Assessment of Transmission Congestion Cost and Locational Marginal Pricing in a Competitive Electricity Market
Bibliographic record
Abstract
In an open-access environment, transmission constraints can result in different energy prices throughout the network. These prices are, in fact, dependent on a number of factors such as the generating unit bid, system load level, network topology and security limits imposed on the transmission network due to thermal, and voltage and stability considerations. Computing these energy prices at all buses in large transmission networks under given system operating conditions can be time-consuming. This paper describes a simple methodology based on the analysis performed by the Hydro One in-house computer program (PROCOSE) to calculate, for a given period of time, transmission congestion cost (TCC) in dollars per unit time and locational marginal pricing (LMP) in dollars per megawatt-hour (MWh) at any selected bus in the transmission system. In addition, the information provided by the program output on congested transmission elements is used to identify buses in the network whose LMPs are representative of the entire network. The computed LMPs at these buses are used to define zones in the network where each zone has its LMP. The proposed methodology can be used to carry out sensitivity studies to determine the impact of changes in system parameters and operating conditions on the LMPs. The proposed method is illustrated using the IEEE Reliability Test System (RTS) and the Hydro One network system.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".