggest the traditional benefits of precooling can be achieved with an additional benefit of reducing CO₂ emissions in grids with high daytime renewable energy penetrations.

Climate change is expected to exacerbate the urban heat island (UHI) effect in cities worldwide, increasing the risk of heat-related morbidity and mortality. Solar reflective ‘cool pavement’ is one of several mitigation strategies that may counteract the negative effects of the UHI effect. An increase in pavement albedo results in less heat absorption, which results in reduced surface temperatures (T _surface). Near surface air temperatures (T _air) could also be reduced if cool pavements are deployed at sufficiently large spatial scales, though this has never been confirmed by field measurements. This field study is the first to conduct controlled measurements of the impacts of neighborhood-scale cool pavement installations. We measured the impacts of cool pavement on albedo, T _surface, and T _air. In addition, pavement albedo was monitored after installation to assess its degradation over time. The field site (∼0.64 km²) was located in Covina, California; ∼30 km east of Downtown Los Angeles. We found that an average pavement albedo increase of 0.18 (from 0.08 to 0.26) corresponded to maximum neighborhood averaged T _surface and T _air reductions of 5 °C and 0.2 °C, respectively. Maximum T _surface reductions were observed in the afternoon, while minimum reductions of 0.9 °C were observed in the morning. T _air reductions were detected at 12:00 local standard time (LST), and from 20:00 LST to 22:59 LST, suggesting that cool pavement decreases T _air during the daytime as well as in the evening. An average albedo reduction of 30% corresponded to a ∼1 °C reduction in the T _surface cooling efficacy. Although we present here the first measured T _air reductions due to cool pavement, we emphasize that the tradeoffs between T _air reductions and reflected shortwave radiation increases are still unclear and warrant further investigation in order to holistically assess the efficacy of cool pavements, especially with regards to pedestrian thermal comfort.