Planning and Service Restoration Using Soft Open Points in Renewable-rich Distribution Networks
Bibliographic record
Abstract
Soft open points (SOPs) are power electronic devices that can be implemented in place of normally open points (NOPs) of distribution networks to realize active power flow control, reactive power compensation, and post-fault service restoration. Two aspects of SOP have been researched in this thesis, which includes the optimal placement and sizing of SOP, and service restoration using SOP in the renewable-rich distribution network. For optimal placement and sizing of Soft Open Points in the renewable-rich distribution networks, a two-stage deterministic optimization methodology is developed. In Stage 1, the loss sensitivity index (LSI) and voltage deviation index (VDI) is used to identify candidate locations of SOPs. A new approach to derive LSI is proposed, where LSI is used to find initial candidate nodes. The candidate nodes are then grouped lateral-wise and used to form the candidate pair of nodes for SOP placement between laterals. In Stage 2, a mathematical optimization model is developed using AC power flow for optimal sizing of SOPs with the objective of power loss minimization. Each voltage source converter (VSC) capacity of the SOP is individually optimized to obtain the optimal capacity. The effectiveness of the developed deterministic optimization method is validated in a renewable rich modified IEEE 33 node test system. Incorporating the uncertainties of distributed generation (DG) and load, the two-stage SOP placement and sizing methodology is further reformulated as a probabilistic optimization method. The objective function is updated, exploring more benefits of SOP in distribution system. A multi-scenario optimization model for SOP placement and sizing is developed to minimize power losses and active power curtailment of DG in the distribution system. SOP’s optimal location search strategy and mathematical formulation are upgraded to a multi-scenario environment. The power factor limit at the substation/grid interconnection is incorporated in the mathematical model as a constraint to analyze its effect on the DG’s active power curtailment. The developed method is validated in a real-life large 404-node unbalanced distribution system operated by our local utility, Saskatoon Light and Power in Saskatoon, Canada, along with a balanced IEEE 33-node test system. The service restoration methodology is developed by coordinating the operation of multiple SOPs and DG units in the distribution networks. The method is formulated in two stages. In stage 1, during a grid/substation fault, the power supply priority is given to the critical loads. In this stage, considering the ramp rate constraint of dispatchable DGs, their real power generation is kept at the same set points as before the fault. In stage 2, the controllable DGs are dispatched to restore the load of the outage area, and maximum load restoration is achieved. In both stages, real and reactive power of SOPs are regulated to maximize service restoration in the outage area. A Mixed-integer nonlinear programming (MINLP) model is developed using AC power flow for centralized (coordinated) and decentralized (uncoordinated) operation of SOPs and DGs with the objective of maximum service restoration. A comparison between the centralized (coordinated operation) optimization and decentralized (uncoordinated operations) optimization of SOPs and DGs is made to verify the performance of the proposed method using the modified IEEE 33-node test system.
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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) | 0.000 | 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".