The Value of Demand Response (DR) to Mitigate Wind Integration Costs in a Smart Grid
Bibliographic record
Abstract
In the last five years the electric grid worldwide has seen increasing amounts of installed wind generation capacity. Over the last five years, North America (USA and Canada) has witnessed wind capacity grow at an annual rate of over 30%. At the same time, increasing investments in smart grid technologies have enabled improvements in energy products such as Demand Response (DR). The utility industry, system operators and regulators are investing heavily to understand and determine the impacts of increasing wind penetration on the power system. As explored below, an often neglected, but important point of interest to the authors has been the effect of increased cycling of large fossil, formerly base loaded power plants due to increasing penetration of variable wind or solar power. Various types of DR programs have been implemented by utilities and system operators and these DR programs may be classified based on the time it takes to call upon a DR event or the energy market that the programs are allowed to participate within. Hence, we may have a “slow” DR that participates in a Day-Ahead market and the events are called upon well in advance. On the other hand, “fast” DR programs would participate in Real-Time and Ancillary Services markets. DR from a power dispatch perspective can be considered a “virtual power plant” providing energy, ancillary service and capacity in energy markets. Energy benefits of DR have been explored extensively, especially in terms of reduced fuel costs due to reduction in demand. In this paper we explore the conceptual use and value of DR in providing benefits associated with reduced damage to a fleet of fossil-fueled power plants if it is used to reduce startups and/or load following/cycling.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".