Business Model for Local Distribution Companies to Promote Renewable Energy
Bibliographic record
Abstract
Decentralized or distributed small renewable power facilities are usually installed in local communities for households and small business companies. These facilities include solar PV, concentrated solar power, and wind power, etc. In order to promote installations of such facilities, governments in many countries have developed a number of policies and business models. For example, in Germany and Canada, electricity feed-in tariff policy and business model were developed; in the USA, tax rebate policies and relevant business models were promoted. These policies and models have in some but not in large scale promoted decentralized small renewable power in local communities. The key issue is that these policies and business models do not provide sufficient incentives to local distribution companies (LDC), nor to renewable power installers and users. This paper’s research covers the creation of a business and communication model, named as LDC model, to incentivize both renewable power installers/users and LDCs. This LDC model can play a key role in promoting decentralized small-scale generation (DSG) with renewable energy in local communities. The core element of the LDC model is a revenue model which serves as an instrument to finance renewable installations for households and small commercial businesses. A case study is undertaken with real data of a power distribution company in Toronto, Canada. This paper concludes that with appropriate government policy and with the development of customized information systems for accessing households and small business via internet, an LDC will be able to take leadership in investing and installing small renewable power, and consequently enlarge the share of renewable energy supply in its local power distribution network.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".