Probabilistic Optimal Reactive Power Planning in Distribution Systems With Renewable Resources in Grid-Connected and Islanded Modes
Bibliographic record
Abstract
Reactive power planning has always been a key research area in power distribution engineering; technically and economically. However, the problem needs to be revisited to consider several aspects of modern distribution systems, such as a high penetration level of renewable resources with intermittent nature; the microgrid concept and the possibility of system operation in grid-connected or isolated single microgrid mode, or isolated multiple microgrids; and probabilistic or hourly load profile. Motivated by these needs and considering all these aspects, this paper presents a generalized approach for probabilistic optimal reactive power planning in modern distribution systems. A new index is defined to probabilistically assess the success of microgrids in terms of real and reactive power adequacy and voltage limit constraints. Afterward, the reactive power planning is performed to reduce the annual energy losses of the grid-connected system and increase the defined microgrid success index. The problem formulation and solution algorithms are presented in this paper. The well-known PG&E 69-bus distribution system is selected as a test case, and through several sensitivity studies, the effect of optimization coefficients on the design and the robustness of the algorithm are investigated. A cost-benefit case study is also presented to determine the optimum total size of distributed reactive sources for the system under 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".