Many actors amongst multiple renewables: A systematic review of actor involvement in complementarity of renewable energy sources
Bibliographic record
Abstract
Although complementarity achieved by combining multiple renewable energy sources (RES) is an important method to increase shares of RES, it is often overlooked in policy prescriptions supporting an energy transition. Complementarity can be implemented by multiple actors, however there has been little attention to which actors are involved, and their roles. A systematic review was conducted to provide an overview of the state of academic literature on the topic of combinations of multiple RES and the involvement of multiple associated actors. The sample included 78 articles using a range of methodologies to analyze varying combinations of wind, solar, bioenergy, hydro, geothermal, and ocean energy, alongside combinations of traditional, new, and supporting energy actors. Studies included contextualized (location specific) agent-based, techno-economic, economic, business model, and qualitative analyses, and decontextualized reviews, agent-based, and optimization models. Multi-actor complementarity is being addressed by diverse disciplines in diverse contexts globally, across a range of geographic scales. The majority of studies focus on solar-wind, although more diverse RES combinations were found in contextualized studies. New actors usually participate alongside traditional system actors. More attention to supporting actors is required. Findings highlight the need for further research beyond the technical benefits of combining multiple RES, to explore the roles of various actors. This can be accomplished by incorporating more context in studies, for example, using the substantial existing body of data and research, and by including a greater range of RES combinations, and incorporating more perspectives of associated actors.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".