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Record W2803436620 · doi:10.55016/ojs/sppp.v11i1.43162

The Alberta Electrical Grid: What to Expect in the Next Few Years in Alberta

2018· article· en· W2803436620 on OpenAlexaboutno aff
Brian Livingston

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsGridEnvironmental scienceGeographyMeteorologyGeodesy

Abstract

fetched live from OpenAlex

The Alberta government has stated that it wants to make significant changes to the supply of electricity to the current electrical grid for the province. These changes include the phasing out of coal generation by 2030, the supply of 30 per cent of electricity from renewables by 2030 and the introduction of a socalled capacity market in addition to the current electrical energy market. The achievement of these objectives will require a number of fundamental changes to the existing electrical grid. This paper provides an overall description of these changes. The paper first examines the current grid structure in which coal and gas provide the base load supply in the amount of 90 per cent of electricity demand, and renewables are a relatively small source of supply for the remaining 10 per cent. It then reviews the current simple energy market in Alberta that uses a single price auction to determine the wholesale price of electricity. The paper then notes that the achievement of these changes will require a large amount of investment in the next 15 years to create new generating capacity that currently does not exist. The Alberta Electric System Operator (AESO) has forecast that by 2032, Alberta will need an additional 7,000 megawatts of gas generation, 5,000 megawatts of wind, 700 megawatts of solar and 350 megawatts of hydro. To put this in context, the Ontario grid currently has 4,213 megawatts of wind (11 per cent of total generating capacity) and 380 megawatts of solar (one per cent of generating capacity). The Alberta government has made two fundamental changes in the electricity market to make this happen. First, it has introduced a Renewable Energy Program (REP) to incent investment in renewables. They asked industry to bid on a 20 year contract for supply of electricity that offered a guaranteed fixed price that was independent of the existing wholesale market. The first round of bidding (REP 1) announced in December 2017 resulted in 600 megawatts of new wind capacity at prices below expectations. No solar proposals were accepted in REP 1, a result that may cause the Alberta government to make new proposals (details still to come) that may permit solar participation. Two new rounds for 2018 (REP 2 for 300 megawatts and REP 3 for 400 megawatts) have requested bids on a similar basis. The one new feature is that REP 2 is limited to investors with an Indigenous equity position of at least 25 per cent. Second, the Alberta government has proposed to introduce a capacity market that would compensate electricity suppliers for merely creating capacity to supply. The capacity market was requested by industry and was announced by the Government of Alberta in November 2016. It is intended to give additional compensation over and above the energy market compensation in order to make it economic for investment in future renewables if the REP guaranteed price structure is terminated, and in future base load and backup gas generation. The paper then describes one possible solution using new storage battery technology as a means of providing backup generation for renewables. Finally, the paper contrasts the proposed Alberta electrical grid with the current Ontario electrical grid. It notes that the current high electricity prices in Ontario have become a high profile political issue there, since consumers are paying all electricity costs. In contrast, the Alberta government has also stated that retail electricity prices will be capped at 6.8 cents per kilowatt hour until 2021. If retail rates exceed that amount, the Alberta government will use carbon tax revenues to pay the difference.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.749
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.007
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.273
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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