A Generalized Optimal Planning Platform for Microgrids of Remote Communities Considering Frequency and Voltage Regulation Constraints
Bibliographic record
Abstract
Access to electricity is a key factor behind development and expansion of modern societies, and electric power systems are the backbone infrastructure for economic growth of nations and communities. However, more than a billion people all over the world have no or limited access to electricity and are deprived of basic services. Furthermore, there are many communities that rely on small-scale isolated microgrids to supply their electric power demands, and many challenges exist in keeping those microgrids operating. The cost of operating isolated microgrids is a major issue which impacts the availability of a proper power network in remote communities. Hence, many organizations, communities and governments around the world are looking into alternative options for electrification of remote communities by considering Renewable Energy (RE) resources, such as wind and solar power, and utilization of Energy Storage Systems (ESS). \nThis thesis investigates the feasibility of RE deployment in remote communities, by proposing a generalized optimal planning platform and conducting comprehensive simulation studies based on real measured data, and evaluates the impact of economic, technical and operation constraints on the planning of an isolated microgrid involving conventional generation, RE resources and ESS. This work suggests that further investigation should be made on the potential impacts of the integration of RE resource on systems operation constraints, such as frequency and voltage regulation, and the results justify the importance of such investigations. Detailed studies on the impact of operation constraints on the planning and sizing of the microgrid are performed. The impact of ESS on planning studies and its potential role in system operation are analyzed. Furthermore, the impact of RE integration on reduction of diesel generation and thus carbon footprint in remote communities is evaluated. The inclusion of a demand response management strategy in microgrid planning problem is considered and its impact on the integration of RE and ESS in remote communities is analyzed. \nThe proposed planning platform is applied to the microgrid of Kasabonika Lake First Nation (KLFN), a northern Ontario remote community. The results indicate that RE and ESS integration projects are achievable considering alternative incentives and funding resources. It is also shown that frequency regulation constraints have remarkable impact on the sizing of the RE units and ESS. A sensitivity analysis is also performed in order to study the effect of variable parameters on the optimal design of the microgrid at KLFN.
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".