Life cycle cost profitability of biomass power plants in South Africa within the international context
Bibliographic record
Abstract
South Africa's renewable energy programme has been widely considered a success. Biomass is one of the selected technologies, on which capacity and tariff caps are set in place. It is unclear whether the price caps allow for sufficient profits for private role-players. The aim of the study is to investigate the potential profit margins for biomass power plant companies entering the programme. Costs throughout the lifespan of the power purchase agreement were determined by using the Levelised Cost of Electricity (LCOE) metric. The method used cost inputs which were determined using a mixture of local and international indicators for three scenarios, the worst case (WC) scenario representing highest input costs, the most likely case (MLC) scenario representing median costs, and best case (BC) scenario representing lowest input costs. The results show that the WC, MLC and BC LCOE for biomass power plants in South Africa are 3.53 ZAR/kWh (0.235 USD/kWh), 1.30 ZAR/kWh (0.086 USD/kWh) and 0.78 ZAR/kWh (0.052 USD/kWh), respectively. In all three scenarios, the bulk of the cost constitute delivered fuel costs. Considering sales tariffs at ZAR1.475/kWh, profit margins for WC, MLC and BC scenarios were determined as −139%, 12% and 47%, respectively. These figures compare favourably with China, the United States of America, and Europe in general, opposed to Canada, where higher profit margins are achievable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".