OPTIMAL OPERATION AND PLANNING OF MICROGRIDS CONSIDERING FREQUENCY STABILITY
Bibliographic record
Abstract
In modern power systems, the traditional power plants consuming fossil fuels are gradually being replaced by renewable plants which bring about several challenges and issues for the safe operation of these systems. Higher penetration of renewable plants weakens the grid’s frequency control capability. The frequency of a power system is an important indicator of any load-generation imbalance. If proper measures are not taken, the frequency-related events as a result of the contingencies might have devastating consequences such as unintentional load shedding, generator tripping, equipment damage, and blackout. The consequences have significantly higher impacts in a small-scale power system such as a microgrid where the penetration level of renewable generation is high, the inertia of the system is low, and the resources capable of providing reserve are limited. Battery energy storage systems (BESSs) have suitable characteristics to provide a wide range of high-power and high-energy services and they can have a significant contribution to frequency control if they are optimally scheduled. In a microgrid setting, the main goal of the frequency stability constraints is to prevent such consequences after the occurrence of credible contingencies and ensure the microgrid can ride through these events. \nThis thesis presents optimization models to take frequency stability constraints into account for microgrid operation and planning studies. In the operation stage, frequency stability constraints are integrated into the day-ahead scheduling model of a grid-connected microgrid considering the unit commitment constraints and the sudden islanding as the contingency. The goal of the proposed algorithm is to ensure that when a grid-connected microgrid suddenly and unintentionally disconnects from the main grid, the microgrid’s frequency stability metrics remain within their safe ranges and the resources can provide sufficient primary frequency response to this end. \nAlso, for the planning stage, optimal sizing of a BESS as a source of power and energy services is studied considering the frequency stability constraints for grid-connected and islanded microgrids. For realistic modeling, a detailed frequency response model of the microgrid elements under different contingency types is developed based on a discretized model of the swing equation. The developed optimization models are linear and relatively fast in obtaining the globally optimum solution with the proposed reformulated mathematical models. A k-means clustering algorithm is also used for scenario reduction purposes and identification of representative days. \nFor all the studies, a microgrid test case comprised of conventional generators, a PV plant, BESS, and loads is used. The discretized swing equation and time-domain model of the primary frequency response of microgrid units are used without sacrificing accuracy. Also, the accelerated decomposition techniques ensure the computational burdens are reasonable and they improve the performance of the algorithms. All the simulations and codings were done in the GAMS and MATLAB environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".