Integrated Planning and Operational Control Of Resilient MEG For Optimal DERs Sizing and Enhanced Dynamic Performance
Bibliographic record
Abstract
In this chapter, an integrated planning and operational control of a microenergy grid (MEG) has been presented to optimally size DERs and to enhance MEG dynamic performance. The design, development, and hardware setup of the proposed MEG have been presented from the planning stage to the operational stage. The planning stage optimizes the size and type of distributed energy resources (DERs) for minimum cost and minimum CO2 emissions. Then, the operational stage evaluates and fine-tunes the dynamic response. Renewable energy sources and natural-gas-based combined heat and power (CHP) are implemented, studied, and integrated into the MEG. A D-FACTS device, green plug-energy economizer (GP-EE) with two DC/AC schemes are proposed and integrated into the MEG. Enhanced heuristic optimization methods can be applied to control the parameter settings of GP-EE to fine-tune the system dynamic response. The proposed controller adapts the global control error to increase the energy efficiency and reliability. Power factor improvement, voltage profile enhancement, loss reduction, and power quality improvement have been realized and achieved. The design and development of the MEG with hardware demonstration have been developed at the Energy Safety and Control Laboratory (ESCL), University of Ontario Institute of Technology. The MEG system included implementation of control strategies for DERs and programmable loads at a laboratory scale. A software system was developed to monitor all MEG parameters and to control the various components. Demonstration with digital simulations has been validated with the results showing the effectiveness and the improved performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".