Using Temperature Sensitivity to Estimate Shiftable Electricity Demand: Implications for power system investments and climate change

TL;DR

The paper investigates how to manage electricity demand variability in grids with rising renewable shares and climate-driven changes by quantifying shiftable, temperature-sensitive demand and its potential to flatten load. It employs a top-down regression linking hourly regional demand to hour-of-day, hour-of-year, day-of-week, and weather signals via cooling and heating degree hours across 31 US regions to estimate the share of temperature-sensitive load, then simulates within-day demand reshaping by shifting a fraction $\alpha$ of this load under various transmission scenarios and a $+2^{\circ}$C climate change. Key findings include that with $\alpha=0.5$, regional daily peaks fall by about 10.1%, base load rises by 22.2%, daily SD declines by 76.9%, and 17.9% of region/days become completely flattenable; CDH has a stronger relationship to demand than HDH, and perfect transmission enhances smoothing but is outpaced by load shifting. Climate-change analysis shows that even under a warming scenario, shifting temperature-sensitive demand substantially reduces the expected increases in peaks and variability, underscoring the value of demand-side flexibility for planning investments in transmission and thermal storage.

Abstract

Growth of intermittent renewable energy and climate change make it increasingly difficult to manage electricity demand variability. Centralized storage can help but is costly. An alternative is to shift demand. Cooling and heating demands are substantial and can be economically shifted using thermal storage. To estimate what thermal storage, employed at scale, might do to reshape electricity loads, we pair fine-scale weather data with hourly electricity use to estimate the share of temperature-sensitive demand across 31 regions that span the continental United States. We then show how much variability can be reduced by shifting temperature-sensitive loads, with and without improved transmission between regions. We find that approximately three quarters of within-day, within-region demand variability can be eliminated by shifting just half of temperature-sensitive demand. The variability-reducing benefits of shifting temperature-sensitive demand complement those gained from improved interregional transmission, and greatly mitigate the challenge of serving higher peaks under climate change.

Figures (13)

• Figure 1: Demand, Flexible Load, Hard Load, and Flattened Demand. The graph shows electricity demand for one region in the Eastern Interconnect on December 8, 2016, together with the estimated flexible load (shaded yellow) and remaining hard demand (assumed unshiftable). Flattened demand is constructed by reshaping all (in blue) or half (in green) the flexible load in each hour.
• Figure 2: Proportional reduction in overall load variability for different shares of temperature-sensitive load being flexible. Each line in the graphs show the reduction in the (A) daily or (B) overall (3-year) standard deviation (SD) of demand for a region when raw demand is optimally flattened using $\alpha$ share of temperature-sensitive load within each day. Each line is colored according to the interconnect in which the corresponding region lies. The thicker black line represents the demand-weighted regional average of daily (A) or overall (B) reduction in SD
• Figure 3: ERCOT Demand in relation to CDH and HDH. The graph shows the ratio of electricity demand to mean demand in relation to average CDH and HDH in the hour, each aggregated over all grids in the region. The graph also shows the (overlaid) frequency distributions of CDH and HDH. Texas has more CDH than HDH, plus a stronger association with CDH than HDH. Graphs for the other regions, 15 in each interconnect, are provided in Supplementary Information (Figure S4 and S5).
• Figure 4: The influence of demand flexibility, transmission, and climate change on daily and overall base and peak load. The aqua colored bars show average values of daily peak and base load divided by the same-day mean (lighter shade) or overall (3-year) mean (darker shade). The red bars indicate the same average values with +2$^\circ$C change in temperature, normalized by the actual historical load. Whiskers mark the 1st and 99th percentiles of daily peak and base demand. Demand flexibility increases from left to right, where $\alpha=0$ is raw demand (left column), $\alpha=0.5$ is demand optimally flattened using half the temperature-sensitive load, and $\alpha=1$ is demand optimally flattened using all of the temperature-sensitive load. Transmission increases from top to bottom, where the first row assumes no connectivity between regions, the second row assumes perfect transmission within interconnects (East, West, ERCOT), and the last row assumes perfect transmission across the contiguous United States.
• Figure S1: ERCOT demand in relation to temperature for each season, related to STAR Methods. The average demand index value according to temperature and season is displayed above a frequency count of temperature. Demand index values are obtained by scaling demand according to regional average demand. The dotted blue line shows 18$^{\circ}$C, the benchmark for determining a cooling or heating degree hour. Compiled hourly data are from 2016 through 2018.