Managing Peak Load in the Past, Present, and Future: Challenges and Opportunities for a Flexible Energy Future
Bibliographic record
Abstract
Taken at face value, peak load management involves controlling or influencing the time of day when electricity is used in homes, businesses, and public facilities. The desire to reduce peak electricity use has been based on the availability of generation resources and their associated costs. During periods of high electricity demand, retail energy providers, including distribution utilities, purchase power from generation sources that are less efficient than baseload generation resources such as hydro, nuclear, or coal. Operating the electrical grid has never been simple, but today the balance of supply and demand is getting more complex. On the supply side, the increasing penetration of renewable and distributed energy sources, such as solar and wind power, makes peak load management more complex. These sources are inherently intermittent, meaning that power generation cannot always be scheduled to meet demand. Additionally, electrification of the buildings and transportation sectors is changing the load profiles of customers and the regions of a distribution utility's service area. Yet, the rise of distributed energy resources (DERs), such as rooftop solar and battery storage, has created both opportunities and challenges for grid operators. Peak load management is rapidly evolving from past practices, with new use cases, economic drivers, hardware and software, and information technologies playing an increasingly important role. A cleaner energy future, while critical to our society and the environment, is not without increasing cost pressures, economic risks, and reliability challenges from extreme weather events. While we know where we are going, it is not exactly clear how we will get there. Nonetheless, the emphasis on peak load management will only increase in scale and sophistication. To better predict and prepare for the rapidly changing energy landscape, this editorial discusses the past and present state of peak load management and how it might be evolving into more flexible load management.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".