Evaluating Hydro-Québec’s Decarbonization Pathways Using Integrated Asset-Centric Electrical Power System Evolution Modeling
Bibliographic record
Abstract
The era of decarbonization is expected to require massive infrastructure investments to upgrade and renew the electrical power system, which puts enormous pressure on electrical utilities. The fragmented organizational structure typical of most utilities today hinder energy leaders’ ability to assess the global monetary and social cost of maintaining grid reliability in the decarbonized future. To provide decision-makers with a consolidated high-level outlook of the power system’s evolution, Hydro-Québec developed an innovative decision-support tool referred to as the Integrated Asset-Centric Electrical Power System Evolution Model or EPSEM. It essentially aggregates asset and risk management data for Hydro-Québec’s generation, transmission, and distribution activities, and simulates the evolution of the electrical system as a whole, generating CapEx, OpEx and asset metric projections based on different energy mixes and forecasted demand scenarios. The paper outlines the model’s main design steps and provides a broad overview of the modeling approach and simulation methodology, particularly its unique data aggregation methodology and holistic and interdisciplinary approach to power system modeling. Examples of the types of results and analytics that can be obtained are presented and discussed. The observed benefits of the EPSEM demonstrate how advanced data-driven tools and a holistic approach to asset management risk analysis and power system planning can help decision-makers chart decarbonization pathways and more efficiently coordinate expansion planning.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".