Analysis of Key Performance Indicators for Local Electricity Markets’ Design
Bibliographic record
Abstract
Local electricity markets (LEMs) are investigated as a solution to provide consumers and prosumers the opportunity to have control over their electricity-related choices and make savings on their energy bills. This work analyzes market design factors, such as the number of update intervals per trading slot, the production-to-consumption (PtC) ratio, and pricing scenarios that influence the performance of an LEM. The decentralized autonomous area agent (D3A) has been used for running LEM simulations under the German regulatory framework. The results of the simulations compared using self-sufficiency, the share of market savings, and the average buying rate revealed that the performance of an LEM is highly dependent on the market design factors. Also, bidding strategy affects the performance of an LEM compared to the share of the local generation. The results imply that LEM can provide better incentives for both prosumers and consumers by providing them with the opportunity to trade their excess generation at prices higher than the feed-in tariff and lower their regular electricity tariff, respectively. With only a 20% reduction in average buying rate, it is also evident that LEMs provide a great opportunity for keeping smaller PV systems active after their 20 years of fixed remuneration under a state-sponsored incentive scheme in Germany.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".