McKenna Peplinski’s research while affiliated with University of Southern California and other places

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.

The California Independent System Operator (CAISO) utilizes a system-wide, voluntary demand response 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 to 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.

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 for 200,000 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 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 r2 values for the dry bulb temperature (0.39) regression were either comparable to or higher than the r2 values for humid heat metrics (0.15-0.40). Our results suggest that using a combination of heat metrics can increase confidence in AC penetration rate estimates, but using dry bulb temperature alone produces similar estimates to other humid heat metrics, which are often more difficult to access, individually. Future work should investigate these results in regions with high humidity.