Toward Sustainable Electricity Markets: Merit-Order Dynamics on Photovoltaic Energy Price Duck Curve and Emissions Displacement
Bibliographic record
Abstract
This paper examines how the slope of the merit-order curve and the share of non-zero-dollar dispatched energy affect photovoltaic (PV) price cannibalization and the declining market value of all generation types. Using historical merit-order data from Alberta, Canada—during its coal-to-gas transition—we simulated the introduction of zero-marginal-cost PV offers. The increased PV penetration rapidly suppresses midday electricity prices, forming a “duck curve” that challenges solar project economics. Emission reductions improve with rising carbon prices, indicating environmental benefits despite declining market revenues. Years with steeper merit-order slopes and lower non-zero-dollar dispatch shares show intensified price cannibalization and a reduced PV market value. The integration of battery storage alongside PV significantly flattened daily price profiles—raising the trough prices during charging and lowering the highest prices during discharging. While this reduces price volatility, it also diminishes the market value of all generation types, as batteries discharge at zero marginal cost during high-price hours. Battery arbitrage remains limited in low- and moderate-price regimes but becomes more profitable under high-price regimes. Overall, these dynamics underscore the challenges of integrating large-scale PV in energy-only markets, where price cannibalization erodes long-term investment signals for clean energy technologies. These insights inform sustainable energy policy design aimed at supporting decarbonization, and investment viability in liberalized electricity markets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".