Alberta's Renewable Energy Market: Developing Contract Issues
Bibliographic record
Abstract
Governments and corporations around the world have increasingly demonstrated leadership in tackling climate change, resulting in new decarbonization policies and initiatives and new corporate environmental, social, and governance mandates. At the same time, wind and solar capital costs have decreased and new technologies, such as battery storage, are becoming more prevalent, thereby increasing the flexibility and utility of renewable energy generation. Developers and investors have already expended significant capital to further the development of renewable generation with, according to the International Energy Agency, renewable energy investment reaching its highest level ever in 2021 — $367 billion internationally. This trend is only expected to increase if the global decarbonization goals are to be realized. Within Canada, Alberta is at the forefront of renewable energy development due in part to its unique power industry structure — a deregulated, competitive, wholesale power market with non-discriminatory system access — which has attracted corporate off-takers and buoyed investment in renewable energy projects in the province, supported by corporate power purchase agreements. The pace of development is striking, and Alberta has rapidly become a preferred destination for renewable energy investment. The Alberta Electric System Operator says that 22 percent of the energy generated in Alberta in 2021 came from renewable energy sources. This is predictedto rise sharply — for instance, there are 61 solar projects currently under development in Alberta that could be completed by the middle of the decade. This article examines several emerging contracting and development challenges currently facing renewable energy projects in Alberta, and aims to provide concrete, practical advice on several discrete contractual issues. Topics covered include, among others: (1) force majeure issues faced by project developers; (2) diverse carbon offset/renewable attributes programs and implications for power purchase arrangements; (3) developments pertaining to ethical procurement, including forced labour and the use of tracing protocols; and (4) the need for increased flexibility in contractual arrangements to accommodate the pairing of wind and solar with storage resources. Although this article is based primarily on experiences gained while developing renewable energy projects in Alberta, the analyses and solutions presented can be applied in many jurisdictions.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 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".