Bibliographic record
Abstract
Smart grid technologies, renewables, energy storage devices and electric vehicles are going to characterize the next generation distribution systems. It is important to note that inclusion of electric vehicles and renewables, inherently due to their natural power profile, result in distribution systems having a peaky load profile with lower asset utilization factors. Optimal planning and operation of distribution systems are important aspects and should consider this changing paradigm. This thesis aims to develop new solutions for optimal planning and operation of distribution systems considering these new technologies and their implications. The thesis specifically aims to use new techniques such as complementarity in conjunction with classical optimization techniques to develop new algorithms for optimal planning and operation of distribution systems. The proposed work includes the following. Two new distribution planning algorithms are proposed that include the installation and optimal sizing of Battery Energy Storage System units in addition to traditional assets, such as feeders and transformers. It incorporates plan and asset lifetimes as a means of establishing the minimum total annualized costs of new and replacement assets, operation and maintenance, and customer interruptions. For a fair comparison, all costs reflect the current year and are annualized over a specific study period. Even though the second technique has the same base as the first method, it is a multi-objective algorithm that uses fuzzy optimization technique to handle multiple contradicting objectives that cannot be combined into a single objective as they are in different units. This method has been developed due to the lack of certainty in how to calculate customer interruption cost in literature. It was proven in both methods that Battery Energy Storage System could be a more economical option compared to expensive underground feeders. Then in order to realize Smart Radial Distribution System of the future, a real-time optimal reconfiguration algorithm is proposed, which uses a classical nonlinear optimization technique and guarantees an optimal solution in the least time. The method optimizes the system loss and is based upon a complementarity technique that transforms a set of discontinuous solution spaces into a single continuously differentiable solution space, thus enabling the use of classical nonlinear optimization techniques without resorting to heuristics. The method is tested on 33-bus and 69-bus systems and the results are better or matching the other methods available in literature while it is significantly faster.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".