Intelligent computational infrastructures for optimized autonomous distributed energy generation in remote communities
Bibliographic record
Abstract
Distributed generation along with smart grid applications are poised to make important contributions to the clean-tech sector and remote communities. The dependence on one source for energy supply does not prove reliable enough when the renewable resource, such as wind or solar, is variable, creating a dependence on external fuel supply and a vulnerability to foreign control. Developing an energy strategy through intelligent energy system simulation and optimization can help communities make informed decisions about their energy investments. This dissertation reasons that distributed renewable energy systems without operative computational infrastructures face a fundamental economic challenge derived from their ad-hoc design and implementation. To address this, it proposes the method of Optimal Operational Awareness (OOA)—a feedback mechanism on the state of, and changes in, the properties of the implemented subsystems and their behaviour, to meet users objectives. Despite many applications of hybrid renewable energy systems, and reputable multi-objective evolutionary algorithms (MOEAs) for optimization, no one has applied MOEAs to dynamic system operation for optimized engagement of system components. This thesis describes an application of the NSGA-II algorithm to the multi-objective optimization of the operation of a stand-alone wind-PV-biomass-diesel system with batteries and CAES storage and a central controller. The simultaneous objectives are to minimize the levelized cost of energy (LCOE), and unmet load (UL) while maximizing the renewable energy ratio (RER). This work provides a case-study evaluation from data collected on-site at the island of Fernando de Noronha (FDN), Brazil. The results show that FDN could move from an annual average of 33% RER and LCOE range of $0.26 - $0.36 per kWh to an increased RER range of 60% - 100% and LCOE of $0.10 - $0.50 per kWh, while maintaining UL of 0%, by increasing its renewable energy generation and storage capacity approximately five times. Furthermore, optimal operational awareness for this configuration shows that despite 100% RER, certain periods experience a high LCOE of $2.00 per kWh, resulting from energy spillage due to oversupply, indicating sub-optimal system sizing and wasted energy to trim by improving system configuration. This work concludes that it is possible to achieve 100% RER, but storage and/or backup diesel generation are important to include in systems with highly variable supply. The cost of electricity decreases as renewable energy penetration increases, but is configuration dependent as well dependent on storage state of charge. Oversizing storage can be just as costly, if not more costly, than supplying energy with diesel generation, thus proper sizing and dispatch strategy are critical to achieve economic electricity supply. Furthermore, the role of multiple renewable energy generators in providing autonomous supply can be more valuable to the user than increased supply cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".