Techno-economic evaluation of electricity pricing structures on photovoltaic and photovoltaic-battery hybrid systems in Canada
Bibliographic record
Abstract
There is limited understanding of how electricity market fluctuations and dynamics, such as changes in electricity pricing schemes and rate structures impact the profitability of hybrid battery and solar photovoltaic (PV) systems. This study provides a techno-economic evaluation of PV and hybrid PV-battery systems using the Solar Alone Multi-objective Advisor (SAMA), an open-source tool used for optimally sizing PV-based systems. The study focuses on the economic implications of time of use (ToU) and tiered rate (TR) pricing structures in Ontario, Canada and examines the potential impact of an investment tax credit. Furthermore, a sensitivity analysis is completed, which evaluates fluctuating battery costs and grid escalation rates, provides new insights into the financial viability of hybrid systems under various economic conditions. The results show the substantial influence of these rate structures, with ToU pricing generally proving more economically advantageous compared to TR pricing. While net-metered PV systems are increasingly attractive due to favorable economic metrics, the addition of battery storage under current cost conditions in Ontario remains less viable. The results support the importance of rate structures to enhance the economic viability of distributed PV systems to help achieve sustainable development goals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".