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  1. Abstract

    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 theFlex Period Response, which estimates how much demand was shifted away from the Flex Alert period, and theRamping 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.

     
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    Free, publicly-accessible full text available December 8, 2024
  2. 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.

     
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    Free, publicly-accessible full text available October 9, 2024
  3. Abstract

    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 ther2values for the dry bulb temperature (DBT) (0.39) regression were either comparable to or higher than ther2values 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.

     
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    Free, publicly-accessible full text available October 1, 2024
  4. 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.

     
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  5. Abstract

    Extreme heat events are increasing in frequency and intensity, challenging electricity infrastructure due to growing cooling demand and posing public health risks to urbanites. In order to minimize risks from increasing extreme heat, it is critical to (a) project increases in electricity use with urban warming, and (b) identify neighborhoods that are most vulnerable due in part to a lack of air conditioning (AC) and inability to afford increased energy. Here, we utilize smart meter data from 180 476 households in Southern California to quantify increases in residential electricity use per degree warming for each census tract. We also compute AC penetration rates, finding that air conditioners are less prevalent in poorer census tracts. Utilizing climate change projections for end of century, we show that 55% and 30% of the census tracts identified as most vulnerable are expected to experience more than 16 and 32 extreme heat days per year, respectively.

     
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  6. Abstract

    Climate change, urbanization, and economic growth are expected to drive increases in the installation of new air conditioners, as well as increases in utilization of existing air conditioning (AC) units, in the coming decades. This growth will provide challenges for a diversity of stakeholders, from grid operators charged with maintaining a reliable and cost-effective power system, to low-income communities that may struggle to afford increased electricity costs. Despite the importance of building a quantitative understanding of trends in existing and future AC usage, methods to estimate AC penetration with high spatial and temporal resolution are lacking. In this study we develop a new classification method to characterize AC penetration patterns with unprecedented spatiotemporal resolution (i.e. at the census tract level), using the Greater Los Angeles Area as a case study. The method utilizes smart meter data records from 180 476 households over two years, along with local ambient temperature records. When spatially aggregated, the overall AC penetration rate of the Greater Los Angeles Area is 69%, which is similar to values reported by previous studies. We believe this method can be applied to other regions of the world where household smart meter data are available.

     
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  7. Abstract

    Reducing energy consumption for urban water management may yield economic and environmental benefits. Few studies provide comprehensive assessments of energy needs for urban water sectors that include both utility operations and household use. Here, we evaluate the energy needs for urban water management in metropolitan Los Angeles (LA) County. Using planning scenarios that include both water conservation and alternative supply options, we estimate energy requirements of water imports, groundwater pumping, distribution in pipes, water and wastewater treatment, and residential water heating across more than one hundred regional water agencies covering over 9 million people. Results show that combining water conservation with alternative local supplies such as stormwater capture and water reuse (nonpotable or indirect potable) can reduce the energy consumption and intensity of water management in LA. Further advanced water treatment for direct potable reuse could increase energy needs. In aggregate, water heating represents a major source of regional energy consumption. The heating factor associated with grid-supplied electricity drives the relative contribution of energy-for-water by utilities and households. For most scenarios of grid operations, energy for household water heating significantly outweighs utility energy consumption. The study demonstrates how publicly available and detailed data for energy and water use supports sustainability planning. The method is applicable to cities everywhere.

     
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  8. Free, publicly-accessible full text available March 1, 2025
  9. Free, publicly-accessible full text available January 1, 2025