Modular multilevel converters with partially integrated battery energy storage
Bibliographic record
Abstract
This thesis investigates Modular Multilevel Converters (MMCs) with partially integrated Battery Energy Storage Systems (BESS), where only a subset of sub-modules (SMs) are equipped with battery energy storage via bidirectional boost converters. The primary goal of incorporating BESS is to provide inertial and frequency support to the grid. These converters are typically studied using Electromagnetic Transient (EMT) simulations. A continuous-time model of the MMC-BESS is developed based on the state-space equations of the converter. The proposed model enhances computational efficiency in two key ways: (i) it significantly reduces the number of nodes in the conventional switching model, thereby shrinking the size of its admittance matrix, and (ii) it avoids computationally expensive re-triangularization of the admittance matrix during normal operation, restricting it to rare instances of converter blocking. This method results in substantial reductions in the simulation time of MMC circuits, making it particularly useful for studies requiring repetitive simulations. The computational efficiency and accuracy of the proposed model are validated by comparing its implementation in the PSCAD/EMTDC simulator against conventional detailed switching models and experimental measurements from a single-phase scaled-down laboratory setup. The Peak Current Mode (PCM) control strategy is used to manage the DC-DC converters integrating the batteries with the MMC. PCM provides a protective layer that limits the current through the battery and DC-DC converters' switches. An Averaged Value Model (AVM) of the MMC-BESS, along with its per-unit representation, is also derived. The AVM is beneficial for low-frequency studies, such as upstream control design and SM capacitor sizing. Finally, the proposed topology and its model are used to explore replacing Manitoba's outdated Bipole I HVDC system with a VSC-HVDC system featuring MMC and MMC-BESS at the rectifier and inverter terminals, respectively. The study aims to stabilize and maintain system frequency while eliminating synchronous condensers and AC line filters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".