Künftige Netzregulierung als Grundpfeiler einer effektiven Liberalisierung des deutschen Stromsektors
Bibliographic record
Abstract
Mit dem 2005 novellierten Energiewirtschaftsgesetz wurde in Deutschland eine staatliche Regulierung der Energienetze eingeführt, die einen diskriminierungsfreien Netzzugang und eine zunächst kostenorientierte Entgeltbindung vorsieht. Wie schon in vielen anderen Ländern soll künftig auch hier eine sogenannte Anreizregulierung etabliert werden, bei der jedem Netzbetreiber eine Entwicklung von Preis- oder Erlösobergrenzen für eine Regulierungsperiode vorgegeben wird. Ausgehend von theoretischen Fundierungen einer Netzentgeltregulierung werden in diesem Beitrag wesentliche Gestaltungselemente einer Anreizregulierung wie Kostenprüfung, Effizienzvergleich, Anpassungsformel und Qualitätsregulierung diskutiert. Je nach Ausgestaltung dieser Elemente werden unterschiedliche Anreize für die Netzbetreiber gesetzt, um erwünschte Aktivitäten zu belohnen und unerwünschte zu sanktionieren. Zugleich müssen mögliche Fehlanreize vermieden werden. So darf die Regulierung nicht allein auf Rationalisierung zielen, sondern muss zugleich auch den Anforderungen der Qualität und der Umstrukturierung des Stromsektors Rechnung tragen. Aspekte der Versorgungssicherheit und -zuverlässigkeit sowie der Servicequalität müssen deshalb frühzeitig im Anreizmechanismus und nach Möglichkeit bereits im Effizienzvergleich explizit berücksichtigt werden. Abstract In Germany state regulation of energy networks was introduced by the amendment of the Energy Utility Act in 2005. This regulation shall enable a non-discriminatory third party access to the transmission and distribution systems and starts with a cost oriented approach of tariff regulation. Following many other countries, Germany is now preparing the introduction of a modern incentive regulation scheme, which will define price or revenue caps for each network operator during a regulatory period. On the basis of theoretical foundations of network regulation, considering both neoclassical and institutional approaches, this article discusses important design elements of incentive regulation like cost accounting, benchmarking, regulation formula and quality regulation. The design of these elements determines the incentives given to network operators, in order to stimulate desired action or to sanction undesired behavior. Most notably, wrong incentives must be avoided. In particular, regulation should not focus on rationalization alone, but has to allow for sufficient quality of supply and the restructuring of the electricity sector. The dimensions of security of supply, reliability, and service quality therefore have to be implemented in the incentive scheme from the beginning. As far as possible, quality aspects should be fully integrated in the benchmarking process. JEL Classification: L51, L94, Q48, L43
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".