The Impact of Electricity Market Interventions by System Operators during Emergency Situations
Bibliographic record
Abstract
This technical brochure from the JWG C2/C5.06 examines the impact of market interventions by system operators during emergency situations. System operators often have the authority to intervene in market processes to restore a power system from abnormal conditions (such as alerts or emergencies) back to normal. However, ongoing market activities can sometimes exacerbate already challenging system situations.<br/>The JWG focused on analyzing the effects of these interventions, supported by real-life examples. The aim is to draw lessons that can help rule-makers regulate this area of market rules clearly and potentially incentivize market participants to operate in a way that minimizes the need for interventions altogether.<br/>To gather information on international practices, a survey was conducted. During the analysis of the responses, it became evident that definitions of “intervention” and “emergency situation” vary significantly around the world. Consequently, the JWG established a definition for intervention to be used in this work, which states:<br/>- An intervention refers to a directive from the system operator to a market participant, mandating a specific action that the participant is obligated to follow.<br/>- This directive involves an action that the participant would not undertake voluntarily without explicit instruction from the system operator.<br/>The Technical Brochure is organized as follows: the initial sections provide an overview of the brochure and list the working group members, along with an Executive Summary. Chapter 1 presents the Introduction, while Chapter 2 discusses markets, emergency situations, and interventions, elaborating on our proposed definitions. Chapter 3 assesses the survey responses, Chapter 4 considers current trends and the outlook for future interventions, and Chapter 5 concludes with recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".