Seasonal Hydropower Storage Dams: Are They Cost-Effective in Providing Reliability for Solar PV?
Bibliographic record
Abstract
For a country to be able to sustain a policy of increasing the use of renewable energy sources to supply electricity, it must be able to continue to provide a reliable electricity supply service to its customers. Typically, electricity reliability is maintained by thermal electricity generation. To substitute solar PV for thermal electricity generation to a significant degree, it is imperative to determine the least-cost complementary technologies that will provide system reliability. In many parts of Africa and Asia, potential sites for seasonal storage dams are available or have been built. In the case studied here, maintaining service reliability by expanding the capacity of the generation plant of a seasonal storage dam in all scenarios is less costly than providing service reliability by a thermal alternative. However, maintaining service reliability while expanding generation by solar PV is in all cases costly. The levelized financial cost of the incremental energy supplied when a reliable service is maintained is between 30% and 89% greater than the levelized cost of a standalone solar PV plant. For the same set of scenarios, the range of the economic levelized cost is 28% to 85% greater with reliability than the standalone solar PV field without reliability. Given the circumstances of the electricity market, the least-cost technology to maintain a reliable service may be specific to the market. The analysis also shows that when the economic opportunity cost of funds increases from 2% to 11.5%, the levelized cost of renewable electricity generation systems doubles. Hence, if the developed countries of the world want low-income countries to maintain policies to reduce the use of fossil fuels to generate electricity, capital subsidies to low-income countries that are facing high economic opportunity costs of funds are likely to be necessary.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".