Multi-objective Optimization for Design and Operation of Distributed Energy Systems through the Multi-energy Hub Network Approach
Bibliographic record
Abstract
A generic framework is developed to study the application of energy hubs and its related network model to demonstrate the optimal design and operation of distributed energy systems (DESs) in urban areas. A novel multi-objective approach based on augmented epsilon constraint technique is employed to carry out this work. As an illustrative example, the proposed model is applied to an urban area in Ontario, Canada. Different scenarios are defined to investigate the effect of energy storage systems and energy exchange within a network on the optimal configuration and operation of the system. Moreover, multi-objective optimization is carried out based on two conflicting objectives, namely, total annual cost and greenhouse gas emission. The findings show that the simultaneous consideration of DESs, storage technologies, and a network of energy exchange between hubs (scenario 4) results in the installation of more DESs and at least 8% reduction of annual cost when compared to other scenarios. Furthermore, lowering the electricity grid emission factor results in higher adoption of renewable energy generation based DESs rather than natural gas based DESs. The sensitivity analysis shows that doubling the electricity tariff rate results in 75% increase in cost, while the pricing of natural gas has no significant effect on overall cost. This demonstrates that the cost is more sensitive to the electricity tariff rate rather than natural gas price for this specific case study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".