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Record W2802332755 · doi:10.55016/ojs/sppp.v11i1.43169

Capacity Market Design: Motivation and Challenges in Alberta’s Electricity Market

2018· article· en· W2802332755 on OpenAlexaffabout
David P. Brown

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectricity marketBusinessElectricityIndustrial organizationEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Alberta’s electricity market is currently undergoing a period of substantial transition. The province should proceed with caution as it switches from an energy-only electricity market to a capacity market by 2021. Many other jurisdictions have already made the changeover and Alberta can learn from their experiences in order to avoid common mistakes and pitfalls that can arise with the deployment of a capacity market.There were growing concerns that the existing electricity market structure would not attract sufficient investment from conventional generation (e.g., natural gas) due to the increased penetration of zero marginal cost renewable generation. As a result, the Alberta government has chosen to transition to a capacity market. For consumers, a capacity market aims to ensure there is sufficient investment in new generation capacity to “keep the lights on” and reduce price swings in the wholesale market. The capacity market will also help the province meet its goals for attracting investors and transitioning away from its dependence on coal-fired electricity generation.However, a switchover is not as simple as it sounds. In an energy-only market, firms are paid solely based on the provision of electricity in hourly wholesale markets. In capacity markets, electricity-generating firms are also paid for providing generation capacity, reflecting the potential to provide electricity at some point in the future. While capacity markets can help ensure there is a reliable supply of electricity, there are several challenges in the implementation of capacity markets. This paper discusses the motivation for the adoption of capacity markets, highlights challenges regulators face when implementing this market design in the context of Alberta, and summarizes the key trade-offs associated with energy-only versus capacity market designs.Relative to an energy-only market, a capacity market is more complex and requires that regulators specify numerous parameters that are essential to the functioning of the market. An essential, but often controversial component is the formulation of the capacity demand curve. A capacity demand curve for Alberta has to be carefully designed to deal with uncertainties in demand growth, given that Alberta’s electricity demand is closely interconnected with the ups and downs of global crude oil prices.Consideration must be given to the perspective of outside investors who – as in any area of economic interest – are wary about uncertainty. Political and regulatory uncertainty can undermine the success of a capacity market. This potential for investor hesitancy could result in incumbent firms, familiar with investing in Alberta, seizing a larger share of the market in an already historically concentrated environment. It is critical that policymakers establish a clear and well-defined trajectory for the future of Alberta’s electricity market design as a whole, not just its capacity market. The capacity market is not a panacea for the potential downfalls of an energy-only market. There are trade-offs associated with both energy-only and capacity market designs. Energy-only markets are arguably more economically efficient with cleaner price signals. However, with political constraints on electricity price-spikes and the expansion of renewables, there is more uncertainty in an energy-only market’s ability to promote investment. A capacity market provides more certainty in terms of generation resource adequacy, but at a potentially higher cost. Despite these tradeoffs, capacity markets are unambiguously more complex. This places a heavy burden on regulators to carefully and correctly set critical capacity market parameters that can have substantive impacts on prices and the associated economic signals.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.066
GPT teacher head0.249
Teacher spread0.183 · 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 designObservational
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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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