Implications of extreme temperatures and socio-economic development on power markets’ peak demand across the world
Bibliographic record
Abstract
Relevance: Extreme temperature events, both heatwaves and cold spells, can put pressure on power systems’ reliability by pushing power demand to record highs. Within the literature assessing the impacts of climate change on the energy sector, gathering new evidence on the drivers of peak load is a pressing issue for multiple reasons. First, peaks in power load must be accommodated by exceptional ramp-up requirements of power generating units, so that in the future adapting to climate change may involve the construction of plentiful under-utilized peak generation plants, putting pressures on the decarbonization goals and increasing stranded assets risks. Furthermore, peak load shocks induced by extreme temperatures can coincide with reduced transmission and distribution capacity, further challenging the operation of electricity grids [1]. Both the empirical and modeling literature assessing the impacts of climate change on the energy sector have generally focused on aggregated electricity demand rather than on its peaks. Few available empirical studies investigate how extreme events can affect peak demand focus on industrialized countries and estimate reduced-form models, that hold adaptation, economic growth, technology, and current infrastructure constant [2,3]. Our paper aims to fill this gap by identifying if and how climatic and socio-economic drivers can affect the magnitude of the peak load response to extreme weather events. Methods: We assess these interrelated dynamics by exploiting high-frequency power demand data collected from load balancing authorities. Specifically, we assemble a novel dataset spanning for the last two decades across more than 100 power markets, comprising both countries (European Member States, Asian and African countries) and large sub-national regions (power markets in Japan, Australia and Russia and Federal States or Provinces in the US, Canada, Brazil and India). The dataset includes: i) daily peak and total load; ii) daily population-weighted exposure to weather from 3 hourly near surface temperature data at 0.25 degrees gridded resolution; iii) quarterly and yearly regional statistics and indicators on demography, economy, education and innovation. We investigate how daily peak load responds to extreme temperatures by adopting a suite of time-series and panel econometric methods that fully exploit the high-frequency and sub-national disaggregation of our dataset. Results: Utilizing the innovative methodological framework proposed, we: i) identify how peak load responds to temperature extremes in different regions; ii) test if and how such response can be modulated by regional climatic and socio-economic characteristics; iii) derive cost implications due to the amplification of peak demand deriving from future increases in the intensity and frequency of extreme events. References: [1] Yalew, S. G., van Vliet, M. T., Gernaat, D. E., Ludwig, F., Miara, A., Park, C., ... & Van Vuuren, D. P. (2020). Nature Energy, 5(10), 794-802. [2] Auffhammer, M., Baylis, P., & Hausman, C. H. (2017). PNAS, 114(8), 1886-1891. [3] Wenz, L., Levermann, A., & Auffhammer, M. (2017). PNAS, 114(38), E7910-E7918.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".