Conservation Voltage Reduction Technique in Renewable-Rich Multi-Energy Systems
Bibliographic record
Abstract
Conservation Voltage Reduction (CVR) is an advanced distribution system management technique implemented by utilities to achieve energy savings and peak shaving by controlling the voltage magnitudes for voltage-dependent loads. With increasing penetration of solar photovoltaics (PVs) in distribution grids, their interfacing inverters can significantly contribute to CVR. This thesis focuses on two aspects of CVR: 1) CVR implementation in renewable-rich multi-energy distribution networks; 2) optimal PV placement to enhance CVR in multi-energy distribution networks. A deterministic programming model is first developed for day-ahead scheduling of voltage regulation devices for CVR implementation, including on-load tap changers (OLTC), capacitor banks (CBs), and PV interfacing smart inverters to power grids. This model minimizes the daily load consumptions and network power losses to provide optimal settings for voltage regulation devices. Natural gas networks are integrated with electric distribution systems to improve reliability and resiliency through energy conversion devices, such as gas-fired DGs (GFDGs) and the power-to-gas (P2G) technology. To address uncertainties of the forecasted load and PV power generation, a two-stage stochastic programming model for CVR implementation in multi-energy distribution networks is then proposed. The first stage finds the optimal tap and switch positions of OLTCs and CBs, respectively, and the base reactive power injection or absorption of PV smart inverters. After the realization of uncertainties via numerous scenarios, the second stage finds the amount of readjustments in reactive power output of PV smart inverters based on their droop characteristics to reach optimal CVR results. A two-step relaxation-based technique is also developed, which is proven to improve the computation speed significantly. The proposed stochastic model is validated by the modified IEEE 33-bus 7-node integrated electricity and natural gas system (IEGS), and the model scalability is then tested on the modified IEEE 123-bus 20-node IEGS. The proposed CVR technique is also validated by comparing with existing methods. To evaluate the proposed technique in real-world applications, the model is extended to unbalanced distribution grids, and is assessed using a 404-bus unbalanced distribution system operated by Saskatoon Light and Power in Saskatoon, Saskatchewan, Canada. The second part of this thesis focuses on optimal placement of PVs along with the capacity of PV smart inverters in multi-energy distribution networks to enhance CVR implementation. A framework is developed through two steps to determine a suitable number of PVs. A mixed-integer quadratically constrained programming (MIQCP) model is solved at the first step, considering an initial optimal number of PVs to be installed, and the amount of reductions in load consumptions and network power losses are calculated; a day-ahead CVR implementation model proposed in the first part of this thesis is then solved for randomly placed PVs, at the second step. The stopping criterion is whether improvements in load consumptions and power losses reductions are considerable (roughly 60% reduction is desired). If the planners need more improvements, the number of installed PVs can be increased, and the whole process is repeated until the stopping criterion is met. The PV optimal placement technique is validated using the IEEE 33-bus 7-node test system. It is found that there is a limit on the number of PVs in load consumption reductions, but integrating more PVs in the system can reduce power losses significantly. The CVR implementation with optimal placed PVs can achieve better results than that using randomly placed PVs.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".