Microgrid Planning in Distribution Networks Through Optimal Allocation and Sizing of Distributed Generation and Microgrid Formation
Bibliographic record
Abstract
Microgrid formation is an effective way to transform a conventional distribution network into its active form due to increasing penetration of renewable distributed generation (DG). Proper planning of microgrids is essential to realize their benefits in distribution networks. In this thesis, microgrid planning in distribution networks have been studied through the following two aspects: 1) optimal placement and sizing of DGs, and 2) microgrid formation with switch placement. Two approaches, deterministic and probabilistic, are proposed for optimal placement of DGs which is the primary step towards microgrid planning. In the deterministic approach, Brute Force search algorithm and Backward Forward Sweep load flow algorithm are used to optimally place and size DGs by minimizing the total power loss in distribution networks. The IEEE 33-node test system is used to validate the proposed method through several case studies considering dispatchable and non-dispatchable DGs and capacitor banks. The proposed method is proven to be effective by comparing with existing methods. By incorporating uncertainties of renewable DGs and loads, a probabilistic method for optimal DG placement by minimizing the total energy loss is proposed, where the planning is formulated as a non-linear programming (NLP) problem. AC optimal power flow (OPF) is used to solve this planning problem by considering operational constraints and uncertainties in loads and renewable power generation of the network. A new index, the Voltage Regulation Index (VRI), is proposed to select candidate locations for DG placement in a distribution network. Four commercially available solar photovoltaic (PV) modules are evaluated and the highest performing one is chosen and utilized in this method. The IEEE 33-node test system and a real 404-node distribution system operated by Saskatoon Light and Power in Saskatoon, Canada are used to validate the proposed method. The proposed method shows superior performance compared to existing methods. A new method for microgrid planning through optimal microgrid formation in distribution network is proposed through a two-stage performance optimization, where the total power loss, the adequacy and reliability of the whole system are optimized in the first stage. A new index, the Microgrid Planning Index (MPI), is defined as the objective of the optimization process. The MPI includes the total power loss index, the self-adequacy index, and the reliability index. In the second stage, isolating switches are allocated at judicious locations within each microgrid to improve the reliability of the system. Brute Force search algorithm and Backward Forward Sweep method are used to solve optimization and load flow problems, respectively. The IEEE 33-node test system is used to validate the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".